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

Rapid Screening of Biomarkers in KYSE-150 Cells Exposed to Polycyclic Aromatic Hydrocarbons via Inkjet Printing Single-Cell Mass Spectrometry

Single-cell analysis by mass spectrometry (MS) is emerging as a powerful tool that not only contributes to cellular heterogeneity but also offers an unprecedented opportunity to predict pathology onset and facilitates novel biomarker discovery. However, the development of single-cell MS analysis techniques with a focus on sample extraction, separation, and ionization methods for volume-limited samples and complexity of cellular samples are still a big challenge. In this study, we present a high-throughput approach to inkjet drop on demand printing single-cell MS for rapid screening of biomarkers of polycyclic aromatic hydrocarbon (PAH) exposure at the KYSE-150 cell, aiming to elucidate the pathogenesis of PAH-induced esophageal cancer. With an analytical bulk KYSE-150 cell throughput of up to 51 cells per minute, the method provides a new opportunity for simultaneous single-cell analysis of multiple biomarkers. We screened 930 characteristic ions from 3,683 detected peak signals and identified 91 distinctive molecules that exhibited significant differences under various concentrations of PAH exposure. These molecules have potential as clinical diagnostic biomarkers. Additionally, the current study identifies specific biomarkers that behave completely opposite in single-cell and multicell lipidomics as the concentration of PAH changes. These biomarkers potentially subdivide KYSE-150 cells into PAH-sensitive and PAH-insensitive types, providing a basis for revealing PAH toxicity and disease pathogenesis from the heterogeneity of cellular metabolism.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Direct sub-atmospheric pressure ionization mass spectrometry: Evaporation/sublimation-driven ionization is amazing, fundamentally, and practically

This paper covers direct sub-atmospheric pressure ionization mass spectrometry (MS). The discovery, applications, and mechanistic aspects of novel ionization processes for use in MS that are not based on the high-energy input from voltage, laser, and/or high temperature but on sublimation/evaporation within a region linking a higher to lower pressure and modulated by heat and collisions, are discussed, including how this new reality has guided a series of discoveries, instrument developments, and commercialization. A research focus, inter alia, is on how best to understand, improve, and use these novel ionization processes, which convert volatile and nonvolatile compounds from solids (sublimation) or liquids (evaporation) into gas-phase ions for analysis by MS providing reproducible, accurate, sensitive, and prompt results. Our perception on how these unprecedented versus traditional ionization processes/methods relate to each other, how they can be made to coexist on the same mass spectrometer, and an outlook on new and expanded applications (e.g., clinical, portable, fast, safe, and autonomous) is presented, and is based on ST's Opening lecture presentation at the Nordic Mass spectrometry Conference, Geilo, Norway, January 2023. Focus will be on matrix-assisted ionization (MAI) and solvent-assisted ionization (SAI) MS covering the period from 2010 to 2023; a potential paradigm shift in the making.

Microfluidic Impedance Cytometry Enabled One-Step Sample Preparation for Efficient Single-Cell Mass Spectrometry

Single-cell mass spectrometry (MS) is significant in biochemical analysis and holds great potential in biomedical applications. Efficient sample preparation like sorting (i.e., separating target cells from the mixed population) and desalting (i.e., moving the cells off non-volatile salt solution) is urgently required in single-cell MS. However, traditional sample preparation methods suffer from complicated operation with various apparatus, or insufficient performance. Herein, a one-step sample preparation strategy by leveraging label-free impedance flow cytometry (IFC) based microfluidics is proposed. Specifically, the IFC framework to characterize and sort single-cells is adopted. Simultaneously with sorting, the target cell is transferred from the local high-salinity buffer to the MS-compatible solution. In this way, one-step sorting and desalting are achieved and the collected cells can be directly fed for MS analysis. A high sorting efficiency (>99%), cancer cell purity (≈87%), and desalting efficiency (>99%), and the whole workflow of impedance-based separation and MS analysis of normal cells (MCF-10A) and cancer cells (MDA-MB-468) are verified. As a standalone sample preparation module, the microfluidic chip is compatible with a variety of MS analysis methods, and envisioned to provide a new paradigm in efficient MS sample preparation, and further in multi-modal (i.e., electrical and metabolic) characterization of single-cells.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

High-Throughput Single-Cell, Single-Mitochondrial DNA Assay Using Hydrogel Droplet Microfluidics

There is growing interest in understanding the biological implications of single cell heterogeneity and heteroplasmy of mitochondrial DNA (mtDNA), but current methodologies for single-cell mtDNA analysis limit the scale of analysis to small cell populations. Although droplet microfluidics have increased the throughput of single-cell genomic, RNA, and protein analysis, their application to sub-cellular organelle analysis has remained a largely unsolved challenge. Here, we introduce an agarose-based droplet microfluidic approach for single-cell, single-mtDNA analysis, which allows simultaneous processing of hundreds of individual mtDNA molecules within >10,000 individual cells. Our microfluidic chip encapsulates individual cells in agarose beads, designed to have a sufficiently dense hydrogel network to retain mtDNA after lysis and provide a robust scaffold for subsequent multi-step processing and analysis. To mitigate the impact of the high viscosity of agarose required for mtDNA retention on the throughput of microfluidics, we developed a parallelized device, successfully achieving ~95 % mtDNA retention from single cells within our microbeads at >700,000 drops/minute. To demonstrate utility, we analyzed specific regions of the single-mtDNA using a multiplexed rolling circle amplification (RCA) assay. We demonstrated compatibility with both microscopy, for digital counting of individual RCA products, and flow cytometry for higher throughput analysis.

Single-cell analytical technologies: uncovering the mechanisms behind variations in immune responses

The immune landscape varies among individuals. It determines the immune response and results in surprisingly diverse symptoms, even in response to similar external stimuli. However, the detailed mechanisms underlying such diverse immune responses have remained mostly elusive. The utilization of recently developed single-cell multimodal analysis platforms has started to answer this question. Emerging studies have elucidated several molecular networks that may explain diversity with respect to age or other factors. An elaborate interplay between inherent physical conditions and environmental conditions has been demonstrated. Furthermore, the importance of modifications by the epigenome resulting in transcriptome variation among individuals is gradually being revealed. Accordingly, epigenomes and transcriptomes are direct indicators of the medical history and dynamic interactions with environmental factors. Coronavirus disease 2019 (COVID-19) has recently become one of the most remarkable examples of the necessity of in-depth analyses of diverse responses with respect to various factors to improve treatment in severe cases and to prevent viral transmission from asymptomatic carriers. In fact, determining why some patients develop serious symptoms is still a pressing issue. Here, we review the current "state of the art" in single-cell analytical technologies and their broad applications to healthy individuals and representative diseases, including COVID-19.

Microfluidic Sampling of Undissolved Components from Subcellular Regions of Living Single Cells for Mass Spectrometry Analysis

Precise sampling of undissolved chemical components from subcellular regions of living single cells is a prerequisite for their in-depth analysis, which could promote understanding of subtle early stage physiological or pathological processes. Here we report a microfluidic method to extract undissolved components from subcellular regions for MS analysis. The target single cell was isolated by the microchamber beneath the microfluidic probe and washed by the injected biocompatible isotonic glucose aqueous solution (IGAS). Then, the sampling solvent was injected to extract undissolved components from the expected subcellular region of the living single cell, where the position and size of the sampling region could be controlled. The components immobilized by undissolved cellular structures were proven to be successfully extracted. Since unextracted subcellular regions were protected by IGAS, the single cell could survive after a tiny part was extracted, providing the possibility of repetitive sampling of the same living cell. Phospholipids extracted from the subcellular regions were successfully identified. The results demonstrated the feasibility of our method for subcellular sampling and analysis.

Exploring the Accumulation Behavior and Heterogeneity of Perfluorooctanesulfonic Acid in Zebrafish Primary Organ Cells by Single-Cell Mass Cytometry

Perfluorooctanesulfonic acid (PFOS) is a commonly found environmental pollutant with potential toxicity and health risks to biosystems and ecosystems. Study of the accumulation behavior and heterogeneity of PFOS in biological primary organ cells provides us significant insights to explore its cytotoxicity, carcinogenicity, and mutagenicity. Here a single-cell mass cytometry system was established for the high-throughput analysis of trace PFOS and the exploration of its accumulation behavior and heterogeneity in zebrafish primary organ cells. The single-cell mass cytometry system applied a ∼25 μm constant-inner-diameter capillary as the single-cell generation and transportation channel with an etched tip-end of 40 μm as the nanoelectrospray emitter for mass spectrometric analysis. The single-cell mass cytometry system showed satisfactory semiquantitative performance and sensitivity for analysis of PFOS in single cells, with a high detection throughput of ∼35 cells/min. Subsequently, the liver, intestine, heart, and brain from PFOS-exposed zebrafish (100 pg/μL, 28 days) were dissociated and prepared as cell suspensions, and the cell suspensions were introduced into the single-cell mass cytometry system for high-throughput analysis of PFOS in individual primary organ cells. Significant cellular accumulation heterogeneities were observed, with the highest content in liver cells, followed by intestine cells, then heart cells, and the lowest in brain cells. In addition, the dynamics of PFOS in the zebrafish liver, intestine, heart, and brain cells showed typical violin plot distributions and were well-described using a gamma (γ) function.

"Iridium Signature" Mass Spectrometric Probes: New Tools Integrated in a Liquid Chromatography-Mass Spectrometry Workflow for Routine Profiling of Nitric Oxide and Metabolic Fingerprints in Cells

Nitric oxide (NO) is a highly reactive signaling molecule involved in diverse biological processes. Simultaneous profiling of NO and associated metabolic fingerprints in a single assay allows more accurate assessments of cell states and offers the possibility to better understand its exact biological roles. Herein, a multiplexing LC-MS workflow was established for simultaneous detection of intracellular NO and various metabolites based on a novel "iridium signature" mass spectrometric probe (Ir-MSP841). This Ir-MSP841 can convert highly liable NO to a stable permanently charged triazole product (Ir-TP852), enabling direct MS detection of NO. This ^191/193Ir-signature mass spectrometric probe-based approach is endowed with overwhelming advantages of interference-free, high quantitative accuracy, and great sensitivity (limit of detection down to 0.14 nM). It also reveals good linearity over a wide concentration range 12.5-500 nM and has been successfully employed for exploring the release behaviors of three representative NO donors in cells. Meanwhile, metabolic profiling results reveal that varying the concentrations of NO has distinct effects on various cellular metabolites. This study provides a robust, sensitive, and versatile method for simultaneous detection of NO and numerous metabolites in a single LC-MS run and expands its applications in biomedical research.

Single-Cell Metabolomics-Based Strategy for Studying the Mechanisms of Drug Action

Studying the mechanisms of drug antitumor activity at the single-cell level can provide information about the responses of cell subpopulations to drug therapy, which is essential for the accurate treatment of cancer. Due to the small size of single cells and the low contents of metabolites, metabolomics-based approaches to studying the mechanisms of drug action at the single-cell level are lacking. Herein, we develop a label-free platform for studying the mechanisms of drug action based on single-cell metabolomics (sMDA-scM) by integrating intact living-cell electro-launching ionization mass spectrometry (ILCEI-MS) with metabolomics analysis. Using this platform, we reveal that non-small-cell lung cancer (NSCLC) cells treated by gefitinib can be clustered into two cell subpopulations with different metabolic responses. The glutathione metabolic pathway of the subpopulation containing 14.4% of the cells is not significantly affected by gefitinib, exhibiting certain resistance characteristics. The presence of these cells masked the judgment of whether cysteine and methionine metabolic pathway was remarkably influenced in the analysis of overall average results, revealing the heterogeneity of the response of single NSCLC cells to gefitinib treatment. The findings provide a basis for evaluating the early therapeutic effects of clinical medicines and insights for overcoming drug resistance in NSCLC subpopulations.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Mass Spectrometry Imaging-Based Single-Cell Lipidomics Profiles Metabolic Signatures of Heart Failure

Heart failure (HF), leading as one of the main causes of mortality, has become a serious public health issue with high prevalence around the world. Single cardiomyocyte (CM) metabolomics promises to revolutionize the understanding of HF pathogenesis since the metabolic remodeling in the human hearts plays a vital role in the disease progression. Unfortunately, current metabolic analysis is often limited by the dynamic features of metabolites and the critical needs for high-quality isolated CMs. Here, high-quality CMs were directly isolated from transgenic HF mice biopsies and further employed in the cellular metabolic analysis. The lipids landscape in individual CMs was profiled with a delayed extraction mode in time-of-flight secondary ion mass spectrometry. Specific metabolic signatures were identified to distinguish HF CMs from the control subjects, presenting as possible single-cell biomarkers. The spatial distributions of these signatures were imaged in single cells, and those were further found to be strongly associated with lipoprotein metabolism, transmembrane transport, and signal transduction. Taken together, we systematically studied the lipid metabolism of single CMs with a mass spectrometry imaging method, which directly benefited the identification of HF-associated signatures and a deeper understanding of HF-related metabolic pathways.