Organs-on-a-chip engineering

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Organs-on-a-chip engineering

3

Felix Kurth1, Erika Gyo¨rvary1, Sarah Heub1, Diane Ledroit1, Samantha Paoletti1, Kasper Renggli2, Vincent Revol1, Marine Verhulsel3, Gilles Weder1 and Fre´de´ric Loizeau1 1

Centre Suisse d’Electronique et de Microtechnique SA, Neuchaˆtel, Switzerland ETH Zu¨rich, Department of Biosystems Science and Engineering, Basel, Switzerland 3 Fluigent SAS, Le Kremlin-Biceˆtre, France

2

Introduction One of the greatest challenges in health care is the lack of physiologically relevant preclinical models (Moraes et al., 2012). Animal cell lines and animal tests are still used to develop drugs for humans, often leading to incorrect correlations between animal and human physiology or between in vitro and in vivo data. Drug manufacturers often fail in drug development as correctly predicting human responses from these methods is not possible (Pound et al., 2004). This can result in high drug costs, fewer new compounds in the pipeline, compounds with only limited efficacy, and good compound candidates lost in (failed) translation. A new way to investigate organ physiology mechanisms and drug-testing platforms for personalized medicine and disease modeling is required.

Definition Traditional two-dimensional (2D) cell cultures can provide indications of compound efficacy and toxicology but cannot model cell functions and physiology, because they lack the three-dimensional (3D) structures found in intact organs. Recent developments and the convergence of biology, materials sciences, engineering, and microtechnologies are bringing us closer to physiologically relevant preclinical models. These technologies make it possible to establish a new concept for preclinical models, that of an organ-on-a-chip (OOC). OOCs have been described as “a fit-for-purpose microfluidic device, containing living engineered organ substructures in a controlled microenvironment, that recapitulates one or more aspects of the organ’s dynamics, functionality and (patho)physiological response in vivo under real-time monitoring” (Thomassen, 2018). This hard-won definition is the result of several expert interviews carried out in the framework of the European Union’s ORCHID project. OOCs can be classified as single-organ systems, Organ on a Chip. DOI: https://doi.org/10.1016/B978-0-12-817202-5.00003-6 © 2020 Elsevier Inc. All rights reserved.

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focusing on the key parameters of a single organ, or as multiorgan platforms, where several organ models’ interactions and responses can be monitored in a single system simultaneously. Since the invention of OOCs nearly 20 years ago, several manufacturing techniques and materials have been developed and matured to make this technology more accessible to researchers worldwide. Nevertheless, challenges remain and many design rules or trade-off need to be made during the design and fabrication of OOCs.

Engineering challenges OOCs exist at the intersection of two areas, the cell and microtissue culture field and the microfabrication technologies field, which was originally developed for the semiconductor industry. While the former is an organic, 3D, and dynamic world, the semiconductor world is inert and flat and favors immutability. Challenges therefore arise when cells cohabit and must survive alongside materials or processes that were not designed for interaction with biological components. As engineers develop OOCs, seeking physiologically relevant solutions, several points in the design and manufacturing phases should receive particular attention.

Design challenges In the design phase the main objective is to create models that are relevant to answering biological questions. By combining structured soft materials with microactuators and micropumps, it is possible to mimic the basic functions of organs or tissues. For example, a lung-on-a-chip platform exposes lung epithelial cells to an airflow as a microcavity expands following a breathing pattern (Huh et al., 2010). In a kidney-on-a-chip platform, kidney epithelial cells lie on a thin, porous membrane separating two flows to filtrate toxins (Jang et al., 2013). Such designs take advantage of the planar limitation of microfabrication technologies to develop simple yet sufficient models. Besides overall organ or tissue function, OOCs must control the migration, location, and morphology of the cultured cells; otherwise the cell phenotype might not be maintained. Mature vascular smooth muscle cells, for instance, typically display an elongated morphology in their native blood vessel (Rhodin, 2014). Cultured in vitro, they can, however, exhibit less elongation and lose their contractility (Alford et al., 2011). Therefore designers need to find ways to grow and mature these cells in their elongated state on an OOC (Sarkar et al., 2005). Not only should the model mimic the chosen organ functions, it should also measure necessary parameters such as cell activity or secretions. Each parameter can generally be monitored in several ways. Depending on the required sensitivity or speed, not all monitoring solutions are appropriate. pH, for example, can be monitored through electrochemical or optical means, each with its pros and cons. It is the designers’ responsibility to select the most relevant solution for a given experiment.

Introduction

How to mimic organ function, maintain cell phenotype, and measure critical parameters are the key questions that must be answered prior to the manufacturing step.

Manufacturing challenges Vascularization, filtration, and separation are key tissue processes that occur at a scale up to a few hundred micrometers. For OOCs to mimic such processes, they must be fabricated with similar resolutions. There are not many manufacturing methods that can produce features with reliable dimensions on such small scales; the only standard manufacturing method currently available comes from the field of microelectronics. For decades, integrated circuits and sensors have been produced on silicon wafers by the deposition and subsequent local etching of various materials with submicron precision. Hence, researchers have naturally been using the same microfabrication processes to create microfluidic chips and OOCs, although they have certain limitations. Microfabrication processes are carried out in a clean-room environment—an infrastructure that involves high maintenance costs and strict policies for avoiding contamination of the equipment and consumables. Researchers have circumvented this limitation mainly by fabricating a master in the clean room using silicon or glass and then molding and replicating their devices in more bio-friendly and cost-efficient materials outside the clean room. Silicone-based polymers such as polydimethylsiloxane (PDMS) are widely used in this context since they provide the right range of elasticity and a welcome optical transparency. Their tendency to absorb many chemicals and, therefore, trap or release drugs unintentionally is problematic and should be assessed carefully (Shirure and George, 2017). Besides the lack of suitable materials, microfabrication environments also employ quality control systems that may not be relevant for cell culture applications. Adapting those systems might become necessary for the production of OOCs, especially during the later phases of industrialization and commercialization. Because of the limitations of microfabrication, engineers have been investigating new fabrication processes and materials to replace or complement the existing ones. Additive manufacturing methods such as inkjet printing bring with them the materials and flexibility that microfabrication lacks; printers have been depositing microdroplets precisely for decades. With some modifications, printers can now print functional biomaterials such as collagen, proteins, and even cells. While still in their infancy, companies such as RegenHU (Fribourg, Switzerland) or Cellink (Gothenburg, Sweden) are developing and manufacturing bioprinters and bio-inks specifically for OOCs or tissue culture applications, making these emerging technologies more and more available to engineers and researchers. While whole devices can now be 3D-printed, additive manufacturing is too lengthy a process to match the production capabilities of microfabrication. A realistic and promising solution would be to combine the production scale of microfabrication with the materials and flexibility of 3D printing to create hybrid

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devices. Mechanical structures with electrical functions can be mass-produced in the clean room while 3D printers add bio-functionalization only where it is needed. Whereas originally only a few infrastructure-intensive and restrictive fabrication methods were available, engineers now have access to more adapted and flexible solutions for manufacturing their OOCs. From the materials to the surface chemistry, engineers have choices when developing OOCs to specifications.

Microengineering Introduction Design, materials selection, and fabrication techniques for manufacturing an OOC platform are not trivial tasks. Several key parameters, such as fluid control, absence of air bubbles, optimal oxygen concentration, and sterility maintenance, must typically be considered. Regardless of these numerous technological challenges, the first thing to do is to clearly define the specific organ-level function(s) to be mimicked. Although several parameters are consciously oversimplified, the engineered microenvironment must remain as close as possible to in vivo conditions. In addition, an OOC platform has to integrate a precisely controlled environment and some monitoring systems. Most physiological tissues are subject to chemical, mechanical, and/or electrical stimulations and exhibit anisotropic behavior, meaning that their properties often depend on the direction of applied stimuli. This complex in vivo condition is tentatively reproduced with the combined use of microfluidic devices with biosurfaces and actuators. It can be integrated onto single chips or parallelized standardized formats such as multiwell plates. Fig. 3.1 shows a general process for the elaboration of an OOC platform. The physicochemical properties of materials—chemistry, topography, and functionalization—exert a significant influence on the cells. In this context, the materials must also fulfill other major requirements, such as compatibility with sterilization methods, visualization tools, and especially fabrication techniques. Built using a fully automated procedure, 3D printing is typically a tool of choice for complex shapes. This section describes the main and critical steps to consider in the development of an OOC platform.

Design The design phase of OOC platforms is often a complex process, as these devices are made of many components that are specific to the different applications intended. For example, with regard to microfluidic architecture, parameters such as the number of channels, 2D/3D conformation, dimensions, geometry, and pattern must be considered. These parameters can play a critical role in establishing an adequate biochemical environment for the cells as well as tissue functionality.

Microengineering

FIGURE 3.1 Process flow diagram for developing an organ-on-a-chip platform.

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The incorporation of bio-surfaces such as membranes that can act as barriers or interfaces and thus better mimic the modeled organ/tissue is therefore common. Moreover, actuators and sensors are often desirable in reproducing in vivo conditions and monitoring important physicochemical parameters. Finally, the choice of chip-to-world connections, such as fluidic and electrical connections, must be made according to the applied conditions in the chip and the ease of their implementation. An overview of the elements that might be integrated into an OOC device is given in Fig. 3.2. A microfluidic device consists mainly of channels. The channel network enables the construction of a scaffold and control of the spatial location of the organ/tissue growth in a so-called mini-bioreactor. The features of this bioreactor depend on the biological tissue: monolayer, multilayer, or 3D cell culture. The microfluidic channels associated with fluid control elements enable the provision of a suitable biochemical environment for organ/tissue growth, stimulation, and analysis in the bioreactor. Spatial control of the biological tissue means several interfaces with multiple cell types, culture media, and surfaces. Classical 2D chip devices are often too simple so a 3D chip approach is required. A more complex microarchitecture, closer to the in vivo situation, can be achieved by the superimposition of channels, biointerfaces, and control elements, resulting in an integrated hybrid, multilayer device (Caplin et al., 2015; Zheng et al., 2016; Zhang et al., 2018). Another aspect of the design of the microfluidic architecture is the channel patterning, that is, the fabrication of defined patterns inside the channels (e.g., microgrooves). The functionalization of channels via geometry typically enables immobilization of the organ/tissue in the bioreactor but also serves as a scaffold

FIGURE 3.2 Schematic overview of the components of an organ-on-a-chip microfluidic device that should be considered during the design phase.

Microengineering

for bio-functionalization of the surface (Sun et al., 2011). In addition, the microchannels support the establishment of an organ-like vascularization system (Haase and Kamm, 2017; Bertassoni et al., 2014; King et al., 2004). Finally, differing channel geometry can be produced, the choice of which depends on the desired flow conditions and the mimicked environment. Fig. 3.3 presents a summary of possible options in the design of microfluidic channels. The design of the microfluidic device and its channels can be assisted by computer simulation to calculate the desired dynamic conditions (flow profile, shear, physical actuation). Detailed information on such methods can be obtained from case studies in the literature (Hagmeyer et al., 2013; Oleaga et al., 2016; Zengerle et al., 1995; Sajay et al., 2017; Borenstein et al., 2002; Kaihara et al., 2000). To model the different barriers and tissue interfaces, plastic membranes are often incorporated into the microfluidic channels of an OOC device (Zhang et al., 2018). In some cases the selected membrane can be porous, to better mimic the properties of a particular tissue. Another approach involves the insertion of a membrane that supports the growth of two cell types, one on each side of the membrane. This multilayer channel construction enables the exposure of each cell type to a different environment while mimicking a tissue junction. This format is widely used for modeling multiple organs, such as brain, liver, gut, heart, skin, and lung (Ahadian et al., 2018; Lee and Sung, 2018; Skardal et al., 2016; Ronaldson-Bouchard and Vunjak-Novakovic, 2018). The incorporation of such membranes into OOC devices is discussed in the “Fabrication process” section.

FIGURE 3.3 Schematic description of the most common options applied to the design of channels in an organ-on-a-chip microfluidic device. I, Inlet; O, outlet; P, pressure.

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An OOC platform also interacts with its external environment. This includes the surrounding physical parameters such as gas, temperature, humidity, and light, and connected external equipment such as readers, off-chip sensors, a fluidcontrol unit, and analytical instruments. The user may also interact directly with the platform for manual sampling. The environmental parameters are typically maintained by placing the OOC platform in an incubator with a controlled temperature and gas supply and protected from light. The footprint of the device must therefore be considered early in the design phase, as the whole OOC platform should be compatible with incubation chambers. In many OOC devices, dynamic conditions are applied, and fluid control can be part of the microdevice body (Huh et al., 2013; Bhatia and Ingber, 2014). Elements such as micropumps (Chen et al., 2017; Sonntag et al., 2015), valves (Chen et al., 2009; Huh et al., 2011), mixers (Polinkovsky et al., 2009; Brennan et al., 2014), or flow constrictors (Rodrı`guez-Villarreal et al., 2010) can be incorporated by specific channel design. Gradient control can also be assisted by channel geometry. The architecture of the microfluidic device must also comprise the elements required for the control and observation of the physiological aspects that are relevant to the application. In other words, it must integrate specific geometries or building blocks to generate desired stimuli (and sensing). When reproducing a function of the body, it is possible that mechanical, biochemical, or biophysical stimuli are required simultaneously (Park et al., 2015). The design strategies for applying these stimuli to the cells/tissues inside the chip are manifold. A mechanical stimulus, for example, can be achieved by the insertion of a stretchable membrane in skin or lung models (Guenat and Berthiaume, 2018), whereas a biochemical stimulus can be achieved by applying a gradient injection of a growth factor (Yum et al., 2014). Concerning observation, if the material used for the fabrication of the microfluidic device is not optically transparent, the design must include optical access, which can be achieved by the insertion of a transparent material. This also implies that the design must anticipate access to this area using a microscope, camera, light source, or other reader. Additional electrical and/or mechanical biochemical actuators and sensors are often needed for complex functions (see the “Stimulation and sensing” section). Embedded control units and sensors have advantages over external instrumentation. The former often enable frequent time-point or even continuous monitoring of a physicochemical parameter and limit the risk of culture contamination that is associated with sampling. Such units can be inserted into the chip construct either directly on the surface of a channel or as a building block in the device body. These on-chip solutions are principally the focus of research, given that they add complexity to the process of manufacturing the chip. As an example, the use of flexible membranes requires mastery of the bonding of at least three individual layers. Since OOC chips are by definition disposable, any additional on-chip function leads to higher costs of disposables. Beyond functionality, designers of OOC platforms should keep in mind that the cost of a device has to be balanced

Microengineering

by the additional information it provides. In general, a solution that makes use of off-chip components will be more cost-efficient than the same chip with on-chip functionalities. Electrical and fluidic connections also play a crucial role in the chip design (Temiz et al., 2015). An external fluid control unit is typically required to provide a dynamic liquid environment, administer biochemicals, supply gases, manage waste, and support in-line and off-line sample analysis. The various methods for building an external fluid control unit are discussed in the “Engineering fluid control for organ-on-a-chips” section. From a design standpoint, the OOC platform usually requires multiple sources of fluids, and their connections to the device must not leak or clog, so physicochemical parameters of fluid control, such as pressure, flow rate, salinity, or pH, must also be considered. The design of the fluidic interface must also preserve sterility inside the chip. This is particularly critical for applications that require time-point sampling for off-line analysis. Here, the sample needs to be delivered or accessed without endangering the remaining culture inside the OOC device. The fluidic ports are frequently placed on top of the microfluidic device and around an observation window, so as not to disturb optical access. Side connections are rare, as they are not suited to multilayer structures, but technologies that allow for 3D bulk chip fabrication (3D printing, micromachining) give more flexibility regarding the positioning of the fluidic ports, which are then more conveniently placed on the sides. These design approaches offer more opportunities for interconnecting the various elements of the microfluidic device. Finally, the format of the device should be considered. Although microfluidic chips are often used as single chips mimicking one or several organs, it is desirable that OOC platforms be compatible with standard formats used in drug development—such as multiwell plates and glass slides—to achieve high throughputs (Ronaldson-Bouchard and Vunjak-Novakovic, 2018).

Materials Material selection is inextricably linked to the design of the OOC device. In the body, cells are surrounded by other cells, fluids, and diverse materials such as protein fibers or minerals. When building an in vitro model, the practical need for a solid support to grow cells and study the biological model thereby generates an interface that is artificial in terms of its geometry and composition. Besides device architecture, then, material selection is therefore crucial to controlling the effect of the device on the cells and mimicking the in vivo microphysiological environment of the tissue and in particular the biointerfaces. In this section, we guide the reader concerning the selection of appropriate materials from which to build an OOC device, providing insight into the requisite criteria and available options. In practice a device is composed of a bulk material that forms its structure and other materials that can be added to customize the surface bioproperties or add functions (materials used for electrical, chemical, and optical sensors are

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discussed in the “Stimulation and sensing” section). It is rare that one single material can be used to fabricate an OOC device, because of the complexity of such systems, and several materials must be used. Multiple criteria must be considered when selecting a material to integrate into an OOC device:

• • • • • •

Biocompatibility Sterilization Physicochemical properties Material function in the device Design and fabrication possibilities Cost

One goal of the OOC approach is to provide a more accurate in vitro model by reconstructing an organ’s environment. Thus even if the experiments are conducted outside the human body, the material selected for the device must be biocompatible. When referring to implantable devices, a biocompatible material would not lead to any undesired or harmful biological effects. Concerning OOC devices and tissue engineering, a biomaterial would be able to perform as a substrate that supports the appropriate cellular activity (Williams, 2008). Common biocompatible materials include silicon substrates, polymers, resins, and hydrogels (Zhang et al., 2018). Sterilization of materials that are in direct or close contact with cells is necessary to avoid microorganismal contamination. Several sterilization techniques are readily available, and their utility is governed by the device-development context. Wet heat (autoclaving), dry heat (flaming, baking), solvents (ethanol), and radiation (ultraviolet light) are common sterilization agents available in most laboratories, while the use of X-rays, gamma rays, or ethylene oxide is generally limited to specialized laboratories or commercialized processes. Sterilization is characterized by its efficiency, measured in microbial inactivation, total sterilization time, operability, and scale-up. When selecting a material, it is important to consider what sterilization methods it would withstand, accounting for material properties such as permeability and thermal or chemical resistance, and verify that its properties or structure will not be altered during the process (dos Santos et al., 2017). As there is no current standard method for either fabricating or sterilizing OOC devices, efforts should be made with regard to the development of appropriate procedures to improve these processes. Various physicochemical properties can be required of the material, depending on the chip design and application. The following properties are essential when developing an OOC device:

• Optical transparency, mostly for observing the culture inside the chip. In practice, the microfluidic chip is either fabricated using an optically transparent material or integrates optical windows into defined observation areas. A material is qualified as optically transparent if it can transmit incident

Microengineering

•

•

•

• •

light with relatively little absorption or reflection. PDMS is typically used for OOC devices, as are glass, hydrogels, and transparent polymers such as poly (methyl methacrylate) (PMMA). Optical microscopy is generally applied as a simple, fast, and noninvasive measurement technique (Yi et al., 2017). Gas permeability, as most human cells require a supply of oxygen. A highly permeable material such as PDMS guarantees that enough oxygen is provided to the cells in the microchannels, while a nonpermeable material such as glass or plastic requires separate oxygenators (Huh et al., 2011) or perfusion. Adsorption of molecules to the surface can potentially alter the physiological response of the organ and the results of drug exposure tests. This issue is recurrent with polymers (Shirure and George, 2017) and is one of the main limitations of PDMS, making its current widespread use questionable (Shirure and George, 2017; Capulli et al., 2014; Berthier et al., 2012). Resistance to chemicals, as a release of compounds caused by degradation could interfere with the mimicked organ functions. Chemical degradation of the material could also cause leakage or the introduction of air plugs, which would spoil the experiment. Thermal resistance, as the material should not undergo dilation or contraction that could alter the properties of the system. Stiffness can also be an important parameter. A material with a stiffness similar to that of the reproduced organ will be more accurate, but such materials are not yet available. For device fabrication, high flexibility of the chip could present both advantages (e.g., the possibility of microfabricating integrated microfluidic channels, valves, or pumps) and disadvantages, such as deformation under experimental conditions such as pressure, causing variations in flow or shear stress (Berthier et al., 2012).

Finally, the costs associated with the production of the microfluidic device with the selected materials are an important factor with regard to the standardization of OOC platforms. Since these platforms mainly target the drug development market, the number of tests required could be considerable. One factor affecting the cost per data point is the availability of the raw material; more important still is the possibility of cost-effective, large-scale production (up-scalable fabrication process). Since the first steps in development of microfluidic devices in the 1950s, many inorganic (silicon, glass, ceramics) and organic (polymers) materials have been used to produce devices in a range of application areas. Extensive reviews of these materials and their advantages and limitations are available in the literature (Berthier et al., 2012; Tsao, 2016; Becker and Locascio, 2002), and details of their properties can be obtained from their manufacturers. The same materials are employed for OOC platforms, and an overview of their relevant properties is provided in Table 3.1. Today, PDMS is mostly used for research purposes and small-scale fabrication. Glass has outstanding physicochemical properties; it remains costly, but

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Table 3.1 Properties of common materials used in organ-on-a-chip microfluidic devices. Material

Stiffness

Sterilization method

Biocompatibility

Optical transparency

Functionalization

Drug absorption

Cost

Polydimethylsiloxane Polycaprolactone Polylactic acida Glass Silicon Polycarbonatea Poly(methyl methacrylate) Polystyrenea COC/COP/CBC

Elastic Elastic Rigid Rigid Rigid Rigid Rigid

A, D, E E B, C A,B, D, E A,D,E A,B,C B, C

1 1 1 1 1 1 1

11 2 2 11 22 11 11

11 1 1 11 11 0 1

2 1 1 11 11 11 11

0 0 0 2 2 11 11

Rigid Rigid

B, C B, C

1 1

11 11

0 1

11 11

11 1

The symbols are defined as the following: 11 “Excellent,” 1 “Good,” 0 “Neutral,” 2 “Poor,” and 22 “Unrealizable”. References (Moraes et al., 2012; Pound et al., 2004; Thomassen, 2018; Huh et al., 2010, 2011, 2013; Jang et al., 2013; Rhodin, 2014; Alford et al., 2011; Sarkar et al., 2005; Shirure and George, 2017; Caplin et al., 2015; Zheng et al., 2016; Zhang et al., 2018; Sun et al., 2011; Haase and Kamm, 2017; Bertassoni et al., 2014; King et al., 2004; Hagmeyer et al., 2013; Oleaga et al., 2016; Zengerle et al., 1995; Sajay et al., 2017; Borenstein et al., 2002; Kaihara et al., 2000; Ahadian et al., 2018; Lee and Sung, 2018; Skardal et al., 2016; Ronaldson-Bouchard and Vunjak-Novakovic, 2018; Bhatia and Ingber, 2014; Chen et al., 2009, 2017; Sonntag et al., 2010, 2015; Polinkovsky et al., 2009; Brennan et al., 2014; Rodrìguez-Villarreal et al., 2010; Park et al., 2015; Guenat and Berthiaume, 2018; Yum et al., 2014; Temiz et al., 2015; Williams, 2008; dos Santos et al., 2017; Yi et al., 2017; Capulli et al., 2014; Berthier et al., 2012; Tsao, 2016; Becker and Locascio, 2002; Zhou, 2017; Pocock et al., 2016; Hamad et al., 2014; Lee and Cho, 2016; Mills et al., 2006; Su et al., 2011; Yavuz et al., 2016). A, Autoclave; B, ethylene oxide; C, gamma rays; CBC, cyclic block copolymer; COC, cyclic olefin copolymer; COP, cyclic olefin polymer; D, ultraviolet light; E, ethanol. a Properties such as transparency, stiffness, and temperature resistance are tunable according to the specific composition.

Microengineering

industrial players in the biomedical market (Micronit, Dolomite, Illumina, IMT) are focusing their efforts on the development of cost-effective fabrication processes. Thermoplastics unlock upscalable fabrication processes, which makes them attractive for production and standardization (Becker and Locascio, 2002). Recent developments in 3D printing techniques in the field have introduced new materials, mostly resins based on polyester/polyether oligomers that have an acrylate or methacrylate group (e.g., polycaprolactone) and biodegradable compounds derived from methacrylate-functionalized polyesters (Zhou, 2017). Although 3D printing would bring automation at a relatively low cost, the formulation of an ink that would possess all the necessary properties for an OOC device is still challenging. In an effort to recreate the physiological and pathological responses of tissues and organs in OOCs, research is being conducted into the incorporation of a new category of materials—known as smart materials—into the chip. Smart materials can be defined as materials with one or more key properties that can be altered in response to a defined stimulus. These compounds can change, for example, their color, shape, rigidity, opacity, or porosity in response to a stimulus such as an alteration in physiological properties (temperature, pH, enzyme concentrations) or an external stimulation (light, electrical current, magnetic field). Materials with smart properties include polymers, proteins, and nanoparticles, and have been reviewed by Verma et al. (2016). In OOC devices, they can act as actuators, sensors, or on-demand self-assembly agents. By way of illustration, it is possible to reproduce mechanical stimulations applying to different biological tissues by including polypyrrole actuators in devices. When an electric field is applied, polypyrrole can expand, stimulating cells mechanically. The stimulation of epithelial cells using this type of actuator is illustrated in Fig. 3.4. Although the association of smart materials and OOC applications appears promising, especially in modeling cells such as muscles or neurons, the incorporation of such smart materials into the design is challenging. Moreover, there is still room for the development of more efficient smart materials that would be sensitive to specific changes in stimulus but stable with regard to storage and to other fluctuations in the environment.

FIGURE 3.4 Schematic illustration of the principle of the in vitro mechanical stimulation of Madin
Darby canine kidney epithelial cells using polypyrrole microactuators. Adapted with permission from Svennersten, K., Berggren, M., Richter-Dahlfors, A., Jager, E.W.H., 2011. Mechanical stimulation of epithelial cells using polypyrrole microactuators. Lab Chip 11, 3287
3293.

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Raw synthetic materials are often not sufficient to recreate the environment within the human body. It is, however, possible to functionalize surfaces to better mimic the 3D microenvironment and to model the interfaces of specific tissues (Ahadian et al., 2018). Hydrogels, which consist of networks of hydrophilic polymer chains that can contain a large amount of water, can be used to attain this goal. They often act as the biomaterial scaffold on which the cells grow, spread, and proliferate. They must therefore possess adequate stiffness to support the cells, permeability to allow oxygen supply, and biocompatibility. Hydrogels can be natural or synthetic. While natural compounds are typically nontoxic and closer to those found in the body, they present several disadvantages compared with synthetic hydrogels, which have physical and chemical properties that can be easily tuned depending on the desired function. For example, the surface of PDMS can be altered by coating with hydrogels (Ergir et al., 2018), attaining a different flexibility to better mimic a given organ. Other modifications can include peptide attachment, photo-crosslinkable moieties, and combination with other materials (Ahadian et al., 2018). It is, however, worth mentioning that synthetic hydrogels are often supplemented with adhesion molecules such as laminin or fibronectin, as they lack natural cell adhesion ligands (Verhulsel et al., 2014). Examples of hydrogels that have been used in the production of OOC platforms and tissue engineering include collagen, Matrigel, poly(ethylene oxide), poly (vinyl alcohol), poly(acrylic acid), gelatin, gelatin methacryloyl, and fibrin (Zhang et al., 2018). One of the most important engineering challenges is to shape such a hydrogel into a stable structure. The choice of fabrication process is therefore critical, with microfabrication and 3D printing presenting very promising prospects (Zhang et al., 2018).

Fabrication process The fabrication of an OOC platform benefits from extensive experience in microfabrication in other fields. It mainly differs from other applications in its greater complexity and in requiring hybrid platforms. Different processes are needed for each of the key elements of an OOC platform, and these elements can be grouped into four categories: the microfluidic chip, the microtissues, the components necessary to generate the desired stimulus, and components for sensors (Ahadian et al., 2018; Yang et al., 2017). We briefly review the classical techniques for microfluidic devices, then focus more closely on two aspects that are inherently linked to OOC platforms, the insertion of a membrane into the device and fabrication using 3D bioprinting, a promising approach that makes it possible to build complete bio-platforms in a continuous manner. Methods for the fabrication of stimulation and sensing features are discussed separately in the “Stimulation and sensing” section.

Microengineering

The fabrication process of the microfluidic device is inextricably linked to its design (multilayer, pattern resolution), its connections with external equipment, and the material selection, where material properties give access to a limited set of techniques. Most often, an OOC device is used for research purposes, which allows for manual production and single use. In such a case, there are numerous possibilities in terms of material selection and available fabrication techniques, and costs are less of a concern. More important constraints are encountered when a device is intended for large-scale production, and costs and upscaling potential are also taken into account (Tsao, 2016; Iliescu et al., 2012). In drug-screening applications the goal is typically to reduce the cost per data point. The manufacturing of the chip body and microstructures is commonly achieved using one of the microfabrication techniques described in Table 3.2. The literature contains helpful reviews that discuss the advantages and limitations of these methods (Ahadian et al., 2018; Faustino et al., 2016; Kuo et al., 2019). In Table 3.3, we provide references to individual elements of the literature that, when combined, describe all the methods used with the most common materials for OOC microfluidic devices. Once the various elements and layers have been produced, the challenge is to ensure both sealing and the connection of the device with the necessary external equipment. These features must be taken into account early in the design phase. In their review, Temiz et al. (2015) describe the different methods for achieving sealing and fluid and electrical connection. The choice of method also depends on the material’s properties and the context in which the device is produced (small vs large scale, single vs multiple use, research vs commercial product). Sealing typically consists of bonding two layers of the same or different materials using an adhesive, or of inserting a gasket to form channel walls. Electrical connections consist of electrodes or contact pads, which should be accessible on one side of the device. The fluidic connections mainly take one of four forms: a gasket, a soft-bonded port, tubing inserted directly into the access hole, or commercially available standard fittings. Apart from conventional 2D chip assembly techniques, OOC platforms often require the addition of a membrane (see the “Stimulation and sensing” section). The selection process of the membrane is critical with regard to the integration of the desired biofunctions into the in vitro model. Depending on the organ model selected, and as part of the general design and fabrication process, the integration of a membrane can be complex. To support readers who would like to develop an OOC device that includes a membrane, we propose a step-by-step approach for membrane selection and integration (Fig. 3.5). Different techniques can be used to combine microfluidic devices and membranes (De Jong et al., 2006). The two main approaches are membrane fabrication during chip fabrication and separate fabrication and later assembly with other chip elements. In this approach the membrane is first produced using a range of processes, depending on the desired material and characteristics. Fig. 3.6 shows the fabrication of a porous membrane to be integrated into a lung-on-a-chip

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Table 3.2 Microfabrication techniques for microfluidic devices. Method

Short description

Principal advantages

Principal limitations

Wet etching

Chemical removal of material layers using a mask, in liquid phase

Short run time Cost-efficient

Not suitable for highaspect-ratio features

Dry etching

Physical removal of material layers using a mask, using plasma or gas

High precision High resolution Upscalable process

Clean-room process Safety

Photolithography

Pattern transfer from a mask to a light-sensitive material via exposure to ultraviolet light, then etching

High precision Cost-effective

Clean-room process Material availability Long processing time Requires planar surface

Soft lithography

Pattern transfer from an elastomeric stamp, mold, or master to the substrate

Applicable to rough and flexible substrates

Clean-room process Long processing time Costs Precision

Microinjection molding

Injection of melted plastic into a mold

Large-scale process

Residual stress in the product Not suitable for highaspect-ratio features

Precision Safety

Short run time Low cost Can circumvent sealing step Hot embossing

Microreplication of structures on a heated plastic substrate

Low residual stress in the product Suitable for high-aspectratio features

Long processing time

Micromilling

Mechanical ablation of the material on the micrometer scale

Large-scale process Suitable for high-aspectratio features

Long processing time Surface roughness Limited resolution Requires further sealing steps

3D printing

Layer-by-layer addition of material ink

Accessible to biomaterials Upscalable process Short run time No need for sealing steps Cost-efficient Versatile

Available materials

Requires further sealing steps

Resolution

Microengineering

Table 3.3 Review of microfabrication and sealing methods associated with materials for microfluidic devices. Class

Material

Method

References

Inorganic

Glass and silicon

Review

Polydimethylsiloxane

Review Rapid prototyping Scaffold removal Review Hot embossing Review

Iliescu et al. (2012) and Wang et al. (2018a) Mcdonald et al. (2000) Anderson et al. (2000)

Polymer

Poly(methyl methacrylate) (from the thermoplastics family) Thermoplastics in general

Micromilling Hot embossing Micrometerinjection molding

Saggiomo and Velders (2015) Chen et al. (2008a) Mathur et al. (2009) Becker and Locascio (2002) Guckenberger et al. (2015) Becker and Heim (2000) Attia et al. (2009)

device (Huh et al., 2013); the membrane can then be inserted between two layers of the device body so that all components are sealed together. The sealing step represents a major challenge for this technique, as it needs to guarantee a tight and robust junction at each interface while preserving membrane structure. The assembly methods to bind the membrane and the microfluidic parts are similar to those employed for chip sealing, and include clamping, gluing, and thermal fusion. The choice of the method greatly depends on the material used to fabricate the membrane. For example, PDMS membranes are widely used as PDMS is really easy to seal (please refer to the “Compression” section for detailed information). Materials other than PDMS—for instance, hard plastics—are best assembled using thermal diffusion bonding (Sonntag et al., 2016). Binding can also be improved by specific surface treatments. Pocock et al. (2016) (Murphy and Atala, 2014) were able to assemble a microdevice made up of a polycarbonate membrane situated between two glass substrates by functionalizing the polycarbonate membrane with ammonia, performing a plasma treatment on the substrates, and heating all the layers together at a low temperature (less than the glass-transition temperature of polycarbonate). Membranes can also be produced and integrated during the device’s fabrication. Depending on the desired membrane properties, it can be fabricated as part

63

FIGURE 3.5 General approach for selecting a membrane for inclusion in an organ-on-a-chip device.

FIGURE 3.6 Example of the fabrication of a porous PDMS membrane by molding using a silicon master and cured PDMS counterpart. In this work, the mold pillars are 10 μm in diameter and 50 μm high, and the final PDMS membrane is 10 μm thick. PDMS, Polydimethylsiloxane. Adapted with permission from Huh, D., et al., 2013. Microfabrication of human organs-on-chips. Nat. Protoc. 8, 11, 2135
2157.

Microengineering

of the chip body by typical microengineering methods or created as a postprocessing step (De Jong et al., 2006). So far we have discussed the fabrication of the microfluidic device and its features. What distinguishes an OOC device from other microfluidic chips is the combination of the device itself with the model tissue. The insertion of this live biological material into a complex system constitutes one of the main challenges in the production of an OOC platform as the cells deposition method has to preserve their viability. In most cases, the microfluidic chip is first fabricated according to the techniques described previously (Table 3.2), and the cells are then manually deposited into the microfluidic channels, for example, using a syringe. The channels are often precoated with the extracellular matrix to promote cell adhesion and provide an appropriate physiological environment. Afterward, the whole chip can be incubated, and an appropriate culture medium is typically flowed through the microchannels to promote cell/tissue growth. Heart-on-a-chip is one example of an initial fabrication of the microfluidic device, here by 3D printing of the chip, sensors, and actuators, followed by seeding the cells (Lind et al., 2017). Here, everything but the cells is printed in a single, continuous step using multiple printer heads, and the self-assembly of the tissue is guided and controlled by printed microstructures. Such postprocessing cell-loading methods are generally suitable for research purposes but involve multiple individual steps that make overall OOC device fabrication more complex. The lack of reproducibility and upscaling limitations constitute a major obstacle when it comes to standardizing OOC platforms, especially when these steps are performed manually. Recent advances in the fabrication process were made possible by the development of 3D bioprinting, a biofabrication strategy of printing viable cells and constituting 3D tissue structures in a single continuous procedure with great accuracy (Avci et al., 2018; Lee and Cho, 2016; Murphy and Atala, 2014; Vijayavenkataraman et al., 2018). The principle is similar to that of conventional 3D printing—the layer-by-layer deposition of bio-inks onto, in this case, a biocompatible scaffold—and can be performed using various printing techniques, including stereo lithography, inkjet, extrusion, and laser-assisted bioprinting. Depending on the process, different cell viabilities, resolutions, and printing speeds can be achieved and inks with different viscosities can be processed. An overview of some of the aforementioned techniques is given in Fig. 3.7. It is worth mentioning that the selected process should be mild enough not to alter the viability of the cells; this is why cells are often encapsulated in biocompatible materials such as hydrogels, to prevent mechanical damage (Yang et al., 2017). The combination of the 3D bioprinting of biological cells and microfluidic devices to build an OOC platform can follow two different approaches (Yi et al., 2017). Two-step fabrication is possible, involving first the production of a microfluidic chip using any conventional microfabrication method and then the 3D bioprinting of cells or organs on the prefabricated chip. This method makes it possible to design OOCs with multiple cellular arrangements and structures. For example, Chang et al. (Malkoc, 2018) were able to fabricate a liver-on-a-chip by

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FIGURE 3.7 Selected biofabrication approaches involving the use of hydrogels as bioinks: (A) inkjet bioprinters, (B) microextrusion bioprinters, and (C) laser-assisted bioprinting. Adapted with permission from Malda, J., et al., 2013. 25th Anniversary article: engineering hydrogels for biofabrication. Adv. Mater. 25, 36, 5011
5028.

3D extrusion-printing an alginate bioink containing specific liver cells onto a prefabricated PDMS chamber and then assembling it with glass microfluidic channels. They were thus able to show that the direct printing of cells onto the microfluidic device led to better structural adaptability of the cells with respect to the design specifications. Nevertheless, this method has several disadvantages, as it still cannot be completely automated because of its sequence of steps, performed on different machines. Manual intervention is therefore needed to assemble the device, which can lead to a lack of reproducibility and to possible contamination in the culture. The single-step fabrication approach is believed to solve these issues: it consists of printing the entire chip device, including the cells, the chip, the microfluidic channels, and eventual sensors and actuators, in only one continuous and automated process. This can be accomplished using various nozzles and biomaterials in the printer. In the last few years, several groups have achieved the one-step production of OOCs such as a liver-on-a-chip (Yi et al., 2017), a nervous system (Johnson et al., 2016), and a kidney (Homan et al., 2016). It is possible to 3D-print actuators using smart materials and embedded sensors directly onto the chip, leading to a well-equipped and monitored device fabricated in only one step (Yang et al., 2017). While the process and the device may be more complex to design, this method would solve the automation and manual handling problems, making it more time-efficient and reproducible. Despite the potential offered by this technology, it presents diverse challenges that must be overcome. First, a higher printing resolution would be required to produce more complex structures, such as capillary networks. A higher printing speed would also be desirable to improve cell viability and decrease overall processing time. Further development is also required before this technology can be scaled up and standardized (Ma et al., 2018). One of the key limitations of 3D

Table 3.4 Overview of the different bioinks used for the bioprinting of scaffolds (Merceron and Murphy, 2015). Hydrogel type

Bioink

Advantages

Disadvantages

3D printing technique

Natural

Collagen

Highly biocompatible, easy to use, low cost Highly biocompatible

Poor structural and mechanical properties, slow cross-linking Melting temperature ,37 C

Inkjet, extrusion Extrusion

High biocompatibility, quick gelation time Highly biocompatible, highly customizable (when mixed to other hydrogels)

Lack structural and mechanical stability, quick degradation time Highly soluble at room temperature

Inkjet

Alginate

Low cost, abundantly available, shape fidelity

Not biomimetic for mammalian cells, high calcium content

Agarose

Good thermal gelation properties

Matrigel

Good thermal gelation properties, good biomimicry High biocompatibility, easily functionalized, versatile mechanical properties (depending on polymerization degree), low cost Good shape fidelity after printing

Not enzymatically biodegradable and not biomimetic in mammals, difficult to print Unfavorable gelation kinetics

Gelatin

Fibrin Hyaluronic acid

Synthetic

Poly (ethylene glycol) Poloxamers

PEG, Polyethylene glycol.

Low cell adhesion, not enzymatically biodegradable

Low cell adhesion, not enzymatically biodegradable, poor long-term structural properties, potential cytotoxicity

Extrusion (when mixed to gelatin) Inkjet, laser, extrusion Inkjet, extrusion Inkjet, laser Inkjet, extrusion

Extrusion

Example of applications Scaffold Sacrificial material (if pure), scaffold (if mixed), additive Adjuvant Scaffold (if mixed)

Scaffold

Sacrificial material, scaffold Single-layered structures Cell encapsulation, cross-linking agent, scaffold Sacrificial material

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bioprinting is the restricted choice of biomaterials that can be used. Material properties must address requirements such as printability (suitable viscous and rheological properties), biocompatibility, degradation kinetics and byproducts (adequate kinetics, nontoxic byproducts), structural and mechanical properties, and material biomimicry (tissue specificity) (Murphy and Atala, 2014). Table 3.4 gathers a list of bioinks that are currently used for 3D bioprinting. Finally, there are still fundamental issues with building a suitable vasculature, which is required to supply the cells with nutrients and oxygen. These issues include the lack of resolution for 3D bioprinters, as the size of vascular networks can reach down to 3 μm while the one for high-resolution bioprinters is around 20 μm. Moreover, the printing time of such small structures must not compromise cells viability, which is still challenging. Various approaches are currently being developed but are still limited by nozzle dimensions and the complexity of the structure (Malkoc, 2018). Overall, there is a clear need to broaden the spectrum of biomaterials that can be used for 3D bioprinting so that this technology can be applied to the production of OOCs and tissues. For this technique to fulfill its potential, advancements in cell sources, printing technologies, and the combination of techniques are necessary.

Engineering fluid control for organ-on-chips Microfluidic chips present a higher surface-to-volume ratio than traditional Petri dishes and well plates, resulting in a lower volume of medium available for each individual cell. Such devices allow the detection of cell-secreted factors at early time points in the experiment, since the limit of detection is reached earlier. In contrast, toxins and waste products saturate faster and nutrients are depleted earlier; the cell culture medium must consequently be renewed more frequently. To overcome this challenge, various perfusion systems can be plugged into the chip or directly integrated with it. In addition to refreshing the medium, cell perfusion can be implemented to reproduce physiological functions of the body, such as laminar flow in blood vessels, or to recreate living cell environments, such as biochemical gradients or cell signaling (Rothbauer et al., 2018). Each type of application requires the integration of equipment with specific performance, versatility, volumetric capacity, and automation properties. Adapting microfluidics to cell culture has introduced some constraints on microfluidic instruments that were not seen with the traditional bioanalytical and physicochemistry applications that initiated this field. These constraints include the following:

• Robustness for long-term experiments: Typical physicochemical experiments •

last from several minutes to 1 day, compared with several weeks for cell culture experiments. Incubator proof: Cells are typically grown in incubators with controlled temperature and CO2 levels. If chips are impermeable to gas, the instrument

Engineering fluid control for organ-on-chips

•

•

•

itself should guarantee that the medium perfused is buffered with appropriate CO2 and O2 concentrations. The instrument should also withstand exposure to high air humidity. Contamination: Cell culturing must be performed in an aseptic environment. Instruments should be equipped with adapted HEPA filters and all parts in contact with the cell culture medium (such as tubing and syringe) should be disposable or autoclavable for reuse. High-throughput: Given the duration and variability of cell biology experiments, samples are typically prepared with at least three replicates at once rather than one sample at a time for physicochemistry experiments. Instruments should therefore be able to perfuse numerous chips in parallel. Footprint: The instruments should have a reduced footprint to fit into the incubator or on a microscope stage for live-cell imaging.

Thus in comparison with traditional in vitro cell cultures in Petri dishes or flasks, fluid control (actuating, directing, monitoring, and controlling the flow of all liquids) is key in the design of OOC environments. Before reviewing fluidcontrol options, it is important to understand the functions of a fluid control system (Fig. 3.8):

• Medium exchange refers to the process of changing the culture medium at regular intervals.

• Perfusion relates to the passage of fluid through a microfluidic system for

• •

• •

medium exchange and mimicking physiological flow conditions. Perfusion rates are typically low and can be pulsatile, constant, or with a defined flow pattern. Controlling the flow rate is particularly important to stimulate the cells mechanically (see the “Mechanical” section on mechanical stimulation). Flow recirculation involves perfusion in a closed circulatory system. Injection or chemical stimulation denotes the delivery of one or more chemical compounds to simulate drug delivery. Notably, implementing concentration gradients both spatially and temporally is particularly challenging. Sampling relates to the action of removing part of the fluid circulating within the chip, typically to perform measurements. The combination of the abovementioned functions.

The main engineering challenge for fluid control in OOCs is the small volume of fluids (Wikswo et al., 2013). In particular, in applications studying signaling and organ-to-organ interactions (Faley et al., 2008), nonphysiological dilutions of metabolites, hormones, and paracrine signals should be avoided. Since the size of the tissues formed in OOCs is up to 1 million times smaller than organs in the human body, a circulation volume as small as 5 μL must be mastered. This, in turn, leads to stringent requirements for the fluidic components—including, for example, the minimization of dead volumes. Moreover, cost comes into play since the parallelization of experiments over days to weeks must be ensured.

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FIGURE 3.8 Example of a complex organ-on-a-chip, where recirculation is performed among the different organs using a pump and on-chip microvalves. Reproduced with permission from Zhang, Y.S., et al., 2017. Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors. Proc. Natl. Acad. Sci. 114, 12, E2293
E2302.

In the following sections, we offer insights into the options available for controlling fluids in OOC systems and assist the reader in evaluating these options for future applications. These sections are organized into four parts. The first is dedicated to liquid actuation options, that is, pumps. The second and third parts

Engineering fluid control for organ-on-chips

review microvalves and flow-control options, respectively. The fourth part discusses practical implementations of flow control in OOCs.

Liquid actuation We list below the microfluidic perfusion systems adapted for cell cultures, their strengths, and their limitations, and indicate some applications for which they are relevant. Table 3.5 gives an overview of the main liquid-actuation methods.

Pipetting robots The simplest fluid actuation solution consists of manual pipetting or pipetting robots to remove and add liquids within the OOC system. Because of the high degree of automation, liquid-handling robots are a competitive solution to exchanging cell culture medium at regular intervals and to administering drugs or removing samples. Airtight ports permit the use of pipetting robots as disposable pumps (CSEM, 2010). Pipetting robots can sequentially serve different microfluidic chips while ensuring sterility (tip exchange, integration into laminar-air-flowcontrolled environments). The flow rate can be easily controlled and altered during the experiment. This approach alone, however, is not adapted for continuous perfusion, since the pipetting head would have to be immobilized during the whole perfusion period and would thus be limited to addressing one chip only over a long time period. Furthermore, pipetting robots generally have a large footprint and are more expensive that alternative solutions.

Gravity-driven flow The flow in the chip results from the difference in liquid height between the fluid inlet and outlet (Ong et al., 2017; Chen et al., 2011a; Ayyapan et al., 2016; Esch et al., 2016). This is the simplest and least-expensive method and can be easily scaled to multiple chips, as the asymmetry in liquid levels is directly set on the chip (Fig. 3.9). Furthermore, chip wells can be positioned so that the spacing is compatible with pipetting robots for industrial use. The main limitation of this technique is that the flow rate is not constant over time, as the liquid height and the associated hydrostatic pressure continuously change. To overcome this obstacle, chips can be placed on a rocking plate and tilted constantly. The frequency and amplitude of rocking can be adjusted to achieve the desired flow rate. One example is the commercial OOC platform OrganoPlate (Mimetas, Leiden, The Netherlands), a 96-well plate format with a related perfusion rocker to control the fluid flow rate and the number of desired rocking cycles. Until recently, the geometry of the flow obtained was limited to bidirectional laminar flow, which may not be physiological for certain tissues and may prevent the formation of the appropriate phenotype for specific cell types, such as endothelial cells, which normally polarize against the flow. Wang et al. have demonstrated long-term recirculating unidirectional perfusion with gravity-driven flow (Wang and Shuler, 2018), circumventing one of the most severe limitations of

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Table 3.5 Overview of the most relevant liquid-actuation mechanisms. Actuation principle Peripheral

Manual or robotized pipetting Gravitydriven flow

Peristaltic pump

Syringe pump

Pressure controller

Membrane pump Viscous drag pump On-chip

Membrane pump

Surfacedriven flow

Features

References

Standardized solution for medium exchange. Limited applicability for perfusion. Large footprint and initial costs Simple and cost-efficient. Easily scalable to multiple chips. Bidirectional, tunable flow rates achievable using tilt tables Simplest solution for recirculating medium. Not applicable for circulating cells due to tube pinching. Not applicable for very low fluid volumes Unidirectional flow with direct control of the flow rates independently of chip geometry or liquid properties. Relatively large footprint and costs High flow stability and response time. Can reproduce complex flow patterns. Relatively small footprint, in particular when multiplexing Small footprint, pulsatile flow, relatively low costs Bidirectional, fast response time, pulsation-free, small footprint

CSEM (2010), Tecan (2019), and Hamilton (2019)

Very low dead volumes and flow rates. Well adapted for recirculating low fluid volumes. Use of polydimethylsiloxane membrane leads to adsorption of small molecules. Increases the complexity and cost of the disposable Unidirectional, passive liquid actuation. Low-cost and compact solution. Pumping rate changes with time (no external control)

Ong et al. (2017), Chen et al. (2011a), Ayyapan et al. (2016), and Esch et al. (2016) Caplin et al. (2015), Maschmeyer et al. (2015a), Kieninger et al. (2018), Alexander et al. (2018a), and Moya et al. (2018) Agarwal et al. (2013), Loskill et al. (2015, 2017), Li and Tian (2018), Misun et al. (2016), and Rennert et al. (2015) Fluigent (2019a), Feaver et al. (2013), and Benam et al. (2015)

Jenke et al. (2017) and Wang and Fu (2018) Dubeau-Laramée et al. (2014), Chauvet et al. (2014), and CSEM (2010) Huang et al. (2008), Jeong and Konishi (2008), Lee et al. (2007), Kim et al. (2006), Wang and Lee (2006), and Materne et al. (2015)

Juncker et al. (2002) and Walker and Beebe (2002)

Engineering fluid control for organ-on-chips

FIGURE 3.9 Example of a gravity-flow-driven cell-based assay. (A) Photo of the bottom of the 32-unit perfusion array. (B) Magnified picture of a flow unit. (C) Scheme of a cross-section of the flow unit and the associated gravity-driven flow. Reproduced with permission from Chen, S.Y.C., Hung, P.J., Lee, P.J., 2011a. Microfluidic array for threedimensional perfusion culture of human mammary epithelial cells. Biomed. Microdevices, 13, 4, 753
758.

simple gravity-driven flow. As in conventional well-plate cultures, these chips can be placed directly into an incubator, and the open wells ensure CO2 exchange to buffer the medium independently of the chip material. Combined with pipetting robots to fill the inlet reservoirs and dispense test compounds, gravity-driven flow is a powerful method to parallelize and standardize fluid flow for OOC.

Peristaltic pumps Peristaltic pumps are widely used in cell culture as they are compact and easy to use and connect (Caplin et al., 2015; Maschmeyer et al., 2015a; Kieninger et al., 2018; Alexander et al., 2018a; Moya et al., 2018) (Fig. 3.10). They can either be used to pump medium from source to waste or to recirculate medium in a chip. The number of samples that can be perfused in parallel varies from 1 to 24, depending on the number of tubes that can be coiled around the pump rotor. Peristaltic pumps deliver pulsatile flow. The frequency and amplitude of oscillations required to obtain a given flow rate vary between pumps by the number of rollers, their rotation frequency, and the diameter of the tubes. Most peristaltic pumps are software-driven to automate protocols such as periodic injection or simple flow variations, the latter including median flow rate increase or decrease (Mazzocchi et al., 2018). Most complex flow patterns, including sinusoidal flow, are difficult to achieve because of flow pulsation. In most cases, all tubes are coiled around one rotor and therefore deliver the same flow rate in every chip, which can be limiting for certain studies. Tube-pinching by the rollers damages the tubes, which must be replaced periodically, and this type of pump should not be used in experiments involving circulating cells. Most peristaltic pumps are designed to fit within an incubator, and the latest generation of micropumps are highly compact (e.g., MP2 micropumps from

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FIGURE 3.10 Scheme of a six-chamber cell-based assay with a low-volume closed-loop fluidic circuit perfusing each sample. Reproduced under a Creative Commons Attributions 4.0 International License from Mazzocchi, A.R., Rajan, S.A.P., Votanopoulos, K.I., Hall, A.R., Skardal, A., 2018. In vitro patient-derived 3D mesothelioma tumor organoids facilitate patient-centric therapeutic screening. Sci. Rep. 8, 1, 1
12.

Elemental Scientific, Omaha, NE, United States, and RP-TX microperistaltic pumps from Takasago Fluidic Systems, Kobe, Japan). Recirculating fluid saves culture medium, which can be highly expensive for primary cells, and enriches the medium with cell-secreted factors. Enclosed circulation, however, requires that medium buffering with CO2 is achieved inside the chip, using either a gaspermeable material or a CO2-independent medium for short time periods (typically up to 2 hours).

Syringe pumps Syringe pumps were traditionally the instrument most frequently used in microfluidics (Agarwal et al., 2013; Loskill et al., 2015, 2017; Li and Tian, 2018; Misun et al., 2016; Rennert et al., 2015). They are also used in the biomedical field and in hospitals, for drug dosing and calibrated injections. A wide range of syringe pumps is available on the market, delivering flow rates of 0.012
300 mL/min. Most syringe pumps are standardized instruments since they are designed to be compatible with a variety of syringes. Their flow stability and intuitive user experience make them the preferred choice of biologists, but their volume capacity is limited by the volume of the syringe. Their footprint and compatibility with incubators are other restrictive factors, as most syringe pumps are

Engineering fluid control for organ-on-chips

not designed to work in a humid environment. To overcome this limitation, syringe pumps can be placed outside the incubator and aspirate the medium from the chip outlet instead of pushing it into its inlet. The source can be a reservoir, sealed with a permeable membrane or a non-gas-tight lid, which is placed in the incubator, connected to the chip, or even designed as part of the chip. Using such a reservoir at the chip fluid inlet also overcomes the problem of medium buffering, which is complex in perfusion mode because syringes and many types of tubing are gas-impermeable. Throughput is also limited since in most cases, a syringe pump is designed for one to two syringes. Some manufacturers have developed accessories to push up to 10 syringes with one plunger simultaneously at identical flow rates (PHD ULTRA, Harvard Apparatus) and individual but connectable syringe pump units. Another drawback of syringe pumps is related to the constant delivery of cell suspensions; since the syringes are fixed on the syringe pumps, cells in suspension begin to sediment rapidly, compromising the experiment as a homogeneous cell suspension can no longer be supplied. The newest commercially available solutions rotate the syringe itself or stirring bottles connected directly to the pump (Cetoni, 2019).

Pressure controller In microfluidic devices, flows are laminar and follow the equation ΔP 5 R 3 Q, where R is the hydrodynamic resistance of the chip, ΔP is the pressure drop between the chip inlet and outlet, and Q is the volumetric flow inside the chip. This equation demonstrates that the flow can be set directly using flow controllers or by adjusting the pressure with pressure controllers. Pressure controllers such as Flow EZ (Fluigent, Paris, France) apply a controlled pressure at the liquid’s surface, which pushes out the fluid at a controlled flow rate. Fig. 3.11 illustrates a typical implementation, where the microfluidic chip is connected to a reservoir. Pressure controllers have shorter response times and better flow stability than standard syringe pumps. When a flow sensor is added to the system, a feedback loop can allow the pressure controller to constantly adjust the pressure to maintain the desired flow rate in the chip. Such a setup provides additional information to the user, as the system records both the pressure and the flow rate over the course of the experiment. This information is valuable for cell culturing as cells can easily clog the chip, resulting in a pressure increase to maintain the flow rate (Bondot et al., 2012). The responsiveness of pressure controllers is particularly useful when reproducing complex flow patterns such as aortic coronary flow. For sensitive cells exposed to a dynamic environment the controlling of all experimental parameters is of major importance. Endothelial cells, for example, have been shown to be sensitive to complex shear stress frequency harmonics (Feaver et al., 2013). Complex flow patterns can be directly coded and implemented in the software that drives the flow controllers. Working with pressure controllers is also particularly convenient when reproducing air
liquid interfaces for OOC models such as skin or lung (Benam et al., 2016).

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FIGURE 3.11 Scheme of a pressure-controlled open fluid circuit. The reservoir is pressurized, resulting in a fluid flow through the microfluidic chip. Reproduced with permission from Fluigent (www.fluigent.com).

Regarding compatibility with the incubator, pressure controllers have a competitive advantage over syringe pumps and peristaltic pumps since the user can adjust the gas composition to pressurize the medium. Furthermore, agitation of the medium can be easily implemented because pressure systems do not restrain tubes in a fixed position. Finally, multiplexing is another major benefit of pressure controllers, as a pressure source or stock solution can be split into multiple pressure or liquid channels, increasing throughput and reducing footprint (Fig. 3.12).

Membrane or diaphragm pumps Membrane and diaphragm pumps are formed from a cavity and two one-way valves. As the membrane or diaphragm is actuated, the volume of the cavity varies, creating repeating cycles or aspiration and expulsion of the liquid within the cavity (Fig. 3.13). Pneumatic, piezoelectric, electrostatic or electroactive, thermal, and electromagnetic forces are used to actuate the flexible membrane. Piezoelectric pumps are the most common, and various products are available commercially for microfluidics. Flow rates ranging from 0.1 μL/min to tens of milliliters per minute can be achieved depending on the size of the cavity, the actuation voltage, and the

Engineering fluid control for organ-on-chips

FIGURE 3.12 Scheme of the use of pressure to multiplex experiments. On the left, a fluidic manifold is connected to the liquid output to perfuse multiple chips from one reservoir. On the right, a pneumatic manifold divides the pressure between multiple sources to perfuse each chip with a dedicated medium. Reproduced with permission from Fluigent (www.fluigent.com, Laura Lelli).

FIGURE 3.13 Example of a piezoelectric membrane pump and its cross-section: (1) piezo; (2) adhesive; (3) actuation diaphragm; (4) inlet valve; (5) outlet valve. Reproduced under a Creative Commons Attributions 4.0 International License from Jenke, C., et al., 2017. The combination of micro diaphragm pumps and flow sensors for single stroke based liquid flow control. Sensors 17, 4, 755.

frequency (Wang and Fu, 2018). Membrane pumps provide a pulsatile flow, and the flow rate can be tuned by changing the actuation frequency.

Viscous drag pumps Viscous drag pumps use the friction between the fluid and a moving surface to convey the fluid from the inlet to the outlet. Fig. 3.14 illustrates a viscous drag pump, with its rotating, disk-shaped surfaces that drag the fluid. A scraping unit conveys fluid to and away from the moving surface. Viscous drag pumps have the advantage of being bidirectional and entirely pulsation-free. Their main drawbacks are the limited pressure generated for aqueous solutions (0.5
1 bar) and

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FIGURE 3.14 Schematic representation of the working principle of the viscous drag pump. Reproduced with permission from the Swiss Center for Electronics and Microtechnology (CSEM).

FIGURE 3.15 The TURBISC pump designed by the CSEM. CSEM, Swiss Center for Electronics and Microtechnology. Reproduced with permission from Swiss Center for Electronics and Microtechnology (CSEM).

that the flow rate achieved at a given rotation speed depends on the back pressure. Thus a flow sensor is often used in combination with viscous drag pump to enable closed loop control of the flow-rate. Fig. 3.15 shows a pump designed by the Swiss Center for Electronics and Microtechnology (CSEM, 2010). It has been used in different microfluidic flow cells (Dubeau-Larame´e et al., 2014; Chauvet et al., 2014) for its small footprint (width ,8 mm), its pulsation-free flow, and small inner volume (about 100 μL).

Engineering fluid control for organ-on-chips

Because the housing and seal are made out of polyetherimide and polyetheretherketone, respectively, the pump is resistant to most chemicals. In particular, this pump has been used for flow cytometry setups in the International Space Station (Dubeau-Larame´e et al., 2014). Due to the high shear rate induced by this pumping principle, the effect on cell viability needs to be verified when pumping cell solutions. For instance, it has been shown that hepatocytes are damaged during the pumping process while yeast cells remain unaffected.

On-chip pumps Beyond the peripheral (off-chip) pumps mentioned above, different on-chip pumps have been investigated and tested. On-chip pumps offer the option to create self-contained, closed microfluidic systems (Boyd-Moss et al., 2016), have inherently lower dead volumes, and reduce the number of fluidic interconnects on the chip. In general, on-chip pumps achieve very low flow rates, but they tend to increase the complexity of the microfluidic manufacturing processes and add costs to the disposable. On-chip pumps use a wide range of actuation principles, either passive (e.g., surface-driven flow, osmosis-driven flow) or active [e.g., membrane pumps, rotary pumps (Darby et al., 2010), bubble and acoustic pumps (Oskooei and Gu¨nther, 2015), and electrochemical, electrokinetic, and electroosmotic pumps]. Several groups have reviewed micropumps, including Au et al. (2011), Byun et al. (2014), and Wang and Fu (2018). Surface-driven-flow and membrane pumps are the most relevant technologies for OOCs because of their compatibility with cell cultures and their technological maturity.

Membrane pumps On-chip membrane pumps are based on the same concept as peripheral membrane pumps (see the “Membrane or diaphragm pumps” section), except that the whole pump is built into the microfluidic chip (Fig. 3.16). Unidirectionality of flow is achieved using check valves (Kim et al., 2006) or the sequential actuation of consecutive valves, thus pushing the displaced volume in the desired direction (Huang et al., 2008; Jeong and Konishi, 2008; Lee et al., 2007; Kim et al., 2006; Wang and Lee, 2006). Various implementations of pneumatic actuation exist, such as peristaltic pumps based on three consecutively arranged membrane valves (Unger et al., 2000; Studer et al., 2004), peristaltic micropumps based on a serpentine geometry (Huang et al., 2008; Wang and Lee, 2006), and the doormat micropump (Grover et al., 2003) (Fig. 3.17). Single-stroke micropumps are based on a single pneumatic control line (Lai and Folch, 2011). Generally, pneumatic micropumps achieve maximum flow rates in the range of nanoliters per second to hundreds of nanoliters per second for an actuation frequency around 20
75 Hz and a driving pressure of some tens to hundreds of kilopascals. The dead volume of these pumps is very low, in the range of hundreds of picoliters.

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FIGURE 3.16 Peristaltic micropump based on the sequential deformation of three pneumatic actuators. Reproduced with permission from Jeong, O.C., Konishi, S., 2008. Fabrication of a peristaltic micro pump with novel cascaded actuators. J. Micromech. Microeng. 18, 025022.

FIGURE 3.17 Scheme of a diaphragm micropump consisting of three doormat microvalves. Reproduced with permission from Grover, W.H., Skelley, A.M., Liu, C.N., Lagally, E.T., Mathies, R.A., 2003. Monolithic membrane valves and diaphragm pumps for practical large-scale integration into glass microfluidic devices. Sens. Actuat. B 89, 315
323.

Another common actuation approach is based on piezoelectric materials, as illustrated in Fig. 3.18 (Tracey et al., 2006). Piezoelectric pumps can achieve higher flow rates than pneumatically actuated pumps, since much higher actuation frequencies can be achieved. Despite the increasing use of polymer materials (PDMS or PMMA), however, piezoelectric pumps remain more expensive than other pumping schemes and thus are not well suited for disposable chips. In general, the main drawback of membrane pumps is the choice of flexible materials for the membrane. Many designs have been demonstrated with PDMS, which limits the scalability and applicability of these pumps because of the difficulty of the manufacturing scale-up and the nonspecific binding of small proteins to PDMS. Nonetheless, various membrane micropumps are available commercially, such as those manufactured by TissUse (Berlin, Germany) (Sonntag et al., 2010;

Engineering fluid control for organ-on-chips

FIGURE 3.18 Operation of a single piezoelectric micropump pumping from left to right. Reproduced with permission from Tracey, M.C., Johnston, I.D., Davis, J.B., Tan, C.K.L., 2006. Dual independent displacement-amplified micropumps with a single actuator. J. Micromech. Microeng. 16, 1444
1452.

FIGURE 3.19 Schemes of capillary pump operation. Reproduced with permission from Juncker, D., et al., 2002. Autonomous microfluidic capillary system. Anal. Chem. 74, 6139
6144.

Materne et al., 2015; Schimek et al., 2013), or Formulatrix (Bedford, MA, United States) (Formulatrix, 2019b).

Surface-driven flow or capillary pumps Surface-driven flow pumps or capillary pumps use the interplay between surface tension, surface chemistry, and topography to move the fluid in the direction that minimizes the free energies between the vapor, fluid, and surfaces (Fig. 3.19). An IBM group from Switzerland has pioneered the use of such capillary pumps (Juncker et al., 2002). Flow rates in the order of tens to hundreds of nanoliters per second have been demonstrated. Surface-driven flow pumps are compact, simple to integrate, and low-cost (Walker and Beebe, 2002). The main drawbacks are the limited volumes that can be actuated and the limitation to unidirectional flow.

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Furthermore, the pumping rate changes with time and cannot be controlled externally.

Valves and bubble traps Valves allow the user to control the fluid flow within a microfluidic chip. Used in combination with liquid actuation, valves permit, for instance, the flow from one channel to another to be redirected or the sequence of fluids flushed through the chip to be alternated. For liquid actuation, it is important to distinguish between off-chip valves and on-chip valves. Many companies offer commercial off-chip products, including solenoid valves. Since the pioneering work of the group led by Andreas Manz in 1993, different technologies have been proposed for on-chip microvalves (Au et al., 2011; Oh and Ahn, 2006). Table 3.6 describes their applicability for OOCs. Table 3.6 Overview of microvalve technologies used in organ-on-a-chip applications. Microvalve category

Applicability for organ-on-achip devices

Electrokinetic

Not applicable because of the use of high voltages and large variability

Pneumatic

Simple integration into microfluidic chips using commercial pressure controllers. However, the use of polydimethylsiloxane may cause issues because of adsorption of hydrophobic molecules

Pinch

Simple integration into microfluidic chips using mechanical actuators

Phase change

Requires integration of heating or cooling elements. Slow actuation time Useful to rectify pressure peaks during cell insertion, thus avoiding damaging the cells Applicable for chip devices using biocompatible electroactive polymers. Easy to control using low-voltage electronics

Burst

Electroactive polymers

References Lee et al. (2008), Kaigala et al. (2008), Jacobson et al. (1999), and Schasfoort et al. (1999) Unger et al. (2000), Studer et al. (2004), Grover et al. (2003), Sundararajan et al. (2005), Hosokawa and Maeda (2000), Yang and Lin (2007), Yoo et al. (2007), Irimia and Toner (2006), and van der Wijngaart et al. (2007) Pemble and Towe (1999), Weibel et al. (2005, 2007), Pilarski et al. (2005), and Formulatrix (2019a) Yang and Lin (2007) and Yoo et al. (2007) Cho et al. (2007) and Chen et al. (2008b) Tanaka et al. (2013), Parker (2015), and Carpi and Smela (2009)

Engineering fluid control for organ-on-chips

FIGURE 3.20 Principle of operation of a bubble trap. The bubbles present in the liquid at the inlet are removed by gas diffusion through a semipermeable membrane.

Bubble traps are often integrated into the microfluidic system to ensure stable operation by removing bubbles circulating in the microfluidics that may block it and potentially disturb the flow or the sensors. Introduced air bubbles are, moreover, lethal for cultured microtissues. Fig. 3.20 illustrates the principle of operation of bubble traps. The bubbles present in the liquid at the inlet are removed by gas diffusion through a semipermeable membrane. The efficiency of bubble removal depends on the contact surface between the liquid and the semipermeable membrane. This effect can be enhanced by using vacuum at the exhaust or by using micropillars in the fluid path to pin the air bubbles (Zhang et al., 2017). Different commercial products are available (Fluigent, 2019b; ElveFlow, 2019a).

Flow sensing Flow-rate and pressure are the two most important parameters to monitor and measure during the operation of OOCs. The flow-rate is indeed directly related to oxygen and nutrients perfusion of the OOC but also the shear stress to which the cells are exposed. Furthermore, exact control of low flow-rates is essential when emulating cell-to-cell interactions or spatiotemporal chemical gradients in order to achieve physiologically relevant concentrations of the different molecules in the medium. Pressure is another important parameter to monitor, both during cell loading into the microphysiological system and during cell perfusion. Cells can be easily damaged during cell loading due to pressure peaks, as reported by Wang et al. (2016). Pressure is also an important aspect of human physiology, in particular when mimicking blood circulation (Chen et al., 2017) or to induce mechanical stimulation (see the “Mechanical” section). Different flow-rate and pressure sensors technologies are introduced and compared in the following paragraphs.

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Table 3.7 Overview of flow-rate sensor technologies. Sensor principle

Response time (ms)

References

Optical/Capacitive flank monitoring Differential-pressure based Thermal calorimetry Thermal time-of-flight Coriolis force Gravimetric balance

0.4 ,1 40 6
25 50
200 ,2000

Zengerle et al. (1995) Richter et al. (1999) Sensirion (2019) Ashauer et al. (1999) Bronkhorst (2019) Sartorius (2019)

Flow-rate sensors A large variety of sensors based on different fields of physics are available (Table 3.7). Selecting the appropriate flow meter adapted to the flow regime and fluid is critical for accurate measurements.

Thermal sensors A common technology relies on the calorimetric method. A microheater provides a minimal amount of heat to the medium monitored (around 1 C). Two temperature sensors, located on each side of the heater, detect temperature variations. The flow rate is then calculated based on the spread of heat, which is directly related to the flow rate. This method of monitoring flow is one of the simplest and least intrusive. It is easy to integrate into microelectromechanical devices, as very small heaters and sensors already exist. Nonetheless, it requires knowledge of the fluid density and specific heat capacity. These values should also be constant for the proper functioning of the sensor. In biological experiments the presence of cells or particles in the fluid may affect the fluid’s properties and the measurement. Furthermore, a change in temperature may not be acceptable to the viability of cells in the solution. Several other thermal flow meters are available that function in a similar way (Kuo et al., 2012). The hot wire uses a resistor as a heater and a sensing element. As the resistance is dependent on the temperature, a relationship between applied tension, temperature, and resulting resistance can be established. Other sensors use so-called time-of-flight sensing (Fig. 3.21). This technique uses only one sensor, which is located downstream of the heater. By observing the heat distribution over time, time-of-flight sensing can deduce fluid velocity and thus the flow rate.

Coriolis mass flow meters Use of mass flow meters in microfluidics is growing as the technology is improved for microscale flows. In a mass flow meter operating on the Coriolis principle (Fig. 3.22) the fluid flows on a vibrating channel. The flow rate through its mass will proportionally affect the frequency, phase shift, or amplitude of the initial vibration. The main advantage of this technology is the independence of

Engineering fluid control for organ-on-chips

FIGURE 3.21 Function principle of a time-of-flight thermal sensor. Reproduced under a Creative Commons Attributions 3.0 International License from Kuo, J.T.W., Yu, L., Meng, E., Kuo, J.T.W., Yu, L., Meng, E. Micromachined thermal flow sensors—a review. Micromachines 3, 3, 550
573.

FIGURE 3.22 Schematic principle of the Coriolis flow sensor. Reproduced under a Creative Commons Attributions 4.0 International License from Lo¨tters, J.C., Lammerink, T.S., Groenesteijn, J., Haneveld, J., Wiegerink, R.J. Integrated thermal and microcoriolis flow sensing system with a dynamic flow range of more than five decades. Micromachines 2012, 3, 194
203.

the measured flow rate from the properties of the liquid. These sensors can monitor gas flow or oils without any specific calibration; however, the technology remains expensive, and the small inner diameter of the fluidic path may not be suitable for biological experiments.

Differential pressure-based flow sensors Differential pressure-based (DPB) sensors calculate the flow rate by measuring the differential pressure across a fluidic restriction of known resistance. The dynamic range and sensitivity of DPB flow sensors can easily be adapted by changing the restriction placed in the channel. The higher the restriction is, the higher is the sensitivity. But the restriction should be selected carefully as a high flow resistance limits the fluidic performance of the system. DPB flow sensors offer a faster response time (,5 ms) than thermal flow sensors. The flow restriction is often realized by using a capillary or a diaphragm placed in the flow path

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FIGURE 3.23 Principle of a pressure sensor
based flow sensor: (1) silicon diaphragm; (2) orifice cone angle; (3) piezo resistors; (4) conducting gasket. Reproduced under a Creative Commons Attributions 4.0 International License from Jenke, C., et al., 2017. The combination of micro diaphragm pumps and flow sensors for single stroke based liquid flow control. Sensors 17, 4, 755.

(Fig. 3.23). The ratio between orifice diameter and diaphragm thickness changes the influence of viscous friction and therefore viscosity and temperature on the differential pressure across the restriction. When the ratio is small, viscous friction dominates (Hagen
Poiseuille equation), while inertia effects dominate for large ratios (Torricelli’s law). For ratios close to 1, both effects must be considered (Hagenbach correction) (Richter et al., 1999). DPB sensors should typically be calibrated for certain liquid classes to achieve reproducible results.

Imaging-based sensors Various methods based on the imaging of moving or flexible elements in the chip have been demonstrated. For instance, microparticle image velocimetry tracks the displacement of particles within the medium using a digital camera. Since images are acquired at fixed intervals, the velocity of the particles can be calculated. Particle image velocimetry is a well-known method for characterizing flow properties in microfluidic chips (Nguyen and Wereley, 2002; Lindken et al., 2009), with the advantage of being contactless and yielding a velocity distribution. It is not, however, adapted for nonresearch applications, since particles often must be introduced into the medium to visualize the flow. Other imaging-based approaches make use of flexible structures such as pillars or posts to measure fluid flow (Mann et al., 2012).

Pressure sensors Pressure is an important parameter to monitor, both during cell loading into the microphysiological system and during cell perfusion. Cells can be easily damaged during cell loading due to pressure peaks, as reported by Wang et al. (2016). Pressure is also an important aspect of human physiology, in particular when mimicking blood circulation (Chen et al., 2017). For this reason, different pressure sensor solutions have been employed in OOCs. Commercial pressure sensors can be used, but they are not adapted to measure localized pressure and typically have a large dead volume. On-chip sensor solutions have been explored; most solutions are based on the deflection of a flexible membrane such as PDMS (Kartalov et al., 2007; Chung et al., 2009;

Engineering fluid control for organ-on-chips

FIGURE 3.24 Example of a micropressure sensor based on a flexible membrane. Reproduced with permission from Wang, L., et al., 2009. Polydimethylsiloxane-integratable micropressure sensor for microfluidic chips. Biomicrofluidics 3, 3, 034105.

Wang et al., 2009; Liu et al., 2013), which is measured either optically or electrically (Fig. 3.24). An interesting alternative has been proposed by Chen et al. (2017) that is based on the use of a sealed capillary. Pressure is directly related to the position of the liquid meniscus in the sealed capillary and can thus be measured with a video camera. Using this sensor, the authors were able to demonstrate that the pressure profile in their chip was identical to the systolic and diastolic pressure cycles observed in humans.

Other sensors Beyond pressure and flow rate, other parameters may require monitoring to ensure the proper operation of the OOC. For instance, the formation of bubbles within the microfluidic chip because of degassing or cellular activity may have a serious impact. Bubbles change the flow resistance of microfluidic channels and, thus, flow rates, but measuring the change in liquids passing through a microfluidic channel may also be important to monitor the process. Flow-front sensors measure changes of phase in front of the sensor as well as changes in the liquid properties. Flow-front sensors based on optical (Nguyen and Truong, 2005) or electromagnetic (Wrasse et al., 2017) approaches have been developed. One example is bubble sensors that are commercialized by various companies (Elveflow, 2019b).

Industrial implementation The engineering of the right fluid control system depends greatly on the organlevel function that a specific OOC needs to mimic. The complexity of the flow

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Table 3.8 Overview of organ-on-a-chip companies and their methods of flow control. Company

Flow control

References

InSphero

Bidirectional, gravity-based flow using a tilt table. Flow rate is calculated from known flow resistance in the microfluidic plate Bidirectional, gravity-based flow using a tilt table. Flow changes path depending on the direction Medium exchange by manual pipetting Membrane-based peristaltic pump

Kim et al. (2015) and InSphero (2019)

Mimetas

AlveoliX TissUse

Emulate

FiberCell Systems CN Bio Innovations Draper Laboratory

Depending on the application, pressure control, peristaltic, or syringe pumps Peristaltic pump Fully automated fluid control

Electrical actuated pumps

Trietsch et al. (2013, 2017) and Mimetas (2019) Stucki et al. (2015, 2018) and Alveolix (2019) Sonntag et al. (2010), Materne et al. (2015), Schimek et al. (2013), and TissUse (2019) Huh et al. (2013), Emulate (2019), and Kasendra et al. (2018) FibreCellSystems (2019) Edington et al. (2018) and Tsamandouras et al. (2017) Coppeta et al. (2017) and Xiao et al. (2017)

control system can be orders of magnitude apart between performing a simple medium exchange or a complex recirculation to simulate metabolism. Flow-control strategies used in commercial OOCs vary. Table 3.8 shows that OOC companies are using conservative approaches for flow control; practically all OOC companies use peripheral (off-chip) liquid actuation strategies. The intent is to achieve high reproducibility and reliability but also to decrease the cost of the disposable chip. Adoption of OOC devices by pharmaceutical companies, however, requires simplicity, easier standardization, and compatibility with standard laboratory automation systems. Only TissUse currently offers an on-chip membrane pump to recirculate the medium, and as their OOC platform aims to stimulate the interplay between urine and blood circuits, liquid actuation with very low dead volumes is beneficial. As a general rule, researchers interested in transferring their solution to the market should consider adopting the platform with the lowest degree of complexity for simulating a particular organ-level function.

Stimulation and sensing

Stimulation and sensing OOC platforms strive for model systems of nearly in vivo quality. To this end and additionally to the already described engineering requirements (the “Microengineering” section), stimulation tools are essential to induce the development of physiological cell functions for each individual organ mimic. Moreover, sensing tools allow measurements with high sensitivity and selectivity for accurate model system interpretation and thus are fundamental for experimental readout, validation, and standardization. Together, stimulation and sensing technologies are key components for the success of OOC and cell analysis platforms. This section discusses optical, mechanical, and electrical methods that enable quantitative analyses specifically for OOC platforms and introduces stimulation strategies used in cell analysis and OOC devices. Since the chemical stimulation of tissue in microfluidic OOCs commonly occurs via liquid perfusion and thus does not require specialized solutions, it is not part of the overview provided here.

Optical Optical assessments are the most commonly used analysis methods for OOC systems. They can take the form either of real-time culture monitoring or of endpoint assays. Whereas real-time culture monitoring is considered noninvasive and measurement is remote, endpoint assays are less time-critical but are often destructive. Optical probes consist of luminescent molecules—that is to say
molecules that emit either a fluorescent or a phosphorescent signal upon light excitation (Lakowicz, 1999). A plethora of luminescent molecules exists, allowing for—for instance—cell viability monitoring, protein expression analyses, and characterizing the impact of engineered devices on biological systems (Johnson and Spence, 2010; Varma et al., 2017). It should be noted that it is best to accurately evaluate molecular probes for optical analysis since they may, under certain conditions, interfere with the biological system studied (Kurth et al., 2018). Most optical assays employed in OOC devices are, in fact, molecular assays developed for conventional 2D cultures; they are already well documented and thus are not discussed here [for an overview, refer to Johnson and Spence (2010) and Varma et al. (2017)]. This section will focus on assays specifically required and partially developed for 3D cell model systems. As it is of the utmost importance for OOC platform control and validation to accurately monitor various parameters in the cell culture environment, there have been increasing efforts to integrate a variety of sensors into the microfluidic cell environment, including dissolved oxygen and CO2 (Grist et al., 2010; Modena et al., 2018). Although optical sensors are available for numerous other parameters and metabolites, such as pH, glucose, and lactate, the optical sensor designs are rather similar: either a hydrophilic fluorophore sensitive to a target

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molecule or metabolite is perfused within the culture medium or the sensitive fluorophore is encapsulated within a solid matrix of different formats. Since dissolved oxygen is one of the key parameters for OOC platform engineering, we will exemplarily focus on oxygen-sensitive optical measurements in the following. The features of optical oxygen sensors mainly depend on: 1. Sensing methods and material The molecular oxygen concentration reversibly quenches both the luminescence intensity and the excited-state lifetime of the luminophore so that optical oxygen sensors are sensitive to changes in its intensity as well as its decay time. Higher luminescent intensities correlate to higher oxygen concentrations, whereas higher luminophore lifetimes correlate to lower oxygen concentrations. Despite the fact that intensity-based sensing has been successfully applied for in vivo and cell culture methods, several publications have concluded that lifetime-based recordings are more robust and suitable for OOC monitoring compared to intensity measurements as fluorescent lifetimes are unaffected by bleaching or impaired photon yield due to proliferating tissue increasingly masking the probe (Grist et al., 2010; Oomen et al., 2016). Key parameters for the choice of the luminophore are the efficiency of the quenching process, stability against photobleaching, compatibility with inexpensive and available excitation sources, and the capability of being discriminated from the autofluorescence background. Examples of molecular fluorescent oxygen indicators include ruthenium-based and metallo-porphyrinbased molecules (Grist et al., 2010). 2. Encapsulation Any probe to be incorporated within a cell culture system must be carefully evaluated for its biocompatibility to exclude toxic effects. To prevent direct interaction between the luminophore and the biological material the fluorophore is thus often encapsulated and immobilized in a polymer, sol
gel, or silica matrix. Important considerations for the choice of the encapsulation matrix are the diffusion constant of oxygen and the solubility of the selected oxygen-sensitive luminophore within it (Oomen et al., 2016). If a sensor is to be reused, it must withstand adequate cleaning and sterilization routines. 3. Format Various formats of optical oxygen sensors exist for microfluidic cell culture devices (Fig. 3.25). The target-sensitive luminescent molecules can be encapsulated within patterned thin films attached to, for instance, the microfluidic channel or chamber bottom surface. The combination of multiple different sensor films next to each other allows for multiparametric readouts in the case that other luminophores with diverse target molecules are incorporated. A more miniaturized but still locally immobilized solution is the

Stimulation and sensing

(A)

(B)

Sensor film

Substrate

Substrate

Sensor film

(C)

(D)

Micro/nanoparticles

Optical fiber Opaque optical isolation Sensor film Aqueous solution (E)

Micro/nanoparticles

(F)

Sensor film

Substrate

Aqueous solution

FIGURE 3.25 The essential advantages of optical sensors for microfluidic bioreactors and OOC systems are the miniature sizes of the optical probes and their remote operation by fluorescence microscopy. To integrate these optical sensors into OOC platforms, molecule-sensitive luminophores can be encapsulated into thin films (A and B), onto optical fibers (C), and into beads of varying sizes (D and E). They are also available as hydrophilic probes dissolved in solution (F). OOC, Organ-on-a-chip. Reproduced with permission from Grist, S.M., Chrostowski, L., Cheung, K.C., 2010. Optical oxygen sensors for applications in microfluidic cell culture. Sensors (Switzerland) 10, 9286
9316.

packing of the sensor molecule into a matrix at the tip of an optical fiber, which serves as a direct light transmitter with minimal photon losses. Packed mobile probes consist of polymer or silica beads enriched with the luminophore of interest either inside the porous core structure or immobilized onto the outer surface of the beads. These beads can be introduced into the platform on demand in solution and their concentration can be adapted to specific needs. Polymer matrices enveloping the beads result in prolonged operational stability due to a decrease in both luminophore leaching and luminophore bleaching, the latter by interaction with environmental

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compounds. Nanoparticle-sized beads become cell permeable and therefore enable, for example, real-time intracellular oxygen monitoring in single cells or across tissues as well as in situ oxygenation studies. Submerging oxygensensitive beads in gel matrices also opens up the possibility of parameter monitoring in often-used gel matrices, such as Matrigel, gelatin methacryloyl, and collagen. Hydrophilic target molecule-sensitive luminophores are, on the contrary, soluble in water and have already served as probes for in vivo imaging. They can easily be dosed in varying concentrations and injected and recovered on demand, when perfusion systems are used. Compared to beads introduced into the device, luminophores do not interfere with bright-field and fluorescence microscopy apart from their emission wavelength. The main disadvantage of this approach is that, since not encapsulated, the probe releases cell-toxic singlet oxygen in its excited state to its environment. The use of such probes must consequently be characterized to prevent metabolic stress. Recent findings have, however, shown that the addition of 10% fetal bovine serum to the solution containing the hydrophilic probe prevents cells from experiencing the toxic side effect (Eyer et al., 2017). 4. System The sensor will finally be part of an optical measurement system consisting of at least a light source to excite the luminescent dye and a detector to detect the luminescent emission. Although detectors can be reduced to the minimum—for instance, a photodiode—most solutions for OOC devices employ imaging setups in order to retrieve the spatial resolution of the organ model. Conventionally used systems include standard fluorescence microscopes, but depending on the OOC device other setups providing sufficient sensitivity and spatial resolution may offer highly affordable alternatives. For example, simple bright field imaging can provide the readout of spherical microtissue sizes as a measure of growth and viability. It is worth mentioning that the use of any system already available in cell culture laboratories will certainly support the establishment of OOC platforms in daily laboratory routines.

Mechanical The principles of mechanical cell stimulation for miniaturized culture models were originally developed for single cells or 2D cultures in the early years of the 21st century (Kim et al., 2009) and have since been further optimized for OOC model systems. While dimensions and durability demanded adaptations, the principles of mechanical force transmission persisted, all with the ultimate goal of inducing mechanotransduction processes unique and relevant for the mimicked in vivo situation. Mechanotransduction processes—that is to say, the translation of an external physical force into an intracellular biochemical signal—are mediated by a plethora of distinct signal cascades (Clapham, 2003; Jaalouk and Lammerding, 2009). Those processes are distinct for unique cell differentiation

Stimulation and sensing

FIGURE 3.26 The principles of mechanical force transmission mimicking the in vivo situation comprise shear forces, strain/stretch, compression, and intracellular contractile forces, as well as gravity. Example models for the microfluidic realization of these scenarios are presented in the right column. The combination of different forces increases complexity but can be beneficial for tissue and multi-tissue applications. Adapted from Kurth, F., Eyer, K., Franco-Obrego´n, A., Dittrich, P.S., 2012. A new mechanobiological era: microfluidic pathways to apply and sense forces at the cellular level. Curr. Opin. Chem. Biol. 16, 400
408 with permission from Elsevier.

states as well as terminally differentiated tissues and rely on particular force paradigms (Fig. 3.26): (1) fluid flow-induced shear stress, (2) tensile strain (stretch), (3) compression, (4) contraction, and (5) gravity. The biological relevance of these cases is rather complex, but at the same time, it is fundamentally important to choose the correct force paradigm for the individual 3D cell model system of interest. Good overviews on the mechanobiological principles can be found in Discher et al. (2005), Vogel and Sheetz (2006), Huang et al. (2004), Ingber (2003), Baker and Chen (2012), and Krishnan et al. (2011). Of all the enumerated forces, gravity is perhaps the most underestimated of all, since it is omnipresent and hardly changes. Due to its constant effect on Earth and the technological difficulty of inducing changes, it has however not yet been of interest for OOC devices. The electromechanical stimulation of tissue, for instance by pulsed electromagnetic fields (Parate et al., 2017), is gaining recognition in regenerative medicine and is listed in Table 3.9, together with all mechanically relevant

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Table 3.9 Mechanically relevant forces to induce and sense mechanotransduction processes realized in organ-on-a-chip devices and cell analysis platforms. The listed reviews discuss options for mechanical cell stimulation, including by means of magnetic bead incorporation (stretch, compression), acoustic waves, substrate microand nanopatterning, and hyper- and hypogravity. Mechanical force

Objective

Technological solution

Fluid shear force

Induce physiological shear/prevent harmful shear

Accurate flow control

Stretch/strain

Dynamic, cyclic stretching

Soft polymer membranes

Compression

Dynamic, cyclic mechanical loading

Reactor design (metal, hard plastic)

Constant or dynamic, cyclic mechanical loading Sense cell-induced contraction force Induce mechanical stimulation by electromagnetic field

Soft polymer membrane

Contraction Electrical

Cell substrates Reviews

Sense mechanotransduction response Varying substrate stiffness to influence cell development

Soft strain gauge sensors Electronic microactuators, pulse electromagnetic fields Transepithelial electrical resistance Substrates and matrices (e.g., gels)

Platforms for the mechanobiological study of organs-on-a-chip/cells

References (examples) Haase and Kamm (2017), Chen et al. (2017), Curto et al. (2017), Varma et al. (2018), Scheinpflug et al. (2018), Kim et al. (2015), Bavli et al. (2016), Li et al. (2015), van Duinen et al. (2017), Jain et al. (2018), Maschmeyer et al. (2015a), and Lohasz et al. (2019) Huh et al. (2010), Benam et al. (2016), Stucki et al. (2015, 2018), Douville et al. (2010), Villenave et al. (2017) Scheinpflug et al. (2018), Li et al. (2010), Hoffmann et al. (2015), Meinert et al. (2017), and Rousselle et al. (2018) Park et al., 2012

Lind et al. (2017), Feinberg et al. (2007), and Grosberg et al. (2011) Svennersten et al. (2011) and Parate et al. (2017)

Curto et al. (2017)

Verhulsel et al. (2014), Gilbert et al. (2010), and Maginet al. (2016) Haase and Kamm (2017), Ahadian et al. (2018), Guenat and Berthiaume (2018), Kim et al. (2009), Kurth et al. (2012), Ergir et al. (2018), and McLean et al. (2018)

Stimulation and sensing

forces. To identify potential applications of mechanotransduction in OOC devices, this section focuses on the engineering details enabling the force transmission of shear forces, stretch, and compression, as well as force readout of tissue contraction.

Stimulation Shear stress The simplest way of introducing mechanical forces on cells is by applying fluid flow-induced shear forces, which represent, for example, blood flow through veins or interstitial flow through the bone matrix. The physical phenomena commence relatively simply with a mere fluid flow through a straight tube in which cells grow attached to the inner surface but can reach high complexity in cases of tube branching or even upon the addition of blood cells into the fluid. These processes are well described in a review by Sebastian and Dittrich (2018) and have direct implications for device design and experimental conditions. Moreover, fluid flow-induced shear forces are very often hard to exclude since continuous nutrient supply and waste removal within cell culture models typically depend on medium perfusion. Shear forces are commonly given in dynes per square centimeter, where 1 dyn=cm2 5 1  1025 N 5 1  1021 Pa:

Physiologically relevant shear forces for endothelial cell culture models range from about 10 to .50 dyn/cm2 (Papaioannou and Stefanadis, 2005; Kim et al., 2007); for bone osteoblasts, effects were reported between 8 and 30 dyn/cm2 in a very simplified cell model (Jeon and Jeong, 2012). Other tissues may have to be protected from shear forces to impede deviating effects from in vivo-like development. As shear forces can impinge already at significantly lower ranges for highly mechanosensitive cells—for instance, progenitor muscle cells have been shown to react at forces ,0.1 dyn/cm2 (Kurth et al., 2015) and Madin
Darby canine kidney cells at 0.3 dyn/cm2 (Curto et al., 2017)—fluid-flow ratios and chip designs have to be considered carefully. For design considerations, we refer the reader to recent reviews of multiorgan systems (Wang et al., 2018b; Rogal et al., 2017; Ronaldson-Bouchard and Vunjak-Novakovic, 2018). Fluid flow-induced shear forces can be approximated according to the wall-shear rate model: τ 56

QUμ ; wUh2

ðrectangular channelÞ

τ 54

QUμ ; πUR3

ðcircular tubeÞ

where Q is the volumetric fluid-flow rate, μ is the dynamic viscosity of the medium, w and h are the channel width and height, respectively, and R is the inner radius of the tube (Squires and Quake, 2005; Efstathopoulos et al., 2008). The dimensions of the channel height or the inner radius affect the fluid

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FIGURE 3.27 Vessel shear stress values in various types of vasculature measured in vivo. Reproduced with permission from Papaioannou, T.G., Stefanadis, C., 2005. Vascular wall shear stress: basic principles and methods. Hell. J. Cardiol. 46, 9
15.

mechanics most and thus determine whether the device is prone to inducing strong or weak shear forces. They are accordingly the most important geometrical constraints for device engineering. Notably, the application of constant shear forces may result in different outcomes than the application of pulsatile shear forces (Chin et al., 2011), which occur, for example, in the case of peristaltic pump-driven medium perfusion. In vivo-derived values for vessel shear rates are available in a review by Papaioannou and Stefanadis (Papaioannou and Stefanadis, 2005); see also Fig. 3.27. Such patient-derived information can be translated to in vivo-mimicking on-chip models to reinforce their medical relevance. In experimental terms, in vitro-induced shear force values and their effect on conventional 2D microfluidic cell culture are summarized in a review by Kim et al. (2007); see also Table 3.10. Since a concise and complete summary of shear rate-induced effects in OOC devices is still lacking in the literature, the interested reader is referred to Table 3.9 at the beginning of the “Mechanical” section; it provides references to scientific works and commercially available 3D culture models, each of which provide relevant flow rates for their devices to induce/circumvent shear forces. An emerging technology for 3D cell models is the direct printing of cells in a gel matrix. These printing processes, however, induce quite significant shear forces that may lead to necrosis and cell disruption, respectively (Zhou, 2017; Vijayavenkataraman et al., 2018). Studies on the long-term effect induced by these shear forces may yield valuable information with which to optimize this versatile

Stimulation and sensing

Table 3.10 Experimentally tested fluid flow
induced shear forces in microfluidic cell cultures. Cell type Vascular endothelial cells Hepatocytes Mouse embryonic stem cells Human endothelial cells Bovine aortic endothelial cells HeLa cells Chinese hamster ovary cells Human osteoblast-like bone cells Skeletal muscle progenitor cells (C2C12) Madin
Darby canine kidney cells

Shear stress ðdyn=cm2 Þ

Reference

10‒100 ,2 6.5 4‒25 20 4‒25 4‒25 4‒25 8‒30 0.09‒0.94 0.3

Li et al. (2005) Powers et al. (2002) Fok and Zandstra (2005) Ranjan et al. (1996) Varma et al. (2018) Ranjan et al. (1996) Ranjan et al. (1996) Ranjan et al. (1996) Jeon and Jeong (2012) Kurth et al. (2015) Curto et al. (2017)

Data partly derived from Kim, L., Toh, Y.-C., Voldman, J., Yu, H., 2007. A practical guide to microfluidic perfusion culture of adherent mammalian cells. Lab Chip 7, 681
694.

technology. New technologies for 3D cell culture model systems may not be the only factors influencing the experimental outcome (un)expectedly. For instance, a careful adaptation of shear forces is recommended for drug-testing models as mechanobiological input has been shown to affect drug delivery yields (Bhise et al., 2014). The diverse effects of fluid flow-induced shear forces on the cell models under investigation depict the complex interaction of biological systems with engineered environments and underscore the necessity of thorough planning and testing.

Stretch: tensile strain The application of mechanical stretch in cellular in vitro models commonly depends on flexible membranes that are either unidirectionally stretched by elongation of a particular membrane area or undergo a more complex deformation due to the application of either liquid or gas (positive or negative) pressure to a circular membrane from one side (Fig. 3.28). The first cell-stretch platforms were developed in the 1990s with the aim to induce uniaxial stretch in simpler 2D cell models, especially muscle. Muscle development inherently depends on cyclic stretching, since the mechanotransduction processes driving myogenesis are orchestrated by stretch-sensitive cation channels that upon activation induce changes in calcium homeostasis, which ultimately regulate cell fate (proliferation or differentiation) (Bassel-Duby and Olson, 2006). Although other tissues do not depend on this mechanical stimulus as strongly, their physiological and pathological states can still be influenced by tensile strain (Huh et al., 2010). The first cellstretching platforms comprised a silicon chamber that was elongated by stepping motors [e.g., Naruse et al. (1998)—a stretch chamber by Strex, Osaka, Japan]. To adapt this principle to OOC devices, the dimensions of the chambers were scaled

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FIGURE 3.28 Organ-on-a-chip models that include mechanical stretch. (A) A PDMS-based chip design for an intestine model undergoing peristalsis-like motions, manufactured by Emulate (emulatebio.com): The gut epithelium is attached to a porous, flexible PDMS membrane in the middle of a microfluidic channel serving as the medium supply. The cell channel is flanked by two vacuum channels that, upon activation, exert cyclic membrane stretch. The medium perfusion-induced shear forces support the recreation of the gut microenvironment. (B) An alveolus-on-a-chip model system for parallelized testing, by AlveoliX (www.alveolix.com). Mechanical stretch of the cell-laden membrane (BM) representing the alveolar barrier consisting of epithelial type I (AT I) and type II (AT II) cells at the air interface and ECs at the liquid interface. The multilayered chip device represents exactly this mechanism, whereby the deformation of the lower microdiaphragm transfers the vacuum force to the upper cell-laden membrane during breathing mode. Medium exchange is enabled by membrane valves at the side of the flow chamber. BM, Basal membrane; ECs, endothelial cells; PDMS, polydimethylsiloxane. Adapted from (A) Villenave, R., et al., 2017. Human gut-on-a-chip supports polarized infection of coxsackie B1 virus in vitro. PLoS One 12, 2, e0169412; (B) Stucki, J.D., et al., 2018. Medium throughput breathing human primary cell alveolus-on-chip model. Sci. Rep. 8, 1, 14359.

down to smaller sizes and incorporated into closed chambers and channels. The membranes are still typically fabricated from silicon (mainly PDMS) and then sandwiched in between adjacent channels or culture compartments (Huh et al., 2013), (Stucki et al., 2015), (Quiro´s-Solano et al., 2016). To allow for

Stimulation and sensing

transmembrane cell-to-cell communication and effective exchange of molecules, the membranes can be perforated during fabrication. The primary focus of these OOC models is lung mimics (Huh et al., 2010, 2013; Benam et al., 2016; Stucki et al., 2015; Douville et al., 2010), but other tissue models are integrated into identical chip systems to study, for example, the intestine (Kim et al., 2012; Villenave et al., 2017). Applied strain forces are given in either linear, surface, circumferential, or radial strain. Linear strain—the predominant variant—is reportedly applied up to 50%, whereas most of the devices induce about 10% (Huh et al., 2013; Stucki et al., 2015; Douville et al., 2010; Villenave et al., 2017). Resulting surface strains are typically slightly higher—that is, up to 60%—and circumferential and radial strains are commonly applied at below 20% (Guenat and Berthiaume, 2018). Despite these differences in induced strain forces, stretching frequencies are relatively similar at around 0.2 Hz for all organ models and seldom exceed frequencies above 1 Hz. Most recent developments combining silicon membranes with microelectromechanical systems technology have, however, opened the way for highly miniaturized membranes that enable the generation of strain rates as high as 870 Hz allowing studies of even extreme physiological conditions such as blunt force trauma (Poulin, 2018). Since silicon-based polymer membranes can absorb hydrophobic molecules not only by surface interactions but also by the diffusion of molecules into the porous polymer, precautions must be taken to not alter concentrations of administered drugs and stimulants (Berthier et al., 2012; Wang et al., 2012; Wong and Ho, 2009). Alternative materials are, however, rarely used as stretchable membranes. For PDMS chips, low-pressure (oxygen) plasma activation and subsequent assembly of the membranes to the adjacent layer(s) results in reliable bonding (Huh et al., 2013; Wagner et al., 2013). In some protocols, the membranes are “glued” in between the adjacent layers, typically during the prepolymer curing process. It is strongly advised to extend such curing processes beyond the time specified in standard protocols (a minimum of 36 hours) to ensure the complete depletion of precursor molecules, which are highly cell-toxic (Kellogg et al., 2014; Regehr et al., 2009). Glues must be thoroughly validated for their biocompatibility. The clamping of membranes is an alternative option but is often prone to fluid leakage and air bubble generation, respectively.

Compression Although all cells within the human body regularly undergo compressive forces due to body movement and gravity, the development and maintenance of only a few tissues inherently depend on constant dynamic and cyclic mechanical loading. This force paradigm is the key stimulation driving bone remodeling processes (Tru¨ssel et al., 2012; Scheinpflug et al., 2018) as well as chondrogenesis (Huang et al., 2010). Some OOC platforms exist to mimic this early phase of chondrogenesis, but many of them lack the input of this mechanical load. Models that include mechanical input to bone cells within artificial scaffolds and chondrocytes have

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nevertheless been presented, though not in the typical OOC community and in comparably large dimensions (Li et al., 2010; Hoffmann et al., 2015; Meinert et al., 2017). Regarding bone, these models do, however, all fail to completely represent the cellular diversity, and thus the complex functions in interplay within in vivo bone tissue. Since the engineering of such a complex tissue still requires technological advancement in integrated scaffold formation, the models currently closest to in vivo conditions actually use in vivo-derived bone for subsequent on-chip culture and analyses (Torisawa et al., 2014). Existing solely in vitro microfluidic chip models, on the contrary, focus on more simplified bone models, very often studying bone marrow stem-cell differentiation or osteocyte orchestration, for example, but not comprising the complex architecture of the calcified bone matrix. Those models that also include dynamic mechanical loading use multilayered soft polymer chips with incorporated flexible membranes that can be pressed onto the cells (Park et al., 2012) and thereby are similar to the chip devices engineered to induce mechanical strain. Applied mechanical load is given in either pressure—for example, for membrane-loaded systems about 1 psi [equivalent to 0.69 N/cm2 (Park et al., 2012)]—or in the description of the experimental setup, such as distance of compression (in micro- or millimeters) or loading (in percentage) (Li et al., 2010; Hoffmann et al., 2015; Meinert et al., 2017).

Sensing To keep OOC models as simple as possible while optimizing robustness and reproducibility, force sensors have so far only rarely been integrated into these devices. As numerous immunocytochemical end-point assays exist, cell response to mechanical forces is often observed optically by means of fluorescent labels. Further options are cell contraction imaging and time-lapse monitoring of changes in cell morphology. A good overview of the specialized integration of molecule toolkits for mechanosensing and mechanoregulation in biological systems is given by Jacobs and Blank (2014). A special case is the monitoring of cell contraction, which primarily focuses on cardiac tissue. Lind et al. presented a thin-sheet signal transducer that, upon cell contraction, bends strongly (Fig. 3.29A) (Lind et al., 2017; Feinberg et al., 2007). Upon substrate sensor deformation, the change in sensor resistance directly correlates to the level of tissue contraction. The formed laminar cardiac tissues were able to generate stress in the range of 1
15 kPa. Other options for the monitoring of cell contractile forces have been developed, particularly for single-cell studies, but have not yet been incorporated in OOC devices, either due to their complex fabrication protocols or to their high optical transparency requirements. Examples for substrate deformation monitoring include soft polymer pillars that bend under cell contraction (Fig. 3.29B) (Mann et al., 2012; Fu et al., 2010; Lam et al., 2012) as well as the optical monitoring of cellinduced fluorescent bead displacements, which are incorporated within a 3D hydrogel of well-defined elasticity (Legant et al., 2010). Just recently, electrical sensors

Stimulation and sensing

FIGURE 3.29 (A) Sensing cardiomyocyte contraction: a laminar cardiomyocyte tissue bends a gauge wire during contraction. The change in sensor resistance correlates to the cantilever deflection. (B) Soft polymer pillars can measure cell contractile forces at high sensitivity, which can be tuned by changing the geometrical ratios. (C) Transepithelial electrical resistance changes following mechanical cell actuation by fluid flow-induced shear forces, as shown by Curto et al. (A) Reprinted by permission from Lind, J.U., et al., 2017. Instrumented cardiac microphysiological devices via multimaterial three-dimensional printing. Nat. Mater. 16, 303
308; (B) Reprinted by permission from Fu, J., et al., 2010. Mechanical regulation of cell function with geometrically modulated elastomeric substrates. Nat. Methods 7, 733
736; and (C) Adapted from Curto, V.F., et al., 2017. Organic transistor platform with integrated microfluidics for in-line multi-parametric in vitro cell monitoring. Microsyst. Nanoeng. 3, 17028.

were shown to measure changes in transepithelial electrical resistance that correlated to changes in fluid flow-induced shear forces, thereby broadening the scope of sensing mechanotransduction processes on-chip (Fig. 3.29C) (Curto et al., 2017).

Electrical Electrochemical sensors Electrochemical sensors combine biological selectivity and electroanalytical sensitivity within one device. They detect analytes of interest on a (bio-)

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functionalized layer of the sensor with the precision and the selectivity of biological recognition (Grieshaber et al., 2008); this catalytic or molecular recognition is then translated into a measurable electrical signal proportional to the concentration of the analyte (The´venot et al., 2001; Ronkainen et al., 2010). Because of their high sensitivity and versatility in application, electrochemical sensors are among the most used and available devices in biomolecular electronics. Here, we highlight electrochemical sensors with varying molecular recognition principles. For a concise overview of the sensors’ modes of action, fabrication techniques, and use in biological studies, please refer to the reviews by Zhang and Hoshino (2014), Bieg et al. (2017), or Primiceri et al. (2013).

Electrochemical enzyme biosensors Enzymes provide high specificity for their target molecule, which is typically bound to the enzymatic pocket. Leveraging this lock-and-key recognition for electrical transducers enables simple, fast, and very precise measurements (Ispas et al., 2012). In addition to substrate detection, enzymatic biosensors can also measure enzymatic inhibition. These inhibition-based enzymatic biosensors monitor the decrease in enzymatic activity caused by the analyte of interest (Amine et al., 2016). An overview of the different enzymatic mechanisms for analyte detection in enzyme-based biosensors is given in Fig. 3.30. Advances in the fields of microfabrication and miniaturization have created numerous new applications for enzyme-based electrochemical sensors in the biomedical and food industries (Ispas et al., 2012; Weltin et al., 2016; Monteiro and Almeida, 2019; Rotariu et al., 2016). The variety of strategies for enzymatic sensor modification allows for versatile use and the integration of the devices with liquid samples (Mross et al., 2015). Enzymatic transducers are widely leveraged

FIGURE 3.30 Mechanisms of enzymatic analyte detection in biosensors. Reproduced under a Creative Commons Attributions 4.0 International License from Asal, M., O¨zen, O¨., ˙ 2018. Recent developments in enzyme, DNA and immuno-based biosensors. ˘ I., Sahinler, ¸ M., Polatoglu, Sensors 18, 6, 1924 (Asal et al., 2018).

Stimulation and sensing

to monitor glucose and lactate metabolism in single-cell systems (Wang, 2008; Moser et al., 2002; Ges et al., 2008; Ges and Baudenbacher, 2010; Weltin et al., 2014), but also for flow through-based devices, in which the biosensors measure the liquid arriving downstream from the cell culture compartments (Misun et al., 2016; Moser et al., 2002; Eklund et al., 2006; Talaei et al., 2015; Boero et al., 2012; Frey et al., 2010; Sassa et al., 2008; Perdomo et al., 2000; Satoh et al., 2008; Dempsey et al., 1997; Brischwein et al., 2003). Applications of enzyme-based electrochemical biosensors for advanced cell culturing and OOC systems have generated significant interest in investigating microtissue metabolism in situ. Weltin et al. measured drug-induced damage to liver microtissues based by lactate secretion with a biosensor insert for standard well culture plates (Fig. 3.31A) (Weltin et al., 2017). Bavli et al. (2016) were able to maintain liver function on-chip for over a month and simultaneously recorded glucose and lactate concentrations with an integrated biosensor. Such real-time measurement has made it possible to predict mitochondrial stress, which is correlated with a shift toward anaerobic glycolysis. Work by Misun et al. (2016) integrated glass plugs containing electrochemical enzyme biosensors with dedicated hanging-drop networks to monitor 3D microtissues (Fig. 3.31B); that study measured glucose and lactate secretion with glucose and lactate oxidase to detect the metabolic activity of colon cancer microtissues in real time. A different approach is to continuously sample the perfusing medium from the cell culture chamber into a cartridge equipped with electrochemical sensors (Fig. 3.31C). In this way the still somewhat limited operational stability of enzymatic electrochemical biosensors can be overcome, as the individual sensors can be easily exchanged and required calibrations be run between measurements. This strategy has been used to quantitatively assess the amount of alanine aminotransferase secreted by a 3D liver model, which directly correlates to liver viability (La Cour et al., 2016).

Electrochemical immunosensors Antibodies and aptamers represent another class of molecular recognition proteins. The specific interaction of antibody or aptamer with the antigen can be leveraged for highly sensitive electrochemical biosensors by immobilizing antibodies, fragments of antibodies, or aptamers on the sensor surface (Ugo and Moretto, 2017; Piro and Reisberg, 2017). The versatility of antibodies and aptamers in detecting trace amounts of analytes—ranging from cells, viruses, and bacteria to small molecules and proteins—has generated considerable interest in their potential as biomarkers (Rezaei et al., 2016; Riahi et al., 2016). Electrochemical immunosensors permit numerous types of electrochemical methodologies, which are typically selected based on the sensitivity required for the desired immunoassay. For a comprehensive overview of electrochemical immunosensors, see Wen et al. (2017). Detection of liver-secreted biomarkers by an integrated immunosensor-on-achip has been demonstrated by Riahi et al. (2016). By continuously monitoring the medium of the OOC system, secreted biomarkers—albumin and

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FIGURE 3.31 Applications of enzyme-based biosensors. (A) Microsensor setup that can be dipped into 96-well cell culture plates to measure 3D liver microtissues. The lactate biosensor operates with a working electrode functionalized with lactate oxidase and a blank electrode to reduce unspecific background. Alternatively, oxygen is measured using a diffusion-limiting membrane with the same electrode layout. (B) Lactate and glucose biosensors on glass plug-ins are integrated into a microfluidic hanging-drop network to measure the metabolic activity of cancer microtissues. (C) Placing the electrochemical biosensors downstream of the cell culture enables the addition or change of specific cartridges on demand. 3D, Three-dimensional. (A) Reprinted from Weltin, A., Hammer, S., Noor, F., Kaminski, Y., Kieninger, J., Urban, G.A., 2017. Accessing 3D microtissue metabolism: lactate and oxygen monitoring in hepatocyte spheroids. Biosens. Bioelectron. 87, 941
948 with permission from Elsevier; (B) Reproduced under a Creative Commons Attributions 4.0 International License from Misun, P.M., Rothe, J., Schmid, Y.R.F., Hierlemann, A., Frey, O., 2016. Multi-analyte biosensor interface for real-time monitoring of 3D microtissue spheroids in hanging-drop networks. Microsyst. Nanoeng. 2, 16022; (C) Reprinted from La Cour, J.B., Generelli, S., Barbe, L., Guenat, O.T., 2016. Low-cost disposable ALT electrochemical microsensors for in-vitro hepatotoxic assessment. Sens. Actuators, B Chem., 228, 360
365 with permission from Elsevier.

transferrin—could be quantified in real time over 5 days. Using electrodes coated with aptamers, Shin et al. (2016) measured the response of cardiac microtissues to drugs. The sensor unit was connected to a culturing chamber and monitored creatine kinase that is secreted upon cardiac injury. Furthermore, label-free in situ biomarker measurements using an electrochemical immunosensor within an OOC system containing liver, heart, and cancer cells have been

Stimulation and sensing

demonstrated by Zhang et al. (2017). The setup consists of various culture and sensor units connected by tubing. This modular approach allows for sensor washing and regeneration steps but renders device operation rather complex because of the required valves and pumps. Integrating electrochemical immunosensors into OOC platforms enables the on-line readout of biomarkers [e.g., cytokines (Chen et al., 2015; Liu et al., 2010) and proteins (Arroyo-Curra´s et al., 2017; Chikkaveeraiah et al., 2012)], providing a tool with which to continuously inspect advanced cell cultures for viability and function in the OOC system. This aspect becomes particularly relevant, if dynamic drug-induced responses of tissue models are investigated.

pH- and ion-sensitive sensors Electrochemical sensors equipped with electronically conductive oxides—for example, iridium, platinum, and ruthenium oxides—can sense the potentiometric difference between sensor and reference electrode to measure pH (Olthuis et al., 1990; Fog and Buck, 1984; Kurzweil, 2009; Anderson et al., 2016; Shaegh et al., 2016). These sensors can also be combined with other sensors, such as those monitoring oxygen and CO2 (Arquint et al., 1994). pH sensors can track the metabolism of cells through extracellular changes in acidity (Thedinga et al., 2007), which has been demonstrated by Ges et al. (2008) in monitoring cardiac metabolism by integrated pH sensors-on-a-chip. Ion-sensitive field-effect transistors (ISFETs) can also monitor pH and can be used to sense redox potential, lactate, and glucose, as well as specific ions at low concentrations in real time. The ion concentration in the solution of interest defines the current through the transistor. Like most ion sensors, ISFETs are potentiometers, meaning that the electrical current at the solid/liquid interface changes with varying ion concentrations. This electrical potential difference is measured according to the Nernst equation. Nano ISFETs in particular have emerged as a promising and versatile sensing approach (Pachauri and Ingebrandt, 2016), and numerous setups have demonstrated applications for pH sensing (Cui et al., 2001; Knopfmacher et al., 2010), chemical sensing (Sudho¨lter et al., 1989), and label-free biosensing (Gao et al., 2012; Stern et al., 2007). Owing to the advantages of direct, ultrasensitive, and label-free detection, ISFETs have also been successfully used as nucleic acid sensors (Duan et al., 2012) and to monitor the kinetics of receptor binding (Wipf et al., 2016) and the intracellular recording of action potentials (Duan et al., 2012). Relatively inexpensive, multiparametric, and downscalable, ISFETs are thus good candidates for integration into OOC devices.

Electrochemical oxygen sensors Oxygen sensing can be electrochemically achieved through the equilibrium potential on solid electrolyte
electrode cells and the Nernst equation. The measurements are logarithmically dependent on the partial pressure of oxygen (Ramamoorthy et al., 2003). Kieninger et al. developed a transparent sensor that they integrated onto the bottom of a conventional cell culture flask (Kieninger et al., 2014); this sensing cell

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culture flask system enabled pericellular oxygen sensing in real time. Other applications of oxygen biosensors measure cellular oxygen consumption rates in a multiparametric silicon chip, monitoring cellular microphysiological patterns on single chips for several days (Brischwein et al., 2003).

Electrical biosensors Electrical impedance spectroscopy Electrical impedance spectroscopy (EIS) provides a method to quantitatively analyze biological systems ranging from single cells to complex 3D microtissues. The advantage of EIS lies in its label-free and nondestructive mode of action, allowing for real-time measurements to retrieve characteristic information, such as cell counts, tissue size, and intracellular features (Foster and Schwan, 1989). Although the measurement of biological systems and even single cells has been employed for almost a century (Am and Cole, 1937; Fricke and Morse, 1925), microfluidic technology has enabled the integration of EIS into perfused microsystems, and this, in turn, permits new ways to investigate biological samples. Flow-through measurements of cells passing over a set of electrodes enable particle counting and distinguish between individual cell types (Sun and Morgan, 2010). These impedance cytometers identify cell size and volume at lower frequencies and characterize cell membrane conductivity and intracellular features at higher frequencies (Sun and Morgan, 2010; Schade-Kampmann et al., 2008). Label-free measuring of biological systems is leveraged to count or characterize, for example, bacteria (Bernabini et al., 2011; DeBlois and Wesley, 1977), viruses (DeBlois and Wesley, 1977), and pollen (Zhang et al., 2005). In addition, cellular systems ranging from yeast to cancer and stem cells have also been investigated with multifrequency EIS approaches (Holmes et al., 2009; Hassan and Bashir, 2014; Cheung et al., 2005; Holmes and Morgan, 2010; Song et al., 2013; Han et al., 2007; Chen et al., 2011b; Haandbæk et al., 2016). Furthermore, microfluidic systems with immobilized or trapped cells have been developed to measure impedimetric parameters of single cells (Malleo et al., 2010; Jang and Wang, 2007; Zhu et al., 2014, 2015). A subtype of EIS, electrical cell-substrate impedance sensing (ECIS), is used to detect the proliferation and viability of attached or spread cells (Wegener et al., 2000). The approach measures the growth and subsequent spreading of the cells on an electrode-patterned surface. The impedance spectra over the sensor array changes by cell movement or total occupied regions of the sensor. ECIS is a wellestablished technology for many applications, ranging from cancer spreading to cytotoxicity and environmental diagnostics (Harman et al., 2016; Peters et al., 2015; Curtis et al., 2009; Xiao and Luong, 2005; Xing et al., 2006). In addition to ECIS, multielectrode arrays can be used in a similar fashion (Obien et al., 2017) as well as for neurobiological applications (Malerba et al., 2018) but are outside the scope of this section.

Stimulation and sensing

Recent advances have made it possible to use EIS even for more complex cell systems such as microtissues. Label-free and nondestructive monitoring of advanced in vitro models with EIS offers a distinct advantage over other methods. Combined with integration into OOC systems, EIS offers drug-effect measurement in real time. Liver microtissues have been monitored during photodynamic treatment in a dedicated EIS device using two needle electrodes (Molckovsky and Wilson, 2001). Using EIS in hydrodynamic traps, Thielecke et al. investigated drug-induced changes in the morphology of spherical microtissues (Thielecke et al., 2001). Kloß et al. (2008) successfully identified apoptosis of multiple microtissues in microcavities in a higher throughput manner using a fourelectrode setup per well (Fig. 3.32A). True integration into OOC devices was achieved by developing an EIS sensor plug for hanging-drop networks (Schmid et al., 2016). This setup made it possible to monitor the growth of cancer

FIGURE 3.32 Applications of EIS for 3D cell culture or organ-on-a-chip devices. (A) Microcavity array for the simultaneous culture of nine human melanoma microtissues. A setup of four electrodes makes it possible to monitor drug-induced apoptosis on-chip using EIS. (B) Plug-in EIS sensors make it possible to track the beating of cardiac microtissues in a hanging-drop network. (C) Multiplexed tilting chip that enables the parallel culture and EIS readout of 15 microtissues. Drug-induced toxicity is measured by rolling the microtissue over the electrodes and assessing the size of the microtissue. 2D, Two-dimensional; 3D, three-dimensional; EIS, electrical impendence spectroscopy. Adapted with permission from Bu¨rgel, S.C., Diener, L., Frey, O., Kim, J.-Y., Hierlemann, A., 2016. Automated, multiplexed electrical impedance spectroscopy platform for continuous monitoring of microtissue spheroids. Anal. Chem. 88, 22, 10876
10883. © 2016, American Chemical Society.

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microtissues and to measure the beating of cardiac microtissues hanging underneath the EIS sensor plug (Fig. 3.32B). Similar to EIS of single cells that pass over electrodes, a gravity-driven tilting platform enabled cancer microtissues to roll over a pair of electrodes in a study of drug-induced effects (Fig. 3.32C) (Bu¨rgel et al., 2016). By multiplexing the assay on a microscopy slide, 15 parallel experiments can be conducted simultaneously to test drug-induced toxicity in real time by means of EIS-measured microtissue size.

Transepithelial electrical resistance sensors The epithelial barrier function is a crucial aspect of multiple organ function and health. The gut, lung, kidney, liver, and blood-brain barrier all rely on epithelia layers to maintain homeostasis, ensure nutrient transport, and filter out harmful pathogens. These organs are widely studied in vitro, and there is a need for both mimicking and probing the biological and morphological properties of those barriers. Transepithelial electrical resistance is a key measure of the permeability of a cell layer to electrolytes present in the medium. Transepithelial electrical resistance is commonly employed in combination with filter membrane inserts by measuring an electric impedance spectrum over a cell barrier grown on top of the filter (Srinivasan et al., 2015). The transepithelial electrical resistance value of a cell barrier is acquired by applying alternating current at one or more frequencies across the cell layer. The associated impedance is measured in ohms and normalized to Ohm  cm2 by multiplying by the membrane area. Multiple apparatuses dedicated to transepithelial electrical resistance measurements in filter membrane systems are commercially available. These systems generally rely on either handheld electrodes inserted on both sides of the insert (EVOM ohmmeter, World Precision Instruments, Sarasota, FL, United States) or use dedicated multiwell holders with integrated electrodes and the ability to measure impedance at multiple frequency points (cellZscope, Mu¨nster, Germany; xCELLigence, ACEA Biosciences, San Diego, CA, United States). The downside of this approach is that the filter membrane has a strong influence on the measurement, which should be compensated. Furthermore, these systems are largely static, while the trend in cell culture is gradually shifting toward perfused systems. Lastly, filter membrane approaches typically have a relatively large surface, requiring large amounts of starting material or long culture times if they are to reach full confluency. In the past few years, impedance measurement systems integrated into microfluidic systems have been reported to bridge the gap between the utility of electrical sensing and the complexity of OOC models. The first approach involves the patterning of electrodes inside a microfluidic cell culture system. In 2017, Ingber et al. (Wyss Institute, Harvard University) reported a PDMS-based microfluidic system with integrated planar electrodes for impedance measurements (Henry et al., 2017). Other groups developed PDMS chips embedded with conductive biocompatible wires that carry the current close to the cell compartments (Griep et al., 2013; Odijk et al., 2015; van der Helm et al., 2016, 2019). These platforms have been used in combination with impedance spectroscopy to

Stimulation and sensing

characterize perfused, membrane-supported models of tissues such as the gut, lung, and blood-brain barrier. Most other reported solutions integrate either inserts linked to perfused microfluidic channels (Alexander et al., 2018b; Maschmeyer et al., 2015b; Zeller et al., 2017) or microfluidic devices in which cell layers are cultured on built-in porous membranes (Shah et al., 2016; Wang et al., 2017) and make use of a single-frequency voltohmmeter for transepithelial electrical resistance measurements. Typical drawbacks of OOC and other microfluidic cell culture systems include the level of standardization, throughput, and compatibility with standard laboratory equipment. To overcome these limitations, Mimetas developed the OrganoPlate (Fig. 3.33), an OOC platform composed of up to 96 microfluidic chips integrated into a 384-well plate. Each chip can be perfused through leveling between reservoirs, which is made continuous by changing the inclination angle at given time intervals using a modified rocker. The platform has been demonstrated for toxicological and drug-transport studies on barrier tissues such as the gut (Trietsch et al., 2017), kidney (Vormann et al., 2018; Vriend et al., 2018), blood-brain barrier, and vasculature (van Duinen et al., 2017, 2018; Wevers et al., 2018), as well as for studying cancer (Moisan et al., 2018; Lanz et al., 2017). Thus far, readouts on the OrganoPlate have largely been optical, including a range of fluorescent and luminescent assays. Recently, Mimetas unveiled an

FIGURE 3.33 Apparatus for on-chip transepithelial electrical resistance measurement in the OrganoPlate (Mimetas). System impedance is characterized by the channel resistance in series with the extracellular matrix-supported cell layer. The electrical connection is made by dipping conductive electrodes into the entry wells of each chip (A1, A3, B1, and B3 in the schematic). Courtesy of Mimetas.

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automated transepithelial electrical resistance measurement device dedicated to the OrganoPlate platform to allow for electrical measurements. This commercially available apparatus is connected to the microfluidic chips by inserting a lid adapter with an integrated electrode array into the access wells of the OrganoPlate. The OrganoTEER performs impedance readouts at multiple frequencies to record the electrical parameters of a tubule for up to 120 tissues in parallel. Further development of the transepithelial electrical resistance measurement system for OOC technologies is crucial for the industrial adoption of OOC as a preclinical development tool. Challenges such as scalability, throughput, and the cross-platform normalization of impedance values will need to be resolved before widespread adoption of these systems is possible.

Commercial sensors Outside university laboratories, several small- and medium-sized enterprises are developing—often in close collaboration with academic groups—integrated commercial biosensors for cell culture systems (Table 3.11). Commercially available biosensors are used for clinical, food, environment, and biothreat applications [for a review of the application of such commercial biosensors, see Bahadir and Sezgintu¨rk (2015)]; few sensors, however, are or can be integrated into lab-on-achip applications. Since the sensor part of these devices is often the central focus, academic laboratories employ rapid prototyping (e.g., PDMS) to engineer the fluid part around the sensor. Commercial entities, in contrast, can develop sensors that are integrated into more robust devices, for example, by injection molding. These methods require a high initial investment for master fabrication but later enable the mass production of sensor chips with a very high reproducibility. In addition, ad- and absorption effects at the media-chip interface, as seen for PDMS (Wong and Ho, 2009), can be reduced significantly. Table 3.11 Examples of commercially available organ-on-a-chip sensors. Manufacturer

Analyte

Sensor type

References

C-CIT Sensors AG

Lactate and glucose

Electrochemistry

MicruX Technologies

Antigens via immobilized antibodies Endothelial proliferation, barrier function, and motility Oxygen

Impedimetric immunosensor

Spichiger and Spichiger-Keller (2011) Ravalli et al. (2016)

Cell-substrate impedance sensing

Szulcek et al. (2014)

Quenching of luminescence

Santoro et al. (2012)

Ibidi GmbH

PreSens Precision Sensing GmbH

(Continued)

References

Table 3.11 Examples of commercially available organ-on-a-chip sensors. Continued Manufacturer

Analyte

Sensor type

References

Cellasys GmbH

Metabolic and morphological components

Alexander et al. (2018a)

Mimetas

Barrier function

SiMPLINext SA Colibri Photonics GmbH Surflay Nanotec GmbH

Barrier function

Impedance, extracellular acidification (pH), oxygen consumption, and temperature Transepithelial electrical resistance Transepithelial electrical resistance Fluorescence lifetime and/or fluorescence intensity

TissUse GmbH Micronit Micro Technologies

3Brain Axion BioSystems

Oxygen

pH, temperature, oxygen, molecular components Cell viability

Fluorescence lifetime and/or fluorescence intensity

Fluorescent optical fiber

Peptide, a protein, and an antibody detection assay Neuronal activity Electrophysiology

Complementary metal oxide semiconductor-based single-photon counting optical sensor CMOS electrophysiology Microelectrode array

www.mimetas.com www.simplinext. com Sonntag et al. (2016) and Bavli et al. (2016) Massing et al. (2016) and Olszyna et al. (2019) Sergachev et al. (2013) Van Dorst et al. (2016)

Lonardoni (2017) www. axionbiosystems. com

References Agarwal, A., Goss, J.A., Cho, A., McCain, M.L., Parker, K.K., 2013. Microfluidic heart on a chip for higher throughput pharmacological studies. Lab Chip 13 (18), 3599
3608. Ahadian, S., et al., 2018. Organ-on-a-chip platforms: a convergence of advanced materials, cells, and microscale technologies. Adv. Healthc. Mater. 7 (2), 1
53. Alexander, F., Eggert, S., Wiest, J., 2018a. A novel lab-on-a-chip platform for spheroid metabolism monitoring. Cytotechnology 70 (1), 375
386. Alexander, F.A., Eggert, S., Wiest, J., Wiest, J., 2018b. Skin-on-a-chip: transepithelial electrical resistance and extracellular acidification measurements through an automated airliquid interface. Genes (Basel) 9 (2). Available from: https://doi.org/10.3390/ genes9020114. Alford, P.W., Nesmith, A.P., Seywerd, J.N., Grosberg, A., Parker, K.K., 2011. Vascular smooth muscle contractility depends on cell shape. Integr. Biol. 3 (11), 1063
1070.

111

112

CHAPTER 3 Organs-on-a-chip engineering

Alveolix, 2019. “Technology”. [Online]. Available from: ,http://www.alveolix.com/ technology/. (accessed 03.02.19.) Am, H.J.C., Cole, K.S., 1937. Transverse electric impedance of Nitella. J. Gen. Physiol. 21, 189
201. Amine, A., Arduini, F., Moscone, D., Palleschi, G., 2016. Recent advances in biosensors based on enzyme inhibition. Biosens. Bioelectron. 76, 180
194. Anderson, J.R., et al., 2000. Fabrication of topologically complex three-dimensional microfluidic systems in PDMS by rapid prototyping. Anal. Chem. 72 (14), 3158
3164. Anderson, M., Moshnikova, A., Engelman, D.M., Reshetnyak, Y.K., Andreev, O.A., 2016. Probe for the measurement of cell surface pH in vivo and ex vivo. Proc. Natl. Acad. Sci. U.S.A. 113 (29), 8177
8181. Arquint, P., Koudelka-Hep, M., van der Schoot, B.H., van der Wal, P., de Rooij, N.F., 1994. Micromachined analyzers on a silicon chip. Clin. Chem. 40 (9), 1805
1809. Arroyo-Curra´s, N., Somerson, J., Vieira, P.A., Ploense, K.L., Kippin, T.E., Plaxco, K.W., 2017. Real-time measurement of small molecules directly in awake, ambulatory animals. Proc. Natl. Acad. Sci. U.S.A. 114 (4), 645
650. ¨ zen, O ¨ ., Sahinler, Asal, M., O ¸ M., Polato˘glu, ˙I., 2018. Recent developments in enzyme, DNA and immuno-based biosensors. Sensors 18 (6), 1924. Ashauer, M., Glosch, H., Hedrich, F., Hey, N., Sandmaier, H., Lang, W., 1999. Thermal flow sensor for liquids and gases based on combinations of two principles. Sens. Actuators, A: Phys. 73 (1
2), 7
13. Attia, U.M., Marson, S., Alcock, J.R., 2009. Micro-injection moulding of polymer microfluidic devices. Microfluid. Nanofluid. 7, 1
28. Au, A.K., Lai, H., Utela, B.R., Folch, A., 2011. Microvalves and micropumps for BioMEMS. Micromachines 2, 179
220. Avci, H., Gu¨zel, F.D., Erol, S., Akpek, A., 2018. Recent advances in organ-on-a-chip technologies and future challenges: a review. Turkish J. Chem. 42 (3), 587
610. Ayyapan, M., Uttamchand, N.K., Rajan, R.A.A., 2016. Mechanical and wear properties of copper-lead alloy prepared by powder metallurgy processing technique. J. Chem. Technol. Metall. 51 (6), 726
734. Bahadir, E.B., Sezgintu¨rk, M.K., 2015. Applications of commercial biosensors in clinical, food, environmental, and biothreat/biowarfare analyses. Anal. Biochem. 478, 107
120. Baker, B.M., Chen, C.S., 2012. Deconstructing the third dimension
how 3D culture microenvironments alter cellular cues. J. Cell Sci. 125, 3015
3024. Bassel-Duby, R., Olson, E.N., 2006. Signaling pathways in skeletal muscle remodeling. Annu. Rev. Biochem. 75 (1), 19
37. Bavli, D., et al., 2016. Real-time monitoring of metabolic function in liver-on-chip microdevices tracks the dynamics of mitochondrial dysfunction. Proc. Natl. Acad. Sci. U.S. A. 113 (16), E2231
E2240. Becker, H., Heim, U., 2000. Hot embossing as a method for the fabrication of polymer high aspect ratio structures. Sens. Actuators 83, 130
135. Becker, H., Locascio, L.E., 2002. Polymer microfluidic devices. Talanta 56 (2), 267
287. Benam, K.H., et al., 2015. Engineered in vitro disease models. Annu. Rev. Pathol. Mech. Dis. 10 (1), 195
262. Benam, K.H., et al., 2016. Small airway-on-a-chip enables analysis of human lung inflammation and drug responses in vitro. Nat. Methods 13, 151
157.

References

Bernabini, C., Holmes, D., Morgan, H., 2011. Micro-impedance cytometry for detection and analysis of micron-sized particles and bacteria. Lab Chip 11 (3), 407
412. Bertassoni, L.E., et al., 2014. Hydrogel bioprinted microchannel networks for vascularization of tissue engineering constructs. Lab Chip 14 (13), 2202
2211. Berthier, E., Young, E.W.K., Beebe, D., 2012. Engineers are from PDMS-land, biologists are from polystyrenia. Lab Chip 12 (7), 1224
1237. Bhatia, S.N., Ingber, D.E., 2014. Microfluidic organs-on-chips. Nat. Biotechnol. 32 (8), 760
772. Bhise, N.S., Ribas, J., Manoharan, V., Shrike, Y., Polini, A., 2014. Organ-on-a-chip platforms for studying drug delivery systems. J. Control. Release 190, 82
93. Bieg, C., Fuchsberger, K., Stelzle, M., 2017. Introduction to polymer-based solid-contact ion-selective electrodes—basic concepts, practical considerations, and current research topics. Anal. Bioanal. Chem. 409 (1), 45
61. Boero, C., Olivo, J., De Micheli, G., Carrara, S., 2012. New approaches for carbon nanotubes-based biosensors and their application to cell culture monitoring. IEEE Trans. Biomed. Circuits Syst. 6 (5), 479
485. Bondot, T., Petit, N., Ritty, S., Kieffer, C.-A., 2012. Flow-rate calibration and control in a microfluidic device. In: Application EP-2720103-A1 Borenstein, J.T., Terai, H., King, K.R., Weinberg, E.J., Kaazempur-Mofrad, M.R., Vacanti, J.P., 2002. Microfabrication technology for vascularized tissue engineering. Biomed. Microdevices 4 (3), 167
175. Boyd-Moss, M., Baratchi, S., Di Venere, M., Khoshmanesh, K., 2016. Self-contained microfluidic systems: a review. Lab Chip 16 (17), 3177
3192. Brennan, M.D., Rexius-Hall, M.L., Elgass, L.J., Eddington, D.T., 2014. Oxygen control with microfluidics. Lab Chip 14, 4305
4318. Brischwein, M., Motrescu, E.R., Cabala, E., Otto, A.M., Grothe, H., Wolf, B., 2003. Functional cellular assays with multiparametric silicon sensor chips. Lab Chip 3 (4), 234
240. Bronkhorst, 2019. Mini CORI-FLOW datasheet M12
M14. [Online]. Available from: ,https://www.bronkhorst.com/products/liquid-flow/mini-cori-flow/m14/. (accessed 30.01.19.) Bu¨rgel, S.C., Diener, L., Frey, O., Kim, J.-Y., Hierlemann, A., 2016. Automated, multiplexed electrical impedance spectroscopy platform for continuous monitoring of microtissue spheroids. Anal. Chem. 88 (22), 10876
10883. Byun, C.K., Abi-Samra, K., Cho, Y.K., Takayama, S., 2014. Pumps for microfluidic cell culture. Electrophoresis 35, 245
257. CSEM, 2010. “Disposable Pump” for Microfluidic Systems CSEM, 2010. TURBISC Pump—Miniaturized Pulsation-Free, Bi-directional Pump for Liquid Handling Caplin, J.D., Granados, N.G., James, M.R., Montazami, R., Hashemi, N., 2015. Microfluidic organ-on-a-chip technology for advancement of drug development and toxicology. Adv. Healthc. Mater. 4 (10), 1426
1450. Capulli, A.K., et al., 2014. Approaching the in vitro clinical trial: engineering organs on chips. Lab Chip 14 (17), 3181
3186. Carpi, F., Smela, E., 2009. Biomedical Applications of Electroactive Polymer Actuators. John Wiley & Sons.

113

114

CHAPTER 3 Organs-on-a-chip engineering

Cetoni, 2019. Syringe stirrer neMIX. [Online]. Available from: ,https://www.cetoni.com/ products/syringe-stirrer-nemix/. (accessed 03.02.19.) Chauvet, A., Tibiletti, T., Caffarri, S., Chergui, M., 2014. A microfluidic flow-cell for the study of the ultrafast dynamics of biological systems. Rev. Sci. Instrum. 85 (10), 103118. Chen, Y., Zhang, L., Chen, G., 2008a. Fabrication, modification, and application of poly (methyl methacrylate) microfluidic chips. Electrophoresis 29, 1801
1814. Chen, J.M., Huang, P.-C., Lin, M.-G., 2008b. Analysis and experiment of capillary valves for microfluidics on a rotating disk. Microfluid. Nanofluid. 4 (5), 427
437. Chen, C.F., Liu, J., Chang, C.C., DeVoe, D.L., 2009. High-pressure on-chip mechanical valves for thermoplastic microfluidic devices. Lab Chip 9 (24), 3511
3516. Chen, S.Y.C., Hung, P.J., Lee, P.J., 2011a. Microfluidic array for three-dimensional perfusion culture of human mammary epithelial cells. Biomed. Microdevices 13 (4), 753
758. Chen, J., et al., 2011b. Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells. Lab Chip 11 (18), 3174
3181. Chen, P., Huang, N.-T., Chung, M.-T., Cornell, T.T., Kurabayashi, K., 2015. Label-free cytokine micro- and nano-biosensing towards personalized medicine of systemic inflammatory disorders. Adv. Drug Deliv. Rev. 95, 90
103. Chen, Y., et al., 2017. A microfluidic circulatory system integrated with capillary-assisted pressure sensors. Lab Chip 17 (4), 653
662. Cheung, K., Gawad, S., Renaud, P., 2005. Impedance spectroscopy flow cytometry: onchip label-free cell differentiation. Cytometry A 65 (2), 124
132. Chikkaveeraiah, B.V., Bhirde, A.A., Morgan, N.Y., Eden, H.S., Chen, X., 2012. Electrochemical immunosensors for detection of cancer protein biomarkers. ACS Nano 6 (8), 6546
6561. Chin, L.K., Yu, J.Q., Fu, Y., Yu, T., Liu, A.Q., Luo, K.Q., 2011. Production of reactive oxygen species in endothelial cells under different pulsatile shear stresses and glucose concentrations. Lab Chip 11 (11), 1856
1863. Cho, H., Kim, H.-Y., Kang, J.Y., Kim, T.S., 2007. How the capillary burst microvalve works. J. Colloid Interface Sci. 306 (2), 379
385. Chung, K., Lee, H., Lu, H., 2009. Multiplex pressure measurement in microsystems using volume displacement of particle suspensions. Lab Chip 9 (23), 3345. Clapham, D.E., 2003. TRP channels as cellular sensors. Nature 426, 517
524. Coppeta, J.R., et al., 2017. A portable and reconfigurable multi-organ platform for drug development with onboard microfluidic flow control. Lab Chip 17 (1), 134
144. Cui, Y., Wei, Q., Park, H., Lieber, C.M., 2001. Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species. Science 293 (5533), 1289
1292 (80-.). Curtis, T.M., et al., 2009. A portable cell-based impedance sensor for toxicity testing of drinking water. Lab Chip 9 (15), 2176
2183. Curto, V.F., et al., 2017. Organic transistor platform with integrated microfluidics for inline multi-parametric in vitro cell monitoring. Microsyst. Nanoeng. 3, 17028. Darby, S., et al., 2010. A metering rotary nanopump for microfluidic systems. Lab Chip 10 (23), 3218
3226. De Jong, J., Lammertink, R.G.H., Wessling, M., 2006. Membranes and microfluidics: a review. Lab Chip 6 (9), 1125
1139.

References

DeBlois, R.W., Wesley, R.K., 1977. Sizes and concentrations of several type C oncornaviruses and bacteriophage T2 by the resistive-pulse technique. J. Virol. 23 (2), p. 227 LP-233. Dempsey, E., et al., 1997. Design and development of a miniaturised total chemical analysis system for on-line lactate and glucose monitoring in biological samples. Anal. Chim. Acta 346 (3), 341
349. Discher, D.E., Janmey, P., Wang, Y.L., 2005. Tissue cells feel and respond to the stiffness of their substrate. Science 310 (5751), 1139
1143 (80-.). Douville, N.J., et al., 2010. Combination of fluid and solid mechanical stresses contribute to cell death and detachment in a microfluidic alveolar model. Lab Chip 11, 609
619. Duan, X., Li, Y., Rajan, N.K., Routenberg, D.A., Modis, Y., Reed, M.A., 2012. Quantification of the affinities and kinetics of protein interactions using silicon nanowire biosensors. Nat. Nanotechnol. 7 (6), 401
407. Duan, X., et al., 2012. Intracellular recordings of action potentials by an extracellular nanoscale field-effect transistor. Nat. Nanotechnol. 7 (3), 174
179. Dubeau-Larame´e, G., Rivie`re, C., Jean, I., Mermut, O., Cohen, L.Y., 2014. Microflow1, a sheathless fiber-optic flow cytometry biomedical platform: demonstration onboard the international space station. Cytometry A 85 (4), 322
331. van Duinen, V., Zhu, D., Ramakers, C., van Zonneveld, A.J., Vulto, P., Hankemeier, T., 2018. Perfused 3D angiogenic sprouting in a high-throughput in vitro platform. Angiogenesis 1
9. Edington, C.D., et al., 2018. Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci. Rep. 8, 4530. Efstathopoulos, E.P., Patatoukas, G., Pantos, I., Benekos, O., Katritsis, D., Kelekis, N.L., 2008. Wall shear stress calculation in ascending aorta using phase contrast magnetic resonance imaging. Investigating effective ways to calculate it in clinical practice. Phys. Med. 24, 175
181. Eklund, S.E., Snider, R.M., Wikswo, J., Baudenbacher, F., Prokop, A., Cliffel, D.E., 2006. Multianalyte microphysiometry as a tool in metabolomics and systems biology. J. Electroanal. Chem. 587 (2), 333
339. ElveFlow, 2019a. Bubble Trap. [Online]. Available from: ,https://www.elveflow.com/ microfluidic-tutorials/microfluidic-reviews-and-tutorials/microfluidic-fittings-and-tubing-resources/why-and-how-to-use-a-microfluidic-bubble-trap/. (accessed 03.02.19.) Elveflow, 2019b. MLS—microfluidic bubble detector. [Online]. Available from: ,https:// www.elveflow.com/microfluidic-flow-control-products/microfluidic-flow-control-module/ microfluidic-liquid-sensor/. (accessed 03.02.19.) Emulate, 2019. Technology. [Online]. Available from: ,https://emulatebio.com/organs-onchips/. (accessed 03.02.19.) Ergir, E., Bachmann, B., Redl, H., Forte, G., Ertl, P., 2018. Small force, big impact: next generation organ-on-a-chip systems incorporating biomechanical cues. Front. Physiol. 9, 1
8. no. October. Esch, M.B., et al., 2016. HHS Public Access 15 (10), 2269
2277. Eyer, K., et al., 2017. Single-cell deep phenotyping of IgG-secreting cells for highresolution immune monitoring. Nat. Biotechnol. 35, 977
982. Faley, S., et al., 2008. Microfluidic platform for real-time signaling analysis of multiple single T cells in parallel. Lab Chip 8 (10), 1700
1712. Faustino, V., Catarino, S.O., Lima, R., Minas, G., 2016. Biomedical microfluidic devices by using low-cost fabrication techniques: a review. J. Biomech. 49 (11), 2280
2292.

115

116

CHAPTER 3 Organs-on-a-chip engineering

Feaver, R.E., Gelfand, B.D., Blackman, B.R., 2013. Human haemodynamic frequency harmonics regulate the inflammatory phenotype of vascular endothelial cells. Nat. Commun. 4, 1511
1525. Feinberg, A.W., Feigel, A., Shevkoplyas, S.S., Sheehy, S., Whitesides, G.M., Parker, K.K., 2007. Muscular thin films for building actuators and powering devices. Science 317 (5843), 1366
1370. FibreCellSystems, 2019. Pumps. [Online]. Available from: ,https://www.fibercellsystems. com/products/pumps/. (accessed 03.02.19.) Fluigent, 2019a. Pressure-based flow control LINEUP series. [Online]. Available from: ,https://www.fluigent.com/product/microfluidic-components/lineup-series/. (accessed 30.01.19.) Fluigent, 2019b. Bubble trap. [Online]. Available from: ,https://www.fluigent.com/product/microfluidic-components/bubble-trap/. (accessed 03.02.19.) Fog, A., Buck, R.P., 1984. Electronic semiconducting oxides as pH sensors. Sens. Actuators 5 (2), 137
146. Fok, E.Y.L., Zandstra, P.W., 2005. Shear-controlled single-step mouse embryonic stem cell expansion and embryoid body
based differentiation. Stem Cells 23 (9), 1333
1342. Formulatrix, 2019a. Valve control. [Online]. Available from: ,http://help.formulatrix.com/ mantis/4.0/Default.htm#Designing_Dispenses/Concepts/Valve_Control.htm. (accessed 03.02.19.) Formulatrix, 2019b. Microfluidic pumps. [Online]. Available from: ,https://formulatrix. com/microfluidic-pumps/. (accessed 03.02.19.) Foster, K.R., Schwan, H.P., 1989. Dielectric properties of tissues and biological materials: a critical review. Crit. Rev. Biomed. Eng. 17 (1), 25
104. Frey, O., Talaei, S., van der Wal, P.D., Koudelka-Hep, M., de Rooij, N.F., 2010. Continuous-flow multi-analyte biosensor cartridge with controllable linear response range. Lab Chip 10 (17), 2226
2234. Fricke, H., Morse, S., 1925. The electric resistance and capacity of blood for frequencies between 800 and 4(1/2) million cycles. J. Gen. Physiol. 9 (2), 153
167. Fu, J., et al., 2010. Mechanical regulation of cell function with geometrically modulated elastomeric substrates. Nat. Methods 7, 733
736. Gao, A., et al., 2012. Enhanced sensing of nucleic acids with silicon nanowire field effect transistor biosensors. Nano Lett. 12 (10), 5262
5268. Ges, I.A., Baudenbacher, F., 2010. Enzyme-coated microelectrodes to monitor lactate production in a nanoliter microfluidic cell culture device. Biosens. Bioelectron. 26 (2), 828
833. Ges, I.A., Dzhura, I.A., Baudenbacher, F.J., 2008. On-chip acidification rate measurements from single cardiac cells confined in sub-nanoliter volumes. Biomed. Microdevices 10 (3), 347
354. Gilbert, P.M., et al., 2010. Substrate elasticity regulates skeletal muscle stem cell selfrenewal in culture. Science 329, 1078
1081 (80-.). Griep, L.M., et al., 2013. BBB on chip: microfluidic platform to mechanically and biochemically modulate blood-brain barrier function. Biomed. Microdevices 15 (1), 145
150. Grieshaber, D., Mackenzie, R., Vo¨ro¨s, J., Reimhult, E., 2008. Electrochemical biosensors— sensor principles and architectures. Sensors 8 (3), 1400
1458.

References

Grist, S.M., Chrostowski, L., Cheung, K.C., 2010. Optical oxygen sensors for applications in microfluidic cell culture. Sensors (Switzerland) 10, 9286
9316. Grosberg, A., Alford, P.W., McCain, M.L., Parker, K.K., 2011. Ensembles of engineered cardiac tissues for physiological and pharmacological study: heart on a chip. Lab Chip 11, 4165
4173. Grover, W.H., Skelley, A.M., Liu, C.N., Lagally, E.T., Mathies, R.A., 2003. Monolithic membrane valves and diaphragm pumps for practical large-scale integration into glass microfluidic devices. Sens. Actuators, B: Chem. 89, 315
323. Guckenberger, D.J., De Groot, T.E., Wan, A.M.D., Beebe, D.J., Young, E.W.K., 2015. Micromilling: a method for ultra-rapid prototyping of plastic microfluidic devices. Lab Chip 15 (11), 2364
2378. Guenat, O.T., Berthiaume, F., 2018. Incorporating mechanical strain in organs-on-a-chip: lung and skin. Biomicrofluidics 12, 042207. Haandbæk, N., Bu¨rgel, S.C., Rudolf, F., Heer, F., Hierlemann, A., 2016. Characterization of single yeast cell phenotypes using microfluidic impedance cytometry and optical imaging. ACS Sensors 1 (8), 1020
1027. Haase, K., Kamm, R.D., 2017. Advances in on-chip vascularization. Regen. Med. 12 (3), 285
302. Hagmeyer, B., Schu¨tte, J., Bo¨ttger, J., Gebhardt, R., Stelzle, M., 2013. Tailoring microfluidic systems for organ-like cell culture applications using multiphysics simulations. SPIE MOEMS-MEMS 8615, 861509. Hamad, K., Kaseem, M., Ko, Y.G., Deri, F., 2014. Biodegradable polymer blends and composites: an overview. Polym. Sci. Ser. A 56 (6), 812
829. Hamilton, 2019. Automated liquid handling. [Online]. Available from: ,https://www. hamiltoncompany.com/automated-liquid-handling. (accessed 03.02.19.) Han, A., Yang, L., Frazier, A.B., 2007. Quantification of the heterogeneity in breast cancer cell lines using whole-cell impedance spectroscopy. Clin. Cancer Res. 13 (1), 139
143. Harman, R.M., Curtis, T.M., Argyle, D.J., Coonrod, S.A., Van de Walle, G.R., 2016. A comparative study on the In vitro effects of the DNA methyltransferase inhibitor 5azacytidine (5-AzaC) in breast/mammary cancer of different mammalian species. J. Mammary Gland Biol. Neoplasia 21 (1
2), 51
66. Hassan, U., Bashir, R., 2014. Coincidence detection of heterogeneous cell populations from whole blood with coplanar electrodes in a microfluidic impedance cytometer. Lab Chip 14 (22), 4370
4381. van der Helm, M.W., et al., 2016. Direct quantification of transendothelial electrical resistance in organs-on-chips. Biosens. Bioelectron. 85, 924
929. van der Helm, M.W., et al., 2019. Non-invasive sensing of transepithelial barrier function and tissue differentiation in organs-on-chips using impedance spectroscopy. Lab Chip. Available from: https://doi.org/10.1039/C8LC00129D. Henry, O.Y.F., Villenave, R., Cronce, M.J., Leineweber, W.D., Benz, M.A., Ingber, D.E., 2017. Organs-on-chips with integrated electrodes for trans-epithelial electrical resistance (TEER) measurements of human epithelial barrier function. Lab Chip 17 (13), 2264
2271. Hoffmann, W., Feliciano, S., Martin, I., de Wild, M., Wendt, D., 2015. Novel perfused compression bioreactor system as an in vitro model to investigate fracture healing. Front. Bioeng. Biotechnol. 3, 10.

117

118

CHAPTER 3 Organs-on-a-chip engineering

Holmes, D., Morgan, H., 2010. Single cell impedance cytometry for identification and counting of CD4 T-cells in human blood using impedance labels. Anal. Chem. 82 (4), 1455
1461. Holmes, D., et al., 2009. Leukocyte analysis and differentiation using high speed microfluidic single cell impedance cytometry. Lab Chip 9 (20), 2881
2889. Homan, K.A., et al., 2016. Bioprinting of 3D convoluted renal proximal tubules on perfusable chips. Sci. Rep. 6, 1
13. Hosokawa, K., Maeda, R., 2000. A pneumatically-actuated three-way microvalve fabricated with polydimethylsiloxane using the membrane transfer technique. J. Micromech. Microeng. 10, 415
420. Huang, H., Kamm, R.D., Lee, R.T., 2004. Cell mechanics and mechanotransduction: pathways, probes, and physiology. Am. J. Physiol. Cell Physiol. 287 (1), C1
C11. Huang, S.B., Wu, M.H., Cui, Z.F., Cui, Z., Lee, G.B., 2008. A membrane-based serpentine-shape pneumatic micropump with pumping performance modulated by fluidic resistance. J. Micromech. Microeng. 18 (12), 045008. Huang, A.H., Farrell, M.J., Mauck, R.L., 2010. Mechanics and mechanobiology of mesenchymal stem cell-based engineered cartilage. J. Biomech. 43, 128
136. Huh, D., Matthews, B.D., Mammoto, A., Montoya-Zavala, M., Hsin, H.Y., Ingber, D.E., 2010. Reconstituting organ-level lung functions on a chip. Science 328 (5986), 1662. LP-1668. Huh, D., Hamilton, G.A., Ingber, D.E., 2011. From 3D cell culture to organs-on-chips. Trends Cell Biol. 21 (12), 745
754. Huh, D., et al., 2013. Microfabrication of human organs-on-chips. Nat. Protoc. 8 (11), 2135
2157. Iliescu, C., Taylor, H., Avram, M., Miao, J., Franssila, S., 2012. A practical guide for the fabrication of microfluidic devices using glass and silicon. Biomicrofluidics 6, 1
16. InSphero, 2019. Technology. [Online]. Available from: ,https://insphero.com/science/ enabling-technology/. (accessed 03.02.19.) Ingber, D.E., 2003. Mechanobiology and diseases of mechanotransduction. Ann. Med. 35, 564
577. Irimia, D., Toner, M., 2006. Cell handling using microstructured membranes. Lab Chip 6, 345
352. Ispas, C.R., Crivat, G., Andreescu, S., 2012. Review: recent developments in enzymebased biosensors for biomedical analysis. Anal. Lett. 45 (2
3), 168
186. Jaalouk, D.E., Lammerding, J., 2009. Mechanotransduction gone awry. Nat. Rev. Mol. Cell Biol. 10, 63
73. Jacobs, M.J., Blank, K., 2014. Joining forces: integrating the mechanical and optical single molecule toolkits. Chem. Sci. 5, 1680
1697. Jacobson, S.C., Ermakov, S.V., Ramsey, J.M., 1999. Minimizing the number of voltage sources and fluid reservoirs for electrokinetic valving in microfluidic devices. Anal. Chem. 71, 3273
3276. Jain, A., et al., 2018. Primary human lung alveolus-on-a-chip model of intravascular thrombosis for assessment of therapeutics. Clin. Pharmacol. Ther. 103, 332
340. Jang, K.-J., et al., 2013. Human kidney proximal tubule-on-a-chip for drug transport and nephrotoxicity assessment. Integr. Biol. 5 (9), 1119
1129. Jang, L.S., Wang, M.H., 2007. Microfluidic device for cell capture and impedance measurement. Biomed. Microdevices 9 (5), 737
743. Jenke, C., et al., 2017. The combination of micro diaphragm pumps and flow sensors for single stroke based liquid flow control. Sensors 17 (4), 755.

References

Jeon, J.H., Jeong, O.C., 2012. Effect of shear stress magnitude on intracellular calcium expression in bone cells. Microelectron. Eng. 97, 329
332. Jeong, O.C., Konishi, S., 2008. Fabrication of a peristaltic micro pump with novel cascaded actuators. J. Micromech. Microeng. 18, 025022. Johnson, B.N., et al., 2016. 3D printed nervous system on a chip. Lab Chip 16 (8), 1393
1400. Johnson, I., Spence, M. (Eds.), 2010. Molecular Probes Handbook, A Guide to Fluorescent Probes and Labeling Technologies. Life Technologies. Juncker, D., et al., 2002. Autonomous microfluidic capillary system. Anal. Chem. 74, 6139
6144. Kaigala, G.V., Hoang, V.N., Backhouse, C.J., 2008. Electrically controlled microvalves to integrate microchip polymerase chain reaction and capillary electrophoresis. Lab Chip 8, 1071
1078. Kaihara, S., et al., 2000. Silicon micromachining to tissue engineer branched vascular channels for liver fabrication. Tissue Eng. 6 (2), 105
117. Kartalov, E.P., Maltezos, G., Anderson, W.F., Taylor, C.R., Scherer, A., 2007. Electrical microfluidic pressure gauge for elastomer microelectromechanical systems. J. Appl. Phys. 102 (8), 84909
849094. Kasendra, M., et al., 2018. Development of a primary human small intestine-on-a-chip using biopsy-derived organoids. Sci. Rep. 8 (1), 2871. Kellogg, R.A., Go´mez-Sjo¨berg, R., Leyrat, A.A., Tay, S., 2014. High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics. Nat. Protoc. 9 (7), 1713
1726. Kieninger, J., Aravindalochanan, K., Sandvik, J.A., Pettersen, E.O., Urban, G.A., 2014. Pericellular oxygen monitoring with integrated sensor chips for reproducible cell culture experiments. Cell Prolif. 47 (2), 180
188. Kieninger, J., Weltin, A., Flamm, H., Urban, G.A., 2018. Microsensor systems for cell metabolism-from 2D culture to organ-on-chip. Lab Chip 18 (9), 1274
1291. Kim, J., et al., 2006. Photopolymerized check valve and its integration into a pneumatic pumping system for biocompatible sample delivery. Lab Chip 6, 1091
1094. Kim, L., Toh, Y.-C., Voldman, J., Yu, H., 2007. A practical guide to microfluidic perfusion culture of adherent mammalian cells. Lab Chip 7, 681
694. Kim, D.-H., Wong, P.K., Park, J., Levchenko, A., Sun, Y., 2009. Microengineered platforms for cell mechanobiology. Annu. Rev. Biomed. Eng. 11, 203
233. Kim, H.J., Huh, D., Hamilton, G., Ingber, D.E., 2012. Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow. Lab Chip 12, 2165
2174. Kim, J.-Y., et al., 2015. 3D spherical microtissues and microfluidic technology for multitissue experiments and analysis. J. Biotechnol. 205, 24
35. King, K.R., Wang, C.C.J., Kaazempur-Mofrad, M.R., Vacanti, J.P., Borenstein, J.T., 2004. Biodegradable microfluids. Adv. Mater. 16 (22), 2007
2012. Kloß, D., et al., 2008. Microcavity array (MCA)-based biosensor chip for functional drug screening of 3D tissue models. Biosens. Bioelectron. 23 (10), 1473
1480. Knopfmacher, O., et al., 2010. Nernst limit in dual-gated Si-nanowire FET sensors. Nano Lett. 10 (6), 2268
2274. Krishnan, R., et al., 2011. Substrate stiffening promotes endothelial monolayer disruption through enhanced physical forces. Am. J. Physiol. Cell. Physiol. 300 (1), C146
C154. Kuo, J.T.W., Yu, L., Meng, E., Kuo, J.T.W., Yu, L., Meng, E., 2012. Micromachined thermal flow sensors—a review. Micromachines 3 (3), 550
573.

119

120

CHAPTER 3 Organs-on-a-chip engineering

Kuo, A.P., Bhattacharjee, N., Lee, Y.-S., Castro, K., Kim, Y.T., Folch, A., 2019. Highprecision stereolithography of biomicrofluidic devices. Adv. Mater. Technol. 1800395, 1800395. Kurth, F., Eyer, K., Franco-Obrego´n, A., Dittrich, P.S., 2012. A new mechanobiological era: Microfluidic pathways to apply and sense forces at the cellular level. Curr. Opin. Chem. Biol. 16, 400
408. Kurth, F., Franco-Obrego´n, A., Casarosa, M., Ku¨ster, S.K., Wuertz-Kozak, K., Dittrich, P. S., 2015. Transient receptor potential vanilloid 2-mediated shear-stress responses in C2C12 myoblasts are regulated by serum and extracellular matrix. FASEB J. 29 (11), 4726
4737. Kurth, F., Dittrich, P.S., Walde, P., Seebach, D., 2018. Influence of the membrane dye R18 and of DMSO on cell penetration of guanidinium-rich peptides. Chem. Biodivers. 15, e1800302. Kurzweil, P., 2009. Metal oxides and ion-exchanging surfaces as pH sensors in liquids: state-of-the-art and outlook. Sensors (Basel) 9 (6), 4955
4985. La Cour, J.B., Generelli, S., Barbe, L., Guenat, O.T., 2016. Low-cost disposable ALT electrochemical microsensors for in-vitro hepatotoxic assessment. Sens. Actuators, B: Chem. 228, 360
365. Lai, H., Folch, A., 2011. Design and dynamic characterization of ‘single-stroke’ peristaltic PDMS micropumps. Lab Chip 11, 336
342. Lakowicz, J.R., 1999. Principles of Fluorescence Spectroscopy, Second ed. Kluwer Academic/Plenum Publishers, New York. Lam, R.H.W., Sun, Y., Chen, W., Fu, J., 2012. Elastomeric microposts integrated into microfluidics for flow-mediated endothelial mechanotransduction analysis. Lab Chip 12 (10), 1865. Lanz, H.L., et al., 2017. Therapy response testing of breast cancer in a 3D high-throughput perfused microfluidic platform. BMC Cancer 17 (1), 709. Lee, D.E., Soper, S., Wang, W.J., 2008. Design and fabrication of an electrochemically actuated microvalve. Microsyst. Technol. 14, 1751
1756. Lee, H., Cho, D.W., 2016. One-step fabrication of an organ-on-a-chip with spatial heterogeneity using a 3D bioprinting technology. Lab Chip 16 (14), 2618
2625. Lee, S.H., Sung, J.H., 2018. Organ-on-a-chip technology for reproducing multiorgan physiology. Adv. Healthc. Mater. 7 (2), 1700419. Lee, S.J., Chan, J.C.Y., Maung, K.J., Rezler, E., Sundararajan, N., 2007. Characterization of laterally deformable elastomer membranes for microfluidics. J. Micromech. Microeng. 17, 843
851. Legant, W.R., Miller, J.S., Blakely, B.L., Cohen, D.M., Genin, G.M., Chen, C.S., 2010. Measurement of mechanical tractions exerted by cells in three-dimensional matrices. Nat. Methods 7, 969
971. Li, X., Tian, T., 2018. Phytochemical characterization of Mentha spicata L. under differential dried-conditions and associated nephrotoxicity screening of main compound with organ-on-a-chip. Front. Pharmacol. 9, 1
10. no. September. Li, Y.-S.J., Haga, J.H., Chien, S., 2005. Molecular basis of the effects of shear stress on vascular endothelial cells. J. Biomech. 38 (10), 1949
1971. Li, Z., Yao, S.-J., Alini, M., Stoddart, M.J., 2010. Chondrogenesis of human bone marrow mesenchymal stem cells in fibrin
polyurethane composites is modulated by frequency and amplitude of dynamic compression and shear stress. Tissue Eng., A 16, 575
584.

References

Li, X., Xu, S., He, P., Liu, Y., 2015. In vitro recapitulation of functional microvessels for the study of endothelial shear response, nitric oxide and [Ca21]i. PLoS One 10 (5), 1
19. Lind, J.U., et al., 2017. Instrumented cardiac microphysiological devices via multimaterial three-dimensional printing. Nat. Mater. 16, 303
308. Lindken, R., Rossi, M., Große, S., Westerweel, J., 2009. Micro-particle image velocimetry (μPIV): recent developments, applications, and guidelines. Lab Chip 9 (17), 2551. Liu, Y., Tuleouva, N., Ramanculov, E., Revzin, A., 2010. Aptamer-based electrochemical biosensor for interferon gamma detection. Anal. Chem. 82 (19), 8131
8136. Liu, M.-C., et al., 2013. Electrofluidic pressure sensor embedded microfluidic device: a study of endothelial cells under hydrostatic pressure and shear stress combinations. Lab Chip 13 (9), 1743. Lohasz, C., Rousset, N., Renggli, K., Hierlemann, A., Frey, O., 2019. Scalable microfluidic platform for flexible configuration of and experiments with microtissue multiorgan models. SLAS Technol. 24 (1), 79
95. Lonardoni, D., Amin, H., Di Marco, S., Maccione, A., Berdondini, L., Nieus, T., 2017. Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks. PLoS Computational Biology 13, e1005672. Loskill, P., Marcus, S.G., Mathur, A., Reese, W.M., Healy, K.E., 2015. μOrgano: a Lego®like plug & play system for modular multi-organ-chips. PLoS One 10 (10), e0139587. Loskill, P., et al., 2017. WAT-on-a-chip: a physiologically relevant microfluidic system incorporating white adipose tissue. Lab Chip 17 (9), 1645
1654. Ma, X., et al., 2018. 3D bioprinting of functional tissue models for personalized drug screening and in vitro disease modeling. Adv. Drug Deliv. Rev. 132, 235
251. Magin, C.M., Alge, D.L., Anseth, K.S., 2016. Bio-inspired 3D microenvironments: a new dimension in tissue engineering. Biomed. Mater. 11 (2), 022001. Malda, J., et al., 2013. 25th anniversary article: engineering hydrogels for biofabrication. Adv. Mater. 25 (36), 5011
5028. Malerba, M., Amin, H., Angotzi, G.N., Maccione, A., Berdondini, L., 2018. In: Ertl, P., Rothbauer, M. (Eds.), Fabrication of Multielectrode Arrays for Neurobiology Applications BT - Cell-Based Microarrays: Methods and Protocols. Springer New York, New York, pp. 147
157. Malkoc, V., 2018. Challenges and the future of 3D bioprinting. J. Biomed. Imaging Bioeng 2 (1), 64
65. Malleo, D., Nevill, J.T., Lee, L.P., Morgan, H., 2010. Continuous differential impedance spectroscopy of single cells. Microfluid. Nanofluid. 9 (2
3), 191
198. Mann, J.M., Lam, R.H.W., Weng, S., Sun, Y., Fu, J., 2012. A silicone-based stretchable micropost array membrane for monitoring live-cell subcellular cytoskeletal response. Lab Chip 12 (4), 731
740. Maschmeyer, I., et al., 2015a. A four-organ-chip for interconnected long-term co-culture of human intestine, liver, skin and kidney equivalents. Lab Chip 15 (12), 2688
2699. Maschmeyer, I., et al., 2015b. Chip-based human liver
intestine and liver
skin cocultures
a first step toward systemic repeated dose substance testing in vitro. Eur. J. Pharm. Biopharm. 95, 77
87. Massing, J., Kaden, D., Ka¨hler, C.J., Cierpka, C., 2016. Luminescent twocolor tracer particles for simultaneous velocity and temperature measurements in microfluidics. Meas. Sci. Technol. 27, 015301.

121

122

CHAPTER 3 Organs-on-a-chip engineering

Materne, E.-M., et al., 2015. The multi-organ chip—a microfluidic platform for long-term multi-tissue coculture. J. Vis. Exp. (98), e52526. Mathur, A., Roy, S.S., Tweedie, M., Mukhopadhyay, S., Mitra, S.K., Mclaughlin, J.A., 2009. Characterisation of PMMA microfluidic channels and devices fabricated by hot embossing and sealed by direct bonding. Curr. Appl. Phys. 9 (6), 1199
1202. Mazzocchi, A.R., Rajan, S.A.P., Votanopoulos, K.I., Hall, A.R., Skardal, A., 2018. In vitro patient-derived 3D mesothelioma tumor organoids facilitate patient-centric therapeutic screening. Sci. Rep. 8 (1), 1
12. McLean, I.C., Schwerdtfeger, L.A., Tobet, S.A., Henry, C.S., 2018. Powering ex vivo tissue models in microfluidic systems. Lab Chip 18 (10), 1399
1410. Mcdonald, J.C., Duffy, D.C., Anderson, J.R., Chiu, D.T., 2000. Fabrication of microfluidic systems in PDMS. Electrophoreses 21, 27
40. Meinert, C., Schrobback, K., Hutmacher, D.W., Klein, T.J., 2017. A novel bioreactor system for biaxial mechanical loading enhances the properties of tissue-engineered human cartilage. Sci. Rep. 7, 16997. Merceron, T.K., Murphy, S.V., 2015. Hydrogels for 3D Bioprinting Applications. Elsevier Inc. Mills, C.A., et al., 2006. Transparent micro- and nanopatterned poly(lactic acid) for biomedical applications. J. Biomed. Mater. Res., A 76 (4), 781
787. Mimetas, 2019. Technology. [Online]. Available from: ,https://mimetas.com/page/technology. (accessed 03.02.19.) Misun, P.M., Rothe, J., Schmid, Y.R.F., Hierlemann, A., Frey, O., 2016. Multi-analyte biosensor interface for real-time monitoring of 3D microtissue spheroids in hanging-drop networks. Microsyst. Nanoeng. 2, 16022. Modena, M.M., Chawla, K., Misun, P.M., Hierlemann, A., 2018. Smart cell culture systems: integration of sensors and actuators into microphysiological systems. ACS Chem. Biol. 13 (7), 1767
1784. Moisan, A., et al., 2018. Mechanistic investigations of diarrhea toxicity induced by antiHER2/3 combination therapy. Mol. Cancer Ther. 17 (7), 1464
1474. Molckovsky, A., Wilson, B.C., 2001. Monitoring of cell and tissue responses to photodynamic therapy by electrical impedance spectroscopy. Phys. Med. Biol. 46 (4), 983
1002. Monteiro, T., Almeida, M.G., 2019. Electrochemical enzyme biosensors revisited: old solutions for new problems. Crit. Rev. Anal. Chem. 49, 44
66. Moraes, C., Mehta, G., Lesher-Perez, S.C., Takayama, S., 2012. Organs-on-a-chip: a focus on compartmentalized microdevices. Ann. Biomed. Eng. 40 (6), 1211
1227. Moser, I., Jobst, G., Urban, G.A., 2002. Biosensor arrays for simultaneous measurement of glucose, lactate, glutamate, and glutamine. Biosens. Bioelectron. 17 (4), 297
302. Moya, A., et al., 2018. Online oxygen monitoring using integrated inkjet-printed sensors in a liver-on-a-chip system. Lab Chip 18 (14), 2023
2035. Mross, S., Pierrat, S., Zimmermann, T., Kraft, M., 2015. Microfluidic enzymatic biosensing systems: a review. Biosens. Bioelectron. 70, 376
391. Murphy, S.V., Atala, A., 2014. 3D bioprinting of tissues and organs. Nat. Biotechnol. 32 (8), 773
785. Naruse, K., Yamada, T., Sai, X.R., Hamaguchi, M., Sokabe, M., 1998. Pp125FAK is required for stretch dependent morphological response of endothelial cells. Oncogene 17 (4), 455
463.

References

Nguyen, N.-T., Truong, T.-Q., 2005. Flow rate measurement in microfluidics using optical sensors. In: First International Conference on Sensing Technology. pp. 28
31 Nguyen, N.-T., Wereley, S.T., 2002. Fundamentals and Applications of Microfluidics. Artech House. Obien, M.E.J., Gong, W., Frey, U., Bakkum, D.J., 2017. CMOS-Based High-Density Microelectrode Arrays: Technology and Applications. Springer, Singapore, pp. 3
39. Odijk, M., et al., 2015. Measuring direct current trans-epithelial electrical resistance in organ-on-a-chip microsystems. Lab Chip 15 (3). Oh, K.W., Ahn, C.H., 2006. A review of microvalves. J. Micromech. Microeng. 16, R13
R39. Oleaga, C., et al., 2016. Multi-Organ toxicity demonstration in a functional human in vitro system composed of four organs. Sci. Rep. 6, 20030. ¨ zu¨m, C., Da¨hne, L., 2019. Label-free bioanalysis based Olszyna, M., Debrassi, A., U on low-Q whispering gallery modes: rapid preparation of microsensors by means of layer-by-layer technology. Adv. Funct. Mater. 29 (2), 1805998. Olthuis, W., Robben, M.A.M., Bergveld, P., Bos, M., Van Der Linden, W.E., 1990. pH sensor properties of electrochemically grown iridium oxide. Sens. Actuators, B: Chem. 2, 247
256. Ong, L.J.Y., Islam, A., DasGupta, R., Iyer, N.G., Leo, H.L., Toh, Y.C., 2017. A 3D printed microfluidic perfusion device for multicellular spheroid cultures. Biofabrication 9, 045005. Oomen, P.E., Skolimowski, M.D., Verpoorte, E., 2016. Implementing oxygen control in chip-based cell and tissue culture systems. Lab Chip 16, 3394
3414. Oskooei, A., Gu¨nther, A., 2015. Bubble pump: scalable strategy for in-plane liquid routing. Lab Chip 15 (13), 2842
2853. Pachauri, V., Ingebrandt, S., 2016. Biologically sensitive field-effect transistors: from ISFETs to NanoFETs. Essays Biochem. 60 (1), 81
90. Papaioannou, T.G., Stefanadis, C., 2005. Vascular wall shear stress: basic principles and methods. Hell. J. Cardiol. 46, 9
15. Parate, D., et al., 2017. Enhancement of mesenchymal stem cell chondrogenesis with shortterm low intensity pulsed electromagnetic fields. Sci. Rep. 7, 9421. Park, S.H., Sim, W.Y., Min, B.H., Yang, S.S., Khademhosseini, A., Kaplan, D.L., 2012. Chip-based comparison of the osteogenesis of human bone marrow- and adipose tissue-derived mesenchymal stem cells under mechanical stimulation. PLoS One 7, e44689. Park, S.-H.L.D., Lim, J., Park, J.Y., 2015. Concise review: stem cell microenvironment on a chip: current technologies for tissue engineering and stem cell biology. Stem Cells Transl. Med. 4, 1352
1368. Parker, 2015. Electroactive Polymer (EAP) Technology—BUL 3800-EAP Overview Pemble, C.M., Towe, B.C., 1999. A miniature shape memory alloy pinch valve. Sens. Actuat. A 77, 145
148. Perdomo, J., Hinkers, H., Sundermeier, C., Seifert, W., Martı´nez Morell, O., Knoll, M., 2000. Miniaturized real-time monitoring system for L-lactate and glucose using microfabricated multi-enzyme sensors. Biosens. Bioelectron. 15 (9
10), 515
522. Peters, M.F., Lamore, S.D., Guo, L., Scott, C.W., Kolaja, K.L., 2015. Human stem cellderived cardiomyocytes in cellular impedance assays: bringing cardiotoxicity screening to the front line. Cardiovasc. Toxicol. 15 (2), 127
139.

123

124

CHAPTER 3 Organs-on-a-chip engineering

Pilarski, P.M., Adamia, S., Backhouse, C.J., 2005. An adaptable microvalving system for on-chip polymerase chain reactions. J. Immunol. Meth. 305, 48
58. Piro, B., Reisberg, S., 2017. Recent advances in electrochemical immunosensors. Sensors 17, 794. Pocock, K.J., et al., 2016. Low-temperature bonding process for the fabrication of hybrid glass
membrane organ-on-a-chip devices. J. Micro/Nanolithogr., MEMS, MOEMS 15 (4), 044502. Polinkovsky, M., Gutierrez, E., Levchenko, A., Groisman, A., 2009. Fine temporal control of the medium gas content and acidity and on-chip generation of series of oxygen concentrations for cell cultures. Lab Chip 9 (8), 1073
1084. Poulin, A., Imboden, M., Sorba, F., Grazioli, S., Martin-Olmos, C., Rosset, S., et al., 2018. An ultra-fast mechanically active cell culture substrate. Sci. Rep. 8, 9895. Pound, P., Ebrahim, S., Sandercock, P., Bracken, M.B., Roberts, I., 2004. Where is the evidence that animal research benefits humans? BMJ 328 (7438), p. 514 LP-517. Powers, M.J., et al., 2002. A microfabricated array bioreactor for perfused 3D liver culture. Biotechnol. Bioeng. 78 (3), 257
269. Primiceri, E., Chiriaco`, M.S., Rinaldi, R., Maruccio, G., 2013. Cell chips as new tools for cell biology
results, perspectives and opportunities. Lab Chip 13 (19), 3789. Quiro´s-Solano, W.F., Gaio, N., Silvestri, C., Pandraud, G., Sarro, P.M., 2016. PEDOT: PSS: a conductive and flexible polymer for sensor integration in organ-on-chip platforms. Procedia Eng. 168, 1184
1187. Ramamoorthy, R., Dutta, P.K., Akbar, S.A., 2003. Oxygen sensors: materials, methods, designs and applicationse. J. Mater. Sci. 38 (21), 4271
4282. Ranjan, V., Waterbury, R., Xiao, Z., Diamond, S.L., 1996. Fluid shear stress induction of the transcriptional activator c-fos in human and bovine endothelial cells, HeLa, and Chinese hamster ovary cells. Biotechnol. Bioeng. 49 (4), 383
390. Ravalli, A., Lozzi, L., Marrazza, G., 2016. Micro-flow immunosensor based on thin-film interdigitated gold array microelectrodes for cancer biomarker detection. Curr. Drug Deliv. 13 (3), 400
408. Regehr, K.J., et al., 2009. Biological implications of polydimethylsiloxane-based microfluidic cell culture. Lab Chip 9, 2132
2139. Rennert, K., et al., 2015. A microfluidically perfused three dimensional human liver model. Biomaterials 71, 119
131. Rezaei, B., Ghani, M., Shoushtari, A.M., Rabiee, M., 2016. Electrochemical biosensors based on nanofibres for cardiac biomarker detection: a comprehensive review. Biosens. Bioelectron. 78, 513
523. Rhodin, J.A.G., 2014. Architecture of the vessel wall. Compr. Physiol. Riahi, R., et al., 2016. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers. Sci. Rep. 6 (1), 24598. Richter, M., Wackerle, M., Woias, P., Hillerich, B., 1999. novel flow sensor with high time resolution based on differential pressure. In: Technical Digest. IEEE International MEMS 99 Conference. 12th IEEE International Conference on Micro Electro Mechanical Systems (Cat. No. 99CH36291). pp. 118
123 Rodrı`guez-Villarreal, A.I., Arundell, M., Carmona, M., Samitier, J., 2010. High flow rate microfluidic device for blood plasma separation using a range of temperatures. Lab Chip 10, 211
219.

References

Rogal, J., Probst, C., Loskill, P., 2017. Integration concepts for multi-organ chips: how to maintain flexibility?!. Futur. Sci. OA 3 (2), FSO180. Ronaldson-Bouchard, K., Vunjak-Novakovic, G., 2018. Organs-on-a-chip: a fast track for engineered human tissues in drug development. Cell Stem Cell 22 (3), 310
324. Ronkainen, N.J., Halsall, H.B., Heineman, W.R., 2010. Electrochemical biosensors. Chem. Soc. Rev. 39 (5), 1747
1763. Rotariu, L., Lagarde, F., Jaffrezic-Renault, N., Bala, C., 2016. Electrochemical biosensors for fast detection of food contaminants
trends and perspective. TrAC, Trends Anal. Chem. 79, 80
87. Rothbauer, M., Zirath, H., Ertl, P., 2018. Recent advances in microfluidic technologies for cell-to-cell interaction studies. Lab Chip 18 (2), 249
270. Rousselle, P., Braye, F., Dayan, G., 2018. Re-epithelialization of adult skin wounds: cellular mechanisms and therapeutic strategies. Adv. Drug Deliv. Rev. Saggiomo, V., Velders, A.H., 2015. Simple 3D printed scaffold-removal method for the fabrication of intricate microfluidic devices. Adv. Sci. 2 (9), 1
5. Sajay, B.N.G., Yin, C.S., Ramadan, Q., 2017. Optimization of micro-fabricated porous membranes for intestinal epithelial cell culture and in vitro modeling of the human intestinal barrier. J. Micromech. Microeng. 27, 124004. Santoro, R., Krause, C., Martin, I., Wendt, D., 2012. On-line monitoring of oxygen as a non-destructive method to quantify cells in engineered 3D tissue constructs. J. Tissue Eng. Regen. Med. 6 (9), 696
701. dos Santos, V., Brandalise, R.N., Savaris, M., 2017. Eng. Biomater. Sarkar, S., Dadhania, M., Rourke, P., Desai, T.A., Wong, J.Y., 2005. Vascular tissue engineering: microtextured scaffold templates to control organization of vascular smooth muscle cells and extracellular matrix. Acta Biomater. 1 (1), 93
100. Sartorius, 2019. Cubis MSA225S datasheet. [Online]. Available from: ,http://balancecanada. com/Documents/MSA225S-000-DU_000.pdf. (accessed 30.01.19.) Sassa, F., Morimoto, K., Satoh, W., Suzuki, H., 2008. Electrochemical techniques for microfluidic applications. Electrophoresis 29 (9), 1787
1800. Satoh, W., Hosono, H., Yokomaku, H., Morimoto, K., Upadhyay, S., Suzuki, H., 2008. Integrated electrochemical analysis system with microfluidic and sensing functions. Sensors 8 (2), 1111
1127. Schade-Kampmann, G., Huwiler, A., Hebeisen, M., Hessler, T., Di Berardino, M., 2008. On-chip non-invasive and label-free cell discrimination by impedance spectroscopy. Cell Prolif. 41 (5), 830
840. Schasfoort, R.B.M., Schlautmann, S., Hendrikse, L., van den Berg, A., 1999. Field-effect flow control for microfabricated fluidic networks. Science 286, 942
945 (80-.). Scheinpflug, J., et al., 2018. Journey into bone models: a review. Genes (Basel) 9 (5), 247. Schimek, K., et al., 2013. Integrating biological vasculature into a multi-organ-chip microsystem. Lab Chip 13 (18), 3588. Schmid, Y.R.F., Bu¨rgel, S.C., Misun, P.M., Hierlemann, A., Frey, O., 2016. Electrical electrical impedance spectroscopy for microtissue spheroid analysis in hanging-drop networks. ACS Sens. 1 (8), 1028
1035. Sebastian, B., Dittrich, P.S., 2018. Microfluidics to mimic blood flow in health and disease. Annu. Rev. Fluid Mech. 50, 483
504.

125

126

CHAPTER 3 Organs-on-a-chip engineering

Sensirion, 2019. LPG10 liquid flow sensor. [Online]. Available from: ,https://www.sensirion. com/en/about-us/company/technology/cmosens-technology-for-liquid-flow/. (accessed 30.01.19.) Sergachev, I., Rusanov, A., Trushkin, E., Sakharov, D., Marx, U., Tonevitsky, A., 2013. Fluorescent optical fiber sensors for cell viability monitoring. Analyst 138 (14), 4066. Shaegh, S.A.M., et al., 2016. A microfluidic optical platform for real-time monitoring of pH and oxygen in microfluidic bioreactors and organ-on-chip devices. Biomicrofluidics 10, 044111. Shah, P., et al., 2016. A microfluidics-based in vitro model of the gastrointestinal human
microbe interface. Nat. Commun. 7 (1), 11535. Shin, S.R., et al., 2016. Aptamer-based microfluidic electrochemical biosensor for monitoring cell-secreted trace cardiac biomarkers. Anal. Chem. 88 (20), 10019
10027. Shirure, V.S., George, S.C., 2017. Design considerations to minimize the impact of drug absorption in polymer-based organ-on-a-chip platforms. Lab Chip 17, 681
690. Skardal, A., Shupe, T., Atala, A., 2016. Organoid-on-a-chip and body-on-a-chip systems for drug screening and disease modeling. Drug Discov. Today 21 (9), 1399
1411. Song, H., et al., 2013. A microfluidic impedance flow cytometer for identification of differentiation state of stem cells. Lab Chip 13 (12), 2300. Sonntag, F., et al., 2010. Design and prototyping of a chip-based multi-micro-organoid culture system for substance testing, predictive to human (substance) exposure. J. Biotechnol. 148 (1), 70
75. Sonntag, F., Gru¨nzner, S., Schmieder, F., Busek, M., Klotzbach, U., Franke, V., 2015. Multilayer based lab-on-a-chip-systems for substance testing. Proc. SPIE 9351, 93510C. Sonntag, F., et al., 2016. Universal lab-on-a-chip platform for complex, perfused 3D cell cultures. In: Proc. of SPIE, vol. 9705. p. 970516 Spichiger, S., Spichiger-Keller, U.E., 2011. New single-use sensors for online measurement of glucose and lactate: the answer to the PAT initiative. Single-Use Technology in Biopharmaceutical Manufacture. John Wiley & Sons, Inc, pp. 295
299. Squires, T.M., Quake, S.R., 2005. Microfluidics: fluid physics at the nanoliter scale. Rev. Mod. Phys. 77 (3), 977
1026. Srinivasan, B., Kolli, A.R., Esch, M.B., Abaci, H.E., Shuler, M.L., Hickman, J.J., 2015. TEER measurement techniques for in vitro barrier model systems. J. Lab. Autom 20 (2), 107
126. Stern, E., et al., 2007. Label-free immunodetection with CMOS-compatible semiconducting nanowires. Nature 445 (7127), 519
522. Stucki, A.O., et al., 2015. A lung-on-a-chip array with an integrated bio-inspired respiration mechanism. Lab Chip 15, 1302
1310. Stucki, J.D., et al., 2018. Medium throughput breathing human primary cell alveolus-onchip model. Sci. Rep. 8 (1), 14359. Studer, V., Hang, G., Pandolfi, A., Ortiz, M., Anderson, W.F., Quake, S.R., 2004. Scaling properties of a low-actuation pressure microfluidic valve. J. Appl. Phys. 95, 393
398. Su, X., Young, E.W.K., Underkofler, H.A.S., Kamp, T.J., January, C.T., Beebe, D.J., 2011. Microfluidic cell culture and its application in high-throughput drug screening: cardiotoxicity assay for hERG channels. J. Biomol. Screen. 16 (1), 101
111. Sudho¨lter, E.J.R., van der Wal, P.D., Skowronska-Ptasinska, M., van den Berg, A., Reinhoudt, D.N., 1989. Ion-sensing using chemically-modified ISFETs. Sens. Actuators 17 (1
2), 189
194.

References

Sun, J., Wang, Y., Qian, Z., Hu, C., 2011. An approach to architecture 3D scaffold with interconnective microchannel networks inducing angiogenesis for tissue engineering. J. Mater. Sci. Mater. Med. 22, 2565
2571. Sun, T., Morgan, H., 2010. Single-cell microfluidic impedance cytometry: a review. Microfluid. Nanofluid. 8, 423
443. Sundararajan, N., Kim, D., Berlin, A.A., 2005. Microfluidic operations using deformable polymer membranes fabricated by single layer soft lithography. Lab Chip 5, 350
354. Svennersten, K., Berggren, M., Richter-Dahlfors, A., Jager, E.W.H., 2011. Mechanical stimulation of epithelial cells using polypyrrole microactuators. Lab Chip 11, 3287
3293. Szulcek, R., Bogaard, H.J., van Nieuw Amerongen, G.P., 2014. Electric cell-substrate impedance sensing for the quantification of endothelial proliferation, barrier function, and motility. J. Vis. Exp. 85, e51300. Talaei, S., van der Wal, P.D., Ahmed, S., Liley, M., de Rooij, N.F., 2015. Enzyme SU-8 microreactors: simple tools for cell-culture monitoring. Microfluid. Nanofluid. 19 (2), 351
361. Tanaka, Y., Fujikawa, T., Kazoe, Y., Kitamori, T., 2013. An active valve incorporated into a microchip using a high strain electroactive polymer. Sens. Actuators, B: Chem. 184, 163
169. Tecan, 2019. Liquid handling and automation. [Online]. Available from: ,https://lifesciences.tecan.com/products/liquid_handling_and_automation. (accessed 03.02.19.) Temiz, Y., Lovchik, R.D., Kaigala, G.V., Delamarche, E., 2015. Lab-on-a-chip devices: How to close and plug the lab? Microelectron. Eng. 132, 156
175. Thedinga, E., et al., 2007. Online monitoring of cell metabolism for studying pharmacodynamic effects. Toxicol. Appl. Pharmacol. 220 (1), 33
44. The´venot, D.R., Toth, K., Durst, R.A., Wilson, G.S., 2001. Electrochemical biosensors: recommended definitions and classification. Biosens. Bioelectron. 16 (1
2), 121
131. Thielecke, H., Mack, A., Robitzki, A., 2001. A multicellular spheroid-based sensor for anti-cancer therapeutics. Biosens. Bioelectron. 16 (4
5), 261
269. Thomassen, 2018. Organ on Chip in Development (ORCHID) TissUse, 2019. Technology. [Online]. Available from: ,https://www.tissuse.com/en/ technology/. (accessed 03.02.19.) Torisawa, Y., et al., 2014. Bone marrow-on-a-chip replicates hematopoietic niche physiology in vitro. Nat. Methods 11, 663
671. Tracey, M.C., Johnston, I.D., Davis, J.B., Tan, C.K.L., 2006. Dual independent displacement-amplified micropumps with a single actuator. J. Micromech. Microeng. 16, 1444
1452. Trietsch, S.J., Israe¨ls, G.D., Joore, J., Hankemeier, T., Vulto, P., 2013. Microfluidic titer plate for stratified 3D cell culture. Lab Chip 13 (18), 3548. Trietsch, S.J., et al., 2017. Membrane-free culture and real-time barrier integrity assessment of perfused intestinal epithelium tubes. Nat. Commun. 8 (1), 262. Tru¨ssel, A., Mu¨ller, R., Webster, D., 2012. Toward mechanical systems biology in bone. Ann. Biomed. Eng. 40 (11), 2475
2487. Tsamandouras, N., Chen, W.L.K., Edington, C.D., Stokes, C.L., Griffith, L.G., Cirit, M., 2017. Integrated gut and liver microphysiological systems for quantitative in vitro pharmacokinetic studies. AAPS J. 19 (5), 1499
1512. Tsao, C.W., 2016. Polymer microfluidics: simple, low-cost fabrication process bridging academic lab research to commercialized production. Micromachines 7 (12), 225.

127

128

CHAPTER 3 Organs-on-a-chip engineering

Ugo, P., Moretto, L.M., 2017. Electrochemical immunosensors and aptasensors. Chemosensors 5 (2), 13. Unger, M.A., Chou, H.P., Thorsen, T., Scherer, A., Quake, S.R., 2000. Monolithic microfabricated valves and pumps by multilayer soft lithography. Science 288, 113
116 (80-.). Van Dorst, B., et al., 2016. Integration of an optical CMOS sensor with a microfluidic channel allows a sensitive readout for biological assays in point-of-care tests. Biosens. Bioelectron. 78, 126
131. van Duinen, V., et al., 2017. 96 perfusable blood vessels to study vascular permeability in vitro. Sci. Rep. 7, 18071. Varma, S., Fendyur, A., Box, A., Voldman, J., 2017. Multiplexed cell-based sensors for assessing the impact of engineered systems and methods on cell health. Anal. Chem. 89, 4663
4670. Varma, S., Garcia-Cardena, G., Voldman, J., 2018. Unraveling endothelial cell phenotypic regulation by spatial hemodynamic flows with microfluidics. In: Proc. MicroTAS 2018. pp. 109
112 Verhulsel, M., Vignes, M., Descroix, S., Malaquin, L., Vignjevic, D.M., Viovy, J.L., 2014. A review of microfabrication and hydrogel engineering for micro-organs on chips. Biomaterials 35 (6), 1816
1832. Verma, R., Adhikary, R.R., Banerjee, R., 2016. Smart material platforms for miniaturized devices: implications in disease models and diagnostics. Lab Chip 16 (11), 1978
1992. Vijayavenkataraman, S., Yan, W.C., Lu, W.F., Wang, C.H., Fuh, J.Y.H., 2018. 3D bioprinting of tissues and organs for regenerative medicine. Adv. Drug Deliv. Rev. 132, 296
332. Villenave, R., et al., 2017. Human gut-on-a-chip supports polarized infection of coxsackie B1 virus in vitro. PLoS One 12 (2), e0169412. Vogel, V., Sheetz, M., 2006. Local force and geometry sensing regulate cell functions. Nat. Rev. Mol. Cell Biol. 7 (4), 265
275. Vormann, M.K., et al., 2018. Nephrotoxicity and kidney transport assessment on 3D perfused proximal tubules. AAPS J. 20 (5), 90. Vriend, J., et al., 2018. Screening of drug-transporter interactions in a 3D microfluidic renal proximal tubule on a chip. AAPS J. 20 (5), 87. Wagner, I., et al., 2013. A dynamic multi-organ-chip for long-term cultivation and substance testing proven by 3D human liver and skin tissue co-culture. Lab Chip 13 (18), 3538
3547. Walker, G.M., Beebe, D.J., 2002. A passive pumping method for microfluidic devices. Lab Chip 2 (3), 131. Wang, C.H., Lee, G.B., 2006. Pneumatically driven peristaltic micropumps utilizing serpentine-shape channels. J. Micromech. Microeng. 16, 341
348. Wang, J., 2008. Electrochemical glucose biosensors. Chem. Rev. 108 (2), 814
825. Wang, Y.N., Fu, L.M., 2018. Micropumps and biomedical applications
a review. Microelectron. Eng. 195, 121
138. Wang, Y.I., Shuler, M.L., 2018. UniChip enables long-term recirculating unidirectional perfusion with gravity-driven flow for microphysiological systems. Lab Chip 18 (17), 2563
2574. Wang, L., et al., 2009. Polydimethylsiloxane-integratable micropressure sensor for microfluidic chips. Biomicrofluidics 3 (3), 034105.

References

Wang, J.D., Douville, N.J., Takayama, S., ElSayed, M., 2012. Quantitative analysis of molecular absorption into PDMS microfluidic channels. Ann. Biomed. Eng. 40, 1862
1873. Wang, X., Phan, D.T.T., Zhao, D., George, S.C., Hughes, C.C.W., Lee, A.P., 2016. An onchip microfluidic pressure regulator that facilitates reproducible loading of cells and hydrogels into microphysiological system platforms. Lab Chip 16 (5), 868
876. Wang, Y.I., Abaci, H.E., Shuler, M.L., 2017. Microfluidic blood-brain barrier model provides in vivo-like barrier properties for drug permeability screening. Biotechnol. Bioeng. 114 (1), 184
194. Wang, T., Chen, J., Zhou, T., Song, L., 2018a. Fabricating microstructures on glass for microfluidic chips by glass molding process. Micromachines 9 (6), 1
15. Wang, Y.I., Carmona, C., Hickman, J.J., Shuler, M.L., 2018b. Multiorgan microphysiological systems for drug development: strategies, advances, and challenges. Adv. Healthc. Mater. 7 (2), 1
29. Wegener, J., Keese, C.R., Giaever, I., 2000. Electric cell-substrate impedance sensing (ECIS) as a noninvasive means to monitor the kinetics of cell spreading to artificial surfaces. Exp. Cell Res. 259 (1), 158
166. Weibel, D.B., Kruithof, M., Potenta, S., Sia, S.K., Lee, A., Whitesides, G.M., 2005. Torque-actuated valves for microfluidics. Anal. Chem. 77, 4726
4733. Weibel, D.B., Siegel, A.C., Lee, A., George, A.H., Whitesides, G.M., 2007. Pumping fluids in microfluidic systems using the elastic deformation of poly(dimethylsiloxane). Lab Chip 7, 1832
1836. Weltin, A., et al., 2014. Cell culture monitoring for drug screening and cancer research: a transparent, microfluidic, multi-sensor microsystem. Lab Chip 14 (1), 138
146. Weltin, A., Kieninger, J., Urban, G.A., 2016. Microfabricated, amperometric, enzymebased biosensors for in vivo applications. Anal. Bioanal. Chem. 408 (17), 4503
4521. Weltin, A., Hammer, S., Noor, F., Kaminski, Y., Kieninger, J., Urban, G.A., 2017. Accessing 3D microtissue metabolism: lactate and oxygen monitoring in hepatocyte spheroids. Biosens. Bioelectron. 87, 941
948. Wen, W., Yan, X., Zhu, C., Du, D., Lin, Y., 2017. Recent advances in electrochemical immunosensors. Anal. Chem. 89 (1), 138
156. Wevers, N.R., et al., 2018. A perfused human blood
brain barrier on-a-chip for highthroughput assessment of barrier function and antibody transport. Fluids Barriers CNS 15 (1), 23. van der Wijngaart, W., Chugh, D., Man, E., Melin, J., Stemme, G., 2007. A lowtemperature thermopneumatic actuation principle for gas bubble microvalves. J. Microelectromech. Syst. 16, 765
774. Wikswo, J.P., et al., 2013. Engineering challenges for instrumenting and controlling integrated organ-on-chip systems. IEEE Trans. Biomed. Eng. 60 (3), 682
690. Williams, D.F., 2008. On the mechanisms of biocompatibility. Biomaterials 29 (20), 2941
2953. Wipf, M., et al., 2016. Label-Free FimH protein interaction analysis using silicon nanoribbon bioFETs. ACS Sens. 1 (6), 781
788. Wong, I., Ho, C.-M., 2009. Surface molecular property modifications for poly(dimethylsiloxane) (PDMS) based microfluidic devices. Microfluid. Nanofluid. 7 (3), 291
306. Wrasse, A.N., Bertoldi, D., Morales, R.E.M., Jose da Silva, M., 2017. Two-phase flow rate measurement using a capacitive sensor and a Venturi meter. In: 2017 IEEE Sensors. pp. 1
3

129

130

CHAPTER 3 Organs-on-a-chip engineering

Xiao, C., Luong, J.H.T., 2005. Assessment of cytotoxicity by emerging impedance spectroscopy. Toxicol. Appl. Pharmacol. 206 (2), 102
112. Xiao, S., et al., 2017. A microfluidic culture model of the human reproductive tract and 28-day menstrual cycle. Nat. Commun. 8, 14584. Xing, J.Z., Zhu, L., Gabos, S., Xie, L., 2006. Microelectronic cell sensor assay for detection of cytotoxicity and prediction of acute toxicity. Toxicol. Vitr. 20 (6), 995
1004. Yang, B.Z., Lin, Q., 2007. A latchable microvalve using phase change of paraffin wax. Sens. Actuators, A: Phys. 134, 194
200. Yang, Q., Lian, Q., Xu, F., 2017. Perspective: fabrication of integrated organ-on-a-chip via bioprinting. Biomicrofluidics 11, 031301. Yavuz, C., Oliaei, S.N.B., Cetin, B., Yesil-Celiktas, O., 2016. Sterilization of PMMA microfluidic chips by various techniques and investigation of material characteristics. J. Supercrit. Fluids 107, 114
121. Yi, H.-G., Lee, H., Cho, D.-W., 2017. 3D printing of organs-on-chips. Bioengineering 4, 10. Yoo, J.C., Choi, Y.J., Kang, C.J., Kim, Y.S., 2007. A novel polydimethylsiloxane microfluidic system including thermopneumatic-actuated micropump and paraffin-actuated microvalve A 2007. Sens. Actuators 139, 216
220. Yum, K., Hong, S.G., Healy, K.E., Lee, L.P., 2014. Physiologically relevant organs on chips. Biotechnol. J. 9 (1), 16
27. Zeller, P., et al., 2017. Hepatocytes cocultured with Sertoli cells in bioreactor favors Sertoli barrier tightness in rat. J. Appl. Toxicol. 37 (3), 287
295. Zengerle, R., Geiger, W., Richter, M., Ulrich, J., Kluge, S., Richter, A., 1995. Transient measurements on miniaturized diaphragm pumps in microfluid systems. Sens. Actuators, A: Phys. 47 (1
3), 557
561. Zhang, J.X.J., Hoshino, K., 2014. Electrical transducers. In: Zhang, J.X.J., Hoshino, K. (Eds.), Molecular Sensors and Nanodevices. Elsevier, Oxford, pp. 169
232. Zhang, Z., Zhe, J., Chandra, S., Hu, J., 2005. An electronic pollen detection method using Coulter counting principle. Atmos. Environ. 39 (30), 5446
5453. Zhang, Y.S., et al., 2017. Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors. Proc. Natl. Acad. Sci. U.S.A. 114 (12), E2293
E2302. Zhang, B., Korolj, A., Lai, B.F.L., Radisic, M., 2018. Advances in organ-on-a-chip engineering. Nat. Rev. Mater. 3 (8), 257
278. Zheng, F., Fu, F., Cheng, Y., Wang, C., Zhao, Y., Gu, Z., 2016. Organ-on-a-chip systems: microengineering to biomimic living systems. Small 12 (17), 2253
2282. Zhou, Y., 2017. The recent development and applications of fluidic channels by 3D printing. J. Biomed. Sci. 24, 80. Zhu, Z., Frey, O., Franke, F., Haandbæk, N., Hierlemann, A., 2014. Real-time monitoring of immobilized single yeast cells through multifrequency electrical impedance spectroscopy. Anal. Bioanal. Chem. 406, 7015
7025. Zhu, Z., Frey, O., Haandbaek, N., Franke, F., Rudolf, F., Hierlemann, A., 2015. Time-lapse electrical impedance spectroscopy for monitoring the cell cycle of single immobilized S. pombe cells. Sci. Rep. 5, 17180. no. October.