Classification of Large Cellular Populations and Discovery of Rare Cells Using Single Cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

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 multimodal mass spectrometry for single-cell analysis and imaging enhancement

Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.

High-Throughput f-LAESI-IMS-MS for Mapping Biological Nitrogen Fixation One Cell at a Time

For the characterization of the metabolic heterogeneity of cell populations, high-throughput single-cell analysis platforms are needed. In this study, we utilized mass spectrometry (MS) enhanced with ion mobility separation (IMS) and coupled with an automated sampling platform, fiber-based laser ablation electrospray ionization (f-LAESI), for in situ high-throughput single-cell metabolomics in soybean (Glycine max) root nodules. By fully automating the in situ sampling platform, an overall sampling rate of 804 cells/h was achieved for high numbers (>500) of tissue-embedded plant cells. This is an improvement by a factor of 13 compared to the previous f-LAESI-MS configuration. By introducing IMS, the molecular coverage improved, and structural isomers were separated on a millisecond time scale. The enhanced f-LAESI-IMS-MS platform produced 259 sample-related peaks/cell, almost twice as much as the 131 sample-related peaks/cell produced by f-LAESI-MS without IMS. Using the upgraded system, two types of metabolic heterogeneity characterization methods became possible. For unimodal metabolite abundance distributions, the metabolic noise reported on the metabolite level variations within the cell population. For bimodal distributions, the presence of metabolically distinct subpopulations was established. Discovering these latent cellular phenotypes could be linked to the presence of different cell states, e.g., proliferating bacteria in partially occupied plant cells and quiescent bacteroids in fully occupied cells in biological nitrogen fixation, or spatial heterogeneity due to altered local environments.

Can single-cell and spatial omics unravel the pathophysiology of pre-eclampsia?

Pre-eclampsia is a leading cause of maternal and fetal morbidity and mortality. Characterised by the onset of hypertension and proteinuria in the second half of pregnancy, it can lead to maternal end-organ injury such as cerebral ischemia and oedema, pulmonary oedema and renal failure, and potentially fatal outcomes for both mother and fetus. The causes of the different maternal end-organ phenotypes of pre-eclampsia and why some women develop pre-eclampsia condition early in pregnancy have yet to be elucidated. Omics methods include proteomics, genomics, metabolomics, transcriptomics. These omics techniques, previously mostly used on bulk tissue and individually, are increasingly available at a single cellular level and can be combined with each other. Multi-omics techniques on a single-cell or spatial level provide us with a powerful tool to understand the pathophysiology of pre-eclampsia. This review will explore the status of omics methods and how they can and could contribute to understanding the pathophysiology of pre-eclampsia.

The endohyphal microbiome: current progress and challenges for scaling down integrative multi-omic microbiome research

As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how "microbiomes within microbiomes" affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales. Video Abstract.

Biodistribution and racemization of gut-absorbed L/D-alanine in germ-free mice

Microbiome-derived metabolites are important for the microbiome-gut-brain axis and the discovery of new disease treatments. D-Alanine (D-Ala) is found in many animals as a potential co-agonist of the N-methyl-D-aspartate receptors (NMDAR), receptors widely used in the nervous and endocrine systems. The gut microbiome, diet and putative endogenous synthesis are the potential sources of D-Ala in animals, although there is no direct evidence to show the distribution and racemization of gut-absorbed L-/D-Ala with regards to host-microbe interactions in mammals. In this work, we utilized germ-free mice to control the interference from microbiota and isotopically labeled L-/D-Ala to track their biodistribution and racemization in vivo. Results showed time-dependent biodistribution of gut-absorbed D-Ala, particularly accumulation of gut-absorbed D-Ala in pancreatic tissues, brain, and pituitary. No endogenous synthesis of D-Ala via racemization was observed in germ-free mice. The sources of D-Ala in mice were revealed as microbiota and diet, but not endogenous racemization. This work indicates the importance of further investigating the in vivo biological functions of gut-microbiome derived D-Ala, particularly on NMDAR-related activities, for D-Ala as a potential signaling molecules in the microbiome-gut-brain axis.

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.

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.

Single-cell mass spectrometry

Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.

Advances in measuring cancer cell metabolism with subcellular resolution

Characterizing metabolism in cancer is crucial for understanding tumor biology and for developing potential therapies. Although most metabolic investigations analyze averaged metabolite levels from all cell compartments, subcellular metabolomics can provide more detailed insight into the biochemical processes associated with the disease. Methodological limitations have historically prevented the wider application of subcellular metabolomics in cancer research. Recently, however, ways to distinguish and identify metabolic pathways within organelles have been developed, including state-of-the-art methods to monitor metabolism in situ (such as mass spectrometry-based imaging, Raman spectroscopy and fluorescence microscopy), to isolate key organelles via new approaches and to use tailored isotope-tracing strategies. Herein, we examine the advantages and limitations of these developments and look to the future of this field of research.

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.

Mass Spectrometry Approaches Empowering Neuropeptide Discovery and Therapeutics

The discovery of insulin in the early 1900s ushered in the era of research related to peptides acting as hormones and neuromodulators, among other regulatory roles. These essential gene products are found in all organisms, from the most primitive to the most evolved, and carry important biologic information that coordinates complex physiology and behavior; their misregulation has been implicated in a variety of diseases. The evolutionary origins of at least 30 neuropeptide signaling systems have been traced to the common ancestor of protostomes and deuterostomes. With the use of relevant animal models and modern technologies, we can gain mechanistic insight into orthologous and paralogous endogenous peptides and translate that knowledge into medically relevant insights and new treatments. Groundbreaking advances in medicine and basic science influence how signaling peptides are defined today. The precise mechanistic pathways for over 100 endogenous peptides in mammals are now known and have laid the foundation for multiple drug development pipelines. Peptide biologics have become valuable drugs due to their unique specificity and biologic activity, lack of toxic metabolites, and minimal undesirable interactions. This review outlines modern technologies that enable neuropeptide discovery and characterization, and highlights lessons from nature made possible by neuropeptide research in relevant animal models that is being adopted by the pharmaceutical industry. We conclude with a brief overview of approaches/strategies for effective development of peptides as drugs. SIGNIFICANCE STATEMENT: Neuropeptides, an important class of cell-cell signaling molecules, are involved in maintaining a range of physiological functions. Since the discovery of insulin's activity, over 100 bioactive peptides and peptide analogs have been used as therapeutics. Because these are complex molecules not easily predicted from a genome and their activity can change with subtle chemical modifications, mass spectrometry (MS) has significantly empowered peptide discovery and characterization. This review highlights contributions of MS-based research towards the development of therapeutic peptides.

Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation

Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons.

Faster, better, and cheaper: harnessing microfluidics and mass spectrometry for biotechnology

High-throughput screening technologies are widely used for elucidating biological activities. These typically require trade-offs in assay specificity and sensitivity to achieve higher throughput. Microfluidic approaches enable rapid manipulation of small volumes and have found a wide range of applications in biotechnology providing improved control of reaction conditions, faster assays, and reduced reagent consumption. The integration of mass spectrometry with microfluidics has the potential to create high-throughput, sensitivity, and specificity assays. This review introduces the widely-used mass spectrometry ionization techniques that have been successfully integrated with microfluidics approaches such as continuous-flow system, microchip electrophoresis, droplet microfluidics, digital microfluidics, centrifugal microfluidics, and paper microfluidics. In addition, we discuss recent applications of microfluidics integrated with mass spectrometry in single-cell analysis, compound screening, and the study of microorganisms. Lastly, we provide future outlooks towards online coupling, improving the sensitivity and integration of multi-omics into a single platform.

The limitless applications of single-cell metabolomics

Single-cell metabolomics (SCM) is currently one of the most powerful tools for performing high-throughput metabolic analysis at the cellular level. The power of single-cell metabolomics to determine the metabolic profiles of individual cells makes it very suitable for decoding cell heterogeneity. SCM bears great potential in cell type identification and differentiation within cell colonies. With the development of various equipment and techniques, SCM analysis has become possible for a wide range of biological samples. Many fields have incorporated this cutting-edge analytic tool to generate fruitful findings. This review article pays close attention to the prevalent techniques utilized in SCM and the exciting new findings and applications developed by studies in phytology, neurology, and oncology using SCM.

High-Throughput Analysis of Tissue-Embedded Single Cells by Mass Spectrometry with Bimodal Imaging and Object Recognition

In biological tissues, cell-to-cell variations stem from the stochastic and modulated expression of genes and the varying abundances of corresponding proteins. These variations are then propagated to downstream metabolite products and result in cellular heterogeneity. Mass spectrometry imaging (MSI) is a promising tool to simultaneously provide spatial distributions for hundreds of biomolecules without the need for labels or stains. Technological advances in MSI instrumentation for the direct analysis of tissue-embedded single cells are dominated by improvements in sensitivity, sample pretreatment, and increased spatial resolution but are limited by low throughput. Herein, we introduce a bimodal microscopy imaging system combined with fiber-based laser ablation electrospray ionization (f-LAESI) MSI with improved throughput ambient analysis of tissue-embedded single cells (n > 1000) to provide insight into cellular heterogeneity. Based on automated image analysis, accurate single-cell sampling is achieved by f-LAESI leading to the discovery of cellular phenotypes characterized by differing metabolite levels.

Lipidomic profiling of single mammalian cells by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI)

To better understand cell-to-cell heterogeneity, advanced analytical tools are in a growing demand for elucidating chemical compositions of each cell within a population. However, the progress of single-cell chemical analysis has been restrained by the limitations of small cell volumes and minute cellular concentrations. Here, we present a rapid and sensitive method for investigating the lipid profiles of isolated single cells using infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS). In this work, HeLa cells were dispersed onto a glass slide, and the cellular contents were ionized by IR-MALDESI and measured using a Q-Exactive HF-X mass spectrometer. Importantly, this approach does not require extraction and/or enrichment of analytes prior to MS analysis. Using this approach, 45 distinct lipid species, predominantly phospholipids, were detected and putatively annotated from the single HeLa cells. The proof-of-concept study demonstrates the feasibility and efficacy of IR-MALDESI-MS for rapid lipidomic profiling of single cells, which provides an important basis for future work on differentiation between normal and diseased cells at various developmental states, which can offer new insights into cellular metabolic pathways and pathological processes. Although not yet accomplished, we believe this approach can be readily used as an assessment tool to compare the number of identified species during source evolution and method optimization (intra-laboratory), and also disclose the complementary nature of different direct analytical approaches for the coverage of different types of endogenous analytes (inter-laboratory).Graphical abstract.

Miniaturized Filter-Aided Sample Preparation (MICRO-FASP) Method for High Throughput, Ultrasensitive Proteomics Sample Preparation Reveals Proteome Asymmetry in Xenopus laevis Embryos

We report a miniaturized filter aided sample preparation method (micro-FASP) for low-loss preparation of submicrogram proteomic samples. The method employs a filter with ∼0.1 mm² surface area, reduces the total volume of reagents to <10 μL, and requires only two sample transfer steps. The method was used to generate 25 883 unique peptides and 3069 protein groups from 1000 MCF-7 cells (∼100 ng protein content), and 13 367 peptides and 1895 protein groups were identified from 100 MCF-7 cells (∼10 ng protein content). Single blastomeres from Xenopus laevis embryos at the 50-cell stage (∼200 ng yolk free protein/blastomere) generated 20 943 unique peptides and 2597 protein groups; the proteomic profile clearly differentiated left and right blastomeres and provides strong support for models in which this asymmetry is established early in the embryo. The parallel processing of 12 samples demonstrates reproducible label free quantitation of 1 μg protein homogenates.

Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era?

Tumor cells detach from the primary tumor or metastatic sites and enter the peripheral blood, often causing metastasis. These cells, named Circulating Tumor Cells (CTCs), display the same spatial and temporal heterogeneity as the primary tumor. Since CTCs are involved in tumor progression, they represent a privileged window to disclose mechanisms of metastases, while -omic analyses at the single-cell level allow dissection of the complex relationships between the tumor subpopulations and the surrounding normal tissue. However, in addition to reporting the proof of concept that we can query CTCs to reveal tumor evolution throughout the continuum of treatment for early detection of resistance to therapy, the scientific literature has also been highlighting the disadvantages of CTCs, which hampers a routine use of this approach in clinical practice. To date, an increasing number of CTC technologies, as well as -omics methods, have been employed, mostly lacking strong comparative analyses. The rarity of CTCs also represents a major challenge, because there is no consensus regarding the minimal criteria necessary and sufficient to define an event as CTC; moreover, we cannot often compare data from of one study with that of another. Finally, the availability of an individual tumor profile undermines the traditional histology-based treatment. Applying molecular data for patient benefit implies a collective effort by biologists, bioengineers, and clinicians, to create tools to interpret molecular data and manage precision medicine in every single patient. Herein, we focus on the most recent findings in CTC −omics to learn how far we have come.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.

Quantification of Drug Molecules in Live Single Cells Using the Single-Probe Mass Spectrometry Technique

Analyzing cellular constituents on the single-cell level through mass spectrometry (MS) allows for a wide range of compounds to be studied simultaneously. However, there is a need for quantitative single-cell mass spectrometry (qSCMS) methods to fully characterize drug efficacy from individual cells within cell populations. In this study, qSCMS experiments were carried out using the Single-probe MS technique. The method was successfully used to perform rapid absolute quantifications of the anticancer drug irinotecan in individual mammalian cancer cells under ambient conditions in real time. Traditional liquid chromatography/mass spectrometry (LC/MS) quantifications of irinotecan in cell lysate samples were used to compare the results from Single-probe qSCMS. This technique showcases heterogeneity of drug efficacy on the single-cell level.

Exploring the Fundamental Structures of Life: Non-Targeted, Chemical Analysis of Single Cells and Subcellular Structures

Cells are a basic functional and structural unit of living organisms. Both unicellular communities and multicellular species produce an astonishing chemical diversity, enabling a wide range of divergent functions, yet each cell shares numerous aspects that are common to all living organisms. While there are many approaches for studying this chemical diversity, only a few are non-targeted and capable of analyzing hundreds of different chemicals at cellular resolution. Here, we review the non-targeted approaches used to perform comprehensive chemical analyses, provide chemical imaging information, or obtain high-throughput single-cell profiling data. Single-cell measurement capabilities are rapidly increasing in terms of throughput, limits of detection, and completeness of the chemical analyses; these improvements enable their application to understand ever more complex physiological phenomena, such as learning, memory, and behavior.

Single-cell Analysis with Microfluidic Devices

A microfluidic device as a pivotal research tool in chemistry and life science is now widely recognized. Indeed, microfluidic techniques have made significant advancements in fundamental research, such as the inherent heterogeneity of single-cells studies in cell populations, which would be helpful in understanding cellular molecular mechanisms and clinical diagnosis of major diseases. Single-cell analyses on microdevices have shown great potential for precise fluid control, cell manipulation, and signal output with rapid and high throughput. Moreover, miniaturized devices also have open functions such as integrating with traditional detection methods, for example, optical, electrochemical or mass spectrometry for single-cell analysis. In this review, we summarized recent advances of single-cell analysis based on various microfluidic approaches from different dimensions, such as in vitro, ex vivo, and in vivo analysis of single cells.

Lipid Analysis of 30 000 Individual Rodent Cerebellar Cells Using High-Resolution Mass Spectrometry

Single-cell measurements aid our understanding of chemically heterogeneous systems such as the brain. Lipids are one of the least studied chemical classes, and their cell-to-cell heterogeneity remains largely unexplored. We adapted microscopy-guided single-cell profiling using matrix-assisted laser desorption/ionization ion cyclotron resonance mass spectrometry to profile the lipid composition of over 30 000 individual rat cerebellar cells. We detected 520 lipid features, many of which were found in subsets of cells; Louvain clustering identified 101 distinct groups that can be correlated to neuronal and astrocytic classifications and lipid classes. Overall, the two most common lipids found were [PC(32:0)+H]⁺ and [PC(34:1)+H]⁺, which were present within 98.9 and 89.5% of cells, respectively; lipid signals present in <1% of cells were also detected, including [PC(34:1)+K]⁺ and [PG(40:2(OH))+Na]⁺. These results illustrate the vast lipid heterogeneity found within rodent cerebellar cells and hint at the distinct functional consequences of this heterogeneity.

Recent advances in single-cell analysis by mass spectrometry

Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.

Analysis of Single-Cell RNA-seq Data by Clustering Approaches

Background:

The recently developed single-cell RNA sequencing (scRNA-seq) has attracted a great amount of attention due to its capability to interrogate expression of individual cells, which is superior to traditional bulk cell sequencing that can only measure mean gene expression of a population of cells. scRNA-seq has been successfully applied in finding new cell subtypes. New computational challenges exist in the analysis of scRNA-seq data.

Objective:

We provide an overview of the features of different similarity calculation and clustering methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We would also like to show that feature selection methods are important to improve clustering performance.

Results:

We first described similarity measurement methods, followed by reviewing some new clustering methods, as well as their algorithmic details. This analysis revealed several new questions, including how to automatically estimate the number of clustering categories, how to discover novel subpopulation, and how to search for new marker genes by using feature selection methods.

Conclusion:

Without prior knowledge about the number of cell types, clustering or semisupervised learning methods are important tools for exploratory analysis of scRNA-seq data.</P>

Investigating Stability and Tautomerization of Gossypol-A Spectroscopy Study

The stability of gossypol was investigated by the spectroscopic method. Gossypol was dissolved in three different solvents (CHCl₃, DMSO, and CH₃OH) under different storage conditions (dark and with nitrogen protection, natural light and with nitrogen protection, ambient air conditions) for different time intervals (0 days, 3 days, 5 days, 7 days, 15 days, 30 days, and 45 days) at room temperature. Then, the stability of gossypol was investigated by ¹H NMR, UV-vis, and HPLC-QTOF-MS spectrometry. Results showed that gossypol existed in aldehyde–aldehyde form in chloroform within five days. Then, both aldehyde–aldehyde and lactol–lactol tautomeric forms existed and maintained a stable solution for 45 days. Gossypol dissolved in methanol mainly existed in aldehyde–aldehyde form. Only a tiny amount of lactol–lactol was found in freshly prepared methanol solution. Gossypol was found to only exist in lactol–lactol form between 30–45 days. Gossypol existed in aldehyde–aldehyde, lactol–lactol, and ketol–ketol forms in dimethyl sulfoxide, and there was a competitive relationship between aldehyde–aldehyde and lactol–lactol form during the 45 days. Among all the solvents and conditions studied, gossypol was found to be highly stable in chloroform. Under the tested conditions, the natural light and atmospheric oxygen had little effect on its stability. Although the spectroscopy data seemed to be changed over time in the three different solvents, it was actually due to the tautomeric transformation rather than molecular decomposition.

Genetically Encoded Fluorescent Proteins Enable High-Throughput Assignment of Cell Cohorts Directly from MALDI-MS Images

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) provides a unique in situ chemical profile that can include drugs, nucleic acids, metabolites, lipids, and proteins. MSI of individual cells (of a known cell type) affords a unique insight into normal and disease-related processes and is a prerequisite for combining the results of MSI and other single-cell modalities (e.g. mass cytometry and next-generation sequencing). Technological barriers have prevented the high-throughput assignment of MSI spectra from solid tissue preparations to their cell type. These barriers include obtaining a suitable cell-identifying image (e.g. immunohistochemistry) and obtaining sufficiently accurate registration of the cell-identifying and MALDI-MS images. This study introduces a technique that overcame these barriers by assigning cell type directly from mass spectra. We hypothesized that, in MSI from mice with a defined fluorescent protein expression pattern, the fluorescent protein's molecular ion could be used to identify cell cohorts. A method was developed for the purification of enhanced yellow fluorescent protein (EYFP) from mice. To determine EYFP's molecular mass for MSI studies, we performed intact mass analysis and characterized the protein's primary structure and post-translational modifications through various techniques. MALDI-MSI methods were developed to enhance the detection of EYFP in situ, and by extraction of EYFP's molecular ion from MALDI-MS images, automated, whole-image assignment of cell cohorts was achieved. This method was validated using a well-characterized mouse line that expresses EYFP in motor and sensory neurons and should be applicable to hundreds of commercially available mice (and other animal) strains comprising a multitude of cell-specific fluorescent labels.

Single-cell assay on microfluidic devices

Advances in microfluidic techniques have prompted researchers to study the inherent heterogeneity of single cells in cell populations.

Advances in mass spectrometry based single-cell metabolomics

Single cell metabolomics using mass spectrometry can contribute to understanding biological activities in health and disease.

True one cell chemical analysis: a review

The constantly growing field of True One Cell (TOC) analysis has provided important information on the direct chemical composition of various cells and cellular components.

Analysis of Single Neurons by Perforated Patch Clamp Recordings and MALDI-TOF Mass Spectrometry

Single-cell mass spectrometry has become an established technique to study specific molecular properties such as the neuropeptide complement of identified neurons. Here, we describe a strategy to characterize, by MALDI-TOF mass spectrometry, neurochemical composition of neurons that were identified by their electrophysiological and neuroanatomical characteristics. The workflow for the first time combined perforated patch clamp recordings with dye loading by electroporation for electrophysiological and neuroanatomical characterization as well as chemical profiling of somata by MALDI-TOF mass spectrometry with subsequent immunohistochemistry. To develop our protocol, we used identified central olfactory neurons from the American cockroach Periplaneta americana. First, the combined approach was optimized using a relative homogeneous, well-characterized neuron population of uniglomerular projection neurons, which show acetylcholine esterase immunoreactivity. The general applicability of this approach was verified on local interneurons, which are a diverse neuron population expressing highly differentiated neuropeptidomes. Thus, this study shows that the newly established protocol is suitable to comprehensively analyze electrophysiological, neuroanatomical, and molecular properties of single neurons. We consider this approach an important step to foster single-cell analysis in a wide variety of neuron types.

Optically Guided Single Cell Mass Spectrometry of Rat Dorsal Root Ganglia to Profile Lipids, Peptides and Proteins

The mammalian dorsal root ganglia (DRG) are located on the dorsal roots of the spinal nerves and contain cell bodies of primary sensory neurons. DRG cells have been classified into subpopulations based on their size, morphology, intracellular markers, response to stimuli, and neuropeptides. To understand the connections between DRG chemical heterogeneity and cellular function, we performed optically guided, high-throughput single cell profiling using sequential matrix-assisted laser desorption/ionization mass spectrometry (MS) to detect lipids, peptides, and several proteins in individual DRG cells. Statistical analysis of the resulting mass spectra allows stratification of the DRG population according to cellular morphology and, presumably, major cell types. A subpopulation of small cells contained myelin proteins, which are abundant in Schwann cells, and mass spectra of several larger cells contained peaks matching neurofilament, vimentin, myelin basic protein S, and thymosin beta proteins. Of the over 1000 cells analyzed, approximately 78 % produced putative peptide-rich spectra, allowing the population to be classified into three distinct cell types. Two signals with m/z 4404 and 5487 were exclusively observed in a cell type, but could not be matched to results of our previous liquid chromatography-MS analyses.

Single Cell Neurometabolomics

Metabolomics, the characterization of metabolites and their changes within biological systems, has seen great technological and methodological progress over the past decade. Most metabolomic experiments involve the characterization of the small-molecule content of fluids or tissue homogenates. While these microliter and larger volume metabolomic measurements can characterize hundreds to thousands of compounds, the coverage of molecular content decreases as sample sizes are reduced to the nanoliter and even to the picoliter volume range. Recent progress has enabled the ability to characterize the major molecules found within specific individual cells. Especially within the brain, a myriad of cell types are colocalized, and oftentimes only a subset of these cells undergo changes in both healthy and pathological states. Here we highlight recent progress in mass spectrometry-based approaches used for single cell metabolomics, emphasizing their application to neuroscience research. Single cell studies can be directed to measuring differences between members of populations of similar cells (e.g., oligodendrocytes), as well as characterizing differences between cell types (e.g., neurons and astrocytes), and are especially useful for measuring changes occurring during different behavior states, exposure to diets and drugs, neuronal activity, and disease. When combined with other omics approaches such as transcriptomics, and with morphological and physiological measurements, single cell metabolomics aids fundamental neurochemical studies, has great potential in pharmaceutical development, and should improve the diagnosis and treatment of brain diseases.

Automated Optically Guided System for Chemical Analysis of Single Plant and Algae Cells Using Laser Microdissection/Liquid Vortex Capture/Mass Spectrometry

Current analytical methods are not capable of providing rapid, sensitive, and comprehensive chemical analysis of a wide range of cellular constitutes of single cells (e.g., lipids, metabolites, proteins, etc.) from dispersed cell suspensions and thin tissues. This capability is important for a number of critical applications, including discovery of cellular mechanisms for coping with chemical or environmental stress and cellular response to drug treatment, to name a few. Here we introduce an optically guided platform and methodology for rapid, automated recognition, sampling, and chemical analysis of surface confined individual cells utilizing a novel hybrid laser capture microdissection/liquid vortex capture/mass spectrometry system. The system enabled automated analysis of single cells by reliably detecting and sampling them either through laser ablation from a glass microscope slide or by cutting the entire cell out of a poly(ethylene naphthalate)-coated membrane substrate that the cellular sample is deposited on. Proof of principle experiments were performed using thin tissues of Allium cepa and cultured Euglena gracilis and Phacus cell suspensions as model systems for single cell analysis using the developed method. Reliable, hands-off laser ablation sampling coupled to liquid vortex capture/mass spectrometry analysis was conducted for hundreds of individual Allium cepa cells in connected tissue. In addition, more than 300 individual Euglena gracilis and Phacus cells were analyzed automatically and sampled using laser microdissection sampling with the same liquid vortex capture/mass spectrometry analysis system. Principal component analysis-linear discriminant analysis, applied to each mass spectral dataset, was used to determine the accuracy of differentiation of the different algae cell lines.

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes. Graphical Abstract ᅟ.

Profiling of Microbial Colonies for High-Throughput Engineering of Multistep Enzymatic Reactions via Optically Guided Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS) imaging has been used for rapid phenotyping of enzymatic activities, but is mainly limited to single-step conversions. Herein we report a label-free method for high-throughput engineering of multistep biochemical reactions based on optically guided MALDI-ToF MS analysis of bacterial colonies. The bacterial cells provide containment of multiple enzymes and access to substrates and cofactors via metabolism. Automated MALDI-ToF MS acquisition from randomly distributed colonies simplifies procedures to prepare strain libraries without liquid handling. MALDI-ToF MS profiling was utilized to screen both substrate and enzyme libraries for natural product biosynthesis. Computational algorithms were developed to process and visualize the resulting mass spectral data sets. For analogues of the peptidic antibiotic plantazolicin, multivariate analyses by t-distributed stochastic neighbor embedding were used to group similar spectra for rapid identification of nonisobaric variants. After MALDI-ToF MS screening, follow-up analyses using high-resolution MS and tandem MS were readily performed on the same sample target. Separately, relative ion intensities of rhamnolipid congeners with various lipid moieties were evaluated to engineer enzymatic specificity. The glycolipid profiles of each colony were overlaid with optical images to facilitate the recovery of desirable mutants. For both the antibiotic and rhamnolipid cases, large populations of colonies were rapidly surveyed at the molecular level, providing information-rich insights not easily obtained with traditional screening assays. Utilizing standard microbiological techniques with routine microscopy and MALDI-ToF MS instruments, this simple yet effective workflow is applicable for a wide range of screening campaigns targeting multistep enzymatic reactions.

MALDI MS Guided Liquid Microjunction Extraction for Capillary Electrophoresis-Electrospray Ionization MS Analysis of Single Pancreatic Islet Cells

The ability to characterize chemical heterogeneity in biological structures is essential to understanding cellular-level function in both healthy and diseased states, but these variations remain difficult to assess using a single analytical technique. While mass spectrometry (MS) provides sufficient sensitivity to measure many analytes from volume-limited samples, each type of mass spectrometric analysis uncovers only a portion of the complete chemical profile of a single cell. Matrix-assisted laser desorption/ionization (MALDI) MS and capillary electrophoresis electrospray ionization (CE-ESI)-MS are complementary analytical platforms frequently utilized for single-cell analysis. Optically guided MALDI MS provides a high-throughput assessment of lipid and peptide content for large populations of cells, but is typically nonquantitative and fails to detect many low-mass metabolites because of MALDI matrix interferences. CE-ESI-MS allows quantitative measurements of cellular metabolites and increased analyte coverage, but has lower throughput because the electrophoretic separation is relatively slow. In this work, the figures of merit for each technique are combined via an off-line method that interfaces the two MS systems with a custom liquid microjunction surface sampling probe. The probe is mounted on an xyz translational stage, providing 90.6 ± 0.6% analyte removal efficiency with a spatial targeting accuracy of 42.8 ± 2.3 μm. The analyte extraction footprint is an elliptical area with a major diameter of 422 ± 21 μm and minor diameter of 335 ± 27 μm. To validate the approach, single rat pancreatic islet cells were rapidly analyzed with optically guided MALDI MS to classify each cell into established cell types by their peptide content. After MALDI MS analysis, a majority of the analyte remains for follow-up measurements to extend the overall chemical coverage. Optically guided MALDI MS was used to identify individual pancreatic islet α and β cells, which were then targeted for liquid microjunction extraction. Extracts from single α and β cells were analyzed with CE-ESI-MS to obtain qualitative information on metabolites, including amino acids. Matching the molecular masses and relative migration times of the extracted analytes and related standards allowed identification of several amino acids. Interestingly, dopamine was consistently detected in both cell types. The results demonstrate the successful interface of optical microscopy-guided MALDI MS and CE-ESI-MS for sequential chemical profiling of individual, mammalian cells.

Single-Cell Mass Spectrometry of Metabolites Extracted from Live Cells by Fluidic Force Microscopy

Single-cell metabolite analysis provides valuable information on cellular function and response to external stimuli. While recent advances in mass spectrometry reached the sensitivity required to investigate metabolites in single cells, current methods commonly isolate and sacrifice cells, inflicting a perturbed state and preventing complementary analyses. Here, we propose a two-step approach that combines nondestructive and quantitative withdrawal of intracellular fluid with subpicoliter resolution using fluidic force microscopy, followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The developed method enabled the detection and identification of 20 metabolites recovered from the cytoplasm of individual HeLa cells. The approach was further validated in ¹³C-glucose feeding experiments, which showed incorporation of labeled carbon atoms into different metabolites. Metabolite sampling, followed by mass spectrometry measurements, enabled the preservation of the physiological context and the viability of the analyzed cell, providing opportunities for complementary analyses of the cell before, during, and after metabolite analysis.