Profiling metabolites and peptides in single cells

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.

Two C-terminal isoforms of Aplysia tachykinin-related peptide receptors exhibit phosphorylation-dependent and phosphorylation-independent desensitization mechanisms

Diversity, a hallmark of G protein-coupled receptor (GPCR) signaling, partly stems from alternative splicing of a single gene generating more than one isoform for a receptor. Additionally, receptor responses to ligands can be attenuated by desensitization upon prolonged or repeated ligand exposure. Both phenomena have been demonstrated and exemplified by the deuterostome tachykinin signaling system, although the role of phosphorylation in desensitization remains a subject of debate. Here, we describe the signaling system for tachykinin-related peptides (TKRPs) in a protostome, mollusk Aplysia. We cloned the Aplysia TKRP precursor, which encodes three TKRPs (apTKRP-1, apTKRP-2a, and apTKRP-2b) containing the FXGXR-amide motif. In situ hybridization and immunohistochemistry showed predominant expression of TKRP mRNA and peptide in the cerebral ganglia. TKRPs and their posttranslational modifications were observed in extracts of central nervous system ganglia using mass spectrometry. We identified two Aplysia TKRP receptors (apTKRPRs), named apTKRPR-A and apTKRPR-B. These receptors are two isoforms generated through alternative splicing of the same gene and differ only in their intracellular C termini. Structure-activity relationship analysis of apTKRP-2b revealed that both C-terminal amidation and conserved residues of the ligand are critical for receptor activation. C-terminal truncates and mutants of apTKRPRs suggested that there is a C-terminal phosphorylation-independent desensitization for both receptors. Moreover, apTKRPR-B also exhibits phosphorylation-dependent desensitization through the phosphorylation of C-terminal Ser/Thr residues. This comprehensive characterization of the Aplysia TKRP signaling system underscores the evolutionary conservation of the TKRP and TK signaling systems, while highlighting the intricacies of receptor regulation through alternative splicing and differential desensitization mechanisms.

Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry

The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.

Picoliter Single-Cell Reactor for Proteome Profiling by In Situ Cell Lysis, Protein Immobilization, Digestion, and Droplet Transfer

Global profiling of single-cell proteomes can reveal cellular heterogeneity, thus benefiting precision medicine. However, current mass spectrometry (MS)-based single-cell proteomic sample processing still faces technical challenges associated with processing efficiency and protein recovery. Herein, we present an innovative sample processing platform based on a picoliter single-cell reactor (picoSCR) for single-cell proteome profiling, which involves in situ protein immobilization and sample transfer. PicoSCR helped minimize surface adsorptive losses by downscaling the processing volume to 400 pL with a contact area of less than 0.4 mm2. Besides, picoSCR reached highly efficient cell lysis and digestion within 30 min, benefiting from optimal reagent and high reactant concentrations. Using the picoSCR-nanoLC-MS system, over 1400 proteins were identified from an individual HeLa cell using data-dependent acquisition mode. Proteins with copy number below 1000 were identified, demonstrating this system with a detection limit of 1.7 zmol. Furthermore, we profiled the proteome of circulating tumor cells (CTCs). Data are available via ProteomeXchange with the identifier PXD051468. Proteins associated with epithelial-mesenchymal transition and neutrophil extracellular traps formation (which are both related to tumor metastasis) were observed in all CTCs. The cellular heterogeneity was revealed by differences in signaling pathways within individual cells. These results highlighted the potential of the picoSCR platform to help discover new biomarkers and explore differences in biological processes between cells.

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

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

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis.

METHODS

Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS).

RESULTS

By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples.

CONCLUSION

This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry

Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.

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.

Scaling up stem cell production: harnessing the potential of microfluidic devices

Stem cells are specialised cells characterised by their unique ability to both self-renew and transform into a wide array of specialised cell types. The widespread interest in stem cells for regenerative medicine and cultivated meat has led to a significant demand for these cells in both research and practical applications. Despite the growing need for stem cell manufacturing, the industry faces significant obstacles, including high costs for equipment and maintenance, complicated operation, and low product quality and yield. Microfluidic technology presents a promising solution to the abovementioned challenges. As an innovative approach for manipulating liquids and cells within microchannels, microfluidics offers a plethora of advantages at an industrial scale. These benefits encompass low setup costs, ease of operation and multiplexing, minimal energy consumption, and the added advantage of being labour-free. This review presents a thorough examination of the prominent microfluidic technologies employed in stem cell research and explores their promising applications in the burgeoning stem cell industry. It thoroughly examines how microfluidics can enhance cell harvesting from tissue samples, facilitate mixing and cryopreservation, streamline microcarrier production, and efficiently conduct cell separation, purification, washing, and final cell formulation post-culture.

Diazo-carboxyl Click Derivatization Enables Sensitive Analysis of Carboxylic Acid Metabolites in Biosamples

Carboxylic acids are central metabolites in bioenergetics, signal transduction, and post-translation protein regulation. However, the quantitative analysis of carboxylic acids as an indispensable part of metabolomics is prohibitively challenging, particularly in trace amounts of biosamples. Here we report a diazo-carboxyl/hydroxylamine-ketone double click derivatization method for the sensitive analysis of hydrophilic, low-molecular-weight carboxylic acids. In general, our method renders a 5- to 2000-fold higher response in mass spectrometry along with improved chromatographic separation. With this method, we presented the near-single-cell analysis of carboxylic acid metabolites in 10 mouse egg cells before and after fertilization. Malate, fumarate, and β-hydroxybutyrate were found to decrease after fertilization. We also monitored the isotope labeling kinetics of carboxylic acids inside adherent cells cultured in 96-well plates during drug treatment. Finally, we applied this method to plasma or serum samples (5 μL) collected from mice and humans under pathological and physiological conditions. The double click derivatization method paves a way toward single-cell metabolomics and bedside diagnostics.

Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry

Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.

Uric acid metabolism promotes apoptosis against Bombyx mori nucleopolyhedrovirus in silkworm, Bombyx mori

The white epidermis of silkworms is due to the accumulation of uric acid crystals. Abnormal silkworm uric acid metabolism decreases uric acid production, leading to a transparent or translucent phenotype. The oily silkworm op50 is a mutant strain with a highly transparent epidermis derived from the p50 strain. It shows more susceptibility to Bombyx mori nucleopolyhedrovirus (BmNPV) infection than the wild type; however, the underlying mechanism is unknown. This study analysed the changes in 34 metabolites in p50 and op50 at different times following BmNPV infection based on comparative metabolomics. The differential metabolites were mainly clustered in six metabolic pathways. Of these, the uric acid pathway was identified as critical for resistance in silkworms, as feeding with inosine significantly enhanced larval resistance compared to other metabolites and modulated other metabolic pathways. Additionally, the increased level of resistance to BmNPV in inosine-fed silkworms was associated with the regulation of apoptosis, which is mediated by the reactive oxygen species produced during uric acid synthesis. Furthermore, feeding the industrial strain Jingsong (JS) with inosine significantly increased the level of larval resistance to BmNPV, indicating its potential application in controlling the virus in sericulture. These results lay the foundation for clarifying the resistance mechanism of silkworms to BmNPV and provide new strategies and methods for the biological control of pests.

Sublimation application of 5-chloro-2-mercaptobenzothiazole matrix for matrix-assisted laser desorption/ionization mass spectrometry imaging of mouse kidney

RATIONALE

Sublimation is a solvent-free technique used to apply a uniform matrix coating over a large sample plate, improving the matrix's purity and enhancing the analyte signal. Although the 5-chloro-2-mercaptobenzothiazole (CMBT) matrix was introduced years ago, there are no reports of its application via sublimation. We investigated the experimental parameters that are optimal for CMBT matrix sublimation on mouse kidney samples. We also evaluated the stability of the sublimed CMBT matrix under a vacuum environment. Using kidney samples prepared with a sublimated CMBT matrix, we conducted matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) analysis of specific phospholipids (phosphatidylcholine and phosphatidylglycerol in the positive ion mode and phosphatidylinositol in the negative ion mode). We also explored various spatial resolutions (50, 20, and 10 μm) and performed sequential MALDI-hematoxylin and eosin (H&E) staining.

METHODS

The CMBT matrix was applied to kidney samples using a sublimation apparatus connected to a vacuum pump to achieve a pressure of 0.05 Torr. The matrix was then subjected to different temperatures and sublimation times to determine the optimal conditions for matrix application. Subsequently, a Q-Exactive mass spectrometer equipped with a Spectroglyph MALDI ion source was employed for MALDI-MSI experiments. Standard protocols were followed for H&E staining after MALDI analysis.

RESULTS

A matrix thickness of 0.15 mg/cm² yielded high-quality images. The sublimated matrix exhibited minimal loss after approximately 20 h of exposure to a vacuum of 7 Torr, indicating its stability under these conditions. Ion images were successfully obtained at spatial resolutions of 50, 20, and 10 μm. Furthermore, orthogonal histological information was obtained through sequential MALDI-H&E staining.

CONCLUSIONS

We demonstrate that samples prepared for MALDI-MSI using sublimation to apply the CMBT matrix yield high-quality mass spectrometric images of mouse kidney sections. We also provide data for the impact of various experimental parameters on image quality (e.g., temperature, time, matrix thickness, and spatial resolution).

Characterizing Intraindividual Podocyte Morphology In Vitro with Different Innovative Microscopic and Spectroscopic Techniques

Podocytes are critical components of the glomerular filtration barrier, sitting on the outside of the glomerular basement membrane. Primary and secondary foot processes are characteristic for podocytes, but cell processes that develop in culture were not studied much in the past. Moreover, protocols for diverse visualization methods mostly can only be used for one technique, due to differences in fixation, drying and handling. However, we detected by single-cell RNA sequencing (scRNAseq) analysis that cells reveal high variability in genes involved in cell type-specific morphology, even within one cell culture dish, highlighting the need for a compatible protocol that allows measuring the same cell with different methods. Here, we developed a new serial and correlative approach by using a combination of a wide variety of microscopic and spectroscopic techniques in the same cell for a better understanding of podocyte morphology. In detail, the protocol allowed for the sequential analysis of identical cells with light microscopy (LM), Raman spectroscopy, scanning electron microscopy (SEM) and atomic force microscopy (AFM). Skipping the fixation and drying process, the protocol was also compatible with scanning ion-conductance microscopy (SICM), allowing the determination of podocyte surface topography of nanometer-range in living cells. With the help of nanoGPS Oxyo^®, tracking concordant regions of interest of untreated podocytes and podocytes stressed with TGF-β were analyzed with LM, SEM, Raman spectroscopy, AFM and SICM, and revealed significant morphological alterations, including retraction of podocyte process, changes in cell surface morphology and loss of cell-cell contacts, as well as variations in lipid and protein content in TGF-β treated cells. The combination of these consecutive techniques on the same cells provides a comprehensive understanding of podocyte morphology. Additionally, the results can also be used to train automated intelligence networks to predict various outcomes related to podocyte injury in the future.

Nanokit coupled electrospray ionization mass spectrometry for analysis of angiotensin converting enzyme 2 activity in single living cell

Angiotensin-converting enzyme 2 (ACE2) is not only an enzyme but also a functional receptor on cell membrane for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, the activity of ACE2 in single living cell is firstly determined using a nanokit coupled electrospray ionization mass spectrometry (nanokit-ESI-MS). Upon the insertion of a micro-capillary into the living hACE2-CHO cell and the electrochemical sorting of the cytosol, the target ACE2 enzyme hydrolyses angiotensin II inside the capillary to generate angiotensin 1-7. After the electrospray of the mixture at the tip of the capillary, the product is differentiated from the substrate in molecular weight to achieve the detection of ACE2 activity in single cells. The further measurement illustrates that the inflammatory state of cells does not lead to the significant change of ACE2 catalytic activity, which elucidates the relationship between intracellular ACE2 activity and inflammation at single cell level. The established strategy will provide a specific analytical method for further studying the role of ACE2 in the process of virus infection, and extend the application of nanokit based single cell analysis.

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.

Characterizing RNA modifications in the central nervous system and single cells by RNA sequencing and liquid chromatography-tandem mass spectrometry techniques

Post-transcriptional modifications to RNA constitute a newly appreciated layer of translation regulation in the central nervous system (CNS). The identity, stoichiometric quantity, and sequence position of these unusual epitranscriptomic marks are central to their function, making analytical methods that are capable of accurate and reproducible measurements paramount to the characterization of the neuro-epitranscriptome. RNA sequencing-based methods and liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques have been leveraged to provide an early glimpse of the landscape of RNA modifications in bulk CNS tissues. However, recent advances in sample preparation, separations, and detection methods have revealed that individual cells display remarkable heterogeneity in their RNA modification profiles, raising questions about the prevalence and function of cell-specific distributions of post-transcriptionally modified nucleosides in the brain. In this Trends article, we present an overview of RNA sequencing and LC-MS/MS methodologies for the analysis of RNA modifications in the CNS with special emphasis on recent advancements in techniques that facilitate single-cell and subcellular detection.

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.

A Microfluidic Cell Co-Culture Chip for the Monitoring of Interactions between Macrophages and Fibroblasts

Macrophages and fibroblasts are two types of important cells in wound healing. The development of novel platforms for studying the interrelationship between these two cells is crucial for the exploration of wound-healing mechanisms and drug development. In this study, a microfluidic chip composed of two layers was designed for the co-culturing of these two cells. An air valve was employed to isolate fibroblasts to simulate the wound-healing microenvironment. The confluence rate of fibroblasts in the co-culture system with different macrophages was explored to reflect the role of different macrophages in wound healing. It was demonstrated that M2-type macrophages could promote the activation and migration of fibroblasts and it can be inferred that they could promote the wound-healing process. The proposed microfluidic co-culture system was designed for non-contact cell-cell interactions, which has potential significance for the study of cell-cell interactions in biological processes such as wound healing, tumor microenvironment, and embryonic development.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

I, Deniega FM, Delica K, Fernandez K, Lebrilla CB, Manalo MN, Heralde III FM, Completo GCJ, Nacario RC. Biological Assay-Guided Fractionation and Mass Spectrometry-Based Metabolite Profiling of Annona muricata L. Cytotoxic Compounds against Lung Cancer A549 Cell Line

Annona muricata L. (Guyabano) leaves are reported to exhibit anticancer activity against cancer cells. In this study, the ethyl acetate extract from guyabano leaves was purified through column chromatography, and the cytotoxic effects of the semi-purified fractions were evaluated against A549 lung cancer cells using in vitro MTS cytotoxicity and scratch/wound healing assays. Fractions F15-16C and F15-16D exhibited the highest anticancer activity in the MTS assay, with % cytotoxicity values of 99.6% and 99.4%, respectively. The bioactivity of the fractions was also consistent with the results of the scratch/wound healing assay. Moreover, untargeted metabolomics was employed on the semi-purified fractions to determine the putative compounds responsible for the bioactivity. The active fractions were processed using LC-MS/MS analysis with the integration of the following metabolomic tools: MS-DIAL (for data processing), MetaboAnalyst (for data analysis), GNPS (for metabolite annotation), and Cytoscape (for network visualization). Results revealed that the putative compounds with a significant difference between active and inactive fractions in PCA and OPLS-DA models were pheophorbide A and diphenylcyclopropenone.

Metabolic flux between organs measured by arteriovenous metabolite gradients

Mammalian organs convert dietary nutrients into circulating metabolites and share them to maintain whole-body metabolic homeostasis. While the concentrations of circulating metabolites have been frequently measured in a variety of pathophysiological conditions, the exchange flux of circulating metabolites between organs is not easily measurable due to technical difficulties. Isotope tracing is useful for measuring such fluxes for a metabolite of interest, but the shuffling of isotopic atoms between metabolites requires mathematical modeling. Arteriovenous metabolite gradient measurements can complement isotope tracing to infer organ-specific net fluxes of many metabolites simultaneously. Here, we review the historical development of arteriovenous measurements and discuss their advantages and limitations with key example studies that have revealed metabolite exchange flux between organs in diverse pathophysiological contexts.

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.

Metabolomics studies of cell-cell interactions using single cell mass spectrometry combined with fluorescence microscopy

Cell-cell interactions are critical for transmitting signals among cells and maintaining their normal functions from the single-cell level to tissues. In cancer studies, interactions between drug-resistant and drug-sensitive cells play an important role in the development of chemotherapy resistance of tumors. As metabolites directly reflect the cell status, metabolomics studies provide insight into cell-cell communication. Mass spectrometry (MS) is a powerful tool for metabolomics studies, and single cell MS (SCMS) analysis can provide unique information for understanding interactions among heterogeneous cells. In the current study, we utilized a direct co-culture system (with cell-cell contact) to study metabolomics of single cells affected by cell-cell interactions in their living status. A fluorescence microscope was utilized to distinguish these two types of cells for SCMS metabolomics studies using the Single-probe SCMS technique under ambient conditions. Our results show that through interactions with drug-resistant cells, drug-sensitive cancer cells acquired significantly increased drug resistance and exhibited drastically altered metabolites. Further investigation found that the increased drug resistance was associated with multiple metabolism regulations in drug-sensitive cells through co-culture such as the upregulation of sphingomyelins lipids and lactic acid and the downregulation of TCA cycle intermediates. The method allows for direct MS metabolomics studies of individual cells labeled with fluorescent proteins or dyes among heterogeneous populations.

Analysis of low-abundance molecules in complex matrices by quadrupole-linear ion trap mass spectrometry using a simultaneous fragmentation and accumulation strategy

RATIONALE

Fast and sensitive analysis of low-abundance molecules in complex matrices has always been a challenge in chemical and biological applications. Mass spectrometry (MS) has been widely used in the fields of chemical and biological analysis due to its unparalleled specificity and sensitivity. However, the MS signals consistently deteriorate in the presence of matrices. Demands for more sensitive and efficient methods to analyze those low-abundance molecules in chemical and biological systems are in urgent need.

METHODS

Based on a home-made quadrupole-linear ion trap (Q-LIT) mass spectrometer, a simultaneous fragmentation and accumulation strategy was developed to improve the sensitivity of the analysis for the low-abundance molecules in complex matrices. Ions were filtered by the quadrupole into the LIT. The precursor ions were fragmented and the product ions were isolated and accumulated in the LIT simultaneously. The fragmentation, isolation and accumulation processes were conducted at the same time. The accumulation time could be controlled to accumulate sufficient product ions.

RESULTS

With this strategy, the signal intensity of targeted molecules could be increased by 2-8 times and by increasing the accumulation time, this could be further enhanced. Those interferences induced by isomers and matrices can be reduced by using our method. We further applied our method to the quantification and analysis of biological samples. Tryptic digested peptides of myoglobin (Mb) were successfully detected by our method.

CONCLUSIONS

We have established a new method with great advantages in the detection of molecules in complex matrices. The application of this method promises better results in the bioanalytical area, especially for the analysis of substances in complex matrices in the future.

Three-dimensional quantitative mass spectrometry imaging in complex system: From subcellular to whole organism

Mass spectrometry imaging (MSI) has been applied for label-free three-dimensional (3D) imaging from position array across the whole organism, which provides high-dimensional quantitative data of inorganic or organic compounds that may play an important role in the regulation of cellular signaling, including metals, metabolites, lipids, drugs, peptides, and proteins. While MSI is suitable for investigation of the spatial distribution of molecules, it has a limitation with visualization and quantification of multiple molecules. 3D-MSI, however, can be applied toward exploring metabolic pathway as well as the interactions of lipid-protein, protein-protein, and metal-protein in complex systems from subcellular to the whole organism through an untargeted methodology. In this review, we highlight the methods and applications of MS-based 3D imaging to address the complexity of molecular interaction from nano- to micrometer lateral resolution, with particular focus on: (a) common and hybrid 3D-MSI techniques; (b) quantitative MSI methodology, including the methods using a stable isotope labeling internal standard (SILIS) and SILIS-free approaches with tissue extinction coefficient or virtual calibration; (c) reconstruction of the 3D organ; (d) application of 3D-MSI for biomarker screening and environmental toxicological research. 3D-MSI quantitative analysis provides accurate spatial information and quantitative variation of biomolecules, which may be valuable for the exploration of the molecular mechanism of the disease progresses and toxicological assessment of environmental pollutants in the whole organism. Additionally, we also discuss the challenges and perspectives on the future of 3D quantitative MSI.

Capillary Electrophoresis Mass Spectrometry for Scalable Single-Cell Proteomics

Understanding the biochemistry of the cell requires measurement of all the molecules it produces. Single-cell proteomics recently became possible through advances in microanalytical sample preparation, separation by nano-flow liquid chromatography (nanoLC) and capillary electrophoresis (CE), and detection using electrospray ionization (ESI) high-resolution mass spectrometry (HRMS). Here, we demonstrate capillary microsampling CE-ESI-HRMS to be scalable to proteomics across broad cellular dimensions. This study established proof-of-principle using giant, ∼250-µm-diameter cells from embryos of the frog Xenopus laevis and small, ∼35-µm-diameter neurons in culture from the mouse hippocampus. From ∼18 ng, or ∼0.2% of the total cellular proteome, subcellular analysis of the ventral-animal midline (V11) and equatorial (V12) cells identified 1,133 different proteins in a 16-cell embryo. CE-HRMS achieved ∼20-times higher sensitivity and doubled the speed of instrumental measurements compared to nanoLC, the closest neighboring single-cell technology of choice. Microanalysis was scalable to 722 proteins groups from ∼5 ng of cellular protein digest from identified left dorsal-animal midline cell (D11), supporting sensitivity for smaller cells. Capillary microsampling enabled the isolation and transfer of individual neurons from the culture, identifying 37 proteins between three different cells. A total of 224 proteins were detected from 500 pg of neuronal protein digest, which estimates to a single neuron. Serial dilution returned 157 proteins from sample amounts estimating to about half a cell (250 pg protein) and 70 proteins from ca. a quarter of a neuron (125 pg protein), suggesting sufficient sensitivity for subcellular proteomics. CE-ESI-HRMS complements nanoLC proteomics with scalability, sensitivity, and speed across broad cellular dimensions.

Lipidomics in Understanding Pathophysiology and Pharmacologic Effects in Inflammatory Diseases: Considerations for Drug Development

The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease.

Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues

Compared with conventional optical microscopy techniques, mass spectrometry imaging (MSI) or imaging mass spectrometry (IMS) is a powerful, label-free analytical technique, which can sensitively and simultaneously detect, quantify, and map hundreds of biomolecules, such as peptides, proteins, lipid, and other organic compounds in cells and tissues. So far, although several soft ionization techniques, such as desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS) have been used for imaging biomolecules, matrix-assisted laser desorption/ionization (MALDI) is still the most widespread MSI scanning method. Here, we aim to provide a comprehensive review of MALDI-MSI with an emphasis on its advances of the instrumentation, methods, application, and future directions in single cell and biological tissues.

Patch-Clamp Proteomics of Single Neurons in Tissue Using Electrophysiology and Subcellular Capillary Electrophoresis Mass Spectrometry

Understanding of the relationship between cellular function and molecular composition holds a key to next-generation therapeutics but requires measurement of all types of molecules in cells. Developments in sequencing enabled semiroutine measurement of single-cell genomes and transcriptomes, but analytical tools are scarce for detecting diverse proteins in tissue-embedded cells. To bridge this gap for neuroscience research, we report the integration of patch-clamp electrophysiology with subcellular shot-gun proteomics by high-resolution mass spectrometry (HRMS). Recording of electrical activity permitted identification of dopaminergic neurons in the substantia nigra pars compacta. Ca. 20-50% of the neuronal soma content, containing an estimated 100 pg of total protein, was aspirated into the patch pipette filled with ammonium bicarbonate. About 1 pg of somal protein, or ∼0.25% of the total cellular proteome, was analyzed on a custom-built capillary electrophoresis (CE) electrospray ionization platform using orbitrap HRMS for detection. A series of experiments were conducted to systematically enhance detection sensitivity through refinements in sample processing and detection, allowing us to quantify ∼275 different proteins from somal aspirate-equivalent protein digests from cultured neurons. From single neurons, patch-clamp proteomics of the soma quantified 91, 80, and 95 different proteins from three different dopaminergic neurons or 157 proteins in total. Quantification revealed detectable proteomic differences between the somal protein samples. Analysis of canonical knowledge predicted rich interaction networks between the observed proteins. The integration of patch-clamp electrophysiology with subcellular CE-HRMS proteomics expands the analytical toolbox of neuroscience.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

Development of multifunctional nanopipettes for controlled intracellular delivery and single-entity detection

The intracellular delivery of biomolecules and nanoscale materials to individual cells has gained remarkable attention in recent years owing to its wide applications in drug delivery, clinical diagnostics, bio-imaging and single-cell analysis. It remains a challenge to control and measure the delivered amount in one cell. In this work, we developed a multifunctional nanopipette - containing both a nanopore and nanoelectrode (pyrolytic carbon) at the apex - as a facile, minimally invasive and effective platform for both controllable single-cell intracellular delivery and single-entity counting. While controlled by a micromanipulator, the baseline changes of the nanopore ionic current (I) and nanoelectrode open circuit potential (V) help to guide the nanopipette tip insertion and positioning processes. The delivery from the nanopore barrel can be facilely controlled by the applied nanopore bias. To optimize the intracellular single-entity detection during delivery, we studied the effects of the nanopipette tip geometry and solution salt concentration in controlled experiments. We have successfully delivered gold nanoparticles and biomolecules into the cell, as confirmed by the increased scattering and fluorescence signals, respectively. The delivered entities have also been detected at the single-entity level using either one or both transient I and V signals. We found that the sensitivity of the single-entity electrochemical measurement was greatly affected by the local environment of the cell and varied between cell lines.

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.

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.

Single Cell Metabolite Detection Using Inertial Microfluidics-Assisted Ion Mobility Mass Spectrometry

Single-cell metabolite measurement remains highly challenging due to difficulties related to single cell isolation, metabolite detection, and identification of low levels of metabolites. Here, as a first step of the technological development, we propose a novel strategy integrating spiral inertial microfluidics and ion mobility mass spectrometry (IM-MS) for single-cell metabolite detection and identification. Cells in methanol suspension are inertially focused into a single stream in the spiral microchannel. This stream of separated cells is delivered to the nanoelectrospray needle to be lysed and ionized and subsequently analyzed in real time by IM-MS. This analytical system enables six to eight single-cell metabolic fingerprints to be collected per minute, including gas-phase collisional cross section (CCS) measurements as an additional molecular descriptor, giving increased confidence in metabolite identification. As a proof of concept, the metabolic profiles of three types of cancer cells (U2OS, HepG2, and HepG2.215) were successfully screened, and 19 distinct lipids species were identified with CCS value filtering. Furthermore, principal component analysis (PCA) showed differentiation of the three cancer cell lines, mainly due to cellular surface phospholipids. Taken together, our technology platform offers a simple and efficient method for single-cell lipid profiling, with additional ion mobility separation of lipids significantly improving the confidence toward identification of metabolites.

SpaceM reveals metabolic states of single cells

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

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.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Chiral Analysis of Lactate during Direct Contact Coculture by Single-Cell On-Probe Enzymatic Dehydrogenation Derivatization: Unraveling Metabolic Changes Caused by d-Lactate

In vitro noncontact cell-based coculture models are frequently employed to study cell-to-cell communication. However, these models cannot accurately represent the complexity of in vivo signaling. d-Lactate is an unusual metabolite produced and released by cancer cells. The characterization of d-lactate is challenging as it shares the same mass but has much lower amounts compared with l-lactate. Herein, d-α-hydroxy acids were specifically recognized and dehydrogenated by d-α-hydroxy acid dehydrogenase. The dehydrogenation products were rapidly quaternized for enhancement of mass signals. An on-probe enzymatic dehydrogenation-derivatization method was proposed for chiral analysis of α-hydroxy acids at the single-cell level. It is a promising amplification methodology and affords over 3 orders of magnitude signal enhancement. Furthermore, direct contact coculture models were used to precisely mimic the tumor microenvironment and explore the communication between cancer and normal cells. Single-cell mass spectrometry (SCMS) was further applied to easily sample cell extracts and study the differences of the aspects of small molecule metabolism in cocultured cells. On the basis of direct contact coculture SCMS, several differential small molecule metabolites and differences of oxidative stress between cocultured and monocultured normal cells were successfully detected. Additionally, d-lactate was discovered as a valuable differential metabolite with application of the two developed methods. It may account for the cancer-associated metabolic behavior of normal cells. These changes could be relieved after d-lactate metabolism-related drug treatment. This discovery may promote the investigation of d-lactate metabolism, which may provide a novel direction for cancer therapy.

Self-aligned sequential lateral field non-uniformities over channel depth for high throughput dielectrophoretic cell deflection

Dielectrophoresis (DEP) enables the separation of cells based on subtle subcellular phenotypic differences by controlling the frequency of the applied field. However, current electrode-based geometries extend over a limited depth of the sample channel, thereby reducing the throughput of the manipulated sample (sub-μL min-1 flow rates and <105 cells per mL). We present a flow through device with self-aligned sequential field non-uniformities extending laterally across the sample channel width (100 μm) that are created by metal patterned over the entire depth (50 μm) of the sample channel sidewall using a single lithography step. This enables single-cell streamlines to undergo progressive DEP deflection with minimal dependence on the cell starting position, its orientation versus the field and intercellular interactions. Phenotype-specific cell separation is validated (>μL min-1 flow and >106 cells per mL) using heterogeneous samples of healthy and glutaraldehyde-fixed red blood cells, with single-cell impedance cytometry showing that the DEP collected fractions are intact and exhibit electrical opacity differences consistent with their capacitance-based DEP crossover frequency. This geometry can address the vision of an "all electric" selective cell isolation and cytometry system for quantifying phenotypic heterogeneity of cellular systems.

Single Cell Mass Spectrometry Quantification of Anticancer Drugs: Proof of Concept in Cancer Patients

In clinical cancer medicine, the current inability to quantify intracellular chemotherapy drug concentrations in individual human cells limits the personalization and overall effectiveness of drug administration. New bioanalytical methods capable of real-time measurement of drug levels in live single cancer cells would allow for more adaptive and personalized administration of chemotherapy drugs, potentially leading to better clinical outcomes with fewer side effects. In this study, we report the development of a new quantitative single cell mass spectrometry (qSCMS) method capable of providing absolute drug amounts and concentrations in single cancer cells. Using this qSCMS system, quantitative analysis of the intracellular drug gemcitabine present in individual bladder cancer cells is reported, including in bladder cancer cells isolated from patients undergoing standard-of-care gemcitabine chemotherapy. The development of single cell pharmacology bioanalytical methods can potentially lead to more effective and safely administered drug medications in patients, especially in the treatment of cancer.

Label-free spectral imaging to study drug distribution and metabolism in single living cells

During drug development, evaluation of drug and its metabolite is an essential process to understand drug activity, stability, toxicity and distribution. Liquid chromatography (LC) coupled with mass spectrometry (MS) has become the standard analytical tool for screening and identifying drug metabolites. Unlike LC/MS approach requiring liquifying the biological samples, we showed that spectral imaging (or spectral microscopy) could provide high-resolution images of doxorubicin (dox) and its metabolite doxorubicinol (dox’ol) in single living cells. Using this new method, we performed measurements without destroying the biological samples. We calculated the rate constant of dox translocating from extracellular moiety into the cell and the metabolism rate of dox to dox’ol in living cells. The translocation rate of dox into a single cell for spectral microscopy and LC/MS approaches was similar (~ 1.5 pM min⁻¹ cell⁻¹). When compared to spectral microscopy, the metabolism rate of dox was underestimated for about every 500 cells using LC/MS. The microscopy approach further showed that dox and dox’ol translocated to the nucleus at different rates of 0.8 and 0.3 pM min⁻¹, respectively. LC/MS is not a practical approach to determine drug translocation from cytosol to nucleus. Using various methods, we confirmed that when combined with a high-resolution imaging, spectral characteristics of a molecule could be used as a powerful approach to analyze drug metabolism. We propose that spectral microscopy is a new method to study drug localization, translocation, transformation and identification with a resolution at a single cell level, while LC/MS is more appropriate for drug screening at an organ or tissue level.

Single-cell microfluidic impedance cytometry: from raw signals to cell phenotypes using data analytics

The biophysical analysis of single-cells by microfluidic impedance cytometry is emerging as a label-free and high-throughput means to stratify the heterogeneity of cellular systems based on their electrophysiology. Emerging applications range from fundamental life-science and drug assessment research to point-of-care diagnostics and precision medicine. Recently, novel chip designs and data analytic strategies are laying the foundation for multiparametric cell characterization and subpopulation distinction, which are essential to understand biological function, follow disease progression and monitor cell behaviour in microsystems. In this tutorial review, we present a comparative survey of the approaches to elucidate cellular and subcellular features from impedance cytometry data, covering the related subjects of device design, data analytics (i.e., signal processing, dielectric modelling, population clustering), and phenotyping applications. We give special emphasis to the exciting recent developments of the technique (timeframe 2017-2020) and provide our perspective on future challenges and directions. Its synergistic application with microfluidic separation, sensor science and machine learning can form an essential toolkit for label-free quantification and isolation of subpopulations to stratify heterogeneous biosystems.