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

Concentric Hybrid Nanoelectrospray Ionization-Atmospheric Pressure Chemical Ionization Source for High-Coverage Mass Spectrometry Analysis of Single-Cell Metabolomics

High-coverage mass spectrometry analysis of single-cell metabolomics remains challenging due to the extremely low abundance and wide polarity of metabolites and ultra-small volume in single cells. Herein, a novel concentric hybrid ionization source, nanoelectrospray ionization-atmospheric pressure chemical ionization (nanoESI-APCI), is ingeniously designed to detect polar and nonpolar metabolites simultaneously in single cells. The source is constructed by inserting a pulled glass capillary coaxially into a glass tube that acts as a dielectric barrier layer. Benefitting from the integrated advantages of nanoESI and APCI, its limit of detection is improved by one order of magnitude to 10 pg mL-1. After the operational parameter optimization, 254 metabolites detected in nanoESI-APCI are tentatively identified from a single cell, and 82 more than those in nanoESI. The developed nanoESI-APCI is successively applied to study the metabolic heterogeneity of human hepatocellular carcinoma tissue microenvironment united with laser capture microdissection (LCM), the discrimination of cancer cell types and subtypes, the metabolic perturbations to glucose starvation in MCF7 cells and the metabolic regulation of cancer stem cells. These results demonstrated that the nanoESI-APCI not only opens a new avenue for high-coverage and high-sensitivity metabolomics analysis of single cell, but also facilitates spatially resolved metabolomics study coupled with LCM.

Lipid fingerprint-based histology accurately classifies nevus, primary melanoma, and metastatic melanoma samples

Probably, the most important factor for the survival of a melanoma patient is early detection and precise diagnosis. Although in most cases these tasks are readily carried out by pathologists and dermatologists, there are still difficult cases in which no consensus among experts is achieved. To deal with such cases, new methodologies are required. Following this motivation, we explore here the use of lipid imaging mass spectrometry as a complementary tool for the aid in the diagnosis. Thus, 53 samples (15 nevus, 24 primary melanomas, and 14 metastasis) were explored with the aid of a mass spectrometer, using negative polarity. The rich lipid fingerprint obtained from the samples allowed us to set up an artificial intelligence-based classification model that achieved 100% of specificity and precision both in training and validation data sets. A deeper analysis of the image data shows that the technique reports important information on the tumor microenvironment that may give invaluable insights in the prognosis of the lesion, with the correct interpretation.

Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution

Metabolic status is crucial for stem cell functions; however, the metabolic heterogeneity of endogenous stem cells has never been directly assessed. Here, we develop a platform for high-throughput single-cell metabolomics (hi-scMet) of hematopoietic stem cells (HSCs). By combining flow cytometric isolation and nanoparticle-enhanced laser desorption/ionization mass spectrometry, we routinely detected >100 features from single cells. We mapped the single-cell metabolomes of all hematopoietic cell populations and HSC subpopulations with different division times, detecting 33 features whose levels exhibited trending changes during HSC proliferation. We found progressive activation of the oxidative pentose phosphate pathway (OxiPPP) from dormant to active HSCs. Genetic or pharmacological interference with OxiPPP increased reactive oxygen species level in HSCs, reducing HSC self-renewal upon oxidative stress. Together, our work uncovers the metabolic dynamics during HSC proliferation, reveals a role of OxiPPP for HSC activation, and illustrates the utility of hi-scMet in dissecting metabolic heterogeneity of immunophenotypically defined cell populations.

Non-Mass Spectrometric Targeted Single-Cell Metabolomics

Metabolic assays serve as pivotal tools in biomedical research, offering keen insights into cellular physiological and pathological states. While mass spectrometry (MS)-based metabolomics remains the gold standard for comprehensive, multiplexed analyses of cellular metabolites, innovative technologies are now emerging for the targeted, quantitative scrutiny of metabolites and metabolic pathways at the single-cell level. In this review, we elucidate an array of these advanced methodologies, spanning synthetic and surface chemistry techniques, imaging-based methods, and electrochemical approaches. We summarize the rationale, design principles, and practical applications for each method, and underscore the synergistic benefits of integrating single-cell metabolomics (scMet) with other single-cell omics technologies. Concluding, we identify prevailing challenges in the targeted scMet arena and offer a forward-looking commentary on future avenues and opportunities in this rapidly evolving field.

Rapid, One-Step Sample Processing for Label-Free Single-Cell Proteomics

Sample preparation for single-cell proteomics is generally performed in a one-pot workflow with multiple dispensing and incubation steps. These hours-long processes can be labor intensive and lead to long sample-to-answer times. Here we report a sample preparation method that achieves cell lysis, protein denaturation, and digestion in 1 h using commercially available high-temperature-stabilized proteases with a single reagent dispensing step. Four different one-step reagent compositions were evaluated, and the mixture providing the highest proteome coverage was compared to the previously employed multistep workflow. The one-step preparation increases proteome coverage relative to the previous multistep workflow while minimizing labor input and the possibility of human error. We also compared sample recovery between previously used microfabricated glass nanowell chips and injection-molded polypropylene chips and found the polypropylene provided improved proteome coverage. Combined, the one-step sample preparation and the polypropylene substrates enabled the identification of an average of nearly 2400 proteins per cell using a standard data-dependent workflow with Orbitrap mass spectrometers. These advances greatly simplify sample preparation for single-cell proteomics and broaden accessibility with no compromise in terms of proteome coverage.

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.

Single-filament imaging mass spectrometry lipidomics in Arthrospira platensis

RATIONALE

Elucidating intra-organismal biochemical and lipid organization in photosynthetic biological cell factories of filamentous cyanobacteria, such as Arthrospira platensis (Spirulina), is important for tracking physiological response mechanisms during growth. Little is known about the filaments' biochemical organization and cellular structure and no label-free imaging techniques exist that provide molecular mapping.

METHODS

We applied ultrahigh-resolution mass spectrometry (MS) with matrix-assisted laser desorption ionization (MALDI) imaging to immobilized Spirulina filaments to investigate the localization of lipids across distinct physiological regions. We optimized matrix selection and deposition methods with the goal of facilitating high spatial, and intra-filament, resolution using untargeted multivariate statistical spectral deconvolution across MS pixels.

RESULTS

Our results demonstrate an improved two-step matrix application with an optimized procedure for intra-organismal lipid profiling to improve analyte sensitivity and achieve higher spatial resolution. We evaluate several conventional matrices, namely 2,5-dihydroxybenzoic acid (DHB), superDHB (sDHB), 1,5-diaminonaphthalene (DAN), and a 50:50 mix of DHB and sDHB, and compare delineation and pixel-based elucidation of intra-filament lipidomics. We identified a total of 1626 features that could be putatively assigned a lipid-like formula based on database query and 46 unique features, with associated lipid assignments that were significantly distinct in their intra-filament location.

CONCLUSIONS

MALDI imaging MS with untargeted statistical spectral deconvolution was used to visualize intra-filament lipidomics organization in Spirulina filaments. Improvements in matrix deposition, including sequential sublimation and pneumatic spraying, increased signal abundance at high spatial resolution and allowed for identification of distinct lipid composition regions. This work outlines a methodology that may be used for micro-ecological untargeted molecular phenotyping.

A roadmap to high-resolution standard microcoil MAS NMR spectroscopy for metabolomics

Current microcoil probe technology has emerged as a significant advancement in NMR applications to biofluids research. It has continued to excel as a hyphenated tool with other prominent microdevices, opening many new possibilities in multiple omics fields. However, this does not hold for biological samples such as intact tissue or organisms, due to the considerable challenges of incorporating the microcoil in a magic-angle spinning (MAS) probe without relinquishing the high-resolution spectral data. Not until 2012 did a microcoil MAS probe show promise in profiling the metabolome in a submilligram tissue biopsy with spectral resolution on par with conventional high-resolution MAS (HR-MAS) NMR. This result subsequently triggered a great interest in the possibility of NMR analysis with microgram tissues and striving toward the probe development of "high-resolution" capable microcoil MAS NMR spectroscopy. This review gives an overview of the issues and challenges in the probe development and summarizes the advancements toward metabolomics.

A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles

Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.

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.

Organic Mass Cytometry Discriminating Cycle Stages of Single Cells with Small Molecular Indicators

Cell cycle is a significant factor toward cellular heterogeneity, so cell cycle discrimination is a precise measurement on the top of single-cell analysis. Single-cell analysis based on organic mass spectrometry has received great attention for its unique ability to profile single-cell metabolome, but the influence of cell cycle on cellular metabolome heterogeneity has been overlooked until now due to the lack of a compatible cell cycle discrimination method. Here, we report a robust protocol based on the combination of three small molecular indicators, consisting of two small molecular labels (Hoechst and docetaxel) and one cellular endogenous compound [phosphocholine (34:1)], to discriminate single cells at different cycle stages in real time by organic mass cytometry. More than 6000 HeLa cells were acquired by an improved organic mass cytometry system to build a cell cycle differentiation model. The model successfully discriminated single HeLa cells, SCC7, and Hep G2 cells, at G₀/G₁, S, and G₂/M stages with larger than 85% sensitivity and larger than 89% specificity. Along with cell cycle discrimination, obvious heterogeneity of amino acids, nucleotides, energy metabolic intermediates, and phospholipids was observed among single cells at different cycle stages by this protocol, further demonstrating the necessity of cell cycle discrimination for cellular metabolome heterogeneity research and the potential of more endogenous small molecular compounds for cell cycle discrimination.

Mouse Pancreatic Peptide Hormones Probed at the Sub-Single-Islet Level: The Effects of Acute Corticosterone Treatment

We combine liquid chromatography coupled with ion mobility spectrometry-mass spectrometry to elucidate how short exposure to corticosterone (Cort) alters the output of mouse pancreatic islet hormones. The workflow enables the robust separation of mouse insulin 1 (Ins1) and insulin 2 (Ins2) and the detection of major islet hormones in a homogenate equivalent to 100-150 islet cells. We show that Ins2 has a unique structure and is degraded much faster than Ins1. Further investigation indicates that Ins2 may populate both T and R states, whereas Ins1 may not. The assemblies of Ins1's B-chain also introduce more structural heterogeneity than Ins2. Collectively, these features account for their unique degradation profiles, the diabetes risk associated with Ins1, and the protective effect of Ins2. In the same experiments, we observe that the ratio of amylin to Ins1 increased significantly in Cort-treated mice (15:1) compared to the control mice (42:1), correlating well with β-cell proliferation observed in immunoassays on the same animal model. We observe no increase in intact full-length insulin levels but more of the truncated forms, indicating that enzymatic activity is accelerated. Our data provide a molecular basis for reduced insulin action induced by Cort and connections between insulin turnover and insulin resistance.

Single-cell omics: A new direction for functional genetic research in human diseases and animal models

Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

Microfluidic device integrating single-cell extraction and electrical lysis for mass spectrometry detection of intracellular compounds

Analysis of cellular composition and metabolism at a single-cell resolution allows gaining more information about complex relationships of cells within tissues or whole living organisms by resolving the variance stemming from the cellular heterogeneity. Mass spectrometry (MS) is a perfect analytical tool satisfying the demanding requirements of detecting and identifying compounds present in such ultralow-volume samples of high chemical complexity. However, the method of sampling and sample ionization is crucial in obtaining relevant information. In this work, we present a microfluidic sampling platform that integrates single-cell extraction from MS-incompatible media with electrical cell lysis and nanoESI-MS analysis of human erythrocytes. Hemoglobin alpha and beta chains (300 amol/cell) were successfully identified in mass spectra of single-erythrocyte lysates.

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.

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows

The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.

Single-Cell Mass Spectrometry of Metabolites and Proteins for Systems and Functional Biology

Molecular composition is intricately intertwined with cellular function, and elucidation of this relationship is essential for understanding life processes and developing next-generational therapeutics. Technological innovations in capillary electrophoresis (CE) and liquid chromatography (LC) mass spectrometry (MS) provide previously unavailable insights into cellular biochemistry by allowing for the unbiased detection and quantification of molecules with high specificity. This chapter presents our validated protocols integrating ultrasensitive MS with classical tools of cell, developmental, and neurobiology to assess the biological function of important biomolecules. We use CE and LC MS to measure hundreds of metabolites and thousands of proteins in single cells or limited populations of tissues in chordate embryos and mammalian neurons, revealing molecular heterogeneity between identified cells. By pairing microinjection and optical microscopy, we demonstrate cell lineage tracing and testing the roles the dysregulated molecules play in the formation and maintenance of cell heterogeneity and tissue specification in frog embryos (Xenopus laevis). Electrophysiology extends our workflows to characterizing neuronal activity in sections of mammalian brain tissues. The information obtained from these studies mutually strengthen chemistry and biology and highlight the importance of interdisciplinary research to advance basic knowledge and translational applications forward.

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.

Quantification of Biocatalytic Transformations by Single Microbial Cells Enabled by Tailored Integration of Droplet Microfluidics and Mass Spectrometry

Improving the performance of chemical transformations catalysed by microbial biocatalysts requires a deep understanding of cellular processes. While the cellular heterogeneity of cellular characteristics, such as the concentration of high abundant cellular content, is well studied, little is known about the reactivity of individual cells and its impact on the chemical identity, quantity, and purity of excreted products. Biocatalytic transformations were monitored chemically specific and quantifiable at the single-cell level by integrating droplet microfluidics, cell imaging, and mass spectrometry. Product formation rates for individual Saccharomyces cerevisiae cells were obtained by i) incubating nanolitre-sized droplets for product accumulation in microfluidic devices, ii) an imaging setup to determine the number of cells in the droplets, and iii) electrospray ionisation mass spectrometry for reading the chemical contents of individual droplets. These findings now enable the study of whole-cell biocatalysis at single-cell resolution.

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.

Portable paper-in-tip spray ionization for the direct mass spectrometric analysis of target analytes in biofluid samples

Portable sampling of target analytes in complex biofluid samples makes mass spectrometric analysis more efficient. This study reports the development of paper-in-tip spray ionization for solid-phase microextraction and in situ electrospray of therapeutic drugs and proteins in complex biological matrices using a piece of hydrophobic paper substrate. This technique possesses a long (more than 8 min) and stable spray duration with only 20 μL of spray solvent. The entire analytical process for a complex sample can be completed in less than 1.5 min and enables high sensitivity (picogram-per-milliliter level) and high quantitation precision.

Computational Methods for Single-Cell Imaging and Omics Data Integration

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

Nanoscale Three-Dimensional Imaging of Drug Distributions in Single Cells via Laser Desorption Post-Ionization Mass Spectrometry

Exploring the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution with mass spectrometry imaging (MSI) techniques is crucial in cellular biology, yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. Herein, a microlensed fiber laser desorption post-ionization time-of-flight mass spectrometer (MLF-LDPI-TOFMS) was developed for the 3D imaging of two anticancer drugs within single cells at a 500 × 500 × 500 nm³ voxel resolution. Nanoscale desorption was obtained with a microlensed fiber (MLF), and a 157 nm post-ionization laser was introduced to enhance the ionization yield. Furthermore, a new type of alignment method for 3D reconstruction was developed on the basis of our embedded uniform circular polystyrene microspheres (PMs). Our findings demonstrate that this 3D imaging technique has the potential to provide information about the 3D distributions of specific molecules at the nanoscale level.

Stable Isotopes for Tracing Cardiac Metabolism in Diseases

Although metabolic remodeling during cardiovascular diseases has been well-recognized for decades, the recent development of analytical platforms and mathematical tools has driven the emergence of assessing cardiac metabolism using tracers. Metabolism is a critical component of cellular functions and adaptation to stress. The pathogenesis of cardiovascular disease involves metabolic adaptation to maintain cardiac contractile function even in advanced disease stages. Stable-isotope tracer measurements are a powerful tool for measuring flux distributions at the whole organism level and assessing metabolic changes at a systems level in vivo. The goal of this review is to summarize techniques and concepts for in vivo or ex vivo stable isotope labeling in cardiovascular research, to highlight mathematical concepts and their limitations, to describe analytical methods at the tissue and single-cell level, and to discuss opportunities to leverage metabolic models to address important mechanistic questions relevant to all patients with cardiovascular disease.

Nanothorn Filter-Facilitated Online Cell Lysis for Rapid and Deep Intracellular Profiling by Single-Cell Mass Spectrometry

Mass spectrometry combined with flow cytometry is emerging for high-throughput single-cell metabolite analysis but still has problems with limited intracellular information coverage. Here, we show a simple and efficient all-in-one system integrating cell injection, cell extraction, online cell lysis, analyte ionization, and mass spectrometric detection for rapid single-HeLa-cell screening with in-depth profiling of cellular metabolites and drugs. Zinc oxide nanothorn-decorated filters with three bore sizes (5.22, 8.36, and 16.75 μm) were fabricated for efficient online lysis of the cell membrane (even nuclear membrane) to facilitate intracellular analyte release and demonstrated to have a size effect for potential subcellular discrimination. The two smaller-bore filters gave 2-11-fold improvements in signal response for representative intracellular metabolites, such as adenosine, glutamine, and leucine/isoleucine. Especially, the smallest-bore filter enabled successful detection of the metabolites in the nucleus, including tetrahydrobiopterin and cyclic guanosine monophosphate. The developed all-in-one system was explored to monitor the uptake of four anticancer drugs, including 5-fluorouracil, doxorubicin, gambogic acid, and paclitaxel in single cells, and further to investigate the drug uptake trends at the subcellular level. The all-in-one system integrates the merits of high-throughput single-cell screening and in-depth intracellular information profiling and is promising for high-coverage single-cell metabolome analysis to serve cell biology research and cancer research.

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.

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.

Mass spectrometry based high-throughput bioanalysis of low molecular weight compounds: are we ready to support personalized medicine?

Liquid chromatography coupled to mass spectrometry (LC-MS) is the gold standard in bioanalysis for the development of quantitative assays to support drug development or therapeutic drug monitoring. High-throughput and low-cost gene sequencing have enabled a paradigm shift from one treatment fits all to personalized medicine (PM). However, gene monitoring provides only partial information about the health state. The full picture requires the combination of gene monitoring with the screening of exogenous compounds, metabolites, lipids, and proteins. This critical review discusses how mass spectrometry–based technologies and approaches including separation sciences, ambient ionization, and ion mobility are/could be used to support high-throughput bioanalysis of endogenous end exogenous low molecular weight compounds. It includes also various biological sample types (from blood to expired air), and various sample preparation techniques.

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.

In Vivo Subcellular Mass Spectrometry Enables Proteo-Metabolomic Single-Cell Systems Biology in a Chordate Embryo Developing to a Normally Behaving Tadpole (X. laevis)*

We report the development of in vivo subcellular high-resolution mass spectrometry (HRMS) for proteo-metabolomic molecular systems biology in complex tissues. With light microscopy, we identified the left-dorsal and left-ventral animal cells in cleavage-stage non-sentient Xenopus laevis embryos. Using precision-translated fabricated microcapillaries, the subcellular content of each cell was double-probed, each time swiftly (<5 s/event) aspirating <5 % of cell volume (≈10 nL). The proteins and metabolites were analyzed by home-built ultrasensitive capillary electrophoresis electrospray ionization employing orbitrap or time-of-flight HRMS. Label-free detection of ≈150 metabolites (57 identified) and 738 proteins found proteo-metabolomic networks with differential quantitative activities between the cell types. With spatially and temporally scalable sampling, the technology preserved the integrity of the analyzed cells, the neighboring cells, and the embryo. 95 % of the analyzed embryos developed into sentient tadpoles that were indistinguishable from their wild-type siblings based on anatomy and visual function in a background color preference assay.

Neuropeptide Localization in Lymnaea stagnalis: From the Central Nervous System to Subcellular Compartments

Due to the relatively small number of neurons (few tens of thousands), the well-established multipurpose model organism Lymnaea stagnalis, great pond snail, has been extensively used to study the functioning of the nervous system. Unlike the more complex brains of higher organisms, L. stagnalis has a relatively simple central nervous system (CNS) with well-defined circuits (e.g., feeding, locomotion, learning, and memory) and identified individual neurons (e.g., cerebral giant cell, CGC), which generate behavioral patterns. Accumulating information from electrophysiological experiments maps the network of neuronal connections and the neuronal circuits responsible for basic life functions. Chemical signaling between synaptic-coupled neurons is underpinned by neurotransmitters and neuropeptides. This review looks at the rapidly expanding contributions of mass spectrometry (MS) to neuropeptide discovery and identification at different granularity of CNS organization. Abundances and distributions of neuropeptides in the whole CNS, eleven interconnected ganglia, neuronal clusters, single neurons, and subcellular compartments are captured by MS imaging and single cell analysis techniques. Combining neuropeptide expression and electrophysiological data, and aided by genomic and transcriptomic information, the molecular basis of CNS-controlled biological functions is increasingly revealed.

Quantitative Approach for Protein Analysis in Small Cell Ensembles by an Integrated Microfluidic Chip with MALDI Mass Spectrometry

Increasing evidence has demonstrated that cells are individually heterogeneous. Advancing the technologies for single-cell analysis will improve our ability to characterize cells, study cell biology, design and screen drugs, and aid cancer diagnosis and treatment. Most current single-cell protein analysis approaches are based on fluorescent antibody-binding technology. However, this technology is limited by high background and cross-talk of multiple tags introduced by fluorescent labels. Stable isotope labels used in mass cytometry can overcome the spectral overlap of fluorophores. Nevertheless, the specificity of each antibody and heavy-metal-tagged antibody combination must be carefully validated to ensure detection of the intended target. Thus, novel single-cell protein analysis methods without using labels are urgently needed. Moreover, the labeling approach targets already known motifs, hampering the discovery of new biomarkers relevant to single-cell population variation. Here, we report a combined microfluidic and matrix-assisted laser desorption and ionization (MALDI) mass spectrometric approach for the analysis of protein biomarkers suitable for small cell ensembles. All necessary steps for cell analysis including cell lysis, protein capture, and digestion as well as MALDI matrix deposition are integrated on a microfluidic chip prior to the downstream MALDI-time-of-flight (TOF) detection. For proof of principle, this combined method is used to assess the amount of Bcl-2, an apoptosis regulator, in metastatic breast cancer cells (MCF-7) by using an isotope-labeled peptide as an internal standard. The proposed approach will eventually provide a new means for proteome studies in small cell ensembles with the potential for single-cell analysis and improve our ability in disease diagnosis, drug discovery, and personalized therapy.

Analysis of Peptide Stereochemistry in Single Cells by Capillary Electrophoresis-Trapped Ion Mobility Spectrometry Mass Spectrometry

Single cell analysis strives to probe molecular heterogeneity in morphologically similar cell populations through quantitative or qualitative measurements of genetic, proteomic, or metabolic products. Here, we applied mass analysis of single neurons to investigate cell-cell signaling peptides. The multiplicity of endogenous cell-cell signaling peptides is a common source of chemical diversity among cell populations. Certain peptides can undergo post-translational isomerization of select residues, which has important physiological consequences. The limited number of single cell analysis techniques that are sensitive to peptide stereochemistry make it challenging to study isomerization at the individual cell level. We performed capillary electrophoresis (CE) with mass spectrometry (MS) detection to characterize the peptide content of single cells. Using complementary trapped ion mobility spectrometry (TIMS) separations, we measured the stereochemical configurations of three neuropeptide gene products derived from the pleurin precursor in individual neurons (N = 3) isolated from the central nervous system of Aplysia californica. An analysis of the resultant mobility profiles indicated >98% of the detectable pleurin-derived peptides exist as the nonisomerized, all-l forms in individual neuron cell bodies. However, we observed 44% of the Plrn2 peptide from the pleurin precursor was present as the isomerized, d-residue-containing form in the nerve tissue. These findings demonstrate an unusual distribution of isomerized peptides in A. californica and establish CE-TIMS MS as a powerful analytical tool for investigating peptide stereochemistry at the single cell level.

Electrofluorochromic Imaging Analysis of Glycan Expression on Living Single Cell with Bipolar Electrode Arrays

The in situ glycan profiling of a single tumor cell plays an important role in personalized cancer treatment. Herein, an integrated microfluidic system was designed for living single-cell trapping and real-time monitoring of galactosyl expression on the surface, combining closed bipolar electrode (BPE) arrays and electrofluorochromic (EFC) imaging. Galactosyl groups on human liver cancer HepG2 cells were used as the model analysts, galactose oxidase (GAO) could selectively oxidize hydroxyl sites of galactosyl groups on the cell surface to aldehydes, and then biotin hydrazide (BH) was used to label the aldehydes by aniline-catalyzed hydrazone ligation. With the biotin-avidin system, nanoprobes were finally introduced to the galactosyl groups on the cell surface with avidin as a bridge, which was prepared by simultaneously assembling ferrocene-DNA (Fc-DNA) and biotin-DNA (Bio-DNA) on gold nanoparticles (AuNPs) due to their large surface area and excellent electrical conductivity. After a labeled single cell was captured in the anodic microchannel, the Fc groups attached on the cell surface were oxidized under suitable potential, and the nonfluorescent resazurin on the cathode was correspondingly reduced to produce highly fluorescent resorufin, collected by fluorescence confocal microscope. The combination of EFC imaging and BPE realized monitoring galactosyl group expression of 5.0 × 10⁸ molecules per cell. Furthermore, the proposed platform had the ability to distinguish a single cancer cell from a normal cell according to the expression level of galactosyl groups and to dynamically monitor the galactosyl group variation on the cell surface, providing a simple and accessible method for the single-cell analysis.

Cell properties assessment using optimized dielectrophoresis-based cell stretching and lumped mechanical modeling

Cells mechanical property assessment has been a promising label-free method for cell differentiation. Several methods have been proposed for single-cell mechanical properties analysis. Dielectrophoresis (DEP) is one method used for single-cell mechanical property assessment, cell separation, and sorting. DEP method has overcome weaknesses of other techniques, including compatibility with microfluidics, high throughput assessment, and high accuracy. However, due to the lack of a general and explicit model for this method, it has not been known as an ideal cell mechanical property evaluation method. Here we present an explicit model using the most general electromagnetic equation (Maxwell Stress Tensor) for single-cell mechanical evaluation based on the DEP method. For proof of concept, we used the proposed model for differentiation between three different types of cells, namely erythrocytes, peripheral blood mononuclear cells (PBMC), and an epithelial breast cancer cells line (T-47D). The results show that, by a lumped parameter that depends on cells' mechanical and electrical properties, the proposed model can successfully distinguish between the mentioned cell types that can be in a single blood sample. The proposed model would open up the chance to use a mechanical assessment method for cell searching in parallel with other methods.

Haematopoietic ageing through the lens of single-cell technologies

Human lifespan is now longer than ever and, as a result, modern society is getting older. Despite that, the detailed mechanisms behind the ageing process and its impact on various tissues and organs remain obscure. In general, changes in DNA, RNA and protein structure throughout life impair their function. Haematopoietic ageing refers to the age-related changes affecting a haematopoietic system. Aged blood cells display different functional aberrations depending on their cell type, which might lead to the development of haematologic disorders, including leukaemias, anaemia or declining immunity. In contrast to traditional bulk assays, which are not suitable to dissect cell-to-cell variation, single-cell-level analysis provides unprecedented insight into the dynamics of age-associated changes in blood. In this Review, we summarise recent studies that dissect haematopoietic ageing at the single-cell level. We discuss what cellular changes occur during haematopoietic ageing at the genomic, transcriptomic, epigenomic and metabolomic level, and provide an overview of the benefits of investigating those changes with single-cell precision. We conclude by considering the potential clinical applications of single-cell techniques in geriatric haematology, focusing on the impact on haematopoietic stem cell transplantation in the elderly and infection studies, including recent COVID-19 research.

Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues

Little is known about the metabolic regulation of rare cell populations because most metabolites are hard to detect in small numbers of cells. We previously described a method for metabolomic profiling of flow cytometrically isolated hematopoietic stem cells (HSCs) that detects 60 metabolites in 10,000 cells (Agathocleous et al., 2017). Here we describe a new method involving hydrophilic liquid interaction chromatography and high-sensitivity orbitrap mass spectrometry that detected 160 metabolites in 10,000 HSCs, including many more glycolytic and lipid intermediates. We improved chromatographic separation, increased mass resolution, minimized ion suppression, and eliminated sample drying. Most metabolite levels did not significantly change during cell isolation. Mouse HSCs exhibited increased glycerophospholipids relative to bone marrow cells and methotrexate treatment altered purine biosynthesis. Circulating human melanoma cells were depleted for purine intermediates relative to subcutaneous tumors, suggesting decreased purine synthesis during metastasis. These methods facilitate the routine metabolomic analysis of rare cells from tissues.