Single-Cell Metabolomics: Technology and Applications

CHAPTER 15

Single-Cell Metabolomics: Technology and Applications Prasad Minakshi*, Mayukh Ghosh†, Rajesh Kumar‡, Harshad Sudhir Patki§, Hari Mohan Saini¶, Koushlesh Ranjank, Basanti Brar*, Gaya Prasad# *Department of Animal Biotechnology, LLR University of Veterinary and Animal Sciences, Hisar, India † Department of Veterinary Biochemistry, Ranchi Veterinary College, Birsa Agricultural University, Ranchi, India ‡ Department of Veterinary Physiology, COVAS, KVASU, Wayanad, India § Department of Veterinary Anatomy and Histology, COVAS, KVASU, Wayanad, India ¶ Center for Medical Biotechnology, M.D. University, Rohtak, India k Department of Veterinary Physiology and Biochemistry, COVAS, SVP University of Agriculture and Technology, Meerut, India # SVP University of Agriculture and Technology, Meerut, India

15.1

INTRODUCTION

Omics technologies deal with the analysis of entire sets of molecules expressed within a cell, a tissue, or an organism under any specific set of conditions. DNA, RNA, and proteins are the subjects of analysis in genomics, transcriptomics, and proteomics, respectively, while metabolites are analyzed through metabolomics. This last type of analysis yields a pan-specific view of the expressed analytes under certain pathophysiological conditions, along with their molecular interactions, providing a holistic vision toward the biological processes along with underlying molecules and mechanisms. Thus, omics technologies provide advantages over the analysis of individual molecules through separate experiments in terms of time, cost, and information. Moreover, rapid developments in instrumentation, along with improvement in data processing and analysis programs through recent bioinformatics tools, continuously remove the barriers of omics technologies and make them practical for purposeful biological applications. Metabolites are arguably the end product of any biological process, and they yield almost immediate information about the phenotype. Every cell of a Single-Cell Omics. https://doi.org/10.1016/B978-0-12-814919-5.00015-4 © 2019 Elsevier Inc. All rights reserved.

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multicellular organism or any unicellular organism continuously performs different metabolic processes to maintain homeostasis, leading to production of a structurally and functionally diverse array of metabolites. Basically, metabolites are endogenous as well as exogenous small molecules, usually lesser than 1.5 kD in size. They comprise sugar and lipids as well as glycolytic products (pyruvate, lactate, etc.), phosphate compounds (adenosine monophosphate (AMP), adenosine diphosphate (ADP), adenosine triphosphate (ATP), etc.), xenobiotics: the products of drug metabolisms, biologically active peptides like neuropeptides operating in cellular signaling that are not degradation products of macro-proteins, etc. In spite of their small size, nucleic acids, minerals, and salts do not qualify as metabolites. This repertoire of diverse molecules varies in terms of their presence and absence or their over- or underproduction with change in physiological condition, environment, and disease condition and with respect to cell types (Onjiko et al., 2015). Rapid turnover of these metabolites is inevitable, as cells respond differently by altering the metabolism during physiological processes like cell differentiation and division, communication, interaction with surroundings, stress responses, pathological conditions like tumors or neurodegenerative disorders, or any other stimuli or assaults. Thus, the analysis of metabolites through metabolomics technologies, yielding immediate information about the underlying biological events, has become a lucrative area of interest for several research groups globally. Unlike metabolomics, other omics technologies often fail to generate information regarding the exact downstream effects and ultimate fates of their analytes. The amount of attention that has been paid to metabolomics technologies has increased in recent times. However, enormous structural diversity, chemical instability due to rapid turnover, and lack of an amplification option render the analysis of metabolites extremely difficult. Precise sampling methods—along with robust analytical techniques such as nuclear magnetic resonance (NMR), sensitive separation-based methods like capillary electrophoresis (CE) or liquid chromatography (LC) and gas chromatography (GC) combined with mass spectrometry (MS), and fluorescence-based analysis methods along with rapidly growing metabolite databases and improved bioinformatics programs—have delivered significant precision to “metabolomics” approaches and have generated a plethora of valuable information underlying several biochemical processes for practical applications of metabolomics. Metabolomics has found successful, wide application in research on animals, plants, and microorganisms. The diverse applications of SCM studies include cancer research, neurobiology, psychological disorders, pharmacology and toxicology, drug discovery, transplant monitoring, metabolic disorders, clinical chemistry, safety assessment of genetically modified (GM) crops, crop improvement, and biofuel generation.

15.1

Amid the increasing importance and applications of metabolomics analysis of an organism or a tissue or certain cell populations, the approach takes a turn toward the emerging field of SCM. Instead of analyzing the average metabolites from a tissue or cell population, SCM techniques focus on isolation of a single target cell and provide insights about the metabolites generated within the target cell at a given point in time (snapshot) under a certain set of conditions; alternatively, SCM can provide information about those target cells individually over time during a life process. The compulsion behind shifting from metabolomics to SCM underlies the fact that multicellular organisms, which have a heterogeneous mass of cells, often behave differently in terms of metabolite generation under similar environmental conditions, and analysis of metabolites of population average masks cellular heterogeneity (Iba´n˜ez et al., 2013a). Under different environment conditions, even genetically similar cells produce different metabolites. Thus, SCM offers much to elucidate this cellular heterogeneity of metabolite generation under particular conditions at particular time intervals, and SCM has better potential to provide holistic and comprehensive insights into researchers’ understanding of organisms’ responses in different physiological conditions, development processes (Onjiko et al., 2015), genetic effect on metabolites generation, disease, treatment effects, biomarker for disease diagnosis and prognosis. When integrated with genomics, transcriptomics, and proteomics analyses, SCM can further reveal the interactions of different gene metabolites during different physiological and pathological conditions. Further, it offers the potential to explore the flows of different physical energy forces within a cell and the effects of those flows so that we can understand bioenergetics channeling in a cell and effect of that channeling on life processes. Thus, the introduction of SCM technology enriches the information through better penetration toward cellular processes. However, at the same time the challenges become more critical. The major challenge in SCM is to instrument sensitivity, which can apply to major metabolites in the nano to attomole range. A typical cell size varies between 15 and 100 μm in diameter, with 1 fL (Escherichia coli) to 500 fL (mammalian cells) volume, where the metabolite concentration is to be expected in a range of two-digit femtomoles (Milo, 2013). The inherent challenges of structural diversity, rapid turnover, and lack of amplification opportunity accompany it. But the major challenges are mostly being surmounted by improving the sample preparation methods and enhancing the sensitivity of mass spectrometric techniques, along with meticulous efforts by several research groups to enrich the metabolome database. This chapter will discuss basic methods of sample preparation for single-cell metabolomics (SCM), techniques to generate metabolomics map of a cell, analysis of the data generated by different bioinformatics tools for coming to a fruitful conclusion, and methods to validate the data—along with application, challenges, and future of SCM.

Introduction

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15.2 SAMPLE PREPARATION METHODS FOR SINGLECELL ISOLATION FOR SCM ANALYSIS The precision of SCM data relies heavily upon proper isolation of individual single target cells from a solid tissue or upon picking up individual cells from a cell suspension. Laser microdissection, microfluidics, fluorescence-based cell sorting, limiting dilutions, and manual cell-picking by micromanipulator are among the most commonly employed single-cell isolation methods in use, depending upon the source of target cells (Fig. 15.1). All these sampling methods have advantages as well as disadvantages (Table 15.1). After isolation, a single-cell can be sampled directly for its metabolite analysis, or it can be subjected to culturing prior to metabolite analysis depending upon the choice of downstream SCM analytical platform. A major concern in sample preparation is to prevent the loss of analyte volume during sampling, as SCM deals with very minute volume, and the sensitivity of the analytical platform has already been pushed to its maximum. At the same time, rapid metabolite turnover during sampling can spoil all the downstream efforts and bring about spurious results if the cellular metabolism has not been quenched properly prior to sampling. The other ways to escape aberrant metabolite generation is to maintain the target single-cell in its native environment until metabolite analysis takes place. Cellular metabolism can be quenched by adding cold organic solvents like methanol during sampling or by shock freezing the target cells to stop the cellular enzyme machinery. Otherwise, microfluidic platforms or lab-on-a chip devices are suitable for single-cell isolation, culturing, and controlled manipulation by varying the culture environment. However, care should be taken that the metabolites of the culture media do not interfere with the cellular metabolites. Further, experimental data requires replicates for statistical interpretation and validation. Therefore, instead of the metabolite being analyzed from an isolated single-cell, multiple single cells from a target population can be subjected to SCM analysis for uniform results. A high-throughput sampling format (microarray) can capture multiple single cells at a time and serve this purpose. Another high-throughput format is fluorescence-based flow cytometry, and its modified formats can be used for separating target cell populations into different subset of populations; however, it is not suitable for untargeted downstream metabolite analysis (Zenobi, 2013). Microfluidic devices also facilitate high-throughput sampling and subsequent mass spectrometric analysis at the same platform. Below, we briefly delineate some salient sample processing methods that have recently been employed for SCM analysis.

15.2.1

Microfluidics/Lab-on-a-Chip Device

Microfluidic devices provide a high-throughput platform where multiple target single cells can be captured, cultured, manipulated, and lysed under controlled

Laser Fig. 2 Fig. 1

A. Inverted microscope

cell

Sample

B. Serial dilution

Stages with micromanipulator

Fig. 3

C. E

D

I

A

A

TISSUE

C

B

A

D

H N B

C

F

S

B

H

G

Fig. 6

Fig. 4

E F

Fig. 5 FIG. 15.1 see the legend on next page

G

FIG. 15.1, Cont’d Various steps for sample preparation of single-cell isolation for SCM analysis. 1: Inverted microscope with micromanipulator: A microneedle is used to isolate single target cells manually from the microtiter plate by visualizing under microscope. The system is attached with computer to visualize and record the activity. 2: Serial dilution: Individual single target cells can be isolated in microtubes or microplate by uniform serial dilution like 1:1 dilution from a homogenous cell population. 3: Laser capture micro-dissection (LCM): A 5–10-μm tissue section containing the target cells is attached to a film or microslide and visualized under microscope. The target cells are manually observed and cut with the help of a microlaser of 1-μm focus area or, (A) alternatively, undesired cells are cut away to remove them. Laser causes melting of the thermoplastic coat, leading to detachment of the target cell from the adhered surface. Then the detached cells of interest can be isolated either by (B) downward falling under gravity or by (C) upward movement for collection. 4: FACS (fluorescence-activated cell sorting): A method to isolate cell coated with fluorescence antibody. It can be used for both negative and positive selection. (A) Cells passing through a thin sheath flow liquid according to their relative size in single layer. (B) Laser beam to provide optical excitation. (C) Optical detectors to capture cell specific signals. (D) On basis of charge applied, fluorescence cells are sorted and separated from their unlabeled counterparts; due to charge difference between labeled and unlabeled, the cells are deflected before reaching the well bottoms. They are deposited in microtiter plate (MTP) where (G) a labeled single-cell or (E) no label cell or (F) a nondeflected cell will be deposited in a different plate. (H) FACS instrument. 5: Magnetic bead–based separation: Similar to FACS, here (A) antibody is attached with magnetic bead and (B) the separation is done on basis of magnetic force. 6: Microfluidics: This is a chipbased system, where cells can be sorted by passing through a microfluidics channel by making them a droplet with the help of oil. (A) Cell insertion point. (B) Oil or liquid insertion. (C) Oil droplet passing through, with channel leading to separation of cell. (D) Cell passing near laser or electric impulse for lysis. (E) Microcooler system with inlet and exhaust of cooling will lead quenching. (F) Unwanted material as waste. (G) Lysed material is subjected to electrophoresis for separation. (H) Material after being subjected to analytical technique like MS or MALDI. (I) Alternately, material can be used for fluorescence detection.

15.2

Sample Preparation Methods for Single-Cell Isolation for SCM Analysis

Table 15.1 Comparison of Different Methods of Isolation of Single-Cell. S. No

Methods

1

Fluorescent-activated cell sorter Laser capture microdissection Microfluidics

2 3 4 5 6

Limiting dilution Manual cell picking/ micromanipulation High-density microarrays

Output/ Time

Automation

Cost

Application

High output

Automatic

High

Low output

Manual

High

Not suitable for metabolites affected by fluoresce methods Unbiased

High output

Automatic

High

Low output Low output

Manual Manual

High output

Automatic

Low Low cost High cost

Need to be developed for all kind of cells Sampling may vary Time-consuming Good for MALDI-MS

conditions—along with facilities for electrophoretic separation of the cellular metabolites, followed by delivery of the content to suitable SCM analytical platforms, such as optical spectroscopy, MS, or other means or by direct delivery of the captured target cell for mass spectrometric analysis. Microfluidics are applied in several array formats, such as patch-clamp array; dynamic single-cell culture array; and integrated microfluidic array plate (iMAP), which uses polydimethylsiloxane (PDMS) as the common construction material and microscale soft lithography for its fabrication. These devices employ multiple meander- shaped hydrodynamic traps to capture the target single cells from an arrayed culture of many individual adherent cells spreading over the microfluidic chip through hydrodynamic or gravity-driven flow. Altered hydrodynamic flow surrounding a PDMS chamber filled with a captured cell imparts self-limitation for accumulation of multiple cells in a single trap. These microchambers not only capture the target single cells but also can open and close rapidly in a controlled fashion, facilitating incubation, washing, lysis, and chemical manipulation for direct analysis of cellular contents in a highthroughput manner.

15.2.2

Nanodevices

Nanoscale devices have already been used for single-cell isolation, chemical manipulation of target cells, and single-cell optical endoscopy. Micromanipulator-coupled nanodevices can be used for manual picking of single cells for downstream analysis. The manual operation renders it a lowthroughput technique and requires automation. Computer imaging–aided identification of target cells followed by their robotic pickup from suspension—using a glass micropipette of 30 μm internal diameter connected

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with a vacuum suction system—has brought much-needed automation to this cell isolation technique. Nanodevices are also used in cellular analysis through atomic force microscopy, scanning conductance microscopy, and several other methods capable of introducing chemicals into the cell itself. Although nanoscale devices have yet to find application as specialized tools for single-cell analysis due to their slowness, controlling difficult but rapid innovations in nanofabrication holds much promise for the relevance of nanoscale devices in SCM in the near future.

15.2.3

High-Density Microarrays for Mass Spectrometry

Sample arraying is another method suitable for single-cell study (Heinemann and Zenobi, 2011; Urban et al., 2010). This is also a chip-based method, where chips are made with recipient sites approximately 50–200 mm in size in a checkerboard arrangement. Microarrays for mass spectrometry (MAMS) needs only a picoliter volume of aliquots, and any excess volume can simply be pulled away for further use. The sample loading method does not require microscale dispensing tools. The sample spreading is precise, placing a single-cell in each array well. This makes Matrix-assisted laser desorption ionization (MALDI) laser-focusing easy, as it is designed in such a way that it matches the MALDI laser size. Two connected benefits of this same sample preparation are the number of slots with single-cell and same focusing; they partly remove the chemical heterogeneity of MALDI spots prepared using conventional supports, and they also reduce the noise level. MAMS is compatible with MALDI-MS analysis, and it has already been applied in analyses of small algae and single yeast cells (Heinemann and Zenobi, 2011).

15.2.4 Direct Sampling Through Nano-Electrospray Ionization Tips This technique is used for “live single-cell video-mass spectrometry” as a direct method of cellular and subcellular metabolite analysis. In this method, target single-cell or cellular contents or subcellular components are sucked through a metal-coated nanospray tip attached to a micromanipulator and directly sprayed to nano-electro spray ionization (nano-ESI) attachment on a mass spectrometer for metabolite analysis. Although the throughput for cellcapturing by this method is relatively low, improvements have been made recently by combining the technique with high-resolution microscopy and computer imaging. Researchers also reported high sensitivity of SCM analysis using this method, and they were able to detect molecules with wide coverage.

15.3

Methods of Sample Preparation for SCM Analysis

15.3 METHODS OF SAMPLE PREPARATION FOR SCM ANALYSIS After multiple target single cells are successfully isolated, the cellular contents need to be processed for delivery to a suitable SCM platform for metabolite analysis. The sample processing method depends upon the type of downstream SCM analytical platform. In general, three types of sample preparation methods are used for SCM analysis. 1. For separation-based analytical platforms such as Capillary electrophoresis coupled with mass spectrometry (CE-MS) or liquid chromatography coupled with mass spectrometry (LC-MS) or gas chromatography coupled with mass spectrometry (GC-MS), after lysis of target cell by chemical or mechanical methods, the cellular contents are separated by CE or liquid or GC and are submitted to mass spectrometric platform for metabolite identification. 2. In the nonseparation-based direct approach, the isolated single cells are co-crystalized with suitable matrix material at the cell isolation platform itself, followed by laser-mediated desorption/ionization of the cellular content (in MALDI) or matrix-free direct desorption/ionization of the isolated single cells through laser to ionize the cellular contents (in LDI or desorption electrospray ionization (DESI)) for mass spectrometric metabolite analysis. 3. Another method also follows direct approach: without any separation of cellular contents, a small volume of cellular content is removed from the target single-cell using a nano-ESI tip under live video microscopic imaging, monitored through computer software and directly submitted to ESI-MS platform without much sample processing for metabolite analysis. Regardless of the type of sampling procedure, careful quenching of metabolic activity prior to sampling or careful maintaining of cells for as long as possible in their native environments is of paramount importance for getting a proper metabolite map. The single target cell’s size, which varies from 1 μm in bacterium to only 500 μm in neuronal cells, often decides the type of sampling procedure. In cases of very low cell size, direct mass spectrometric analysis of the metabolite, instead of the cell lysis and separation-based method, may yield better coverage. Highthroughput cell isolation/capture platforms like microfluidic devices or microarray formats provide flexible simultaneous options for cell lysis, chemical manipulation, separation of the cellular contents, or direct co-crystallization of the isolated cells with suitable matrix for mass spectrometric analysis at

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the same platform. Different cell lysis methods are employed like physical and chemical methods for extraction of metabolites. Among physical lysis methods, application of high-strength electrical fields to cells is mostly used in microfluidic devices. Alternatively, lysis with the help of laser can also be achieved, as shown by Iba´n˜ez et al. (2013a). In yeast cells, these methods have also been used: (1) a solid-state laser emitting pulses of 4-ns duration at a wavelength of 355 nm and at a repetition of 200 Hz, or (2) freezing and/or thawing, and cell disruption by rapid decompression, osmotic shock, or mechanical means (Nassar et al., 2017). The choice of lysis method depends on the platform to be used for analysis. Chemical analysis is chosen most often for MS, using nanospray or CE where various detergents or chemicals are used for lysis of cells. For LC-MS in general, 1:1 cold mixture of methanol and 0.85% ammonium bicarbonate (Liu et al., 2014) or only methanol (Sitnikov et al., 2016) or methanol solution with acetic acid (Nemes et al., 2012) to facilitate analyte extraction and quench enzymatic processes ex vivo is used in 2–5 μL volume. For GC-MS, the samples are treated with ethanol/methanol or other suitable chemicals for deproteinization, followed by evaporation with a vacuum dryer or under a gentle stream of nitrogen gas; then derivatives of BSTFA/TMCS are used to derivatize volatile metabolites for analysis by GC-MS (Hampe et al., 2017; Yang et al., 2016). The metabolites isolated by these methods are directly subjected to analysis or are further separated as needed.

15.4

ANALYTICAL METHODS FOR SCM

Precise single-cell isolation and proper sample processing are followed by analysis of metabolites through suitable analytical means. SCM theoretically encompasses analysis of all the cellular metabolites in a particular time frame. Still, the identified metabolites, which is in the range of hundreds of thousands, along with several unidentified metabolites make up the entire cellular metabolome. Qualitative or quantitative identification of the entire set of metabolites all at once is not truly feasible, mainly for two reasons: 1. the capacity of the analytical method to recognize or differentiate among the structurally related or diverse small molecules from a minute quantity of sample volume isolated from a single-cell, which is termed the “resolution” of the SCM platform; and 2. the threshold capacity of the analytical method to detect the limiting concentration of metabolites reliably from the minute analyte volumes, which is referred to as the “sensitivity” of the SCM platform. Thus SCM practically depicts the qualitative or quantitative analysis of a cluster of metabolites under a specific set of condition.

15.4

Analytical Methods for SCM

The number of analytes in SCM may range from a few to a few hundred. It may target certain particular metabolites of an isolated single-cell either to detect their presence or absence or to estimate their level of expression under certain conditions, which conforms to “targeted SCM.” Otherwise, the SCM platform may be directed toward detection of known metabolites as well as identification of newer unknown ones qualitative or quantitatively expressed within a single-cell under certain condition to get maximum analytes coverage which forms the basis of “untargeted SCM.” The purpose of SCM analysis is to decide the most suitable approach for obtaining biologically significant results. Because the untargeted approach relies most upon using the analytical platform that has maximum sensitivity, direct mass spectrometry imaging (MSI) of the target single-cell, without any cell lysis and separation of cellular metabolites, arguably the most sensitive SCM platform, is often the method of choice for this purpose. However, the reliability of detection, option for quantitation, and exact structural characterization of the analytes often get compromised. Similarly, targeted metabolomics relies in particular on using more robust and reliable methods, along with the option for quantitation of target metabolites using internal standards such as separation-based methods combined with MS like CE-MS, LC-MS, or GC-MS or a tagging approach like fluorescent tagging. Most of these methods have the inherent drawback of lower sensitivity, and only a very few metabolites can be analyzed through these platforms. Depending on the purpose of the analysis, several analytical platforms are being used for SCM analysis with a plethora of modifications. Following are the major basic methods of metabolite analysis from single target cells.

15.4.1 Separation-Based Methods Coupled With Mass Spectrometry MS is arguably the most sensitive method for detecting the wide array of metabolites present in femtomole-to-attomol concentration within a single-cell. Moreover, its label-free and highly informative analysis, along with its compatibility with multiple array formats, makes MS the most commonly used method for SCM analysis. It also facilitates data integrations among different metabolomics databases, as well as options for correlating the metabolomic data with other omics datasets for fruitful biological annotations. After lysis of a target single-cell, the complex cellular metabolome can be separated into its components electrophoretically by CE or chromatographically through LC and GC on an automated platform such as a microfluidic device. This can be followed by direct delivery of the separated metabolites to a coupled MS platform for metabolite identification, quantitation, or downstream analysis (Fig. 15.2).

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FIG. 15.2 Mass spectrometry–based analysis of single-cell metabolite. (A) Showing cell metabolites separation by chromatography/electrophoresis. (B) They are passed through nebulizer. (B)–(E): are parts of Mass Spectrometer (MS). Under high voltage metabolites passed to nebulizer. (C) ICP (inductively coupled plasma) passes them to quadrupole mass analyzer of MS. (D) After according to m/z ratio, analytes will fly to the detector. (E): Signal generated. (F) Signal is recorded and m/z spectra of whole cellular metabolites are obtained.

MS employs different methods for ionization of the delivered metabolites. MALDI is one of the soft ionization methods that provides ample sensitivity for SCM analysis. The analytes are co-crystalized with different matrix materials, inducing desorption/ionization of the metabolites by a laser beam, such as nitrogen lasers (337 nm) or frequency-tripled and quadrupled Nd:YAG lasers (355 nm and 266 nm, respectively). This generates fragmented metabolite ions, which pass through a mass analyzer under an electric/magnetic field to reach the detector, producing signals in terms of mass spectrometric peaks respective to each metabolite. Each metabolite has a distinct mass-to-charge (m/z) ratio, as well as separate retention time that facilitates the identification of each metabolite. The six general types of mass analyzer can be used alone or in combination to separate ions in a MS; the types include the Quadrupole (Q) Mass Analyzer, the Time of Flight Mass Analyzer (TOF-MS), the Magnetic Sector Mass

15.4

Analytical Methods for SCM

Analyzer, the Electrostatic Sector Mass Analyzer, the Ion Trap Mass Analyzers (includes 3D-Quadrupole ion traps, Cylindrical ion trap (CIT), Linear quadrupole ion trap (LTQ), and Orbitrap), and the Fourier Transform ion cyclotron resonance Mass Analyzer (FTMS). The most common combination with MALDI is the TOF-MS. However, generation of intense matrix background signals in MALDI-MS at the 500-Da range creates a critical inconvenience for metabolite identification, which mostly produces m/z signals at the similar range. The strategy to overcome this problem includes a change of matrices to generate low background noise—like 9-aminoacridine (9-AA), which produces only few well-defined signals— along with the added advantage of promoting negative metabolite ion formation. It has already been used in several SCM studies with encouraging sensitivity. Several matrix-free methods, like desorption ionization on silicon (DIOS) or silicon nanopost arrays (NAPAs), have been tried successfully, with the sensitivity ranging from a subfemtomole level to even a subattomole level in single-cell analysis. An alternative ionization approach for mass spectrometric analyses includes ESI-based methods. This is also a soft ionization method, but it produces greater number of multiple-charged ions than MALDI, which produces relatively fewer multiple-charged ions. In this alternative technique, the metabolites are sprayed through a nano-ESI tip in which a high voltage is applied to generate an aerosol of charged metabolites, followed by their channeling through a suitable mass analyzer to the detector for metabolite identification. Because these soft ionization techniques cause very less fragmentation, limited structural information about the metabolites can be obtained if a single MS is used. Instead, coupling tandem mass spectrometry (MS/MS) with the soft ionization technique can improve the resolution and robustness of metabolite identification. MS/MS employs collision cells for fragmentation of the ions between multiple mass analyzers, yielding better structural information. Commonly employed fragmentation methods include collision-induced dissociation (CID), electron capture dissociation (ECD), and electron transfer dissociation (ETD). MS is also used for direct analysis of the target single-cell metabolome without coupling with any separation-based method by mass spectrometric imaging method. This will be discussed in a later section of this chapter.

15.4.1.1 Capillary Electrophoresis Coupled With Mass Spectrometry CE-MS is a separation-based analytical method where the captured/isolated single cells are lysed with suitable reagents to release the complex cellular metabolites, followed by the separation of metabolites as per their physicochemical

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properties in specially designed capillary channel to deliver the contents ultimately to a mass spectrometric chamber for metabolite identification. Electrophoresis in a capillary requires a very minute volume, usually in nanoliters, which suits the exact needs of the SCM technique. Separations of metabolites are mostly performed through two-dimensional electrophoresis (2DE). First-dimensional separation by isoelectric focusing (IEF) based on the charge of metabolites is followed by second-dimensional separation based on their molecular weight. Various forms of CE—such as CE-ESI, CE-ICP ionization, CE-atmospheric pressure chemical ionization (APCI), and CE-atmospheric pressure photoionization (APPI) ( Jecklin et al., 2010)—have already been used with excellent separation of molecules before submitting to MS. Further, CE can be used for supplying specific chemicals to the patched cells joined with capillary for studying a cell’s physiological response to a particular chemical/metabolite and then studying the cell’s metabolomic profile using MS (Aerts et al., 2014; Lapainis et al., 2009). Analysis of endogenous nucleotides in neuron cells has been performed by CE-MS (Liu et al., 2014). Here, the CE-ESI system was hyphenated to a micrOTOF mass spectrometer, and separation was performed by pressure-assisted electrophoresis using silica gel to get better resolution. Prior to that, platinum or stainless steel emitters were used as a coaxial sheath-flow nanospray interface and 20-mM ammonium bicarbonate (NH4HCO3) was used as the background electrolyte (BGE). Ammonium salts work efficiently as BGEs for the formation of gaseous ions in electrospray because of their volatility; hence they minimize the deposits on the sprayer and inlet of the mass spectrometer. This is followed by MS in a mass range of m/z 300–800 to generate the data. The MS systems generally used by researchers are the micrOTOF ESI-TOF mass spectrometer or a maXis ESI-QqTOF tandem MS with 5 ppm mass accuracy and  8000 fwhm (full width at half-maximum) resolution at a 2-Hz spectral acquisition rate (Nemes et al., 2012). This method is effective for analyzing the anionic compounds, but it is difficult for positive ion compounds. Microfluidic/lab-on-a chip devices are often equipped with electrophoresis in a capillary, or a microfluidic channel is particularly advantageous for cellular analyses because it offers favorable scaling laws that allow miniaturization to a volume regime that is compatible with individual cell measurements. Generally, not only does microfluidics include miniaturization of the channels, but also it employs a high-throughput automated method in which cell cooling, sorting, and lysis can be done on the chip. The channel for electrophoresis is fabricated in quartz, glass, or plastic, but any kind of material can be used for electrophoresis as needed, such as silica capillaries or agarose. The electrophoresis channel generally has a width of 50 μm and deepness of 10 μm, and thus it requires very little sample. Since the system is highly automated and can directly be coupled with MS, the chance of data variation due to manual error can be minimized.

15.4

Analytical Methods for SCM

15.4.1.2 Liquid Chromatography Coupled With Mass Spectrometry The complex cellular metabolome can be separated using LC columns, or its variants like nano-LC or HPLC, followed by direct delivery of the metabolites to a coupled MS platform for their qualitative or quantitative analysis. Theoretically the method can be applied for single-cell metabolite analysis, but dilution of metabolites in LC columns and sensitivity of the detection system limit its practical application as a conventional method of SCM analysis. However, metabolites from a pool of 100 MCF-7 positive breast cancer cells have been successfully analyzed using a nano-LC system coupled with MS platform using chemical vapor-assisted ionization after 12C-/13C-dansyl labeling of the metabolites (Luo and Li, 2017).

15.4.1.3 Gas Chromatography Coupled With Mass Spectrometry GC is one of the preferred methods for analysis of volatile compounds, and coupling with MS platform can provide robust, reliable, and quantitative means of metabolite analysis using internal standards. The major disadvantage of this method for single-cell metabolomic analysis is its narrow coverage of metabolite identification, i.e., low sensitivity. However, the method can be used successfully in combination with other suitable sensitive methods for increasing the coverage of metabolite identification and data validation. Sweedler et al. have employed GC-MS data of metabolite analysis from a mammalian peripheral sensory-motor system to correlate with MSI analysis data for metabolomic biomarker identification (Rubakhin et al., 2015).

15.4.2

Mass Spectrometry Imaging

In MSI, which is arguably the most sensitive method for SCM analysis, isolated intact single cells are directly subjected to mass spectrometric analysis without any cell lysis or separation of metabolites being performed. The label-free analysis and vast coverage of metabolite identification makes MSI a method of choice for single-cell metabolite analysis. However, labeling with fluorescent or other chemical tags like GFP (green fluorescent protein) hinders the normal metabolic process of the target molecules; in addition, only a few tagged molecules can be targeted at one time, thus reducing the coverage of metabolite identification. Because MSI lacks the option of chromatographic separation before analysis, resolution of MSI platform should be set at the maximum level to differentially identify closely related or unrelated metabolites from the complex cellular metabolome. Several strategies are being employed to increase the resolution from micron to submicron level; one of these is to reduce the laser spot area by changing the diameter of the optical fiber that guides the laser beam for desorption ionization. Second, multiple data acquisition by multiplex MSI

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using a combination of multiple mass analyzers can generate high-resolution mass spectra. Recently a combination of Orbitrap and LTQ has performed highresolution MS/MS imaging with minimum time added; this is an extra advantage, as multiple scanning with different mass analyzers followed by their compilation can generate a huge amount of data causing handling difficulties as well as causing analysis to take longer. Polarity-switching of mass spectrometric imaging is another approach to obtain the greatest coverage of different metabolites present in the complex metabolome. Generally, mass spectrometric analysis is performed in either positive ion mode or negative ion mode. Due to its intrinsic ionic propensity, a metabolite can suitably be identified in only one of these two ion modes; thus MSI operated in the other ion mode will suppress the metabolite ionization and its identification. As an example, the basic metabolites have an intrinsic propensity to lose an electron to become positively charged and thus detected in positive ion mode sensitively; similarly, acidic compounds are conveniently deprotonated to be detected sensitively in negative ion mode. Therefore, to get the maximum signal, comprehensive metabolites should include MSI in both positive ion mode and negative ion mode. Among the commonly used platforms, MALDI-MSI is not equipped with the option of polarity-switching; however, operating in both polarities followed by multiplexing of the obtained signal is an option that has been used by some research groups. Alternatively, the recently introduced ESI-MS platform that has the option of simultaneous alternative operation in positive and negative ion mode can also be used. Further, the sensitivity of the MSI platform is of paramount importance. Applying different types of newer matrices, such as 9-AA or silver, gold, or titanium oxide nanoparticles, has reduced background noise around the m/z 500 range, which is common for traditional matrices creating difficulties for metabolite analysis that usually generates signal around the similar m/z range. Alternatively, matrix-free LDI methods, silicon-based methods like DIOS, or nanostructure-initiator MS (NIMS) can be used to reduce background noise at a lower m/z range. Here we describe some popular MSI platforms that are being used for SCM analysis.

15.4.2.1 Matrix-Assisted Laser Desorption Ionization/Mass Spectrometry Imaging MALDI-MSI is a very useful technique, as it not only provides details of metabolites to the subcellular level but can also help find the exact cellular location of these metabolites. Due to its inherent advantage of label-free analysis, it does not require any prior knowledge of a molecule as needed in labeled methods (Fig. 15.3). MS can be combined with any desorption ionization technique to get MS images of molecules. However, in MALDI, the laser can be focused at micron-to-submicron size, so it is possible to get subcellular information. Further, the multiplex MS imaging technique can identify unknown compounds directly without chromatographic

15.4

(D)

Analytical Methods for SCM

(E)

(C) (B)

Successful ion path

Quadrapole mass analyzer

Mass spectrometer

Ion path

Electron ion beam

(i) (ii) (iii)

(A)

(F)

(vii) (iv)

a

125

100

(v)

151

(vi)

177 195

205 67 82 0 50

(G)

179

% 111 139 142 167 115 100

150 m/z

222 240

200

250

Mass spectra from fragmented metabolites

FIG. 15.3 Different formats of Mass Spectrometry Imaging (MSI). (A) Different plates of (i) Desorption Ionization on Silicon (DIOS), (ii) NALDI, (iii) Silicon Nanopost Array (NAPA), (iv) Nanostructure-Initiator Mass Spectrometry (NIMS), (v) Surface-assisted Laser Desorption/Ionization (SALDI), (vi) Surface-enhanced Laser Desorption/Ionization (SELDI), (vii) Matrix-Assisted Laser Desorption/Ionization Mass spectrometry (MALDI-MS). In all plates, laser is supplied to the wells containing single target cell for ionization. (B) MALDI plate well with sample. (C) Application of laser in well for soft ionization. (D) Metabolite ions are generated from well. (E) Charged ions fly under the gas flow within mass spectrometer to enter into quadrupole mass analyzer. (F) Quadrupole mass analyzer in which they get separated according to their mass-to-charge (m/z) ratio, ultimately reaching to the detector. (G) Generation of m/z spectra for analysis of whole metabolite profile.

separation. Reducing the laser spot size enhances the sensitivity of the results that can be achieved by a number of methods, such as using a pinhole, multiple focusing lenses or beam expanders, modifications to beam-delivery optics, coaxial laser illumination, oversampling, optical fibers, and transmission geometry setups. Further, by using different matrices it is possible to enhance the desorption/ionization of specific classes of analytes in MALDIMSI for getting preferably better results in specific experiments (Rubakhin et al., 2015). Newly introduced matrices—such as 9-AA; 1,8-di(piperidinyl)naphthalene (DPN); inorganic nanoparticles made up of carbon, silver, gold, etc.; or graphene oxide—have given better results for identification of

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low-molecular-weight metabolites and lipids than conventional matrices like trihydroxyacetophenone (THAP), α-Cyano-4-hydroxycinnamic acid (CHCA), 1,5-Diaminonaphthalene (DAN), 2,5-Dihydroxybenzoic acid (DHB), etc. Thus nanoparticle-assisted laser desorption/ionization (NALDI) MS is creating ample promise and gaining importance. An advantage of MALDI-TOF MS over other MS (LC-MS or GC-EI-MS) is that it can provide localized information of metabolites, as homogenization of the sample is not a necessary prerequisite in separation-based MS platforms. However, the chromatography-based separation and spatial resolution may get compromised (Hansen and Lee, 2018). Also, laser irradiation can mask many molecules present in small amounts because of ionization of abundant membrane lipid and housekeeping proteins (Fujii et al., 2015).

15.4.2.2

Secondary Ion Mass Spectrometry Imaging

The achievable spatial resolution through MALDI-MSI is around <1 μm, but secondary ion mass spectrometry (SIMS) has better resolution capacity at the range of 50–10 nm (Kollmer et al., 2012; Svatosˇ, 2011). Thus nano-SIMS finds suitable finer biological applications like analysis of subcellular metabolite allocation, analysis of calcium channeling, or microbial metabolite analysis using stable isotope labeling (Slaveykova et al., 2009). In this technique, a high-energy primary ion (Cs+, O2 + , O, Ar+, and Ga+) beam is directed toward the solid surfaces of subjected analytes to generate secondary mass spectra, which are recorded. But the high-energy primary ion source distorts the integrity of the macromolecules, thus making it difficult to detect intact molecules. Moreover, lower secondary ion yields also compromise the sensitivity of SIMS in spite of its high-resolution capacity (Zenobi, 2013). Recent progress in this technique through the use of “cluster” ion sources such as C60 + , Bi3+, and Au4004+, referred as “cluster SIMS,” has improved the “intact molecule” detection capacity to metabolite level, but secondary signal generation needs further improvement (Rubakhin et al., 2013).

15.4.2.3 Desorption Ionization on Silicon (DIOS) Mass Spectrometry Imaging MALDI-based methods employ matrices for inducing ionization. This often generates background noise at low m/z range, creating difficulties for metabolite identification. To get rid of this disadvantage, matrix-free laser desorption ionization (LDI) methods have been employed. The matrix-free LDI approach called DIOS, which is a variant of surface-assisted laser desorption ionization (SALDI), employs porous silicon substrate to trap analytes deposited on its surface and employs a laser irradiation—mediated soft ionization method to ionize the analytes, followed by mass spectrometric detection (Fig. 15.3).

15.4

Analytical Methods for SCM

15.4.2.4 Nanostructure-Initiator Mass Spectrometry Imaging NIMS is a modification of DIOS and a surface-based matrix-free approach. A nanostructured silicon surface trap is used to adsorb the liquid (initiator) compounds, which subsequently are released by soft ionization radiation to generate the analyte ions directed to mass spectrometric detection (Fig. 15.3) (Northen et al., 2007; Woo et al., 2008). NIMS imaging has a sensitivity of around attomole range, suitable for analysis of low-molecular weight metabolites. NIMS can also be applied for the imaging of endogenous metabolite-like sterols or carbohydrates that show low ionization efficiency on other ionization methods such as MALDI. Thus NIMS has been successfully employed in imaging of sucrose from flower stem or cholesterol imaging from mouse brain (Patti et al., 2010). As no matrix material is used in NIMS platform, the spatial resolution depends solely on the irradiating laser spot area, and immediate destruction of the target tissue slice following NIMS analysis makes other histological or biochemical analysis difficult (Miura et al., 2012).

15.4.2.5 Silicon Nanopost Array Mass Spectrometry Imaging NAPAs are also an effective matrix-free nanophotonic platform for LDI, acting as an alternative to MALDI, operating with minimal sample preparation to detect small molecules in a rapid and sensitive manner (Fig. 15.3) (Korte et al., 2016). Careful nanofabrication produces a uniform array that acts as nanoscopic receiver of the incident laser energy to transfer it onto the deposited target cell or tissue section for generation of metabolite ions directed to mass spectrometer for detection. Nanophotonic ionization produces less background noise, and sensitivity can be achieved in the zeptomole range by adjusting the aspect ratio of the nanoposts (Stopka et al., 2016). NAPA-MS has already been used to analyze metabolites from yeast cells under oxidative stress in a semiquantitative manner (Walker et al., 2013).

15.4.2.6 Desorption Electrospray Ionization Mass Spectrometry Imaging This platform employs a soft ionization technique in which a fine spray of charged droplets collides with the sample surface, releasing the metabolites; this is followed by their ionization and analyses using a mass spectrometer (Taka´ts et al., 2004). The range of ionizable target molecules is wider in DESI than in other ionizing methods like MALDI and NIMS, as it can ionize varied molecules like lipids and free fatty acids, lipid-soluble vitamins, sugars, and cholesterol after proper derivatization (Wu et al., 2009). However, the spatial resolution is poorest in the DESI method, in the range of around 200 μm (Wiseman et al., 2008). Moreover, sensitivity of detection of the lowabundance small metabolites is often masked by the presence of other abundant molecules. However, minimal sample preparation and operation in ambient conditions make the platform more applicable if the resolution and

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coverage are improved, maybe by altering the ionization inducing chemical reagents (Taka´ts et al., 2004). Recently, endogenous metabolites and lipids from a single human cheek cell were successfully profiled through nano-DESI MS analysis in a quantitative manner by using suitable internal standards (Bergman and Lanekoff, 2017).

15.4.2.7 Laser Ablation Electrospray Ionization Mass Spectrometry Imaging This is an ionization method under ambient environment conditions (Nemes and Vertes, 2007). The isolated single cells are ablated using a mid-infrared (mid-IR) laser of a 2.94-μm wavelength, which gets absorbed through the water to the analytes of the target cell, generating gas phase metabolite particles that are then ionized by spraying charged droplets from the ESI source. Then the ionized metabolite particles are directed to mass spectrometric analyses. The laser beam is guided through an etched tip of a GeO2-based glass fiber covering about a 30-μm cell surface area to produce the spatial resolution in the range of 30 μm (Shrestha and Vertes, 2009). Thus the technique is mostly suitable for analysis of large plant cells or a cluster of animal cells. However, an extremely low chemical background and a simple sampling process are the advantages of this technique, and improved sensitivity can make laser ablation electrospray ionization (LAESI) widely applicable for smaller single-cell analysis (Amantonico et al., 2010a).

15.4.2.8 Live Single-Cell Video-Mass Spectrometry Using Nano-ESI Mass Spectrometry Imaging In this technique the isolated cell content is sucked using a gold-coated glass capillary nanoelectrospray tip that is coupled to a micromanipulator being observed on a computer via video microscopy. The cell content in the nanoESI tip is delivered directly to a Q-TOF mass spectrometer, along with the ionization solvent of acetonitrile containing 0.5% formic acid for MS analysis (Fig. 15.4). Briefly, cells are kept in their native environment until first targeted by using nanospray tip with positive inner pressure under microscope with the help of a micromanipulator or another suitable method. Afterward, the inner pressure of a syringe coupled with the nanospray tip is released to capture the cell, cellular content, or subcellular organelle, and the tip is immediately frozen to quench the enzymes. When analysis is to be carried out, the tip is directly loaded to a nano-ESI platform coupled with MS. More than 100–1000 molecular signals are being generated within minutes from a nanoliter volume of cellular contents (Lapainis et al., 2009; Hiyama et al., 2015).

15.4.2.9

Single-Probe Mass Spectrometry

This direct sampling technique uses a single-probe tip, along with quartz tubing fused with a silica capillary. A laser pipette puller sucks the cellular contents

15.4

Analytical Methods for SCM

FIG. 15.4 Single-cell video-mass spectrometry. (A) Cell observation under stereomicroscope, (B) Isolation of cell or cellular content through nano-ESI (electrospray ionization) syringe. (C) Showing syringe for animal (thin) and plant (thick) cell material isolation. (D) Loading cell sap in nebulizer or nano-ESI system. (E) Digital microscope between nebulizer and MS part for imaging. (F) MS system. (G) Nebulizer. (H): Getting spectrum on computer screen.

from the single target cell, and the contents are delivered to a nano-ESI emitter for ionization, followed by mass spectrometric analyses of the cellular contents. Metabolomic analysis of single algal cells under nitrogen deprivation has been performed by this technique using LTQ Orbitrap mass analyzer; the work revealed several metabolites along with their up and down regulation (Sun et al., 2018).

15.4.2.10

3D MALDI Mass Spectrometry Imaging

This technique employs consecutive sectioning of the target sample, followed by high-resolution two-dimensional mass spectrometric imaging of each section and compilation of the 2D MS images to get a three-dimensional spatial

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cellular metabolite map. The first report of single-cell 3D-MSI has depicted spatial distribution of several lipid components in fertilized individual zebrafish embryos. The work exploited MALDI as an ionization method using a mixture of DHB, iron oxide nanoparticles, and DAN as matrix material and using an external 355-nm frequency-tripled Nd:YAG laser for ionization along with aLTQ-Orbitrap mass analyzer to get around 5 μm resolution (Duen˜as et al., 2017).

15.4.2.11

Microsystem-Based MS

Microfluidic chips or microarray systems are generally equipped with the facility of direct MALDI or LDI mass spectrometric imaging after isolation and manipulation of single target cells on the same platform. The MAMS chips described previously are an example of such a high-throughput array system to perform SCM analysis of multiple target single cells; this method has already been used for metabolite analysis of single yeast cells (Iba´n˜ez et al., 2013b; Amantonico et al., 2008).

15.4.3 15.4.3.1

Nonmass Spectrometry–Based Methods Fluorescence Sensor Detection–Based Metabolomics

With fluorescence-based sensors, detection of biomolecules including metabolites becomes possible after discovery of fluorescence protein (FP) from Aequorea jellyfish and corals. When the engineered FP comes in contact with the target metabolites, the spectroscopic property of FP changes; this change can be used to identify metabolites. Various metabolite sensors have been developed by using either two FPs or one FP. In two FP-based sensors, one works as a F€ orster resonance energy transfer (FRET) donor and the other as an acceptor pair (Fig. 15.5). When both FPs interact with particular metabolites, their conformational change leads to a change in the fluorescence level of FP and acts as a reporter of biochemical events. Another method uses only a single FP molecule, which is located in the center of an 11-strand β-barrel structure from which cell protoplasm can easily pass. When metabolites of interest pass through, it causes change in the configuration or property of FP, leading to a change in fluorescence level, which is detected and quantified. A FRET sensor has already been developed for Zn2+, glutamate, cAMP, cGMP, phosphoinositides, inositol 1,4,5-triphosphate (IP3), diacylglycerol, and bacterial quorum-sensing signaling molecules, maltose, ribose, glucose/galactose, arabinose, sucrose, amino acids (glutamate), and ions (phosphate) etc. (Okumoto, 2010). This system has the capability to add on microfluidic devices for SCM, which can be used for detection of a few metabolites whose sensors have already been developed for final confirmation of data. Development of a microchip-based fluorescence system for metabolomics purposes is still in its early stages (Li et al., 2016). To date, only a

15.4

Analytical Methods for SCM

Ligand

(A)

(C)

(B) FIG. 15.5 F€orster resonance energy transfer (FRET). (A) Two FRET molecules in yellow and light dotted green are shown near each other with ligand interaction site in red and ligand molecule in circular green. (B) When ligand attaches to the FRET molecules at the ligand interaction site, the fluorescence level of molecule changes. The yellow FRET molecules become less fluorescent, while the green molecules increase in fluorescence. This change in fluorescence detects and confirms the presence of targeted ligand/metabolites. (C) Showing single FRET, when ligand enters tunnel, leading to change in fluorescence level. These events take place due to conformational change of molecules.

few molecules at a time can be identified by this method in a cell. In this microchipbased system, isolated cells are tagged with FP proteins and after process of lysis are subjected to electrophoretic separation of metabolites. Metabolites are separated with electrophoresis and multiple metabolites are simultaneously identified with sensors using laser-based fluorescence detection. The system may contain two laser in visible and near infra range for excitation and three optical channels for collection of reflected near-infra red, green and red florescence lights (Li et al., 2016). This technology can be used to develop simultaneous detection of numerous metabolites with high detection sensitivity by using multiple laser excitation and exploiting structural diversity of fluorescent labels in the future for whole metabolomics. Standardization of the technique is needed to overcome the signal problem that occurs due to changes in conditions like pH, temperature, and polarity of solvent.

15.4.3.2 Raman Spectroscopy Raman spectroscopy is a nondestructive technique to detect and quantify metabolites in a cell. The technique is based on applying laser in the visible,

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Rayleigh filter

Beam splitter

CCD

342

Mirror Lens

Diffraction grating

Objective Laser at selected λ

Sample Mirror Microscope enclosure

FIG. 15.6 Raman spectroscopy. Laser is supplied from source. It is filtered and falls on the cell with the help of a deflector. This leads to excitation of molecules, which emits a photon of different spectra, which is received by the detector.

near-infrared, or near-ultraviolet range to illuminate the cell, which can be tagged by fluorescence or radioactive staining (Wagner, 2009). Although some photons are absorbed by molecules, most are scattered after they interact with the molecule. Most molecules change from their ground state while receiving the luminescence, and later they change back to their original ground state. During this process, photons with a longer wavelength (λ) (Stokes scattered photons) than the incident laser beam (λ0) are emitted. All incident and Stokes wavelengths along with the Raman wave number (in cm1) of the scattered photon are measured to generate the Raman spectrum. The excitation wavelength, the power of the laser, the temperature, the polarization ability of the respective molecule, and the concentration of the Raman active molecule all affect the intensity of the Raman band (Fig. 15.6). These complex Raman band spectra and intensities can reveal the quantities of several metabolites within a cell, such as phenylalanine, its derivatives, and phenylalanine-containing peptides, adenine, guanine, hypoxanthine, xanthine, guanosine, uric acid, AMP, cytochrome c, etc. Raman spectroscopy cannot give a whole metabolomics profile, but in combination with fluorescence or radioactive material it is a very good method for studying the kinetics of those particular metabolites in living intact single cells. No staining is required in this technique, but staining is possible only for the biomolecules whose Raman spectra is recognizable. However, in combination of MS this method can best be used for kinetics and toxicological studies (Cherney et al., 2007).

15.5

15.5

ANALYSIS OF DATA

The huge amount of raw data generated by MS or non-MS-based platforms requires various bioinformatics programs equipped with advanced software to obtain biologically meaningful conclusions. MS-based analysis mostly relies on searching for target analytes against an established metabolome database for target identification and structural information. This target identification is entirely dependent on the spectral databases and comparison with authentic standard reference spectra. One drawback is that the identification of unknown metabolites is difficult due to commercial unavailability of an internal standard; secondly, overlapping MS peaks due to poor resolution often hinder proper target identification. High-resolution MS/MS can help to minimize this problem. Although MS analysis is mostly directed toward qualitative target identification, quantitative or semiquantitative analysis can also be performed using suitable internal standards and carefully monitoring the retention time and peak area. Various kinds of purpose-oriented functional analyses can also be performed using suitable in silico bioinformatics tools along with integration with other omics databases. Moreover, putative metabolic pathways for any target metabolite can be predicted using different pathway analysis programs. Established Metabolic Databases and Software for SCM analysis I. Human Metabolome Database (www.hmdb.ca): This database which is freely available, is the world’s largest metabolites electronic database. First released in 2007, itis now available in a fourth version, which contains data on 114,100 metabolites data—far more than the data on 2180 metabolites, released in the first version. The data contain nearly 18,557 detected and quantified metabolites. These data are in expanding mode. II. Pubchem (http://pubchem.ncbi.nlm.nih.gov): This is a freely accessible chemical database along with biological activity assays. Released in 2004, it has more than 93.9 million compound entries, 236 million substance entries, and bioactivity results from 1.25 million high-throughput screening programs, contributed by more than 80 database vendors. III. Metabolights (www.ebi.ac.uk/metabolights): This is a cross-species and cross-platform metabolomic analysis database established in 2012. It contains primary research data as well as metadata for metabolomic studies. The data from the Metabolights database is downloadable, and data-sharing can be done. The platform is compatible with NMR spectroscopy and MS platforms. IV. KEGG (www.genome.jp/kegg): This database is a repository of manually drawn pathway maps of numerous metabolites. It

Analysis of Data

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V.

VI.

VII.

VIII.

IX.

X.

XI.

provides resources for understanding high-level functions, networking, and utilities of the biological system—such as the cell, the organism, and the ecosystem—from molecular-level information. Metlin (http://metlin.scripps.edu): This devoted metabolomic database, implemented in 2005, contains a repository of metabolite information and MS/MS data of over 10,000 distinct metabolites, 50,000 high-resolution ESI-QTOF MS/MS spectra, and 160,000 in silico predicted unique fragment structures, for use in metabolomics experiments. Yeastnet (www.comp-sys-bio.org/yeastnet): This is a public database of consensus yeast metabolic networks as reconstructed from the genome sequence and literature. Yeast Metabolome Database (YMDB) (www.ymdb.ca): This is a manually curated database of small molecule metabolites found in or produced by Saccharomyces cerevisiae. The database covers metabolites described in textbooks, scientific journals, metabolic reconstructions, and other electronic databases. YMDB currently contains entries for 16,042 small molecules, with 909 associated enzymes and 149 associated transporters. ChemBioFinder (http://chembiofinder.cambridgesoft.com): This is an online chemistry and biology reference database with almost two million compounds indexed and linked to other websites. Chemspider (www.chemspider.com): This is a free chemical structure database providing fast text and structure search access to more than 63 million molecules from over 280 data sources. Chemical structure, spectra, or curated data can be deposited to this database, which uses a crowdsourcing approach to create an online chemical dictionary. Massbank (www.massbank.jp): This is a public repository of highquality mass spectral data-sharing and analysis, useful for the chemical identification and structure elucidation of chemical compounds detected by MS. Massbank provides a merged ESI-MS spectral data for each compound which improves the metabolite identification. MetaboAnalyst (http://www.metaboanalyst.ca): This database offers knowledge about a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. This also provides option for statistical analysis, functional interpretation and heatmap visualization. Diverse data types are compatible with this platform including compound concentrations, NMR/MS spectral bins, NMR/MS peak intensity table, NMR/MS peak lists, and LC/ GC-MS spectra.

15.5

XII. LIPID MAPS (http://www.lipidmaps.org/): This is a devoted lipid database provides structures and annotations of biologically relevant lipids containing more than 43,111 unique lipid structures. Structural information is supplied to the database from various sources like LIPID MAPS Consortium’s core laboratories and partners; lipids identified by LIPID MAPS experiments; biologically relevant lipids manually curated from other databases and public sources; journals and computationally generated structures. XIII. Mouse Multiple tissue Metabolome Database (MMMDB) (http:// mmmdb.iab.keio.ac.jp): This is a freely available metabolomic database provides comprehensive and quantitative metabolomic information for multiple tissues from single mice. MS peaks of both identified and unknown metabolites are submitted from untargeted metabolomic experiments. XIV. Metabolomics Workbench (http://www.metabolomicsworkbench. org/): This repository contains over 60,000 entries of NMR and MS metabolomics data from small and large metabolic studies on cells, tissues and organisms. Meta data of targeted as well as untargeted metabolic studies such as MS peak height/area values, LC retention times, NMR binned areas, etc. needs to be submitted for authentication. XV. XCMS Online (https://xcmsonline.scripps.edu/): This is a cloudbased metabolomic analysis platform to process untargeted metabolomic data. Raw LC-MS data along with a data-sharing option enable its 12,000-plus users to collectively share more than 120,000 jobs. XCMSplus is equipped with features enabling complete metabolomics analysis, including detection, retention time correction, alignment, annotation, and statistical analysis. XVI. LipidBlast (http://fiehnlab.ucdavis.edu/projects/LipidBlast): This is an in silico MS/MS database for lipid identification from various organisms, including plants, bacteria, algae, animals, humans, and viruses. XVII. MapMan (https://mapman.gabipd.org/): This tool facilitates conversion of large gene expression data into metabolic pathway maps, particularly for plant metabolomics. XVIII. OpenMS (https://www.openms.de/): This is an open source C ++ based data analysis and management program for raw LC/MS data processing software development. XIX. MS-Dial (prime.psc.riken.jp/Metabolomics_Software/MS-DIAL/): This is a computational MS-based metabolomics and lipidomics program for peak identification and analysis, particularly for small molecules, which supports any chromatography/mass spectrometry approach (GC/MS, GC-MS/MS, LC/MS, LC-MS/MS, etc.).

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XX. Metabolites Biological Role (MBRole) (csbg.cnb.csic.es/mbrole2/): This tool performs overrepresentation (enrichment) analysis of categorical annotations for a set of target compounds, it also integrates chemical and biological information from different databases. XXI. Metabolite Pathway Enrichment Analysis (MPEA): This is a pathway enrichment analysis program for metabolic profiling, data visualization, and interpretation for systemic analysis. XXII. Integrated Molecular Pathway Level Analysis (IMPaLA) (https:// omictools.com/impala-tool): This tool provides option for integration with other omic data and overrepresentation of metabolite expression data to yield information regarding the biochemical bases of pathways. XXIII. LipidBank (http://www.lipidbank.jp/): This is an open, public database of different natural lipids, including fatty acids, glycerolipids, sphingolipids, steroids, and various vitamins. XXIV. Chemical Entities of Biological Interest (ChEBI) (http://www.ebi. ac.uk/chebi/): This is a freely available dictionary of molecular entities focused on “small” chemical compounds only molecular entities not directly encoded by the genome are included, and thus in general nucleic acids, proteins and peptides derived from proteins by cleavage are not found within ChEBI and suits for metabolite information. XXV. Reactome Pathway Database (https://reactome.org/): This is a pathway database coupled with advanced bioinformatics tools for the visualization, interpretation, and analysis of pathway knowledge. It also provides options for overrepresentation and expression analysis. XXVI. MetaCyc (https://metacyc.org/): This is a curated database of experimentally elucidated metabolic pathways from all domains of life. It contains data of 2642 pathways from 2941 different organisms.

15.6 APPLICATION AND POTENTIAL OF SCM TECHNOLOGY Multicellular organisms are made up of heterogeneous kinds of cells that respond differently to various kinds of epigenetic and genetic factors, to the cell microenvironment, to disease conditions, and to disease development. Therefore, SCM has the potential to be applied in nearly all fields of biology, from the generation of a metabolome map of a single-cell organism or each kind of cell in a multicellular organism, to the study of disease development, secretion

15.7

Challenges to and the Future of Single-Cell Metabolomics

study, and drug designing (Nassar et al., 2017) and its effect on different kinds of cells to disease diagnosis, disease treatment and prognosis of treatment. Previously, SCM was demonstrated in a number of cells using different platforms of analysis, ranging from single-cell organisms like yeast to multicellular complex organisms like the plants and animals exemplified in Table 15.2. However, for a number of organisms like bacteria and protozoans and for cells like RBCs, SCM analysis has not yet been done (Table 15.2). Apart from generating data, SCM has also been used for purposes such as analysis of cancer cells (Zhang et al., 2018), study of pathways (Heinemann and Zenobi, 2011; Whitmore et al., 2007), growth analysis, and drug toxicity studies (Nassar et al., 2017; Sasportas et al., 2014).

15.7 CHALLENGES TO AND THE FUTURE OF SINGLECELL METABOLOMICS SCM techniques are at the initial phase of development, and a major challenge is the isolation of target single cells. The everchanging metabolism inside a cell, along with transport and degradation of metabolites, further complicates the condition. Therefore, a very rapid mechanism is needed to extract a cell and its metabolites without disturbing the cellular microenvironment (Okumoto, 2010). Some metabolites are themselves short-lived, and their modifications—like neurotransmitters, calcium ion, inositol phosphates, and cAMP—are very fast, further increasing the challenges in metabolomics study. Further processing leads to changes in temperature, light, or pH, which also affects the metabolites’ composition in cells. Particular species of bacteria and other microorganisms are more sensitive to cold temperature (Pinu et al., 2017). A second major challenge is the issue of sensitivity of the analysis platform. Even though the analysis of different species—from yeast to multicellular organisms, including various types of plant and animal cells (Table 15.2)—has been performed, analysis of smaller microorganisms like bacteria and amoeba is yet to be performed. Moreover, cells of multicellular organisms, RBCs, and other types of cells are also missing from SCM analysis. This indicates a need for the fine-tuning of analytical platforms for SCM analysis, as these organisms are already used for proteomics and transcriptomics study of single-cell level (Taniguchi et al., 2010). Further use of SCM analysis with respect to variable biological conditions is limited; most of the work done until now is reported to signify the capability of a platform to do the SCM. The application of NMR for SCM is still missing because of its sensitivity limit (Fessenden, 2016). However, advances in instrumentation and in data processing techniques will soon make it possible to do SCM on all kinds of cells under different conditions. The enormous potential of SCM to unearth knowledge about life processes and the integration of SCM with single-cell proteomics, transcriptomics, and

347

Table 15.2 Application of SCM Technology for Identification of Various Metabolites in Different Species Using Multiple Platforms.

Species

Cell Type

Analytical Approach

Number/Type of Metabolites Identified

Closterium acerosum

Single-cell

MALDI-MS

4

Yeast

Single-cell

MAMS

Protozoa, Entamoeba histolytica Frog, Xenopus laevis Aplysia californica, sea slug Lytechinus pictus

No, SCM multiple cell used

CE-TOF-MS

Embryo cell Neuron cell Single eggs

CE-μESI-MS CE-ESI-MS LAESI-MS

Neurons

CE-ESI-TOF-MS

500 μm or more in length and 50 μm maximum width ADP, GDP, ATP, GTP, 5–40 μm and acetyl-CoA 48 intermediary metabolites More than 80 300 metabolites 500 μL More than nine but 90–100 μm unidentified 300 distinct 25–500 mm

Basophil leukemia cell In different cell cycle

Tryptophan histidine 29 metabolites and 54 lipids 216 35

More than 16 1–5 pL sample Up to 246 metabolites, 40–100 μm amino acids, sugars, lipids, hormone 90–100 μm

Average Cell Size/ Volume

Reference Amantonico et al. (2010b) Amantonico et al. (2008) Jeelani et al. (2017) Onjiko et al. (2015) Nemes et al. (2011) Shrestha and Vertes (2009) Nemes et al. (2013)

Aplysia californica and Rattus norvegicus Mouse Human HepG2/C3A cancer stem Human Onion, Allium cepa

WBC Bulb epidermis

MS/MS Capillary microsampling MS with fluorescence microscopy Mass spectrometry LAESI-MS

Pelargonium zonale Raphanus sativus

Plant leaf Leaf, stem, and root

Live nano-ESI-LTQ-MS Video-mass spectrometry

Lytechinus pictus

Single eggs

LAESI-MS

Mesembryanthemum crystallinum Torenia hybrida

Epidermal bladder cells (EBC), trichomes Torenia hybrida

GC-TOF Nano-HPLC-MS

Different plant cell

Cell sap

UV-MALDI-TOF

Hypericum perforatum

Petal and leaf

LDI-TOF/MS

Geraniaceae Pelargonium zonale Maize

Leaf cell content

Live nano-ESI-MS

Shrestha and Vertes (2009) 668 different molecular 30–40 μm Barkla and VeraEstrella (2015) features 5 2–4 pL volume from cell Kajiyama et al. (2006) Organic compounds In picoliters Gholipour et al. (2012) €lscher et al. Secondary metabolites 1 μL Ho (2009) More than 12 1–20 μm Tejedor et al. (2012)

Root epidermal and cortex cell Single-cell

MALDI imaging

TCA cycle components 5–50 μm

NMR-based

80

Chicken egg

Range of 12 μm 18 μm

Mizuno et al. (2008) Zhang et al. (2018)

10–20 μm 20–120 μL

Hiyama et al. (2015) Shrestha and Vertes (2009) Tejedor et al. (2009) Fujii et al. (2015)

50–60 g

Hansen and Lee (2018) Roy et al. (2016)

References

genomics holds promise for resolving mystery of the life process and disease process that as yet are not fully understood. This will definitely increase our understanding of the biological processes, as all are interdependent. Technological augmentation is heading in this direction by combining different highthroughput methods with enough multiplexing options.

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