Microfluidic-Mass Spectrometry Interfaces for Translational Proteomics

Review

Microfluidic-Mass Spectrometry Interfaces for Translational Proteomics R. Daniel Pedde,1,2 Huiyan Li,2 Christoph H. Borchers,2,3,4,5,* and Mohsen Akbari1,6,7,* Interfacing mass spectrometry (MS) with microfluidic chips (mchip-MS) holds considerable potential to transform a clinician’s toolbox, providing translatable methods for the early detection, diagnosis, monitoring, and treatment of noncommunicable diseases by streamlining and integrating laborious sample preparation workflows on high-throughput, user-friendly platforms. Overcoming the limitations of competitive immunoassays currently the gold standard in clinical proteomics mchip-MS can provide unprecedented access to complex proteomic assays having high sensitivity and specificity, but without the labor, costs, and complexities associated with conventional MS sample processing. This review surveys recent mchip-MS systems for clinical applications and examines their emerging role in streamlining the development and translation of MS-based proteomic assays by alleviating many of the challenges that currently inhibit widespread clinical adoption. Accelerating the Translation of MS-Based Proteomics Recent advances in clinical proteomics (see Glossary) have yielded biological insights that hold considerable potential to revolutionize the methods by which clinicians address noncommunicable diseases (e.g., heart disease, stroke, cancer, and diabetes) [1–3]. Probing biomarkers in clinical samples has opened promising avenues for the early detection, diagnosis, and monitoring of diseases based on proteomic signatures [3–6]. Since the vast majority of drugs target disease-specific proteins [2], proteomics also has substantial utility in the development, selection, monitoring, and evaluation of novel biopharmaceuticals [4,5,7–9]. The composition, properties, and behavior of the human proteome as an integrated system, however, remain largely elusive [1,10]. Human blood contains tens of thousands of different proteins (along with their proteoforms) spanning at least ten orders of magnitude in concentration, where high-abundance proteins (e.g., human serum albumin (HSA) and immunoglobulins) often veil promising biomarker targets that are present at low levels [11,12]. This necessitates the use of high-performance analytical methods coupled with advanced sample preparation techniques to probe the proteome with increased sensitivity [13–17]. However, competitive immunoassays (IAs) the current gold standard in clinical proteomics suffer from crossreactivity and interferences, limited dynamic range, and challenges with distinguishing proteoforms [5,15,18–20]. There is a trend toward mass spectrometry (MS)-based techniques (Box 1), which facilitate the quantification of proteins, often encompassing various post-translational modifications (PTMs), with high specificity [1,10]. However, the remaining challenges associated with the time-consuming and laborious sample preparation requirements, high sample demand, and complex instrumentation [10,21–24] limit the use of MS in clinical applications that require timely intervention [15,20,25] and pose a major

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Trends Interfacing microfluidics with mass spectrometry (mchip-MS) provides attractive solutions to overcome the limitations of competitive immunoassays (the current gold standard) and conventional mass spectrometerybased approaches. The automation and integration of complex sample processing protocols, enabled by mchip-MS, hold considerable promise to streamline clinical mass spectrometry-based workflows on user-friendly platforms. mChip-MS could accelerate the development of rapid, high-throughput bioanalytical workflows and serve a pioneering role in their clinical translation.

1 Laboratory for Innovations in Microengineering (LiME), Department of Mechanical Engineering, University of Victoria, 3800 Finnerty Rd., Victoria, BC, V8P 5C2, Canada 2 University of Victoria-Genome British Columbia Proteomics Centre, University of Victoria, 3101-4464 Markham St., Victoria, BC, V8Z 7X8, Canada 3 Department of Biochemistry and Microbiology, University of Victoria, 3800 Finnerty Rd., Victoria, BC, V8P 5C2, Canada 4 Gerald Bronfman Department of Oncology, McGill University, 5100 de Maisonneuve Blvd. West, Suite 720, Montreal, QC, H4A 3T2, Canada 5 Proteomics Centre, Jewish General Hospital, McGill University, 3755 Cote-Ste-Catherine Road, Montreal, QC, H3T 1E2, Canada

Box 1. Concepts in Mass Spectrometry (MS)-Based Proteomics Top-Down vs. Bottom-Up Proteomics MS-based proteomics employs two complementary strategies: top-down (i.e., the analysis of intact proteins) and bottom-up proteomics (i.e., the analysis of proteolytic digests) [10] (Figure I). Both techniques rely on precursor selection, fragmentation, and tandem MS (MS/MS) analysis to separate ionized analytes based on their mass-tocharge ratios, resulting in a mass spectrum. Clinical proteomics currently relies largely on bottom-up approaches; however, recent advances in top-down proteomics have opened several avenues for improved assays by enabling the site-specific identification of post-translational modifications (PTMs) with full sequence coverage [71].

*Correspondence: [email protected] (C.H. Borchers) and [email protected] (M. Akbari). Intensity

Separaon

Top-down approach

6 Centre for Biomedical Research (CBR), University of Victoria, 3800 Finnerty Rd., Victoria, BC, V8P 5C2, Canada 7 Centre for Advanced Materials and Related Technologies (CAMTEC), University of Victoria, 3800 Finnerty Rd., Victoria, BC, V8P 5C2, Canada

PTMs

m/z

Protein mixture

Boom-up approach

MS/MS Digeson (pepdes)

MS

Figure I. Top-Down and Bottom-Up Approaches in MS-Based Proteomics. The top-down workflow relies on the analysis of protein separations without digestion. After MS detection, specific proteoforms can be selected (highlighted in spectrum) and fragmented by MS/MS to identify the PTM sites with full sequence coverage. The bottom-up workflow relies on the enzymatic digestion of proteins (typically with trypsin) into small peptides for analysis. Precursors are selected and fragmented by MS/MS for protein identification; however, only partial sequence coverage is achieved due to undetected peptides.

Biomarker Pipeline The pipeline leading to the final clinical validation of biomarkers comprises a sequence of preclinical steps: biomarker discovery, qualification, verification, and validation (Figure II ) [4,15]. Here, an inverse relationship exists between the number of quantified analytes and the number of samples. For example, candidate biomarkers are identified among thousands of analytes in several samples during the discovery phase of the biomarker pipeline. After qualification and verification of select biomarkers, an assay is optimized for only a few candidate analytes, which are tested across thousands of patient samples in the validation phase.

Phase Discovery – Idenfy candidate biomarkers Qualificaon – Confirm sample abundance Verificaon – Assess candidate specificity Validaon – Validate and opmize assay

Number of analytes

Number of samples

1000s

10s

30–100

10s

10s 4–10

100s 1000s

Figure II. Preclinical Steps in the Biomarker Pipeline and the Inverse Relationship between the Number of Analytes and Samples for Each Phase. Adapted from [4].

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bottleneck in the validation phase of the biomarker pipeline (where an assay must be tested against thousands of patient samples to meet rigorous validation standards [3,4]). Accordingly, there is an urgent need to develop efficient technologies that streamline complex proteomic workflows for clinical translation, while maintaining high specificity, sensitivity, reproducibility, and repeatability, in order to fully exploit the utilization of clinically-relevant biomarkers [4,26,27]. Interfacing microfluidics the science and technology of systems that process or manipulate fluids at the submillimeter scale [28] with MS (i.e., mchip-MS) holds considerable promise to accelerate the clinical translation of MS-based proteomic assays. In many cases, mchip-MS has realized (i) integrated complex MS sample preparation strategies on automated and userfriendly platforms; (ii) shorter analysis times (due to the increased rates of molecular diffusion and other transport phenomena at small length scales), especially in the case of protein separations; (iii) reduced sample and reagent consumption, test equipment size, and overall cost per assay; and (iv) parallelized workflows to support dramatically increased throughput for diagnostic screening and other batch processing applications [6,28–32]. To achieve widespread adoption and clinical translation, however, these systems must be equipped with reliable and robust MS coupling hardware, as well as undergo rigorous evaluation according to regulatory guidelines. The development and evolution of mchip-MS systems and their extensive applications have been well-documented [33]; recent articles have focused on the single-device integration of sample preparation workflows [33,34] and the incentives for mchip-MS adoption [35]. This review examines the emerging role of mchip-MS in accelerating the clinical translation of MS-based proteomic assays by describing recent mchip-MS advancements, novel clinical applications, and considerations surrounding the regulatory approval and clinical implementation of MS-based workflows.

Infrastructure in mChip-MS Systems

mChip-MS relies on coupling suitable microfluidic platforms (for sample processing) with soft ionization techniques, which minimize fragmentation while ionizing delicate proteomic samples for MS analysis. Soft ionization is most commonly achieved using either electrospray ionization (ESI) or matrix-assisted laser desorption/ionization (MALDI), which ionize analytes from solutions and dried samples respectively (Figure 1, Key Figure) [36]. Here, ESI predominates since it permits suitable flow rates for microdevices (typically 1–20 mL/min) and is well-suited to online coupling with microfluidic separation techniques, especially for probing complex samples [32,36–38]. MALDI offers several advantages including attomole detection sensitivity, high tolerance to salts and other contaminants, and simplified mass spectra for easy interpretation; however, it is generally limited to offline coupling [32,38,39]. Furthermore, mchip-MS requires a robust coupling interface to efficiently transfer the analytes from the microdevice to the MS system (Figure 2) [37]. Since the genesis of mchip-MS, microfluidics and MS coupling methods have evolved to facilitate robust microdevice integration, accommodate a wider range of flow rates, and (in many cases) enable straightforward and low-cost fabrication. Microfluidic Platforms Microfluidic platforms typically employ analog microfluidics, droplet microfluidics, or digital microfluidics (DMF) and combinations thereof (Figure 1). Analog and droplet-based microfluidics enable the respective manipulation of continuous fluid streams and discrete droplets, typically enclosed in microchannels, using active or passive pumping mechanisms. As an alternative, DMF systems employ insulated electrode arrays to dispense, mix, merge, and split discrete samples by applying successive voltage potentials [24]. With the exception of DMF systems (which require dielectric and electrically conductive materials), microdevice construction relies on several common materials such as silicon, glass, poly(dimethylsiloxane) (PDMS), and thermoplastics [i.e., poly(methyl methacrylate) (PMMA), polycarbonate (PC),

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Glossary Accuracy: the closeness of the mean test results, obtained by a method, to the true value (concentration) of an analyte. Analog microfluidics: continuous fluid streams are manipulated in enclosed microchannels, typically by means of active (e.g., flow- or pressure-based) or passive (e.g., capillary flow) pumping mechanisms. Biomarkers: measurable indicators of specific biological states, particularly those relevant to the risk of contraction, the presence, or the stage of disease. Coefficient of variation (CV): a statistical measure that describes the amount of variability in a distribution (the standard deviation divided by the mean). Dead volume: a portion of internal volume that is not subject to the driving flow (outside of the flow field). Digital microfluidics (DMF): the manipulation of discrete droplets on an insulated electrode array by applying suitable electrode voltage sequences to vary the substrate wettability. Droplet microfluidics: discrete droplets are encapsulated and dispersed in an immiscible oil phase for manipulation, typically in enclosed microchannels. Electroosmotic flow: the use of an applied electric potential across a liquid medium to induce fluid motion, typically within a capillary or microchannel. Electrospray ionization (ESI): analytes are ionized from charged droplets of solution that are infused through a high-voltage emitter into ambient bath gas. Immunoassays (IAs): bioanalytical technique that quantifies a target analyte based on the reaction of an antibody and antigen (analyte). Limit of detection: lowest concentration at which a target analyte has a certain probability of being detected. Liquid-phase lithography: direct photopatterning of a liquid-phase prepolymer mixture to create functional and structural components. Mass spectrometry (MS): analytical technique that sorts ionized species based on their mass-to-charge ratio (m/z). Matrix-assisted laser desorption/ ionization (MALDI): analyte

polystyrene (PS), and cyclic olefin copolymer (COP)]; a detailed material comparison, along with an elegant description of microfluidic concepts, was recently published by Sackmann and colleagues [28]. Owing to their well-established methods and widespread use, analog microfluidics are frequently employed in mchip-MS systems to automate techniques such as protein digestion [39], separation (e.g., liquid chromatography (LC), capillary electrophoresis (CE), and isoelectric focusing (IEF)) [37,40–42], isobaric labelling [39], and affinity extraction [25,43]. Their continuous and laminar flow characteristics make analog systems ideal for applications that require diffusive mixing or the generation of precise charge or concentration gradients (e.g., electrophoretic and chromatographic separations). Droplet microfluidics provide attractive platforms for reaction miniaturization and dispersion-free transport with minimal adsorptive surface interactions, since they limit diffusion and Taylor dispersion caused by fluid shear in continuous flow [44]. Droplet-based systems have been adopted for applications that benefit from the processing of discrete samples such as when interfacing MS to existing systems (e.g., LC [45–48]) or coupling two separation techniques [49]. However, additional complexities arise from the need for droplet generation, which is especially challenging at low frequencies (<1 Hz) [44]. As a promising alternative to microchannel-based methods, DMF systems manipulate discrete droplets with precise control in either open (i.e., exposed to air) or closed systems (i.e., sandwiched between plates) [14,50]. Droplets in closed systems are typically dispersed in a filler fluid (e.g., oil or Pluronic additives) that can enable droplet actuation at lower voltages, reduce nonspecific surface adsorption effects, and inhibit droplet evaporation [30]. Open DMF, on the other hand, provides an ideal platform for integrating external hardware such as electrospray emitters and capillary- or fiber-based systems (e.g., solid phase microextraction) [50,51]. Compared to microchannel-based approaches, DMF is better suited for interfacing solid materials (e.g., dried blood spots [50–54] and dried urine samples [55]), as well as for automating complex sample preparation techniques (e.g., protein immuno-depletion [14]). However, DMF systems introduce several complexities related to microfabrication and operation, due to the addition of electronically conductive and dielectric components. Furthermore, DMF chips are not amenable to storing preloaded liquid sample and reagents, which is useful for streamlining complex protocols [56]. Though microfluidic technology has been embraced as an invaluable tool in biology and clinical research, several limitations still hinder the adoption of novel microfluidic techniques into mainstream use [29,57,58]. Here, the ‘world-to-chip’ interface presents a significant challenge : the advantages of microfluidics (e.g., rapid analyses and reduced consumption) are often sacrificed due to challenges with accurate samples and reagent delivery at small scales [52,56]. The small geometries inherent to microfluidics also pose several limitations on the loading capacity and resolving power of miniaturized separation systems such as LC and CE [59]. Furthermore, the high surface-to-volume ratios present critical limitations due to (i) the increased rate of analyte adsorption onto solid surfaces [56] and (ii) unwanted electroosmotic flow within electrophoretic separations (e.g., CE and IEF) [60]; researchers often employ coatings (e.g., silanes [42,61] and polyamines [60]) to reduce the effects of surface interactions. Accordingly, the development of practical microfluidic components and standardized procedures (for user-friendly sample collection, pretreatment, chip loading, and on-chip processing) are crucial prerequisites for widespread adoption [58].

cocrystallization with a sacrificial matrix (consisting of small, highly absorbent molecules) that is desorbed and ionized using laser pulse ablation. Offline coupling: the output from one device is passed to another (manually or automatically) for subsequent processing. Online coupling: the output from one device is directly adapted to the input of another for continuous processing. Piezoelectric: material which generates motion due to an applied electric potential, or generates an electric potential when subjected to mechanical loading, or both. Post-translational modifications (PTMs): covalent or enzymatic modification of a protein during or after synthesis (e.g., phosphorylation, glycosylation, and glycation). Precision: variation in measurements when the procedure is applied repeatedly to multiple aliquots of a single biological sample. Proteoforms: highly related protein molecules, arising from a single gene, that differ due to variations in genetics, RNA transcript splicing, and post-translational modifications. Proteome: the entire complement of proteins that is expressed in a sample at a given time. Proteomics: the large-scale study of proteomes and their structure and function. Repeatability: variation in repeated measurements on the same sample using the same system and operator. Reproducibility: variation in measurements when the operator, instrumentation, time, or location is changed. Sensitivity: ability to discriminate between different concentrations of the same substance. Specificity: ability to discriminate between a target analyte and other substances that are present in the sample. Taylor dispersion: an increase in the diffusivity of a species caused when fluid shear enhances the dispersion in the direction of flow. Venturi pump: drives fluid flow using a vacuum that is induced by the increase in velocity as another fluid passes through a constricted region.

mChip-MS Coupling Methods The first mchip-MS coupling methods were all introduced in the same volume of Analytical Chemistry (vol. 69, 1997). Figeys, Ning, and Aebersold connected glass chips to a

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Key Figure

Technologies Used in a Typical mChip-MS Workflow

Analog microfluidics

(A)

(B)

Workflow

Droplet microfluidics

Sample preparaon

On-chip processing Droplet generaon (C)

Filler fluid

μChip-MS coupling

Digital microfluidics (DMF)

Ionizaon and analysis by MS

Hydrophobic coang Closed system

Substrate Dielectric Electrodes

(F)

Mass spectrometry (MS)

Open system

Evaporaon

To mass analyzer

Ionizaon Emier (D)

Electrospray ionizaon (ESI) Laser ablaon

Analyte ion

Desorpon Analyte and matrix cocrystallizaon

(E)

Matrix-assisted laser desorpon/ionizaon (MALDI)

Figure 1. Samples are prepared, loaded, and processed using analog, droplet, or digital microfluidic technology (shown in the box). (A) Analog microfluidics process fluids in continuous streams, whereas (B) droplet microfluidics manipulate segmented droplets that are typically suspended in an immiscible phase. As an alternative to microchannel-based techniques, (C) digital microfluidics manipulate discrete droplets (on either an open substrate or enclosed between two plates) by altering the voltage potential across embedded electrodes. Through mchip-MS coupling, the samples are typically ionized using either (D) electrospray ionization (where samples are ionized from charged solutions dispensed from a high-voltage emitter) or (E) matrix-assisted laser desorption/ionization (where samples are desorbed and ionized using laser pulse ablation of co-crystallized analyte/matrix samples) for analysis by mass spectrometry.

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(A)

(D)

Capillary sampling probe

µchip-ESI interfaces

Diced chip

Closed Capillary emier

(B)

Open

Pulled glass (E)

(C)

Polymer film

Mulple emiers

(F)

(G)

Piezoelectric dispenser

(H)

Sample

µchip-MALDI interfaces

Immiscible phase Capillary Evaporaon

(I)

(J)

(K)

Peel

Punch

Slip

Figure 2. Methods for Coupling Microfluidics with Mass Spectrometry (MS). Methods include (top) electrospray ionization (mchip-ESI) and (bottom) matrixassisted laser desorption/ionization (mchip-MALDI). Electrospray emitters for channel-based systems are formed by (A) dicing/cutting the chip to taper a microchannel outlet, (B) glass pulling, and (C) parallel emitter fabrication using silicon microfabrication. For DMF, (D) embedded capillary emitters and capillary sampling probes are common for closed and open systems, respectively. (E) ‘Microfluidic origami’ using folded polymeric films can be used to construct low-cost DMF emitters. MALDI analysis is often achieved by automated target spotting using (F) embedded capillaries, (G) piezoelectric microdispensers, or (H) droplet generators, coupled to a programmable positioning stage. Alternatively, the chip substrate can be directly analysed by MALDI-MS via (I) substrate punching, (J) reversible bonding, or (K) slipping operations to expose cocrystallized samples.

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microsprayer using etched channels and fused capillary tubing, while thin gold leads provided high voltage to the sample reservoirs for analysis by ESI-MS [62]. Xue and colleagues demonstrated ESI infusion directly from the planar edge of a chip using multiple channel outlets for parallel emission [63]. Little and coworkers introduced ‘MALDI-on-a-chip,’ where a piezoelectric pipette manipulated nanoliter-sized DNA samples in an array of individual etched wells on open silicon chips for semi-automated analysis by MALDI-MS [64]. Interfacing microfluidics with ESI-MS (mchip-ESI-MS) (Figure 2A–E) relies on the robust incorporation of a high-voltage emitter that ensures minimal dead volume [31]. In droplet-based systems, ESI-MS coupling is challenging since stable plume formation is inhibited by the alternating flow of sample and oil droplets [47]. Similarly, the coupling of DMF to ESI adds additional complexities since the droplets are typically at ambient pressure, and the operating voltages for DMF and ESI are dissimilar (requiring AC and DC respectively) [65]. Since early chip-MS designs suffered from dead volumes associated with the capillary-chip junction as well as droplet spreading, researchers now favour alternatives methods of emitter integration such as (i) cutting or dicing the chip to taper the outlet of embedded microchannels (Figure 2A) [31,40,44,60,61,66–70], (ii) monolithic integration using glass pulling (Figure 2B) [37], and (iii) parallel fabrication using silicon microfabrication (Figure 2C) [71]. DMF to ESI interfaces typically employ sandwiched capillary emitters [53,55] and Venturi pump-based sampling capillaries connected to external emitters [65,72] for closed and open systems respectively (Figure 2D). The focus has now turned to developing inexpensive and scalable fabrication methods that enable robust emitter integration. For example, a highly sensitive and stable mchip-ESI-MS interface was recently developed by bonding a PS base, featuring an embedded capillary emitter and electrodes, to a PDMS microfluidic chip [73]. Another study employed a variation of liquid-phase lithography to facilitate the seamless and dead-volume-free integration of capillary emitters with glass-polymer chips [74]. By avoiding complex bonding procedures and the need for cleanroom processes, this approach provides access to robust mchip-MS interfaces in any laboratory. For DMF to ESI coupling, ‘microfluidic origami’ (folding disposable emitters from flexible polymeric films as shown in Figure 2E) offers a low-cost and straightforward method to alleviate challenges associated with capillary alignment and external hardware while exhibiting similar performance [75,76]. These attractive coupling solutions hold considerable promise to reduce variability and construct robust mchip-ESI-MS interfaces on a large scale. Interfacing microdevices with MALDI-MS (mchip-MALDI-MS) (Figure 2F–K) generally employs offline coupling, although online systems have been realized [77]. Offline techniques can be divided into two categories: (i) sample spotting onto MALDI target plates [25,45–48] and (ii) direct MALDI analysis using the microdevice substrate as the target [38,39,41–43,78–80]. Analog spotting platforms often rely on droplet ejectors for target spotting, such as embedded capillaries [20] (Figure 2F) or integrated piezoelectric microdispensers [25] (Figure 2G), coupled with a programmable stage for automated positioning. Droplet-based systems, however, can enable essentially contact-free spotting of discrete samples without such dispensers by exploiting their segmented flow (Figure 2H) [45–48]. Compared to standard pipetting techniques, the automated generation of small, uniform, and concentrated target samples, enabled by microfluidics, offers improved mass spectrometric sensitivity and reproducibility [81]. Repeated nanodroplet depositions (i.e., on-spot analyte enrichment) have been shown to promote rapid evaporation, reduce spatial variation, and realize significantly lower detection limits [25]. Furthermore, the automation of microarray spotting can reduce the processing time and variability as well as facilitate operations that are not amenable to conventional techniques. For example, a droplet-based system coupled to nano-LC (nano-LC-microarray-MALDI-MS) was shown to enable the conservation of time-resolved chromatographic separations (at 1 fraction per second) on large microarray targets (containing up to 26 000 spots) [45–48].

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The time, labor, complexity, and overall cost of mchip-MALDI-MS workflows can be further reduced by performing both sample processing and MALDI-MS detection on the same platform [78]. For example, researchers have employed dedicated mounts to secure punched pieces from the microfluidic substrate for MALDI-MS analysis (Figure 2I) [43,79]. A ‘stick-andpeel’ technique was demonstrated by reversibly bonding PDMS to a conductive indium-tin oxide slide [39,80]; PDMS peeling after microfluidic processing exposes the matrix/analyte crystals for direct analysis (Figure 2J). Slip chip designs enable similar operation using rigid substrates such as glass, where the device is slid open for in situ MALDI detection (Figure 2K) [42]. Though these systems are advantageous in eliminating the target spotting step, they generally require either (i) the use of coated glass [39,42,78,80] or silicon substrates [41], or (ii) a series of surface modifications (in the case of polymeric substrates) [43,79].

Applications of mChip-MS in Translational Proteomics By exploiting the power of MS and offering microfluidic solutions to address the challenges associated with laborious workflows and complex instrumentation, mchip-MS holds considerable promise to realize translational platforms for clinical research and implementation. Proteomic mchip-MS systems have been recently developed for the early detection and diagnosis of disease [20,25,43,45,48,50–54], the monitoring of disease progression and therapeutic response [40,67,71,82–84], and the development and characterization of novel therapeutic agents [66,70,79] (Table 1). Disease Diagnosis and Prognosis In cases where timely and sensitive measurement is crucial, the labor-intensive work and slow diagnostic turnarounds associated with conventional IAs can be detrimental [20,25]. For example, arginine vasopressin (AVP) is an antidiuretic peptide hormone used as a biomarker for hemorrhagic shock and congestive heart failure [20,25]. Nguyen and colleagues developed an analog mchip-MALDI-MS system for the selective aptamer-based capture and enrichment, thermally-induced elution, and integrated capillary spotting of AVP, demonstrating a label-free approach with substantially reduced analysis time (several hours) in comparison to IAs (3–11 days) [20]. With the recent addition of on-spot analyte enrichment (i.e., repeated deposition on the same spot to increase analyte concentration) using piezoelectric microdispensing, their system approaches the clinically-relevant detection sensitivity of AVP (10–300 pM) in human plasma within one hour of analysis time [25]. This demonstrates the promising capabilities of mchip-MS systems in rapid diagnostics for late-phase hemorrhagic shock prevention. Similarly, point-of-care microfluidic systems for alternative biomarkers, such as brain natriuretic peptide (BNP) and troponin as markers for heart failure and stroke [58], could revolutionize the detection and monitoring of noncommunicable diseases if coupled with MS. mChip-MS systems provide streamlined platforms to probe disease-associated protein PTMs such as glycosylation [45], phosphorylation [48], and glycation [40,71], among others, which may be altered due to the presence or progress of a disease [43]. Using a disposable centrifugal microfluidic disk, Quaranta and coworkers performed high-throughput sample processing and glycosylation-pattern analysis of transferrin, using specific protein affinity capture from serum, N-linked glycan enzymatic release, and glycan pattern analysis by MALDI-MS [43]. Notably, the analysis could be completed on up to 54 parallel 1-mL samples in approximately 3.5 hours. Additionally, they achieved true positive rates ranging from 75 to 79% when quantifying carbohydrate-deficient transferrin, exceeding the performance of classical approaches (i.e., 74%) in diagnosing chronic alcoholism [43]. The droplet-based nano-LC-microarray-MALDIMS platform has been employed to probe both phosphorylation and glycosylation [45,48]. For glycosylation, treated spots (containing separated glycans and peptides) and untreated spots (containing the intact glycopeptides) were generated through application of PNGase-F (an amidase that selectively removes glycan portions) on every second spot of a fractionated nano-

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Table 1. Recent mChip-MS Platforms for Clinical Proteomicsa On-chip processes

Analysis

MF

Coupling method

MS

Refs

Amyloid b peptides (Alzheimer disease)

Analog

Direct substrate analysis

MALDI

[41]

Diseasediagnostics Isoelectric focusing

[42]

Liquid chromatography

Cancer screening

[78]

Various

Insulin

[80]

Plasma depletion

Hemopexin

Solid phase extraction

Dried blood spot analysis

DMF

[14] ESI

Extraction and derivatization

[52]

Sandwiched capillary

[53]

Droplet

Capillary sampling probe

[49]

Enzyme screening

Hybrid

Direct substrate analysis

MALDI

[94]

HSA glycation (diabetes)

Analog

Parallel emitters

ESI

[71]

Enzyme inhibition reactions (Angiotensin I,II) Time-resolved reaction

Manual

Disease monitoring and management Liquid chromatography Capillary electrophoresis

Tapered corner emitter

[40]

Neuronal release

[67]

Solid phase extraction

Adipocyte release

Commercial spray tip

Rare cell isolation

Cancer (CTCs)

Manual

Digestion and labelling

Bcl-2 Protein

Direct substrate analysis

Affinity enrichment

Transferrin

IgM glycosylation

[82] MALDI

[39] [43]

Vasopressin Droplet-based nano-LC-microarray fractionation (time-resolved)

[83]

Droplet

Piezoelectric microdispenser

[25]

Automated droplet microarray spotting

[45]

Enzyme cleavage

[47]

Phosphorylation

[48]

Development and monitoring of biotherapeutics Affinity enrichment

Glycosylation profiling of mAbs

Capillary electrophoresis

Analog

Direct substrate analysis

MALDI

[79]

Tapered corner emitter

ESI

[61]

ADC (antitumor)

a

[66]

Mixing and incubation

Furosemide

Tapered corner emitter

[70]

Electrochromotography

Benzodiazepines

Pulled glass emitter

[37]

Headings are abbreviated for microfluidics (MF) and mass spectrometry (MS).

LC run [48]. Comparing these spots enabled the first detailed profiling of human serum immunoglobulin M (IgM) site-specific glycosylation previously unattainable due to the number of glycosylation sites and variety of glycoforms in comparison to other antibody classes. Using a similar approach with phosphatase digests, the system enabled the detection of low-level phosphopeptides that are typically missed using conventional MS [45]. DMF-based mchip-MS systems enable the automated extraction and analysis of dry samples such as dried blood spots (DBSs) a sampling and storage vehicle for clinical analysis [50–53]. In 2011, a DMF microfluidic platform was introduced in which DBS analytes were extracted, mixed with internal standards, derivatized, and reconstituted for MS analysis [54]. By including a sandwiched pulled glass emitter for robust coupling, as well as a control system to eliminate manual intervention, the group recently achieved comparable performance to conventional sample processing and off-line tandem MS analysis of succinylacetone (a marker for

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tyrosinemia) and other DBS analytes on a rapid, inexpensive, and automated platform [53]. The same group also developed a DMF magnetic-bead-based solid phase extraction (SPE) device for the online cleanup of DBS sample extracts to improve the signal-to-noise ratio of low-level analytes such as sitamaquine [52]. The integrated DMF-SPE protocol (i.e., dispensing of bead bed, solvent activation, washing, sample loading, washing, and elution) facilitates a high throughput (15 samples per hour) with minimal solvent consumption or required maintenance. Disease Monitoring and Management mChip-MS systems for PTM quantification hold considerable promise to advance the detection and management of diseases such as diabetes (i.e., by studying glycation) [40]. There have been ongoing efforts to incorporate the measurement of clinical markers (e.g., glycated HSA) with existing assays to yield insights into the average blood glucose level over a period of 2–3 weeks prior to blood collection, which is unattainable by glucose meters and glycated hemoglobin (HbA1c) assays alone [40,71]. Mao and Wang developed a rapid top-down assay, coupled to ESI using monolithic multinozzle emitters, to monitor an individual's blood glycemia and to gauge cardiovascular risks and oxidative stress by concurrently measuring glucose, HbA1c, glycated HSA, and several other analytes in only 5 mL of blood, on a unified platform [71]. The top-down approach was used to increase accuracy and enabled the identification of PTMs. Though this study focused on the three most abundant proteins, the chip design also included an extraction segment for enriching the sample for low-level biomarkers if necessary. Redman and colleagues assessed hemoglobin and HSA glycation using chip-based CE and MS detection in combination with a clinically employed immunoassay to measure HbA1c in whole blood lysates with high correlation to clinically-derived levels in less than 3 minutes [40]. Through the successful evaluation of larger patient sample populations, these techniques could yield promising advances in diabetes theranostics and management. In several cases, microfluidic technology has been exploited in the isolation of rare cells (e.g., stem cells, progenitor cells, and circulating tumor cells (CTCs)), which can serve as tools in therapeutic monitoring through both targeted and discovery proteomic profiling [82]. To address the challenges associated with the limited volumes of body fluid samples and the extremely low abundance of rare cells therein, microfluidics can facilitate the high recovery of target cells in low volumes with minimal losses. In a notable study, microfluidic-based cell isolation followed by acoustics-assisted cell lysis, proteolytic digestion, and LC-MS analysis enabled the identification of over 4000 proteins from the injection of only 100–200 cells a significant improvement over previous techniques (hundreds of proteins in 500–1000 cells) [82]. Through streamlining and automation using mchip-MS, this system could serve as an effective therapeutic monitoring tool for cancer patients. Examining the release of cells and tissues in response to stimulation can provide further insights into the application of essential therapeutics for restoration and repair. Croushore and colleagues employed a PDMS microdevice for on-chip neuronal network culture and microvalvecontrolled selective stimulation to characterize the dynamics of neurotransmitter and neuropeptide release patterns using offline MALDI-MS, providing information on the physiological requirements for release [84]. A low-volume region for low-density cell culture enabled the detection of small differences due to chemical heterogeneities among cell types and populations in sparse networks, which are typically undetectable in the analysis of high-density or larger cultures. Li and colleagues employed a microchip CE-MS platform to quantify the chemical stimulus-induced neurotransmitter release from neurons using online coupling to ESI-MS [67]. Their three-layer glass-PDMS device comprising a cell perfusion chamber, pneumatic pressure valves, an electrophoretic separation channel, and a nano-ESI emitter facilitated the simultaneous label-free quantification of essential monoamine (i.e., dopamine and serotonin) and amino acid (i.e., aspartic acid and glutamic acid) neurotransmitters to study

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the dynamics of release from neuronal cells in response to varying chemical stimuli. Notable differences in release dynamics were observed between the two monoamine neurotransmitters, suggesting that they are packaged into separate vesicle pools that respond differently to chemical cues. Another cell-secretion monitoring tool employed PDMS-based pneumatic valves to automatically control an on-chip injection loop for downstream collection of cell perfusate and injection onto an in-line SPE-ESI-MS system to directly monitor and identify extracellular molecules [83]. The valves provided fluidic isolation to maintain a constant pressure within the cell chamber. Development and Monitoring of Biotherapeutics Therapeutic monitoring tools have widespread applications for both clinical care and drug development. By examining a patient’s response to treatment, therapeutic monitoring provides a feedback tool to tailor effective, personalized treatment strategies. The development of biotherapeutics using monoclonal antibodies (mAbs) a multibillion dollar industry relies on similar technologies for proteomic analyses [60]; candidate therapeutic agents must be rigorously characterized to ensure clinically significant bioactivity, drug effectiveness, and quality. However, several challenges pose an increasing demand for the development of novel technologies to characterize protein-based biopharmaceuticals. For example, protein aggregation and product purification pose significant barriers to development [8,9]. Furthermore, conventional chromatographic and electrophoretic techniques are incapable of capturing the complexities of mAbs PTMs on a single platform [60]. mChip-MS systems hold considerable potential to accelerate the development of novel biopharmaceuticals and provide an effective means of therapeutic monitoring in clinical settings. Thuy and Thorsén employed their centrifugal-based microfluidic disk to characterize the effects of glycosylation on the serum clearance rate of therapeutic mAbs (spiked into human serum). Here, the mAb glycan profiles were artificially modified to simulate the conditions due to different mAb clearance rates during circulation. When analyzing the glycan profiles through the rapid and parallelized automation of immunoaffinity capture, enzymatic glycan release, purification, and MALDI-MS analysis, they completed the preparation of 54 samples in parallel in approximately 4 hours, requiring only 0.06 mg of the target antigen per data point [79]. Redman et al. employed chip-based CE separation, coupled to ESI-MS with a corner-diced integrated emitter, to generate electrophoretic mobility data (for identifying mAb charge variants) and to simplify the resulting mass spectra [66]. They demonstrated a simple, generic strategy to analyze the charge heterogeneity of mAbs and antibody drug conjugates (ADCs) at the intact protein level a step toward the development of highly specific chemotherapeutic treatment strategies. Owing to the high throughput and automation capabilities of microfluidics, mchip-MS offers effective solutions for the time-resolved analysis of biological interactions. For example, characterizing real-time protein-ligand binding dynamics is crucial in developing new therapeutic agents and providing new biological insights [70,85], since many biological processes rely on protein-ligand interactions (e.g., signal transduction, enzymatic catalysis, and immune response). Cong and colleagues developed a mchip-MS platform that features protein and ligand inlet channels, a multi-lamellar flow mixer, an automated and variable incubation chamber, and an integrated ESI source for the time-resolved monitoring of protein-ligand binding dynamics [70]. They demonstrated the ability to monitor the binding dynamics of human carbonic anhydrase and furosemide (a drug used to treat fluid build-up due to heart failure, liver scarring, or kidney disease) on a millisecond timescale using label-free detection, automated operation, rapid mixing, and low sample consumption. Through further development, their system could fulfill the growing demand for robust, automated, and high-throughput screening of protein–protein interaction networks [86].

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Validation and Clinical Translation of mChip-MS Systems

Through the development and improvement of powerful mchip-MS platforms over recent years, several novel devices have emerged with potential to transform clinical care. However, their clinical translation is largely inhibited by the slow adoption of both MS-based proteomic assays and microfluidic technology. Despite the abundance of novel protein biomarkers recently uncovered in clinical samples, their regulatory approval has been slow, primarily due to issues surrounding preclinical verification and validation [3,4,15,18,19]. For successful translation to preclinical studies and routine clinical care, MS-based proteomic assays must first achieve the sensitivity, robustness, and throughput comparable to (or exceeding) those of IAs [22]. Their performance must be validated according to regulatory guidelines (i.e., through developing standard curves, evaluating variability, and completing parallelism experiments [3]) a costly, arduous, and multiyear process [18]. Thus, a defined regulatory framework combined with standardized technologies and methodologies is crucial to streamline the translation of MS-based proteomic workflows to achieve widespread adoption and regulatory approval [3]. Microfluidic technology holds considerable promise to accelerate the development, validation, and clinical translation of MS-based proteomics; however, the commercialization of microfluidics is plagued by a lack of customer acceptance and market adoption [57,58]. For end users to adopt new practices and instrumentation, the technological alternatives must offer significant operational or economic advantages since they often require synchronization with existing hardware, integration into current workflows, and additional training [57]. Dissuaded by the risks associated with market adoption, first-user premiums, and uneven regulatory requirements, investors often opt to finance alternative technologies with well-established market routes – another major setback in the commercialization of microfluidics [29,58]. In this light, engineers should focus on the end-user experience in system design by providing straightforward, compatible, and versatile solutions. Regulatory Approval Assay validation guidelines are outlined by regulatory bodies such as the Clinical Laboratory Improvement Amendments (CLIA), Clinical Laboratory Standards Institute (CLSI), Food and Drug Administration (FDA), and European Medicines Agency [3,87]. For general MS-based bioanalytics, the FDA imposes six fundamental performance-validation parameters for premarket approval: accuracy, precision, specificity, sensitivity, reproducibility, and stability [88]. To ensure accuracy, for example, a coefficient of variation (CV) of less than 20% must be demonstrable (<15% at the limit of detection) [88]. Additionally, the execution of the assay by non-experts, FDA-approval of the instrumentation and software, and variability control and correction are essential requisites for the clinical implementation MS-based assays [3]. Assays used in immediate patient care must also satisfy CLSI standards (Table 2) [87]. Specific regulations involving the essential performance criteria for MS-based assays for peptides and proteins remain elusive, and consensus has yet to be reached [89]. Current regulations fail to consider proteomic factors such as peptide selection and stability, internal standards, and calibrators [87]. In addition, more stringent validation criteria should be applied in assessing specifications such as precision and accuracy, especially in assay development [3]. Grant and Hoofnagle proposed a list of experiments for suitable validation of assay parameters such as peptide stability, linearity, lower limit of quantification, and interferences [87]. Three tiers with varying validation requirements (based on the intended application) have also been introduced to ensure that a proteomic assay is fit-for-purpose: (i) clinical diagnostic testing for a single, or small number of, analyte(s), (ii) clinical, epidemiological, or translational research involving tens to hundreds of peptides/proteins, with or without assessment of PTMs,

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Table 2. Regulatory Guidelines for the Clinical Validation of Mass Spectrometry-Based Assays Regulatory guidelines for clinical validation Accuracy

Demonstrate with a minimum of five trials per concentration using three concentrations in the expected range, where the CV should be less than 15% except at the limit of detection Quality control checks at a variety of concentrations that differ from the levels utilized in the assay using an appropriate matrix with known concentration(s) of spiked internal standard(s) Compare with traditional methods (e.g., IAs) and validate against reference standards using predetermined criteria for accuracy

Precision

Demonstrate repeatability and reproducibility separately for each analyte while testing entire workflow Thoroughly investigate the environmental, matrix, material, and procedural variables in each step of the workflow to determine their effects on analyte estimation and identify sources of variation

Sensitivity

Demonstrate limit of detection that is comparable to or exceeding the performance specifications of current clinical IAs

Specificity

Crossreactivity and interference caused by nonspecific binding and polymorphisms must be individually evaluated with the analyte of interest

and (iii) exploratory studies involving tens to hundreds of analytes [19], where the requirements for Tier 1 validation are the most stringent [89]. Factors Inhibiting Clinical Translation The variability of MS-based proteomic assays is a major challenge in clinical adoption [5,90]. Imprecision between batches (i.e., repeatability) and between laboratories (i.e., reproducibility) is often attributed to a lack of standard instrumentation, materials, protocols, and methods for assay validation [87], where prominent sources of variability have been identified and demonstrated in large-scale interlaboratory studies [26,91]. Consequently, laboratories often develop their own assays independently a timely, costly, labour-intensive, and complex process which are plagued by irreproducible results that cannot be easily compared [21]. Moreover, variability associated with sample preparation (especially in bottom-up approaches) as well as the choice of internal standards and surrogate peptides often contribute to irreproducibility [3,5,15]. The requirement for highly trained personnel, due to the high complexity of instrumentation associated with MS-based workflows, presents another inhibitory factor in clinical translation [21]. As pointed out by Nilsson and colleagues, this results in a division of labour between biologists and clinicians (who generate and store the samples) and mass spectrometrists (who typically process samples, operate MS equipment, and evaluate the resulting data, which can compromise data quality due to a lack of accountability and overall management [1]. Final issues to consider include the limitations associated with conventional MS sample preparation techniques, which are often laborious [92], as well as their sample demands [22]. Streamlining Clinical Translation with mChip-MS mChip-MS systems have enabled significant advances in MS-based proteomics, overcoming many of the limitations inherent to competitive IAs and conventional MS (Table 3). The use of microfluidic interfaces can provide virtually any clinic or laboratory (having MS capabilities) with high-performance proteomic analytical tools by facilitating the rapid and automated execution of MS-based assays by non-experts [28–32] and significantly reducing the size and operating cost of sample processing equipment [29,30]. Additionally, the remaining questions about the sensitivity, specificity, reproducibility, and accuracy of MS-based methods [26,91] can potentially be addressed by incorporating microfluidic systems. Through recent refinements, several mchip-MS systems have achieved clinically-relevant limits of detection with suitable precision

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Table 3. Benefits and Shortcomings of Analytical Technologies for Quantitative Proteomics Advantages

Disadvantages

Immunoassays (IAs)

Well-established methods (gold standard) [18] High sensitivity, repeatability, and reproducibility [18] Many off-the-shelf assay kits available [5] Extensive industry and manufacturer support [5]

Susceptibility to interference and crossreactivity (low specificity) [5,15,18,19] Difficult to distinguish proteoforms [18] Low interlaboratory reproducibility Limited multiplexing capabilities [20] Limited dynamic range (< 2 orders of magnitude) [19] Limited antibodies available [20] Time-consuming and labor-intensive protocols [20]

Mass Spectrometry (MS)

High specificity and repeatability [1] Ability to distinguish between protein isoforms and PTMs [5,18] Extensive dynamic range [18] Does not require genetically tagged proteins or specific antibody reagents [5] Multiplexing capabilities [5] Enables absolute quantification using targeted techniques [15] Reduced long-term costs in comparison to IAs [10,15,21]

High cost of acquisition [10,15] Need for highly trained personnel [1,10,21] Time-consuming and labour-intensive protocols [21,22,92] Limited analysis of large and intact proteins [22] Lack of clinically approved assays, off-the-shelf assay kits, and vendor-supplied interfaces [10,21] Susceptibility to irreproducible and incomparable results due to individual assay development [21]

Microfluidic-MS Interfaces (mChip-MS)

All advantages of MS Precise spatiotemporal control of fluids in an assay [28,29] Reduced sample and reagent consumption (lower cost) [29–32] Increased throughput and shorter analysis times (e.g., separations) [29–32] Automation and parallelization capabilities (e.g., batch processing) [28] Reduction in size and operating cost of test equipment [29,30] Integration of complex protocols on user-friendly interfaces [28,31]

Challenges associated with ‘world-to-chip’ interfacing [52,56] Susceptibility to analyte surface adsorption due to high surface-to-volume ratios [56] Lack of commercialized products; generally limited to academic research [28,29] Accurate detection is difficult due to small volumes and narrow bands [31]

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[40,43,53,80], providing a bioanalytical toolbox to maximize the information garnered from low volumes of complex samples. Streamlining laborious MS-based sample preparation workflows on universal, straightforward, and automated platforms should improve reproducibility across laboratories [3] and enable operation by a general clinical technologist [21] eliminating the division of labour. Rapid, automated, and parallelized development of standard curves and correlation plots, enabled by mchip-MS, could substantially reduce the time and cost associated with biomarker validation. The ability to reduce sample processing time can also minimize variations caused by material instability and realize suitable platforms for applications that require timely clinical intervention. Analytical sensitivity can be increased through the use of (i) automated preliminary separation steps (without significant sample preparation complexities or substantial processing time), (ii) reduced flow rates (for ESI), and (iii) advanced spotting techniques such as on-spot analyte enrichment (for MALDI). Additionally, interferences due to contamination and sample carryover can be substantially minimized through the use of disposable devices (i.e., enabled by low-cost mass production), which is critical in clinical biomarker screening to reduce false positive identifications [78].

Concluding Remarks and Future Perspectives Over a decade ago, Whitesides identified the development of bioassays for (i) monitoring therapeutic response and for (ii) early biomarker detection as two promising avenues for microfluidic technology [29]. Recently, the marriage between microfluidic devices and MS systems has provided a glimpse of this reality; however, the aforementioned benefits of mchipMS can only be realized through careful design and rigorous validation of the integrated system, where the most suitable mchip-MS system for each application has yet to be determined (see Outstanding Questions). The focus should turn to developing standardized, user-friendly, and robust mchip-MS instrumentation to achieve widespread adoption and translation. Additional studies should investigate the potential of mchip-MS in analyzing large biomarker panels for complex diseases (e.g., cancer) or the development of personal omics profiles [93] to reveal various medical risks and open avenues for personalized medicine. Until we can rapidly scan the depths of the human proteome, biologists and clinicians will continue to face the inherent limitations of our current tools. Just as the development and automation of techniques such as the polymerase chain reaction has accelerated the field of genomics, perhaps mchip-MS is the key to high-throughput quantitative proteomics. In fact, mchip-MS may soon provide the long-awaited high-value application for microfluidic systems (necessary for their mainstream adoption and commercialization [28,29,57]) and may play a pioneering role in the clinical translation of MS-based proteomics. Acknowledgements We are grateful to Genome Canada and Genome British Columbia for financial support (project codes 204PRO for operations and 214PRO for technology development). C.H.B. is grateful for support from the Leading Edge Endowment Fund (University of Victoria) and for support from the Segal McGill Chair in Molecular Oncology at McGill University (Montreal, Quebec, Canada). C.H.B. is also grateful for support from the Warren Y. Soper Charitable Trust and the Alvin Segal Family Foundation to the Jewish General Hospital (Montreal, Quebec, Canada). M.A. would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Foundation for Innovation for supporting this work. H.L. would like to thank NSERC for support through the Postdoctoral Fellowships Program.

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Trends in Biotechnology, October 2017, Vol. 35, No. 10