Lipid Heterogeneity between Astrocytes and Neurons Revealed by Single-Cell MALDI-MS Combined with Immunocytochemical Classification

Probe-based mass spectrometry approaches for single-cell and single-organelle measurements

Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.

Advances in mass spectrometry-enabled multiomics at single-cell resolution

Biological organisms are multifaceted, intricate systems where slight perturbations can result in extensive changes in gene expression, protein abundance and/or activity, and metabolic flux. These changes occur at different timescales, spatially across cells of heterogeneous origins, and within single-cells. Hence, multimodal measurements at the smallest biological scales are necessary to capture dynamic changes in heterogeneous biological systems. Of the analytical techniques used to measure biomolecules, mass spectrometry (MS) has proven to be a powerful option due to its sensitivity, robustness, and flexibility with regard to the breadth of biomolecules that can be analyzed. Recently, many studies have coupled MS to other analytical techniques with the goal of measuring multiple modalities from the same single-cell. It is with these concepts in mind that we focus this review on MS-enabled multiomic measurements at single-cell or near-single- cell resolution.

Single-Cell Untargeted Lipidomics Using Liquid Chromatography and Data-Dependent Acquisition after Live Cell Selection

We report the development and validation of an untargeted single-cell lipidomics method based on microflow chromatography coupled to a data-dependent mass spectrometry method for fragmentation-based identification of lipids. Given the absence of single-cell lipid standards, we show how the methodology should be optimized and validated using a dilute cell extract. The methodology is applied to dilute pancreatic cancer and macrophage cell extracts and standards to demonstrate the sensitivity requirements for confident assignment of lipids and classification of the cell type at the single-cell level. The method is then coupled to a system that can provide automated sampling of live, single cells into capillaries under microscope observation. This workflow retains the spatial information and morphology of cells during sampling and highlights the heterogeneity in lipid profiles observed at the single-cell level. The workflow is applied to show changes in single-cell lipid profiles as a response to oxidative stress, coinciding with expanded lipid droplets. This demonstrates that the workflow is sufficiently sensitive to observing changes in lipid profiles in response to a biological stimulus. Understanding how lipids vary in single cells will inform future research into a multitude of biological processes as lipids play important roles in structural, biophysical, energy storage, and signaling functions.

Screening Metabolic Biomarkers in KRAS Mutated Mouse Acinar and Human Pancreatic Cancer Cells via Single-Cell Mass Spectrometry

Pancreatic cancer is a highly aggressive and rapidly progressing disease, often diagnosed in advanced stages due to the absence of early noticeable symptoms. The KRAS mutation is a hallmark of pancreatic cancer, yet the underlying mechanisms driving pancreatic carcinogenesis remain elusive. Cancer cells display significant metabolic heterogeneity, which is relevant to the pathogenesis of cancer. Population measurements may obscure information about the metabolic heterogeneity among cancer cells. Therefore, it is crucial to analyze metabolites at the single-cell level to gain a more comprehensive understanding of metabolic heterogeneity. In this study, we employed a 3D-printed ionization source for metabolite analysis in both mice and human pancreatic cancer cells at the single-cell level. Using advanced machine learning algorithms and mass spectral feature selection, we successfully identified 23 distinct metabolites that are statistically significantly different in KRAS mutant human pancreatic cancer cells and mouse acinar cells bearing the oncogenic KRAS mutation. These metabolites encompass a variety of chemical classes, including organic nitrogen compounds, organic acids and derivatives, organoheterocyclic compounds, benzenoids, and lipids. These findings shed light on the metabolic remodeling associated with KRAS-driven pancreatic cancer initiation and indicate that the identified metabolites hold promise as potential diagnostic markers for early detection in pancreatic cancer patients.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Investigating the Metabolic Heterogeneity of Cancer Cells Using Functional Single-Cell Selection and nLC Combined with Multinozzle Emitter Mass Spectrometry

Tumor metastasis and cancer recurrence are often a result of cell heterogeneity, where specific subpopulations of tumor cells may be resistant to radio- or chemotherapy. To investigate this physiological and phenotypic diversity, single-cell metabolomics provides a powerful approach at the chemical level, where distinct lipid profiles can be found in different tumor cells. Here, we established a highly sensitive platform using nanoflow liquid chromatography (nLC) combined with multinozzle emitter electrospray ionization mass spectrometry for more in-depth metabolomics profiling. Our platform identified 15 and 17 lipids from individual osteosarcoma (U2OS) and glioblastoma (GBM) cells when analyzing single-cell samples. Additionally, we used the functional single-cell selection (fSCS) pipeline to analyze the subpopulations of cells with a DNA damage response (DDR) in U2OS cells and fast migration in GBM cells. Specifically, we observed a down-regulation of polyunsaturated fatty acids (PUFAs) in U2OS cells undergoing DDR, such as fatty acids FA 20:3; O2 and FA 17:4; O3. Furthermore, ceramides (Cer 38:0; O3) and triglycerides (TG 36:0) were found to be down-regulated in fast-migrating GBM cells compared to the slow-migrating subpopulation. These findings suggest the potential roles of these metabolites and/or lipids in the cellular behavior of the subpopulations.

Recent developments in ionization techniques for single-cell mass spectrometry

The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.

Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging

Single-cell (SC) analysis provides unique insight into individual cell dynamics and cell-to-cell heterogeneity. Here, we utilize trapped ion mobility separation coupled with dual-polarity ionization mass spectrometry imaging (MSI) to enable high-throughput in situ profiling of the SC lipidome. Multimodal SC imaging, in which dual-polarity-mode MSI is used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage. High-spatial resolution SC-MSI identifies both inter- and intracellular lipid heterogeneity; this heterogeneity is further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterize SC lipidome alteration in response to stearoyl-CoA desaturase 1 inhibition and, additionally, identify cell-layer specific lipid distribution patterns in mouse cerebellar cortex. This integrated multimodal SC-MSI technology enables high-resolution spatial mapping of intercellular and cell-to-cell lipidome heterogeneity, SC lipidome remodeling induced by pharmacological intervention, and region-specific lipid diversity within tissue.

Lipid Signatures and Inter-Cellular Heterogeneity of Naı̈ve and Lipopolysaccharide-Stimulated Human Microglia-like Cells

Microglia are non-neuronal cells, which reside in the central nervous system and are known to play an important role in health and disease. We investigated the lipidomic phenotypes of human naı̈ve and stimulated microglia-like cells by atmospheric-pressure scanning microprobe matrix-assisted laser desorption/ionization mass spectrometry imaging (AP-SMALDI MSI). With lateral resolutions between 5 and 1.5 μm pixel size, we were able to chart lipid compositions of individual cells, enabling differentiation of cell lines and stimulation conditions. This allowed us to reveal local lipid heterogeneities in naı̈ve and lipopolysaccharide (LPS)-stimulated cells. We were able to identify individual cells with elevated triglyceride (TG) levels and could show that the number of these TG-enriched cells increased with LPS stimulation as a hallmark for a proinflammatory phenotype. Additionally, the observed local abundance alterations of specific phosphatidylinositols (PIs) indicate a cell specific regulation of the PI metabolism.

Microlensed fiber allows subcellular imaging by laser-based mass spectrometry

Mass spectrometry imaging (MSI) enables the chemical mapping of molecules and elements in a label-free, high-throughput manner. Because this approach can be accomplished rapidly, it also enables chemical changes to be monitored. Here, we describe a protocol for MSI with subcellular spatial resolution. This is achieved by using a microlensed fiber, which is made by grinding an optical fiber. It is a universal and economic technique that can be adapted to most laser-based mass spectrometry methods. In this protocol, the output of laser radiation from the microlensed fiber causes laser ablation of the sample, and the resulting plume is mass spectrometrically analyzed. The microlensed fiber can be used with matrix-assisted laser desorption ionization, laser desorption ionization, laser ablation electrospray desorption ionization and laser ablation inductively coupled plasma, in each case to achieve submicroscale imaging of single cells and biological tissues. This report provides a detailed introduction of the microlensed fiber design and working principles, sample preparation, microlensed fiber ion source setup and multiple MSI platforms with different kinds of mass spectrometers. A researcher with a little background (such as a trained graduate student) is able to complete all the steps for the experimental setup in ~2 h, including fiber test, laser coupling and ion source modification. The imaging time spent mainly depends on the size of the imaging area. It is suggested that most existing laser-based MSI platforms, especially atmospheric pressure applications, can achieve breakthroughs in spatial resolution by introducing a microlensed fiber module.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.

Origins of nervous tissue susceptibility to ferroptosis

Ferroptosis is a newly defined form of programmed cell death. It possesses unique processes of cell demise, cytopathological changes, and independent signal regulation pathways. Ferroptosis is considered to be deeply involved in the development of many diseases, including cancer, cardiovascular diseases, and neurodegeneration. Intriguingly, why cells in certain tissues and organs (such as the central nervous system, CNS) are more sensitive to changes in ferroptosis remains a question that has not been carefully discussed. In this Holmesian review, we discuss lipid composition as a potential but often overlooked determining factor in ferroptosis sensitivity and the role of polyunsaturated fatty acids (PUFAs) in the pathogenesis of several common human neurodegenerative diseases. In subsequent studies of ferroptosis, lipid composition needs to be given special attention, as it may significantly affect the susceptibility of the cell model used (or the tissue studied).

Single-Cell and Subcellular Analysis Using Ultrahigh Resolution 21 T MALDI FTICR Mass Spectrometry

The mammalian brain contains ∼20,000 distinct lipid species that contribute to its structural organization and function. The lipid profiles of cells change in response to a variety of cellular signals and environmental conditions that result in modulation of cell function through alteration of phenotype. The limited sample material combined with the vast chemical diversity of lipids makes comprehensive lipid profiling of individual cells challenging. Here, we leverage the resolving power of a 21 T Fourier-transform ion cyclotron resonance (FTICR) mass spectrometer for chemical characterization of individual hippocampal cells at ultrahigh mass resolution. The accuracy of the acquired data allowed differentiation of freshly isolated and cultured hippocampal cell populations, as well as finding differences in lipids between the soma and neuronal processes of the same cell. Differences in lipids include TG 42:2 observed solely in the cell bodies and SM 34:1;O2 found only in the cellular processes. The work represents the first mammalian single cells analyzed at ultrahigh resolution and is an advance in the performance of mass spectrometry (MS) for single-cell research.

Chemical Decrosslinking-Based Peptide Characterization of Formaldehyde-Fixed Rat Pancreas Using Fluorescence-Guided Single-Cell Mass Spectrometry

Approaches for the characterization of proteins/peptides in single cells of formaldehyde-fixed (FF) tissues via mass spectrometry (MS) are still under development. The lack of a general method for selectively eliminating formaldehyde-induced crosslinking is a major challenge. A workflow is shown for the high-throughput peptide profiling of single cells isolated from FF tissues, here the rodent pancreas, which possesses multiple peptide hormones from the islets of Langerhans. The heat treatment is enhanced by a collagen-selective multistep thermal process assisting efficient isolation of islets from the FF pancreas and, subsequently, their dissociation into single islet cells. Hydroxylamine-based chemical decrosslinking helped restore intact peptide signals from individual isolated cells. Subsequently, an acetone/glycerol-assisted cell dispersion was optimized for spatially resolved cell deposition onto glass slides, while a glycerol solution maintained the hydrated state of the cells. This sample preparation procedure allowed peptide profiling in FF single cells by fluorescence-guided matrix-assisted laser desorption ionization MS. Here, 2594 single islet cells were analyzed and 28 peptides were detected, including insulin C-peptides and glucagon. T-distributed stochastic neighbor embedding (t-SNE) data visualization demonstrated that cells cluster based on cell-specific pancreatic peptide hormones. This workflow expands the sample availability for single-cell MS characterization to a wide range of formaldehyde-treated tissue specimens stored in biobanks.

Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry

Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.

Development of a 3D-Printed Ionization Source for Single-Cell Analysis

Understanding the physiologies and pathologies of diseases requires a thorough understanding of metabolic heterogeneity in cells. This technical note presents a 3D printing technology for manufacturing an ionization source that is specially adapted for mass spectrometry-based single-cell analysis. This all-in-one 3D-printed electrospray ionization source integrates the sample introduction, metabolite extraction, and ionization into one device, simplifying the process of single-cell analysis and improving the reproducibility of the measurement. We successfully used it for high-throughput analysis of three types of cancer cells (around 17 cells/min) and used the t-distributed stochastic neighbor embedding algorithm to distinguish different cell types based on detected metabolites. By simply adjusting the printing parameters of the 3D-printed ionization source, it can be applied to cells with different sizes. The proposed 3D-printed ionization source promises to open new possibilities for single-cell analysis.

Longitudinal spatial mapping of lipid metabolites reveals pre-symptomatic changes in the hippocampi of Huntington's disease transgenic mice

In Huntington's disease (HD), a key pathological feature includes the development of inclusion-bodies of fragments of the mutant huntingtin protein in the neurons of the striatum and hippocampus. To examine the molecular changes associated with inclusion-body formation, we applied MALDI-mass spectrometry imaging and deuterium pulse labelling to determine lipid levels and synthesis rates in the hippocampus of a transgenic mouse model of HD (R6/1 line). The R6/1 HD mice lacked inclusions in the hippocampus at 6 weeks of age (pre-symptomatic), whereas inclusions were pervasive by 16 weeks of age (symptomatic). Hippocampal subfields (CA1, CA3 and DG), which formed the highest density of inclusion formation in the mouse brain showed a reduction in the relative abundance of neuron-enriched lipids that have roles in neurotransmission, synaptic plasticity, neurogenesis, and ER-stress protection. Lipids involved in the adaptive response to ER stress (phosphatidylinositol, phosphatidic acid, and ganglioside classes) displayed increased rates of synthesis in HD mice relative to WT mice at all the ages examined, including prior to the formation of the inclusion bodies. Our findings, therefore, support a role for ER stress occurring pre-symptomatically and potentially contributing to pathological mechanisms underlying HD.

Advances in high-throughput mass spectrometry in drug discovery

High-throughput (HT) screening drug discovery, during which thousands or millions of compounds are screened, remains the key methodology for identifying active chemical matter in early drug discovery pipelines. Recent technological developments in mass spectrometry (MS) and automation have revolutionized the application of MS for use in HT screens. These methods allow the targeting of unlabelled biomolecules in HT assays, thereby expanding the breadth of targets for which HT assays can be developed compared to traditional approaches. Moreover, these label-free MS assays are often cheaper, faster, and more physiologically relevant than competing assay technologies. In this review, we will describe current MS techniques used in drug discovery and explain their advantages and disadvantages. We will highlight the power of mass spectrometry in label-free in vitro assays, and its application for setting up multiplexed cellular phenotypic assays, providing an exciting new tool for screening compounds in cell lines, and even primary cells. Finally, we will give an outlook on how technological advances will increase the future use and the capabilities of mass spectrometry in drug discovery.

An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue

Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.

Single cell metabolism: current and future trends

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Mass spectrometry imaging to explore molecular heterogeneity in cell culture

Molecular analysis on the single-cell level represents a rapidly growing field in the life sciences. While bulk analysis from a pool of cells provides a general molecular profile, it is blind to heterogeneities between individual cells. This heterogeneity, however, is an inherent property of every cell population. Its analysis is fundamental to understanding the development, function, and role of specific cells of the same genotype that display different phenotypical properties. Single-cell mass spectrometry (MS) aims to provide broad molecular information for a significantly large number of cells to help decipher cellular heterogeneity using statistical analysis. Here, we present a sensitive approach to single-cell MS based on high-resolution MALDI-2-MS imaging in combination with MALDI-compatible staining and use of optical microscopy. Our approach allowed analyzing large amounts of unperturbed cells directly from the growth chamber. Confident coregistration of both modalities enabled a reliable compilation of single-cell mass spectra and a straightforward inclusion of optical as well as mass spectrometric features in the interpretation of data. The resulting multimodal datasets permit the use of various statistical methods like machine learning-driven classification and multivariate analysis based on molecular profile and establish a direct connection of MS data with microscopy information of individual cells. Displaying data in the form of histograms for individual signal intensities helps to investigate heterogeneous expression of specific lipids within the cell culture and to identify subpopulations intuitively. Ultimately, t-MALDI-2-MSI measurements at 2-µm pixel sizes deliver a glimpse of intracellular lipid distributions and reveal molecular profiles for subcellular domains.

Profiling 26,000 Aplysia californica neurons by single cell mass spectrometry reveal neuronal populations with distinct neuropeptide profiles

Neuropeptides are a chemically diverse class of cell-to-cell signaling molecules that are widely expressed throughout the central nervous system, often in a cell-specific manner. While cell-to-cell differences in neuropeptides is expected, it is often unclear how exactly neuropeptide expression varies among neurons. Here we created a microscopy-guided, high-throughput single cell matrix-assisted laser desorption/ionization mass spectrometry approach to investigate the neuropeptide heterogeneity of individual neurons in the central nervous system of the neurobiological model Aplysia californica, the California sea hare. In all, we analyzed more than 26,000 neurons from 18 animals and assigned 866 peptides from 66 prohormones by mass matching against an in silico peptide library generated from known Aplysia prohormones retrieved from the UniProt database. Louvain–Jaccard (LJ) clustering of mass spectra from individual neurons revealed 40 unique neuronal populations, or LJ clusters, each with a distinct neuropeptide profile. Prohormones and their related peptides were generally found in single cells from ganglia consistent with the prohormones’ previously known ganglion localizations. Several LJ clusters also revealed the cellular colocalization of behaviorally related prohormones, such as an LJ cluster exhibiting achatin and neuropeptide Y, which are involved in feeding, and another cluster characterized by urotensin II, small cardiac peptide, sensorin A, and FRFa, which have shown activity in the feeding network or are present in the feeding musculature. This mass spectrometry–based approach enables the robust categorization of large cell populations based on single cell neuropeptide content and is readily adaptable to the study of a range of animals and tissue types.

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models

Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

Referenced Kendrick Mass Defect Annotation and Class-Based Filtering of Imaging MS Lipidomics Experiments

Because of their diverse functionalities in cells, lipids are of primary importance when characterizing molecular profiles of physiological and disease states. Imaging mass spectrometry (IMS) provides the spatial distributions of lipid populations in tissues. Referenced Kendrick mass defect (RKMD) analysis is an effective mass spectrometry (MS) data analysis tool for classification and annotation of lipids. Herein, we extend the capabilities of RKMD analysis and demonstrate an integrated method for lipid annotation and chemical structure-based filtering for IMS datasets. Annotation of lipid features with lipid molecular class, radyl carbon chain length, and degree of unsaturation allows image reconstruction and visualization based on each structural characteristic. We show a proof-of-concept application of the method to a computationally generated IMS dataset and validate that the RKMD method is highly specific for lipid components in the presence of confounding background ions. Moreover, we demonstrate an application of the RKMD-based annotation and filtering to matrix-assisted laser desorption/ionization (MALDI) IMS lipidomic data from human kidney tissue analysis.

Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging

Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.

Hybrid Ionization Source Combining Nanoelectrospray and Dielectric Barrier Discharge Ionization for the Simultaneous Detection of Polar and Nonpolar Compounds in Single Cells

Single-cell metabolomics is expected to deliver fast and dynamic information on cell function; therefore, it requires rapid analysis of a wide variety of very small quantities of metabolites in living cells. In this work, a hybrid ionization source that combines nanoelectrospray ionization (nanoESI) and dielectric barrier discharge ionization (DBDI) is proposed for single-cell analysis. A capillary with a 1 μm i.d. tip was inserted into cells for sampling and then directly used as the nanoESI source for ionization of polar metabolites. In addition, a DBDI source was employed as a post-ionization source to improve the ionization of apolar metabolites in cells that are not easily ionized by ESI. By increasing the voltage of the DBDI source from 0 to 3.2 kV, the classes of detected metabolites can be shifted from mostly polar to both polar and apolar to mainly apolar. Plant cells (onion) and human cells (PANC-1) were investigated in this study. After optimization, 50 compounds in onion cells and 40 compounds in PANC-1 cells were observed in ESI mode (3.5 kV) and an additional 49 compounds in onion cells and 73 compounds in PANC-1 cells were detected in ESI (3.5 kV)-DBDI (2.6 kV) hybrid mode. This hybrid ionization source improves the coverage, ionization efficiency, and limit of detection of metabolites with different polarities and could potentially contribute to the fast-growing field of single-cell metabolomics.

Intact living-cell electrolaunching ionization mass spectrometry for single-cell metabolomics

A novel living-cell mass spectrometry method allows a whole cell to enter entirely into the MS inlet and ionize with almost no sample dilution and matrix interference, which greatly improves the sensitivity of single-cell metabolite detection.

Identification of Lipid Heterogeneity and Diversity in the Developing Human Brain

The lipidome is currently understudied but fundamental to life. Within the brain, little is known about cell-type lipid heterogeneity, and even less is known about cell-to-cell lipid diversity because it is difficult to study the lipids within individual cells. Here, we used single-cell mass spectrometry-based protocols to profile the lipidomes of 154 910 single cells across ten individuals consisting of five developmental ages and five brain regions, resulting in a unique lipid atlas available via a web browser of the developing human brain. From these data, we identify differentially expressed lipids across brain structures, cortical areas, and developmental ages. We inferred lipid profiles of several major cell types from this data set and additionally detected putative cell-type specific lipids. This data set will enable further interrogation of the developing human brain lipidome.

Phospholipid Profiles for Phenotypic Characterization of Adipose-Derived Multipotent Mesenchymal Stromal Cells

Multipotent mesenchymal stromal cells (MSC) have emerged as therapeutic tools for a wide range of pathological conditions. Yet, the still existing deficits regarding MSC phenotype characterization and the resulting heterogeneity of MSC used in different preclinical and clinical studies hamper the translational success. In search for novel MSC characterization approaches to complement the traditional trilineage differentiation and immunophenotyping assays reliably across species and culture conditions, this study explored the applicability of lipid phenotyping for MSC characterization and discrimination. Human peripheral blood mononuclear cells (PBMC), human fibroblasts, and human and equine adipose-derived MSC were used to compare different mesodermal cell types and MSC from different species. For MSC, cells cultured in different conditions, including medium supplementation with either fetal bovine serum or platelet lysate as well as culture on collagen-coated dishes, were additionally investigated. After cell harvest, lipids were extracted by chloroform/methanol according to Bligh and Dyer. The lipid profiles were analysed by an untargeted approach using liquid chromatography coupled to mass spectrometry (LC-MS) with a reversed phase column and an ion trap mass spectrometer. In all samples, phospholipids and sphingomyelins were found, while other lipids were not detected with the current approach. The phospholipids included different species of phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI) and phosphatidylserine (PS) in all cell types, whereas phosphatidylglycerol (PG) species were only present in MSC. MSC from both species showed a higher phospholipid species diversity than PBMC and fibroblasts. Few differences were found between MSC from different culture conditions, except that human MSC cultured with platelet lysate exhibited a unique phenotype in that they exclusively featured PE O-40:4, PG 38:6 and PG 40:6. In search for specific and inclusive candidate MSC lipid markers, we identified PE O-36:3 and PG 40:7 as potentially suitable markers across culture conditions, at which PE O-36:3 might even be used across species. On that basis, phospholipid phenotyping is a highly promising approach for MSC characterization, which might condone some heterogeneity within the MSC while still achieving a clear discrimination even from fibroblasts. Particularly the presence or absence of PG might emerge as a decisive criterion for future MSC characterization.

Photoactivatable Reporter to Perform Multiplexed and Temporally Controlled Measurements of Kinase and Protease Activity in Single Cells

Peptide bioreporters were developed to perform multiplexed measurements of the activation of epidermal growth factor receptor kinase (EGFR), Akt kinase (Akt/protein kinase B), and proteases/peptidases in single cells. The performance characteristics of the three reporters were assessed by measuring the reporter's proteolytic stability, kinetic constants for EGFR and Akt, and dephosphorylation rate. The reporter displaying optimal performance was composed of 6-carboxyfluorescein (6-FAM) on the peptide N-terminus, an Akt substrate sequence employing a threonine phosphorylation site for Akt, followed by a tri-D arginine linker, and finally an EGFR substrate sequence bearing a phosphatase-resistant 7-(S)-hydroxy-1,2,3,4-tetrahydroisoquinoline-3-carboxylic acid (L-htc) residue as the EGFR phosphorylation site. Importantly, use of a single electrophoretic condition separated the mono- and diphosphorylated products as well as proteolytic forms permitting the quantitation of multiple enzyme activities simultaneously using a single reporter. Because the Akt and EGFR substrates were linked, a known ratio (EGFR/Akt) of the reporter was loaded into cells. A photoactivatable version of the reporter was synthesized by adding two 4,5-dimethoxy-2-nitrobenzyl (DMNB) moieties to mask the EGFR and Akt phosphorylation sites. The DMNB moieties were readily photocleaved following exposure to 360 nm light, unmasking the phosphorylation sites on the reporter. The new photoactivatable reporter permitted multiplexed measurements of kinase signaling and proteolytic degradation in single cells in a temporally controlled manner. This work will facilitate the development of a new generation of multiplexed activity-based reporters capable of light-initiated measurement of enzymatic activity in single cells.

High-Throughput Single-Cell Mass Spectrometry Reveals Abnormal Lipid Metabolism in Pancreatic Ductal Adenocarcinoma

Even populations of clonal cells are heterogeneous, which requires high-throughput analysis methods with single-cell sensitivity. Here, we propose a rapid, label-free single-cell analytical method based on active capillary dielectric barrier discharge ionization mass spectrometry, which can analyze multiple metabolites in single cells at a rate of 38 cells/minute. Multiple cell types (HEK-293T, PANC-1, CFPAC-1, H6c7, HeLa and iBAs) were discriminated successfully. We found evidence for abnormal lipid metabolism in pancreatic cancer cells. We also analyzed gene expression in a cancer genome atlas dataset and found that the mRNA level of a critical enzyme of lipid synthesis (ATP citrate lyase, ACLY) was upregulated in human pancreatic ductal adenocarcinoma (PDAC). Moreover, both an ACLY chemical inhibitor and a siRNA approach targeting ACLY could suppress the viability of PDAC cells. A significant reduction in lipid content in treated cells indicates that ACLY could be a potential target for treating pancreatic cancer.

Single-Neuron RNA Modification Analysis by Mass Spectrometry: Characterizing RNA Modification Patterns and Dynamics with Single-Cell Resolution

The entire collection of post-transcriptional modifications to RNA, known as the epitranscriptome, has been increasingly recognized as a critical regulatory layer in the cellular translation machinery. However, contemporary methods for the analysis of RNA modifications are limited to the detection of highly abundant modifications in bulk tissue samples, potentially obscuring unique epitranscriptomes of individual cells with population averages. We developed an approach, single-neuron RNA modification analysis by mass spectrometry (SNRMA-MS), that enables the detection and quantification of numerous post-transcriptionally modified nucleosides in single cells. When compared to a conventional RNA extraction approach that does not allow detection of RNA modifications in single cells, SNRMA-MS leverages an optimized sample preparation approach to detect up to 16 RNA modifications in individual neurons from the central nervous system of Aplysia californica. SNRMA-MS revealed that the RNA modification profiles of identified A. californica neurons with different physiological functions were mostly cell specific. However, functionally homologous neurons tended to demonstrate similar modification patterns. Stable isotope labeling with CD3-Met showed significant differences in RNA methylation rates that were dependent on the identity of the modification and the cell, with metacerebral cells (MCCs) displaying the fastest incorporation of CD3 groups into endogenous RNAs. Quantitative SNRMA-MS showed higher intracellular concentrations for 2'-O-methyladenosine and 2'-O-methylcytidine in homologous R2/LPl1 cell pairs than in MCCs. Overall, SNRMA-MS is the first analytical approach capable of simultaneously quantifying numerous RNA modifications in single neurons and revealing cell-specific modification profiles.

An Integrated Strategy for Mass Spectrometry-Based Multiomics Analysis of Single Cells

Single-cell-based genomics and transcriptomics analysis have revealed substantial cellular heterogeneity among seemingly identical cells. Knowledge of the cellular heterogeneity at multiomics levels is vital for a better understanding of tumor metastasis and drug resistance, stem cell differentiation, and embryonic development. However, unlike genomics and transcriptomics studies, single-cell characterization of metabolites, proteins, and post-translational modifications at the omics level remains challenging due to the lack of amplification methods and the wide diversity of these biomolecules. Therefore, new tools that are capable of investigating these unamplifiable "omes" from the same single cells are in high demand. In this work, a microwell chip was prepared and the internal surface was modified for hydrophilic interaction liquid chromatography-based tandem extraction of metabolites and proteins and subsequent protein digestion. Next, direct electrospray ionization mass spectrometry was adopted for single-cell metabolome identification, and a data-independent acquisition-mass spectrometry approach was established for simultaneous proteome profiling and phosphoproteome analysis without phosphopeptide enrichment. This integrated strategy resulted in 132 putatively annotated compounds, more than 1200 proteins, and the first large-scale phosphorylation data set from single-cell analysis. Application of this strategy in chemical perturbation studies provides a multiomics view of cellular changes, demonstrating its capability for more comprehensive investigation of cellular heterogeneity.

Incorporating Electro-Epoxidation into Electrospray Ionization Mass Spectrometry for Simultaneous Analysis of Negatively and Positively Charged Unsaturated Glycerophospholipids

In this study, we develop an alternating current (AC)-induced electro-epoxidation reaction and incorporate it into nanoelectrospray ionization for locating carbon-carbon double-bonds in positively and negatively charged forms of lipids simultaneously. An AC voltage plays multiple roles in this method, including initiation of the electro-epoxidation of carbon-carbon double-bonds in both charged states of lipids and protonation/deprotonation of lipids for detection in both ion modes. Moreover, the rapid switch between native lipids and their electro-epoxidation products can be achieved at different AC voltages. The efficacy of the present method was demonstrated in mixtures of lipid standards and in a biological polar lipid extract. The advantages of simultaneous detection of negatively and positively charged unsaturated lipids, the low sample consumption, and on-demand electro-epoxidation should allow its wide applications in lipid-related research.

Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques

Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

Discriminating Leukemia Cellular Heterogeneity and Screening Metabolite Biomarker Candidates using Label-Free Mass Cytometry

Discriminating various leukocyte subsets with specific functions is critical due to their important roles in the development of many diseases. Here, we proposed a general strategy to unravel leukocytes heterogeneity and screen differentiated metabolites as biomarker candidates for leukocyte subtypes using the label-free mass cytometry (CyESI-MS) combined with a homemade data processing workflow. Taking leukemia cells as an example, metabolic fingerprints of single leukemia cells were obtained from 472 HL-60, 416 THP-1, 313 U937, 356 Jurkat, and 366 Ramos cells, with throughput up to 40 cells/min. Five leukemia subtypes were clearly distinguished by unsupervised learning t-SNE analysis of the single-cell metabolic fingerprints. Cell discrimination in the mixed leukemia cell samples was also realized by supervised learning of the single-cell metabolic fingerprints with high recovery and good repetition (98.31 ± 0.24%, -102.35 ± 4.82%). Statistical analysis and metabolite assignment were carried out to screen characteristic metabolites for discrimination and 36 metabolites with significant differences were annotated. Then, differentiated metabolites for pairwise discrimination of five leukemia subtypes were further selected as biomarker candidates. Furthermore, discriminating cultured leukemia cells from human normal leukocytes, separated from fresh human peripheral blood, was performed based on single-cell metabolic fingerprints as well as the proposed biomarker candidates, unveiling the potential of this strategy in clinical research. This work makes efforts to realize high-throughput single-leukocyte metabolic analysis and metabolite-based discrimination of leukocytes. It is expected to be a powerful means for the clinical molecular diagnosis of hematological diseases.

Recent developments in Raman spectral analysis of microbial single cells: Techniques and applications

The conventional microbial cell analyses are mostly population-averaged methods that conceal the characteristics of single-cell in the community. Single-cell analysis can provide information on the functional and structural variation of each cell, resulting in the elimination of long and tedious microbial cultivation techniques. Raman spectroscopy is a label-free, noninvasive, and in-vivo method ideal for single-cell measurement to obtain spatially resolved chemical information. In the current review, recent developments in Raman spectroscopic techniques for microbial characterization at the single-cell level are presented, focusing on Raman imaging of single cells to study the intracellular distribution of different components. The review also discusses the limitation and challenges of each technique and put forward some future outlook for improving Raman spectroscopy-based techniques for single-cell analysis. Raman spectroscopic methods at the single-cell level have potential in precision measurements, metabolic analysis, antibiotic susceptibility testing, resuscitation capability, and correlating phenotypic information to genomics for cells, the integration of Raman spectroscopy with other techniques such as microfluidics, stable isotope probing (SIP), and atomic force microscope can further improve the resolution and provide extensive information. Future focuses should be given to advance algorithms for data analysis, standardized reference libraries, and automated cell isolation techniques in future.

SpaceM reveals metabolic states of single cells

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

Single-cell lipidomics with high structural specificity by mass spectrometry

Single-cell analysis is critical to revealing cell-to-cell heterogeneity that would otherwise be lost in ensemble analysis. Detailed lipidome characterization for single cells is still far from mature, especially when considering the highly complex structural diversity of lipids and the limited sample amounts available from a single cell. We report the development of a general strategy enabling single-cell lipidomic analysis with high structural specificity. Cell fixation is applied to retain lipids in the cell during batch treatments prior to single-cell analysis. In addition to tandem mass spectrometry analysis revealing the class and fatty acyl-chain for lipids, batch photochemical derivatization and single-cell droplet treatment are performed to identify the C=C locations and sn-positions of lipids, respectively. Electro-migration combined with droplet-assisted electrospray ionization enables single-cell mass spectrometry analysis with easy operation but high efficiency in sample usage. Four subtypes of human breast cancer cells are correctly classified through quantitative analysis of lipid C=C location or sn-position isomers in ~160 cells. Most importantly, the single-cell deep lipidomics strategy successfully discriminates gefitinib-resistant cells from a population of wild-type human lung cancer cells (HCC827), highlighting its unique capability to promote precision medicine.

macroMS: Image-Guided Analysis of Random Objects by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Mass spectrometry imaging is well-suited to characterizing sample surfaces for their chemical content in a spatially resolved manner. However, when the surface contains small objects with significant empty spaces between them, more efficient approaches to sample acquisition are possible. Image-guided mass spectrometry (MS) enables high-throughput analysis of a diverse range of sample types, such as microbial colonies, liquid microdroplets, and others, by recognizing and analyzing selected location targets in an image. Here, we describe an imaging protocol and macroMS, an online software suite that can be used to enhance MS measurements of macroscopic samples that are imaged by a camera or a flatbed scanner. The web-based tool enables users to find and filter targets from the optical images, correct optical distortion issues for improved spatial location of selected targets, input the custom geometry files into an MS device to acquire spectra at the selected locations, and finally, perform limited data analysis and use visualization tools to aid locating samples containing compounds of interest. Using the macroMS suite, an enzyme mutant library of Saccharomyces cerevisiae and nL droplet arrays of Escherichia coli and Pseudomonas fluorescens have been assayed at a rate of ∼2 s/sample.

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.

NMR Methods for Determining Lipid Turnover via Stable Isotope Resolved Metabolomics

Lipids comprise diverse classes of compounds that are important for the structure and properties of membranes, as high-energy fuel sources and as signaling molecules. Therefore, the turnover rates of these varied classes of lipids are fundamental to cellular function. However, their enormous chemical diversity and dynamic range in cells makes detailed analysis very complex. Furthermore, although stable isotope tracers enable the determination of synthesis and degradation of complex lipids, the numbers of distinguishable molecules increase enormously, which exacerbates the problem. Although LC-MS-MS (Liquid Chromatography-Tandem Mass Spectrometry) is the standard for lipidomics, NMR can add value in global lipid analysis and isotopomer distributions of intact lipids. Here, we describe new developments in NMR analysis for assessing global lipid content and isotopic enrichment of mixtures of complex lipids for two cell lines (PC3 and UMUC3) using both 13C6 glucose and 13C5 glutamine tracers.