Monitoring metabolic responses to chemotherapy in single cells and tumors using nanostructure-initiator mass spectrometry (NIMS) imaging

Chemo-Selective Single-Cell Metabolomics Reveals the Spatiotemporal Behavior of Exogenous Pollutants During Xenopus Laevis Embryogenesis

In-depth profiling of embryogenesis-associated endogenous and exogenous metabolic changes can reveal potential bio-effects resulting from human-made chemicals and underlying mechanisms. Due to the lack of potent tools for monitoring spatiotemporal distribution and bio-transformation behavior of dynamic metabolites at single-cell resolution, however, how and to what extent environmental chemicals may influence or interfere embryogenesis largely remain unclear. Herein, a zero-sample-loss micro-biopsy-based mass spectrometric platform is presented for quantitative, chemo-selective, high-coverage, and minimal-destructive profiling of development-associated cis-diol metabolites, which are critical for signal transduction and epigenome regulation, at both cellular level and tissue level of Xenopus laevis. Using this platform, three extraordinary findings that are otherwise hard to achieve are revealed: 1) there are characteristically different cis-diol metabolic signatures among oocytes, anterior and posterior part of tailbud-stage embryos; 2) halogenated cis-diols heavily accumulate at the posterior part of tailbud-stage embryos of Xenopus laevis; 3) dimethachlon, a kind of exogenous fungicide that is widely used as pesticide, may be bio-transformed and accumulated in vertebrate animals in environment. Thus, this study opens a new avenue to simultaneously monitoring intercellular and intraembryonic heterogeneity of endogenous and exogenous metabolites, providing new insights into metabolic remolding during embryogenesis and putting a warning on potential environmental risk.

Molecular imaging of bacterial biofilms-a systematic review

The formation of bacterial biofilms in the human body and on medical devices is a serious human health concern. Infections related to bacterial biofilms are often chronic and difficult to treat. Detailed information on biofilm formation and composition over time is essential for a fundamental understanding of the underlying mechanisms of biofilm formation and its response to anti-biofilm therapy. However, information on the chemical composition, structural components of biofilms, and molecular interactions regarding metabolism- and communication pathways within the biofilm, such as uptake of administered drugs or inter-bacteria communication, remains elusive. Imaging these molecules and their distribution in the biofilm increases insight into biofilm development, growth, and response to environmental factors or drugs. This systematic review provides an overview of molecular imaging techniques used for bacterial biofilm imaging. The techniques included mass spectrometry-based techniques, fluorescence-labelling techniques, spectroscopic techniques, nuclear magnetic resonance spectroscopy (NMR), micro-computed tomography (µCT), and several multimodal approaches. Many molecules were imaged, such as proteins, lipids, metabolites, and quorum-sensing (QS) molecules, which are crucial in intercellular communication pathways. Advantages and disadvantages of each technique, including multimodal approaches, to study molecular processes in bacterial biofilms are discussed, and recommendations on which technique best suits specific research aims are provided.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Surface-assisted laser desorption/ionization mass spectrometry imaging: A review

In the last decades, surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has attracted increasing interest due to its unique capabilities, achievable through the nanostructured substrates used to promote the analyte desorption/ionization. While the most widely recognized asset of SALDI-MS is the untargeted analysis of small molecules, this technique also offers the possibility of targeted approaches. In particular, the implementation of SALDI-MS imaging (SALDI-MSI), which is the focus of this review, opens up new opportunities. After a brief discussion of the nomenclature and the fundamental mechanisms associated with this technique, which are still highly controversial, the analytical strategies to perform SALDI-MSI are extensively discussed. Emphasis is placed on the sample preparation but also on the selection of the nanosubstrate (in terms of chemical composition and morphology) as well as its functionalization possibilities for the selective analysis of specific compounds in targeted approaches. Subsequently, some selected applications of SALDI-MSI in various fields (i.e., biomedical, biological, environmental, and forensic) are presented. The strengths and the remaining limitations of SALDI-MSI are finally summarized in the conclusion and some perspectives of this technique, which has a bright future, are proposed in this section.

Mass Spectrometry Imaging of Metabolites by Nanostructure Initiator Mass Spectrometry with Fluorinated Gold Nanoparticles

Nanostructure initiator mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) enables the detection and spatial localization of a breath of polar metabolites and lipids with high spatial resolution and ultrasensitivity. Here we describe the methods and procedures for the synthesis and application of f-AuNPs for NIMS of small molecule metabolites and lipids in biological tissues, encompassing sample preparation, mass spectrometric detection, and data analysis and interpretation.

The Application of Single-Cell Technologies in Cardiovascular Research

Cardiovascular diseases (CVDs) are the leading cause of deaths in the world. The intricacies of the cellular composition and tissue microenvironment in heart and vasculature complicate the dissection of molecular mechanisms of CVDs. Over the past decade, the rapid development of single-cell omics technologies generated vast quantities of information at various biological levels, which have shed light on the cellular and molecular dynamics in cardiovascular development, homeostasis and diseases. Here, we summarize the latest single-cell omics techniques, and show how they have facilitated our understanding of cardiovascular biology. We also briefly discuss the clinical value and future outlook of single-cell applications in the field.

Quantitation of amiodarone and N-desethylamiodarone in single HepG2 cells by single-cell printing-liquid vortex capture-mass spectrometry

Quantitative measure of a drug and its associated metabolite(s) with single-cell resolution is often limited by sampling throughput or other compromises that limit broad use. Here, we demonstrate the use of single-cell printing-liquid vortex capture-mass spectrometry (SCP-LVC-MS) to quantitatively measure the intracellular concentrations of amiodarone (AMIO) and its metabolite, N-desethylamiodarone (NDEA), from thousands of single cells across several AMIO incubation concentrations ranging from 0 to 10 μM. Concentrations obtained by SCP-LVC-MS were validated through comparison with average assays and traditional measurement of cells in bulk. Average of SCP-LVC-MS measurements and aggregate vial collection assay the concentrations differed by < 5%. Both AMIO and NDEA had clear log-normal distributions with similar standard deviation of concentrations in the cell population. The mean of both AMIO and NDEA intracellular concentrations were positively correlated with AMIO incubation concentration, increasing from 0.026 to 0.520 and 0.0055 to 0.048 mM for AMIO and NDEA, respectively. The standard deviation of AMIO and NDEA log-normal distribution fits were relatively similar in value across incubation concentrations, 0.15-0.19 log₁₀ (mM), and exhibited a linear trend with respect to each other. The single cell-resolved conversion ratio of AMIO to NDEA increased with decreasing incubation concentration, 7 ± 2%, 18 ± 3%, and 20 ± 7% for 10.0, 1.0, and 0.1 μM AMIO incubation concentrations, respectively. Association with simultaneously measured lipids had several ions with statistically significant difference in intensity but no clear correlations with AMIO intracellular content was observed.

Microfluidic Single-Cell Proteomics Assay Chip: Lung Cancer Cell Line Case Study

Cancer is a dynamic disease involving constant changes. With these changes, cancer cells become heterogeneous, resulting in varying sensitivity to chemotherapy. The heterogeneity of cancer cells plays a key role in chemotherapy resistance and cancer recurrence. Therefore, for effective treatment, cancer cells need to be analyzed at the single-cell level by monitoring various proteins and investigating their heterogeneity. We propose a microfluidic chip for a single-cell proteomics assay that is capable of analyzing complex cellular signaling systems to reveal the heterogeneity of cancer cells. The single-cell assay chip comprises (i) microchambers (n = 1376) for manipulating single cancer cells, (ii) micropumps for rapid single-cell lysis, and (iii) barcode immunosensors for detecting nine different secretory and intracellular proteins to reveal the correlation among cancer-related proteins. Using this chip, the single-cell proteomics of a lung cancer cell line, which may be easily masked in bulk analysis, were evaluated. By comparing changes in the level of protein secretion and heterogeneity in response to combinations of four anti-cancer drugs, this study suggests a new method for selecting the best combination of anti-cancer drugs. Subsequent preclinical and clinical trials should enable this platform to become applicable for patient-customized therapies.

THE SPACE DIMENSION AT THE MICRO LEVEL: MASS SPECTROMETRY IMAGING OF DRUGS IN TISSUES

Mass spectrometry imaging (MSI) has seen remarkable development in recent years. The possibility of getting quantitative or semiquantitative data, while maintaining the spatial component in the tissues has opened up unique study possibilities. Now with a spatial window of few tens of microns, we can characterize the events occurring in tissue subcompartments in physiological and pathological conditions. For example, in oncology-especially in preclinical models-we can quantitatively measure drug distribution within tumors, correlating it with pharmacological treatments intended to modify it. We can also study the local effects of the drug in the tissue, and their effects in relation to histology. This review focuses on the main results in the field of drug MSI in clinical pharmacology, looking at the literature on the distribution of drugs in human tissues, and also the first preclinical evidence of drug intratissue effects. The main instrumental techniques are discussed, looking at the different instrumentation, sample preparation protocols, and raw data management employed to obtain the sensitivity required for these studies. Finally, we review the applications that describe in situ metabolic events and pathways induced by the drug, in animal models, showing that MSI makes it possible to study effects that go beyond the simple concentration of the drug, maintaining the space dimension. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Charting Metabolism Heterogeneity by Nanostructure Imaging Mass Spectrometry: From Biological Systems to Subcellular Functions

The study of metabolism heterogeneity is essential to understand the role of metabolites in supporting and regulating biological functions. To this end, several mass spectrometry imaging (MSI) approaches have been proposed for the detection of small molecule metabolites. However, high noise from the ionization matrix and low metabolome coverage hinder their applicability for untargeted metabolomics studies across space. In this context, nanostructure imaging (/initiator) mass spectrometry (NIMS) and NIMS with fluorinated gold nanoparticles (f-AuNPs) are attractive strategies for comprehensive MSI of metabolites in biological systems, which can provide heterogeneous metabolome coverage, ultrahigh sensitivity, and high lateral resolution. In particular, NIMS with f-AuNPs permits the simultaneous detection of polar metabolites and lipids in a single and cohesive analytical session, thus allowing the systems-level interpretation of metabolic changes. In this Perspective article, we discuss the use of NIMS and f-AuNPs in the exploration of metabolism heterogeneity and provide a critical outlook on future applications of this technology for revealing the metabolic architecture that supports biological functions in health and disease, from whole organisms to tissues, single cells, and subcellular compartments.

Organic matrix-free imaging mass spectrometry

Mass spectrometry (MS) is an ideal tool for analyzing multiple types of (bio)molecular information simultaneously in complex biological systems. In addition, MS provides structural information on targets, and can easily discriminate between true analytes and background. Therefore, imaging mass spectrometry (IMS) enables not only visualization of tissues to give positional information on targets but also allows for molecular analysis of targets by affording the molecular weights. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS is particularly effective and is generally used for IMS. However, the requirement for an organic matrix raises several limitations that get in the way of accurate and reliable images and hampers imaging of small molecules such as drugs and their metabolites. To overcome these problems, various organic matrix-free LDI IMS systems have been developed, mostly utilizing nanostructured surfaces and inorganic nanoparticles as an alternative to the organic matrix. This minireview highlights and focuses on the progress in organic matrix-free LDI IMS and briefly discusses the use of other IMS techniques such as desorption electrospray ionization, laser ablation electrospray ionization, and secondary ion mass spectrometry.

Imaging of cancer lipid metabolism in response to therapy

Lipids represent a diverse array of molecules essential to the cell's structure, defense, energy, and communication. Lipid metabolism can often become dysregulated during tumor development. During cancer therapy, targeted inhibition of cell proliferation can likewise cause widespread and drastic changes in lipid composition. Molecular imaging techniques have been developed to monitor altered lipid profiles as a biomarker for cancer diagnosis and treatment response. For decades, MRS has been the dominant non-invasive technique for studying lipid metabolite levels. Recent insights into the oncogenic transformations driving changes in lipid metabolism have revealed new mechanisms and signaling molecules that can be exploited using optical imaging, mass spectrometry imaging, and positron emission tomography. These novel imaging modalities have provided researchers with a diverse toolbox to examine changes in lipids in response to a wide array of anticancer strategies including chemotherapy, radiation therapy, signal transduction inhibitors, gene therapy, immunotherapy, or a combination of these strategies. The understanding of lipid metabolism in response to cancer therapy continues to evolve as each therapeutic method emerges, and this review seeks to summarize the current field and areas of unmet needs.

Applications of metabolomics to study cancer metabolism

Reprogrammed metabolism supports tumor growth and provides a potential source of therapeutic targets and disease biomarkers. Mass spectrometry-based metabolomics has emerged as a broadly informative technique for profiling metabolic features associated with specific oncogenotypes, disease progression, therapeutic liabilities and other clinically relevant aspects of tumor biology. In this review, we introduce the applications of metabolomics to study deregulated metabolism and metabolic vulnerabilities in cancer. We provide examples of studies that used metabolomics to discover novel metabolic regulatory mechanisms, including processes that link metabolic alterations with gene expression, protein function, and other aspects of systems biology. Finally, we discuss emerging applications of metabolomics for in vivo isotope tracing and metabolite imaging, both of which hold promise to advance our understanding of the role of metabolic reprogramming in cancer.

Detecting Low-Abundance Molecules at Single-Cell Level by Repeated Ion Accumulation in Ion Trap Mass Spectrometer

Low-abundance metabolites or proteins in single-cell samples are usually undetectable by mass spectrometry (MS) due to the limited amount of substances in single cells. This limitation inspired us to further enhance the sensitivity of commercial mass spectrometers. Herein, we developed a technique named repeated ion accumulation by ion trap MS, which is capable of enhancing the sensitivity by selectively and repeatedly accumulating ions in a linear ion trap for up to 25 cycles. The increase in MS sensitivity was positively correlated with the number of repeated cycles. When ions were repeatedly accumulated for 25 cycles, the sensitivity of adenosine triphosphate detection was increased by 22-fold within 1.8 s. Our technique could stably detect low-abundance ions, especially MSⁿ ions, at the single-cell level, such as 5-methylcytosine hydrolyzed from sample equivalent to ∼0.2 MCF7 cell. The strategy presented in this study offers the possibility to aid single-cell analysis by enhancing MS detection sensitivity.

Single-cell mass spectrometry with multi-solvent extraction identifies metabolic differences between left and right blastomeres in the 8-cell frog (Xenopus) embryo

Single-cell metabolic mass spectrometry enables the discovery (untargeted) analysis of small molecules in individual cells. Using single-cell capillary electrophoresis high-resolution mass spectrometry (CE-HRMS), we recently uncovered small-molecule differences between embryonic cells located along the animal-vegetal and dorsal-ventral axes of the 16-cell frog (Xenopus laevis) embryo, raising the question whether metabolic cell heterogeneity also exists along the left-right body axis. To address this question, we here advance single-cell CE-HRMS for identifying and quantifying metabolites in higher analytical sensitivity, and then use the methodology to compare metabolite production between left and right cells. Our strategy utilizes multiple solvents with complementary physicochemical properties to extract small molecules from single cells and improve electrophoretic separation, increasing metabolite ion signals for quantification and tandem HRMS. As a result, we were able to identify 55 different small molecules in D1 cells that were isolated from 8-cell embryos. To quantify metabolite production between left and right cells, we analyzed n = 24 different D1 cells in technical duplicate-triplicate measurements. Statistical and multivariate analysis based on 80 of the most repeatedly quantified compounds revealed 10 distinct metabolites that were significantly differentially accumulated in the left or right cells (p < 0.05 and fold change ≥1.5). These metabolites were enriched in the arginine-proline metabolic pathway in the right, but not the left D1 cells. Besides providing analytical benefits for single-cell HRMS, this work provides new metabolic data on the establishment of normal body asymmetry in the early developing embryo.

Single-cell profiling approaches to probing tumor heterogeneity

Tumor heterogeneity is a major hindrance in cancer classification, diagnosis and treatment. Recent technological advances have begun to reveal the true extent of its heterogeneity. Single-cell analysis (SCA) is emerging as an important approach to detect variations in morphology, genetic or proteomic expression. In this review, we revisit the issue of inter- and intra-tumor heterogeneity, and list various modes of SCA techniques (cell-based, nucleic acid-based, protein-based, metabolite-based and lipid-based) presently used for cancer characterization. We further discuss the advantages of SCA over pooled cell analysis, as well as the limitations of conventional techniques. Emerging trends, such as high-throughput sequencing, are also mentioned as improved means for cancer profiling. Collectively, these applications have the potential for breakthroughs in cancer treatment.

Mass spectrometry imaging of therapeutics from animal models to three-dimensional cell cultures

Mass spectrometry imaging (MSI) is a powerful label-free technique for the investigation of the spatial distribution of molecules at complex surfaces and has been widely used in the pharmaceutical sciences to understand the distribution of different drugs and their metabolites in various biological samples, ranging from cell-based models to tissues. Here, we review the current applications of MSI for drug studies in animal models, followed by a discussion of the novel advances of MSI in three-dimensional (3D) cell cultures for accurate, efficient, and high-throughput analyses to evaluate therapeutics.

Analysis of metabolites in single cells-what is the best micro-platform?

This review covers new innovations and developments in the field of single-cell level analysis of metabolites, involving the role of microfluidic and microarray platforms to manipulate and handle the cells prior their detection. Microfluidic and microarray platforms have shown great promise. The latest developments demonstrate their potential to identify a particular cell or even an ensemble of cells (sharing a common property or phenotype) that co-exist in a much larger cell population. The reason for this is the capability of these platforms to perform several complex analytical processes, such as: cleanup, sorting, derivatization, separation, and detection, with great robustness, speed, and reduced sample/reagent consumption. Here, we present several examples that illustrate the rapid strides that have been made for the routine analysis of metabolites by coupling different microfluidics and microarrays devices to a wide range of analytical detectors (e.g. fluorescent microscopy, electrochemical, and mass spectrometry). Herein, we also present selected examples detailing the use of microfluidics and microarrays in the visualization of the natural occurring cell-to-cell heterogeneity in isogenic populations, in particular during the response to external cues. The possibility to accurate monitor the cell-to-cell heterogeneity based on different levels of key metabolites is of clinical relevance, since cell-to-cell heterogeneity can influence, for example, the outcome of a drug treatment.

Mass spectrometry-based characterization of endogenous peptides and metabolites in small volume samples

Technologies to assay single cells and their extracellular microenvironments are valuable in elucidating biological function, but there are challenges. Sample volumes are low, the physicochemical parameters of the analytes vary widely, and the cellular environment is chemically complex. In addition, the inherent difficulty of isolating individual cells and handling small volume samples complicates many experimental protocols. Here we highlight a number of mass spectrometry (MS)-based measurement approaches for characterizing the chemical content of small volume analytes, with a focus on methods used to detect intracellular and extracellular metabolites and peptides from samples as small as individual cells. MS has become one of the most effective means for analyzing small biological samples due to its high sensitivity, low analyte consumption, compatibility with a wide array of sampling approaches, and ability to detect a large number of analytes with different properties without preselection. Having access to a flexible portfolio of MS-based methods allows quantitative, qualitative, untargeted, targeted, multiplexed, and spatially resolved investigations of single cells and their similarly scaled extracellular environments. Combining MS with on-line and off-line sample conditioning tools, such as microfluidic and capillary electrophoresis systems, significantly increases the analytical coverage of the sample's metabolome and peptidome, and improves individual analyte characterization/identification. Small volume assays help to reveal the causes and manifestations of biological and pathological variability, as well as the functional heterogeneity of individual cells within their microenvironments and within cellular populations. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification

Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules' rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., "shotgun" lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available.

Nanostructure imaging mass spectrometry: the role of fluorocarbons in metabolite analysis and yoctomole level sensitivity

Nanostructure imaging mass spectrometry (NIMS) has become an effective technology for generating ions in the gas phase, providing high sensitivity and imaging capabilities for small molecules, metabolites, drugs, and drug metabolites. Specifically, laser desorption from the nanostructure surfaces results in efficient energy transfer, low background chemical noise, and the nondestructive release of analyte ions into the gas phase. The modification of nanostructured surfaces with fluorous compounds, either covalent or non-covalent, has played an important role in gaining high efficiency/sensitivity by facilitating analyte desorption from the nonadhesive surfaces, and minimizing the amount of laser energy required. In addition, the hydrophobic fluorinated nanostructure surfaces have aided in concentrating deposited samples into fine micrometer-sized spots, a feature that further facilitates efficient desorption/ionization. These fluorous nanostructured surfaces have opened up NIMS to very broad applications including enzyme activity assays and imaging, providing low background, efficient energy transfer, nondestructive analyte ion generation, super-hydrophobic surfaces, and ultra-high detection sensitivity.

Single-cell Western blotting after whole-cell imaging to assess cancer chemotherapeutic response

Intratumor heterogeneity remains a major obstacle to effective cancer therapy and personalized medicine. Current understanding points to differential therapeutic response among subpopulations of tumor cells as a key challenge to successful treatment. To advance our understanding of how this heterogeneity is reflected in cell-to-cell variations in chemosensitivity and expression of drug-resistance proteins, we optimize and apply a new targeted proteomics modality, single-cell western blotting (scWestern), to a human glioblastoma cell line. To acquire both phenotypic and proteomic data on the same, single glioblastoma cells, we integrate high-content imaging prior to the scWestern assays. The scWestern technique supports thousands of concurrent single-cell western blots, with each assay comprised of chemical lysis of single cells seated in microwells, protein electrophoresis from those microwells into a supporting polyacrylamide (PA) gel layer, and in-gel antibody probing. We systematically optimize chemical lysis and subsequent polyacrylamide gel electrophoresis (PAGE) of the single-cell lysate. The scWestern slides are stored for months then reprobed, thus allowing archiving and later analysis as relevant to sparingly limited, longitudinal cell specimens. Imaging and scWestern analysis of single glioblastoma cells dosed with the chemotherapeutic daunomycin showed both apoptotic (cleaved caspase 8- and annexin V-positive) and living cells. Intriguingly, living glioblastoma subpopulations show up-regulation of a multidrug resistant protein, P-glycoprotein (P-gp), suggesting an active drug efflux pump as a potential mechanism of drug resistance. Accordingly, linking of phenotype with targeted protein analysis with single-cell resolution may advance our understanding of drug response in inherently heterogeneous cell populations, such as those anticipated in tumors.

Ion mobility derived collision cross sections to support metabolomics applications

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC-TW-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches.

Mass spectrometry-based metabolomics of single yeast cells

Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In this study, we demonstrate that, with the microarrays for mass spectrometry platform, we are able to observe this variability. We monitor environmentally (2-deoxy-D-glucose) and genetically (ΔPFK2) perturbed Saccharomyces cerevisiae cells at the single-cell, few-cell, and population levels. Correlation plots between metabolites from the glycolytic pathway, as well as with the observed ATP/ADP ratio as a measure of cellular energy charge, give biological insight that is not accessible from population-level metabolomic data.

Metabolomics in the studies of islet autoimmunity and type 1 diabetes

The metabolome is sensitive to genetic and environmental factors contributing to complex diseases such as type 1 diabetes (T1D). Metabolomics is the study of biochemical and physiological processes involving metabolites. It is therefore one of the key platforms for the discovery and study of pathophysiological phenomena leading to T1D and the development of T1D-associated complications. Although the application of metabolomics in T1D research is still rare, metabolomic research has already advanced across the full spectrum, from disease progression to the development of diabetic complications. Metabolomic studies in T1D have contributed to an improved etiopathogenic understanding and demonstrated their potential in the clinic. For example, metabolomic data from recent T1D studies suggest that a specific metabolic profile, or metabotype, precedes islet autoimmunity and the development of overt T1D. These early metabolic changes are attributed to many biochemical pathways, thus suggesting a systemic change in metabolism which may be inborn. Based on this evidence, the role of the metabolome in the progression to T1D is therefore to facilitate specific biochemical processes associated with T1D, and to contribute to the development of a vulnerable state in which disease is more likely to be triggered. This may have important implications for the understanding of T1D pathophysiology and early disease detection and prevention.