Progress toward single cell metabolomics

DAPNe with micro-capillary separatory chemistry-coupled to MALDI-MS for the analysis of polar and non-polar lipid metabolism in one cell

The cellular metabolome is considered to be a representation of cellular phenotype and cellular response to changes to internal or external events. Methods to expand the coverage of the expansive physiochemical properties that makeup the metabolome currently utilize multi-step extractions and chromatographic separations prior to chemical detection, leading to lengthy analysis times. In this study, a single-step procedure for the extraction and separation of a sample using a micro-capillary as a separatory funnel to achieve analyte partitioning within an organic/aqueous immiscible solvent system is described. The separated analytes are then spotted for MALDI-MS imaging and distribution ratios are calculated. Initially, the method is applied to standard mixtures for proof of partitioning. The extraction of an individual cell is non-reproducible; therefore, a broad chemical analysis of metabolites is necessary and will be illustrated with the one-cell analysis of a single Snu-5 gastric cancer cell taken from a cellular suspension. The method presented here shows a broad partitioning dynamic range as a single-step method for lipid analysis demonstrating a decrease in ion suppression often present in MALDI analysis of lipids. Graphical Abstract ᅟ.

Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry

The chemical differences between individual cells within large cellular populations provide unique information on organisms' homeostasis and the development of diseased states. Even genetically identical cell lineages diverge due to local microenvironments and stochastic processes. The minute sample volumes and low abundance of some constituents in cells hinder our understanding of cellular heterogeneity. Although amplification methods facilitate single-cell genomics and transcriptomics, the characterization of metabolites and proteins remains challenging both because of the lack of effective amplification approaches and the wide diversity in cellular constituents. Mass spectrometry has become an enabling technology for the investigation of individual cellular metabolite profiles with its exquisite sensitivity, large dynamic range, and ability to characterize hundreds to thousands of compounds. While advances in instrumentation have improved figures of merit, acquiring measurements at high throughput and sampling from large populations of cells are still not routine. In this Perspective, we highlight the current trends and progress in mass-spectrometry-based analysis of single cells, with a focus on the technologies that will enable the next generation of single-cell measurements.

Metabolomics: A Primer

Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future.

Label-free Quantification of Proteins in Single Embryonic Cells with Neural Fate in the Cleavage-Stage Frog (Xenopus laevis) Embryo using Capillary Electrophoresis Electrospray Ionization High-Resolution Mass Spectrometry (CE-ESI-HRMS)

Quantification of protein expression in single cells promises to advance a systems-level understanding of normal development. Using a bottom-up proteomic workflow and multiplexing quantification by tandem mass tags, we recently demonstrated relative quantification between single embryonic cells (blastomeres) in the frog (Xenopus laevis) embryo. In this study, we minimize derivatization steps to enhance analytical sensitivity and use label-free quantification (LFQ) for single Xenopus cells. The technology builds on a custom-designed capillary electrophoresis microflow-electrospray ionization high-resolution mass spectrometry platform and LFQ by MaxLFQ (MaxQuant). By judiciously tailoring performance to peptide separation, ionization, and data-dependent acquisition, we demonstrate an ∼75-amol (∼11 nm) lower limit of detection and quantification for proteins in complex cell digests. The platform enabled the identification of 438 nonredundant protein groups by measuring 16 ng of protein digest, or <0.2% of the total protein contained in a blastomere in the 16-cell embryo. LFQ intensity was validated as a quantitative proxy for protein abundance. Correlation analysis was performed to compare protein quantities between the embryo and n = 3 different single D11 blastomeres, which are fated to develop into the nervous system. A total of 335 nonredundant protein groups were quantified in union between the single D11 cells spanning a 4 log-order concentration range. LFQ and correlation analysis detected expected proteomic differences between the whole embryo and blastomeres, and also found translational differences between individual D11 cells. LFQ on single cells raises exciting possibilities to study gene expression in other cells and models to help better understand cell processes on a systems biology level.

High resolution laser mass spectrometry bioimaging

Mass spectrometry imaging (MSI) was introduced more than five decades ago with secondary ion mass spectrometry (SIMS) and a decade later with laser desorption/ionization (LDI) mass spectrometry (MS). Large biomolecule imaging by matrix-assisted laser desorption/ionization (MALDI) was developed in the 1990s and ambient laser MS a decade ago. Although SIMS has been capable of imaging with a moderate mass range at sub-micrometer lateral resolution from its inception, laser MS requires additional effort to achieve a lateral resolution of 10μm or below which is required to image at the size scale of single mammalian cells. This review covers untargeted large biomolecule MSI using lasers for desorption/ionization or laser desorption and post-ionization. These methods include laser microprobe (LDI) MSI, MALDI MSI, laser ambient and atmospheric pressure MSI, and near-field laser ablation MS. Novel approaches to improving lateral resolution are discussed, including oversampling, beam shaping, transmission geometry, reflective and through-hole objectives, microscope mode, and near-field optics.

Single-Cell Metabolite Profiling of Stalk and Glandular Cells of Intact Trichomes with Internal Electrode Capillary Pressure Probe Electrospray Ionization Mass Spectrometry

In this report, we developed the pressure probe electrospray ionization-mass spectrometry with internal electrode capillary (IEC-PPESI-MS) which enables high spatial-resolution cell sampling, precise postsampling manipulation, and high detection sensitivity. Using this technique, a comparative in situ single-cell metabolite profiling of stalk and glandular cells, the two adjacent cell types comprising a trichome unit in tomato plants (Solanum lycopersicum L.), were performed to clarify the extent of metabolic differentiation between two cell types as well as among different types of trichomes. Owing to high sensitivity of the system, less than a picoliter cell sap from a single stalk cell sufficiently yielded a number of peaks of amino acids, organic acids, carbohydrates, and flavonoids. The minimal cell sap removal from a stalk cell without severe disturbance of trichome structure enabled sequential analysis of adjacent glandular cell on the same trichome, which showed the presence of striking differences in metabolite compositions between two adjacent cell types. Comparison among different types of trichome also revealed significant variations in metabolite profiles, particularly in flavonoids and acyl sugars compositions. Some metabolites were found only in specific cell types or particular trichome types. Although extensive metabolomics analysis of glandular cells of tomato trichomes has been previously documented, this is the first report describing cell-to-cell variations in metabolite compositions of stalk and glandular cells as well as in different trichome types. Further application of this technique may provide new insights into distinct metabolism in plant cells displaying variations in shape, size, function and physicochemical properties.

Nanomanipulation-Coupled Matrix-Assisted Laser Desorption/ Ionization-Direct Organelle Mass Spectrometry: A Technique for the Detailed Analysis of Single Organelles

We describe a novel technique combining precise organelle microextraction with deposition and matrix-assisted laser desorption/ionization (MALDI) for a rapid, minimally invasive mass spectrometry (MS) analysis of single organelles from living cells. A dual-positioner nanomanipulator workstation was utilized for both extraction of organelle content and precise co-deposition of analyte and matrix solution for MALDI-direct organelle mass spectrometry (DOMS) analysis. Here, the triacylglycerol (TAG) profiles of single lipid droplets from 3T3-L1 adipocytes were acquired and results validated with nanoelectrospray ionization (NSI) MS. The results demonstrate the utility of the MALDI-DOMS technique as it enabled longer mass analysis time, higher ionization efficiency, MS imaging of the co-deposited spot, and subsequent MS/MS capabilities of localized lipid content in comparison to NSI-DOMS. This method provides selective organellar resolution, which complements current biochemical analyses and prompts for subsequent subcellular studies to be performed where limited samples and analyte volume are of concern. Graphical Abstract ᅟ.

Direct metabolomics for plant cells by live single-cell mass spectrometry

Live single-cell mass spectrometry (live MS) provides a mass spectrum that shows thousands of metabolite peaks from a single live plant cell within minutes. By using an optical microscope, a cell is chosen for analysis and a metal-coated nanospray microcapillary tip is used to remove the cell's contents. After adding a microliter of ionization solvent to the opposite end of the tip, the trapped contents are directly fed into the mass spectrometer by applying a high voltage between the tip and the inlet port of the spectrometer to induce nanospray ionization. Proteins are not detected because of insufficient sensitivity. Metabolite peaks are identified by exact mass or tandem mass spectrometry (MS/MS) analysis, and isomers can be separated by combining live MS with ion-mobility separation. By using this approach, spectra can be acquired in 10 min. In combination with metabolic maps and/or molecular databases, the data can be annotated into metabolic pathways; the data analysis takes 30 min to 4 h, depending on the MS/MS data availability from databases. This method enables the analysis of a number of metabolites from a single cell with rapid sampling at sub-attomolar-level sensitivity.

Effect of Intrinsic Noise on the Phenotype of Cell Populations Featuring Solution Multiplicity: An Artificial lac Operon Network Paradigm

Heterogeneity in cell populations originates from two fundamentally different sources: the uneven distribution of intracellular content during cell division, and the stochastic fluctuations of regulatory molecules existing in small amounts. Discrete stochastic models can incorporate both sources of cell heterogeneity with sufficient accuracy in the description of an isogenic cell population; however, they lack efficiency when a systems level analysis is required, due to substantial computational requirements. In this work, we study the effect of cell heterogeneity in the behaviour of isogenic cell populations carrying the genetic network of lac operon, which exhibits solution multiplicity over a wide range of extracellular conditions. For such systems, the strategy of performing solely direct temporal solutions is a prohibitive task, since a large ensemble of initial states needs to be tested in order to drive the system--through long time simulations--to possible co-existing steady state solutions. We implement a multiscale computational framework, the so-called "equation-free" methodology, which enables the performance of numerical tasks, such as the computation of coarse steady state solutions and coarse bifurcation analysis. Dynamically stable and unstable solutions are computed and the effect of intrinsic noise on the range of bistability is efficiently investigated. The results are compared with the homogeneous model, which neglects all sources of heterogeneity, with the deterministic cell population balance model, as well as with a stochastic model neglecting the heterogeneity originating from intrinsic noise effects. We show that when the effect of intrinsic source of heterogeneity is intensified, the bistability range shifts towards higher extracellular inducer concentration values.

Fluorescence-based tools for single-cell approaches in food microbiology

The better understanding of the functioning of microbial communities is a challenging and crucial issue in the field of food microbiology, as it constitutes a prerequisite to the optimization of positive and technological microbial population functioning, as well as for the better control of pathogen contamination of food. Heterogeneity appears now as an intrinsic and multi-origin feature of microbial populations and is a major determinant of their beneficial or detrimental functional properties. The understanding of the molecular and cellular mechanisms behind the behavior of bacteria in microbial communities requires therefore observations at the single-cell level in order to overcome "averaging" effects inherent to traditional global approaches. Recent advances in the development of fluorescence-based approaches dedicated to single-cell analysis provide the opportunity to study microbial communities with an unprecedented level of resolution and to obtain detailed insights on the cell structure, metabolism activity, multicellular behavior and bacterial interactions in complex communities. These methods are now increasingly applied in the field of food microbiology in different areas ranging from research laboratories to industry. In this perspective, we reviewed the main fluorescence-based tools used for single-cell approaches and their concrete applications with specific focus on food microbiology.

Metabolomics in the natural products field--a gateway to novel antibiotics

Metabolomics is a high throughput analytical technique used to globally measure low molecular weight metabolites, allowing simultaneous metabolic comparison of different biological samples and thus highlighting differentially produced compounds as potential biomarkers. Although microbes are renowned as prolific sources of antibiotics, the traditional approach for new anti-infectives discovery is time-consuming and labor-intensive. In this review, the use of NMR- or MS-based metabolomics is proposed as an efficient approach to find antimicrobials in microbial single- or co-cultures.

Single-cell mass spectrometry reveals small molecules that affect cell fates in the 16-cell embryo

Spatial and temporal changes in molecular expression are essential to embryonic development, and their characterization is critical to understand mechanisms by which cells acquire different phenotypes. Although technological advances have made it possible to quantify expression of large molecules during embryogenesis, little information is available on metabolites, the ultimate indicator of physiological activity of the cell. Here, we demonstrate that single-cell capillary electrophoresis-electrospray ionization mass spectrometry is able to test whether differential expression of the genome translates to the domain of metabolites between single embryonic cells. Dissection of three different cell types with distinct tissue fates from 16-cell embryos of the South African clawed frog (Xenopus laevis) and microextraction of their metabolomes enabled the identification of 40 metabolites that anchored interconnected central metabolic networks. Relative quantitation revealed that several metabolites were differentially active between the cell types in the wild-type, unperturbed embryos. Altering postfertilization cytoplasmic movements that perturb dorsal development confirmed that these three cells have characteristic small-molecular activity already at cleavage stages as a result of cell type and not differences in pigmentation, yolk content, cell size, or position in the embryo. Changing the metabolite concentration caused changes in cell movements at gastrulation that also altered the tissue fates of these cells, demonstrating that the metabolome affects cell phenotypes in the embryo.

Making a big thing of a small cell--recent advances in single cell analysis

Single cell analysis is an emerging field requiring a high level interdisciplinary collaboration to provide detailed insights into the complex organisation, function and heterogeneity of life. This review is addressed to life science researchers as well as researchers developing novel technologies. It covers all aspects of the characterisation of single cells (with a special focus on mammalian cells) from morphology to genetics and different omics-techniques to physiological, mechanical and electrical methods. In recent years, tremendous advances have been achieved in all fields of single cell analysis: (1) improved spatial and temporal resolution of imaging techniques to enable the tracking of single molecule dynamics within single cells; (2) increased throughput to reveal unexpected heterogeneity between different individual cells raising the question what characterizes a cell type and what is just natural biological variation; and (3) emerging multimodal approaches trying to bring together information from complementary techniques paving the way for a deeper understanding of the complexity of biological processes. This review also covers the first successful translations of single cell analysis methods to diagnostic applications in the field of tumour research (especially circulating tumour cells), regenerative medicine, drug discovery and immunology.

Negative dielectrophoretic capture and repulsion of single cells at a bipolar electrode: the impact of faradaic ion enrichment and depletion

This paper describes the dielectrophoretic (DEP) forces generated by a bipolar electrode (BPE) in a microfluidic device and elucidates the impact of faradaic ion enrichment and depletion (FIE and FID) on electric field gradients. DEP technologies for manipulating biological cells provide several distinct advantages over other cell-handling techniques including label-free selectivity, inexpensive device components, and amenability to single-cell and array-based applications. However, extension to the array format is nontrivial, and DEP forces are notoriously short-range, limiting device dimensions and throughput. BPEs present an attractive option for DEP because of the ease with which they can be arrayed. Here, we present experimental results demonstrating both negative DEP (nDEP) attraction and repulsion of B-cells from each a BPE cathode and anode. The direction of nDEP force in each case was determined by whether the conditions for FIE or FID were chosen in the experimental design. We conclude that FIE and FID zones generated by BPEs can be exploited to shape and extend the electric field gradients that are responsible for DEP force.

Cell-type specific metabolic profiling of Arabidopsis thaliana protoplasts as a tool for plant systems biology

Flow cytometry combined with cell sorting of protoplasts has previously been used successfully for transcript profiling of the Arabidopsis thaliana root. We have developed the technique further, and in this paper we present a robust and reliable method for metabolite profiling in specific cell types isolated from Arabidopsis roots. The method uses a combination of fluorescence-activated cell sorting and gas chromatography-time of flight-mass spectrometry analysis. Cortical and endodermal cells from the green fluorescent protein (GFP)-expressing enhancer trap line J0571 were analysed and compared with non-GFP-expressing cells and intact root tissue. Of the metabolites identified, several showed significant differences in concentration between cell types. Multivariate statistical analysis was used to compare metabolite patterns between cell and tissue types, showing that the patterns differed substantially. Isolation of specific cell populations combined with highly sensitive MS-analysis will be a powerful tool for future studies of plant metabolism, and can also be combined with transcript and protein profiling for in-depth analyses of cellular processes.

Review of methods to probe single cell metabolism and bioenergetics

Single cell investigations have enabled unexpected discoveries, such as the existence of biological noise and phenotypic switching in infection, metabolism and treatment. Herein, we review methods that enable such single cell investigations specific to metabolism and bioenergetics. Firstly, we discuss how to isolate and immobilize individuals from a cell suspension, including both permanent and reversible approaches. We also highlight specific advances in microbiology for its implications in metabolic engineering. Methods for probing single cell physiology and metabolism are subsequently reviewed. The primary focus therein is on dynamic and high-content profiling strategies based on label-free and fluorescence microspectroscopy and microscopy. Non-dynamic approaches, such as mass spectrometry and nuclear magnetic resonance, are also briefly discussed.

Analysis of endogenous nucleotides by single cell capillary electrophoresis-mass spectrometry

Analytical technologies that enable investigations at the single cell level facilitate a range of studies; here a lab-fabricated capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS) platform was used to analyze anionic metabolites from individual Aplysia californica neurons. The system employs a customized coaxial sheath-flow nanospray interface connected to a separation capillary, with the sheath liquid and separation buffer optimized to ensure a stable spray. The method provided good repeatability of separation and reliable detection sensitivity for 16 mono-, di- and triphosphate nucleosides. For a range of anionic analytes, including cyclic adenosine monophosphate (cAMP), adenosine diphosphate (ADP) and adenosine triphosphate (ATP), the detection limits were in the low nanomolar range (<22 nM). A large Aplysia R2 neuron was used to demonstrate the ability of CE-ESI-MS to quantitatively characterize anionic metabolites within individual cells, with 15 nucleotides and derivatives detected. Following the method validation process, we probed smaller, 60 μm diameter Aplysia sensory neurons where sample stacking was used as a simple on-line analyte preconcentration approach. The calculated energy balance ([ATP] + 0.5 × [ADP])/([AMP] + [ADP] + [ATP]) of these cells was comparable with the value obtained from bulk samples.

Identification of a common non-apoptotic cell death mechanism in hereditary retinal degeneration

Cell death in neurodegenerative diseases is often thought to be governed by apoptosis; however, an increasing body of evidence suggests the involvement of alternative cell death mechanisms in neuronal degeneration. We studied retinal neurodegeneration using 10 different animal models, covering all major groups of hereditary human blindness (rd1, rd2, rd10, Cngb1 KO, Rho KO, S334ter, P23H, Cnga3 KO, cpfl1, Rpe65 KO), by investigating metabolic processes relevant for different forms of cell death. We show that apoptosis plays only a minor role in the inherited forms of retinal neurodegeneration studied, where instead, a non-apoptotic degenerative mechanism common to all mutants is of major importance. Hallmark features of this pathway are activation of histone deacetylase, poly-ADP-ribose-polymerase, and calpain, as well as accumulation of cyclic guanosine monophosphate and poly-ADP-ribose. Our work thus demonstrates the prevalence of alternative cell death mechanisms in inherited retinal degeneration and provides a rational basis for the design of mutation-independent treatments.

Signal transduction: From the atomic age to the post-genomic era

We have come a long way in the 55 years since Edmond Fischer and the late Edwin Krebs discovered that the activity of glycogen phosphorylase is regulated by reversible protein phosphorylation. Many of the fundamental molecular mechanisms that operate in biological signaling have since been characterized and the vast web of interconnected pathways that make up the cellular signaling network has been mapped in considerable detail. Nonetheless, it is important to consider how fast this field is still moving and the issues at the current boundaries of our understanding. One must also appreciate what experimental strategies have allowed us to attain our present level of knowledge. We summarize here some key issues (both conceptual and methodological), raise unresolved questions, discuss potential pitfalls, and highlight areas in which our understanding is still rudimentary. We hope these wide-ranging ruminations will be useful to investigators who carry studies of signal transduction forward during the rest of the 21st century.

Real-time quantification of protein expression and translocation at individual cell resolution using imaging-dish-based live cell array

Cell populations represent intrinsically heterogeneous systems with a high level of spatiotemporal complexity. Monitoring and understanding cell-to-cell diversity is essential for the research and application of intra- and interpopulation variations. Optical analysis of live cells is challenging since both adherent and nonadherent cells change their spatial location. However, most currently available single-cell techniques do not facilitate treatment and monitoring of the same live cells over time throughout multistep experiments. An imaging-dish-based live cell array (ID-LCA) has been developed and produced for cell handling, culturing, and imaging of numerous live cells. The dish is composed of an array of pico scale cavities-pico wells (PWs) embossed on its glass bottom. Cells are seeded, cultured, treated, and spatiotemporally measured on the ID-LCA, while each cell or small group of cells are locally constrained in the PWs. Finally, predefined cells can be retrieved for further evaluation. Various types of ID-LCAs were used in this proof-of-principle work, to demonstrate on-ID-LCA transfection of fluorescently tagged chimeric proteins, as well as the detection and kinetic analysis of their induced translocation. High variability was evident within cell populations with regard to protein expression levels as well as the extent and dynamics of protein redistribution. The association of these parameters with cell morphology and functional parameters was examined. Both the new methodology and the device facilitate research of the translocation process at individual cell resolution within large populations and thus, can potentially be used in high-throughput fashion.

A peptide-based method for 13C Metabolic Flux Analysis in microbial communities

The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, ¹³C Metabolic Flux Analysis (¹³C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during ¹³C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of ¹³C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based ¹³C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species.

Microbial communities underlie a variety of important biochemical processes ranging from underground cave formation to gold mining or the onset of obesity. Metabolic fluxes describe how carbon and energy flow through the microbial community and therefore provide insights that are rarely captured by other techniques, such as metatranscriptomics or metaproteomics. The most authoritative method to measure fluxes for pure cultures consists of feeding the cells a labeled carbon source and deriving the fluxes from the ensuing metabolite labeling pattern (typically amino acids). Since we cannot easily separate cells of metabolite for each species in a community, this approach is not generally applicable to microbial communities. Here we present a method to derive fluxes from the labeling of peptides, instead of amino acids. This approach has the advantage that peptides can be assigned to each species in a community in a high-throughput fashion through modern proteomic methods. We show that, by using this method, it is theoretically possible to recover the same amount of information as through the standard approach, if enough peptides are used. We computationally tested this method with a well-characterized simple microbial community consisting of two species.

Stimulation and release from neurons via a dual capillary collection device interfaced to mass spectrometry

Neuropeptides are cell to cell signaling molecules that modulate a wide range of physiological processes. Neuropeptide release has been studied in sample sizes ranging from single cells and neuronal clusters, to defined brain nuclei and large brain regions. We have developed and optimized cell stimulation and collection approaches for the efficient measurement of neuropeptide release from neuronal samples using a dual capillary system. The defining feature is a capillary that contains octadecyl-modified silica nanoparticles on its inner wall to capture and extract releasates. This collection capillary is inserted into another capillary used to deliver solutions that chemically stimulate the cells, with solution flowing up the inner capillary to facilitate peptide collection. The efficiency of peptide collection was evaluated using six peptide standards mixed in physiological saline. The extracted peptides eluted from these capillaries were characterized via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with low femtomole detection limits. Using the capillary collection system in small custom-fabricated culturing chambers, individual cultured neurons and neuronal clusters from the model animal Aplysia californica were stimulated with distinct neuronal secretagogues and the releasates were collected and characterized using MALDI-TOF MS.

MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data

Background

Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable.

Results

We present Multiresolution Correlation Analysis (MCA), a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. We demonstrate that MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network, followed by application to previously published single-cell qPCR data from mouse embryonic stem cells. We show that MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations.

Conclusions

MCA is a useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.

The emergence of proteome-wide technologies: systematic analysis of proteins comes of age

During the lifetime of a cell proteins can change their localization, alter their abundance and undergo modifications, all of which cannot be assayed by tracking mRNAs alone. Methods to study proteomes directly are coming of age, thereby opening new perspectives on the role of post-translational regulation in stabilizing the cellular milieu. Proteomics has undergone a revolution, and novel technologies for the systematic analysis of proteins have emerged. These methods can expand our ability to acquire information from single proteins to proteomes, from static to dynamic measures and from the population level to the level of single cells. Such approaches promise that proteomes will soon be studied at a similar level of dynamic resolution as has been the norm for transcriptomes.

Quantitative metabolomics: a phantom?

'Mass specs are precise but biology is not!' is a frequently heard argument when quantitative experimental data do not fit into the overall picture. The problem with this opinion is that the significance of measured biological data becomes a matter of gut feeling. Doubtlessly, the measurement precision of modern mass spectrometers is far better than the reproducibility of biological experiments. However, precisely for this reason, technical reproduction of mass spectrometric measurements neither characterizes the whole experiment from cell cultivation to producing biological data nor says anything about systematic errors in the overall measurement procedure. Taking quantitative metabolomics as a fruitful example, we deal with the question of why it is so difficult to say something precise about imprecision in biology.

Patch clamp electrophysiology and capillary electrophoresis-mass spectrometry metabolomics for single cell characterization

The visual selection of specific cells within an ex vivo brain slice, combined with whole-cell patch clamp recording and capillary electrophoresis (CE)–mass spectrometry (MS)-based metabolomics, yields high chemical information on the selected cells. By providing access to a cell’s intracellular environment, the whole-cell patch clamp technique allows one to record the cell’s physiological activity. A patch clamp pipet is used to withdraw ∼3 pL of cytoplasm for metabolomic analysis using CE–MS. Sampling the cytoplasm, rather than an intact isolated neuron, ensures that the sample arises from the cell of interest and that structures such as presynaptic terminals from surrounding, nontargeted neurons are not sampled. We sampled the rat thalamus, a well-defined system containing gamma-aminobutyric acid (GABA)-ergic and glutamatergic neurons. The approach was validated by recording and sampling from glutamatergic thalamocortical neurons, which receive major synaptic input from GABAergic thalamic reticular nucleus neurons, as well as neurons and astrocytes from the ventral basal nucleus and the dorsal lateral geniculate nucleus. From the analysis of the cytoplasm of glutamatergic cells, approximately 60 metabolites were detected, none of which corresponded to the compound GABA. However, GABA was successfully detected when sampling the cytoplasm of GABAergic neurons, demonstrating the exclusive nature of our cytoplasmic sampling approach. The combination of whole-cell patch clamp with single cell cytoplasm metabolomics provides the ability to link the physiological activity of neurons and astrocytes with their neurochemical state. The observed differences in the metabolome of these neurons underscore the striking cell to cell heterogeneity in the brain.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Abscisic acid-responsive guard cell metabolomes of Arabidopsis wild-type and gpa1 G-protein mutants

Individual metabolites have been implicated in abscisic acid (ABA) signaling in guard cells, but a metabolite profile of this specialized cell type is lacking. We used liquid chromatography-multiple reaction monitoring mass spectrometry for targeted analysis of 85 signaling-related metabolites in Arabidopsis thaliana guard cell protoplasts over a time course of ABA treatment. The analysis utilized ∼ 350 million guard cell protoplasts from ∼ 30,000 plants of the Arabidopsis Columbia accession (Col) wild type and the heterotrimeric G-protein α subunit mutant, gpa1, which has ABA-hyposensitive stomata. These metabolomes revealed coordinated regulation of signaling metabolites in unrelated biochemical pathways. Metabolites clustered into different temporal modules in Col versus gpa1, with fewer metabolites showing ABA-altered profiles in gpa1. Ca(2+)-mobilizing agents sphingosine-1-phosphate and cyclic adenosine diphosphate ribose exhibited weaker ABA-stimulated increases in gpa1. Hormone metabolites were responsive to ABA, with generally greater responsiveness in Col than in gpa1. Most hormones also showed different ABA responses in guard cell versus mesophyll cell metabolomes. These findings suggest that ABA functions upstream to regulate other hormones, and are also consistent with G proteins modulating multiple hormonal signaling pathways. In particular, indole-3-acetic acid levels declined after ABA treatment in Col but not gpa1 guard cells. Consistent with this observation, the auxin antagonist α-(phenyl ethyl-2-one)-indole-3-acetic acid enhanced ABA-regulated stomatal movement and restored partial ABA sensitivity to gpa1.

Recent developments in capillary and microchip electroseparations of peptides (2011-2013)

The review presents a comprehensive survey of recent developments and applications of capillary and microchip electroseparation methods (zone electrophoresis, ITP, IEF, affinity electrophoresis, EKC, and electrochromatography) for analysis, isolation, purification, and physicochemical and biochemical characterization of peptides. Advances in the investigation of electromigration properties of peptides, in the methodology of their analysis, including sample preseparation, preconcentration and derivatization, adsorption suppression and EOF control, as well as in detection of peptides, are presented. New developments in particular CE and CEC modes are reported and several types of their applications to peptide analysis are described: conventional qualitative and quantitative analysis, determination in complex (bio)matrices, monitoring of chemical and enzymatical reactions and physical changes, amino acid, sequence and chiral analysis, and peptide mapping of proteins. Some micropreparative peptide separations are shown and capabilities of CE and CEC techniques to provide relevant physicochemical characteristics of peptides are demonstrated.

Metabolomics: playing piñata with single cells

Mass spectrometry advances in single-cell metabolomics enable the discovery of a new biological insight that is not accessible from population-level studies. A new study reveals that single baker's yeast cells provide sufficient material to study chemical and genetic inhibition of glycolysis and identifies metabolic subpopulations that would be invisible in bulk.

The applications of single-cell genomics

We all start out as a single totipotent cell that is programmed to produce a multicellular organism. How do individual cells make those complex developmental switches? How do single cells within a tissue or organ differ, how do they coordinate their actions or go astray in a disease process? These are long-standing and fundamental questions in biology that are now becoming tractable because of advances in microfluidics, DNA amplification and DNA sequencing. Methods for studying single-cell transcriptomes (or at least the polyadenylated mRNA fraction of it) are by far the furthest ahead and reveal remarkable heterogeneity between morphologically identical cells. The analysis of genomic DNA variation is not far behind. The other 'omics' of single cells pose greater technological obstacles, but they are progressing and promise to yield highly integrated large data sets in the near future.

Towards high resolution analysis of metabolic flux in cells and tissues

Metabolism extracts chemical energy from nutrients, uses this energy to form building blocks for biosynthesis, and interconverts between various small molecules that coordinate the activities of cellular pathways. The metabolic state of a cell is increasingly recognized to determine the phenotype of not only metabolically active cell types such as liver, muscle, and adipose, but also other specialized cell types such as neurons and immune cells. This review focuses on methods to quantify intracellular reaction flux as a measure of cellular metabolic activity, with emphasis on studies involving cells of mammalian tissue. Two key areas are highlighted for future development, single cell metabolomics and noninvasive imaging, which could enable spatiotemporally resolved analysis and thereby overcome issues of heterogeneity, a distinctive feature of tissue metabolism.

Qualitative and quantitative metabolomic investigation of single neurons by capillary electrophoresis electrospray ionization mass spectrometry

Single-cell mass spectrometry (MS) empowers metabolomic investigations by decreasing analytical dimensions to the size of individual cells and subcellular structures. We describe a protocol for investigating and quantifying metabolites in individual isolated neurons using single-cell capillary electrophoresis (CE) coupled to electrospray ionization (ESI) time-of-flight (TOF) MS. The protocol requires ∼2 h for sample preparation, neuron isolation and metabolite extraction, and 1 h for metabolic measurement. We used the approach to detect more than 300 distinct compounds in the mass range of typical metabolites in various individual neurons (25-500 μm in diameter) isolated from the sea slug (Aplysia californica) central and rat (Rattus norvegicus) peripheral nervous systems. We found that a subset of identified compounds was sufficient to reveal metabolic differences among freshly isolated neurons of different types and changes in the metabolite profiles of cultured neurons. The protocol can be applied to the characterization of the metabolome in a variety of smaller cells and/or subcellular domains.