High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

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.

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

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

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Metabolomics: A useful tool for ischemic stroke research

Ischemic stroke (IS) is a multifactorial and heterogeneous disease. Despite years of studies, effective strategies for the diagnosis, management and treatment of stroke are still lacking in clinical practice. Metabolomics is a growing field in systems biology. It is starting to show promise in the identification of biomarkers and in the use of pharmacometabolomics to help patients with certain disorders choose their course of treatment. The development of metabolomics has enabled further and more biological applications. Particularly, metabolomics is increasingly being used to diagnose diseases, discover new drug targets, elucidate mechanisms, and monitor therapeutic outcomes and its potential effect on precision medicine. In this review, we reviewed some recent advances in the study of metabolomics as well as how metabolomics might be used to identify novel biomarkers and understand the mechanisms of IS. Then, the use of metabolomics approaches to investigate the molecular processes and active ingredients of Chinese herbal formulations with anti-IS capabilities is summarized. We finally summarized recent developments in single cell metabolomics for exploring the metabolic profiles of single cells. Although the field is relatively young, the development of single cell metabolomics promises to provide a powerful tool for unraveling the pathogenesis of IS.

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

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

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Recent progress in mass spectrometry for single-cell metabolomics

Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.

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.

Single-cell sequencing: expansion, integration and translation

With the rapid advancement in sequencing technologies, the concept of omics has revolutionized our understanding of cellular behaviors. Conventional omics investigation approaches measure the averaged behaviors of multiple cells, which may easily hide signals represented by a small-cell cohort, urging for the development of techniques with enhanced resolution. Single-cell RNA sequencing, investigating cell transcriptomics at the resolution of a single cell, has been rapidly expanded to investigate other omics such as genomics, proteomics and metabolomics since its invention. The requirement for comprehensive understanding of complex cellular behavior has led to the integration of multi-omics and single-cell sequencing data with other layers of information such as spatial data and the CRISPR screening technique towards gained knowledge or innovative functionalities. The development of single-cell sequencing in both dimensions has rendered it a unique field that offers us a versatile toolbox to delineate complex diseases, including cancers.

Negative Magnetophoresis Focusing Microchips Online-Coupled with ICP-MS for High-Throughput Single-Cell Analysis

High-throughput single-cell analysis is critical to elucidate the cell heterogeneity. Recently, droplet microchips using oil/gas phases to generate single-cell encapsulated droplets have been combined with inductively coupled plasma-mass spectrometry (ICP-MS) for determination of trace elements in single cells with a throughput of dozens of cells per min. To improve the sample throughput and avoid the oil phase introduced into ICP-MS, herein, a negative magnetophoresis focusing microchip was established and online-coupled to ICP-MS for single-cell analysis. MCF-7 cells in the paramagnetic salt solution were introduced into the designed focusing microchannel, in which they were focused into a single stream under both the magnetic repulsion force and inertial lift force, and then were introduced into ICP-MS for online single-cell analysis. The important parameters including the chip design, the concentration of the paramagnetic salt solution, flow rate, cell density, and dwell time were optimized. Under the optimal conditions, a high sample throughput of 1390 cells min^-1 was obtained. The established online analytical system was applied to study the uptake behaviors of MCF-7 cells for Zn²⁺ and ZnO nanoparticles (NPs) at a single-cell level. The single-cell analysis results indicate that MCF-7 cells displayed more remarkable heterogeneity when they were treated with ZnO NPs, and the uptake content of ZnO NPs by MCF-7 cells was less than that of Zn²⁺. Compared with other droplet microdevice-ICP-MS analysis systems, the developed system has the advantages of simple design and fabrication, no organic phase, a high throughput, and a low sample consumption (only 5 μL).

Fabrication of Biomaterials and Biostructures Based On Microfluidic Manipulation

Biofabrication technologies are of importance for the construction of organ models and functional tissue replacements. Microfluidic manipulation, a promising biofabrication technique with micro-scale resolution, can not only help to realize the fabrication of specific microsized structures but also build biomimetic microenvironments for biofabricated tissues. Therefore, microfluidic manipulation has attracted attention from researchers in the manipulation of particles and cells, biochemical analysis, tissue engineering, disease diagnostics, and drug discovery. Herein, biofabrication based on microfluidic manipulation technology is reviewed. The application of microfluidic manipulation technology in the manufacturing of biomaterials and biostructures with different dimensions and the control of the microenvironment is summarized. Finally, current challenges are discussed and a prospect of microfluidic manipulation technology is given. The authors hope this review can provide an overview of microfluidic manipulation technologies used in biofabrication and thus steer the current efforts in this field.

Dynamic metabolic change of cancer cells induced by natural killer cells at single-cell level studied by label-free mass cytometry

Natural killer cells (NK cells) are important immune cells which have attracted increasing attention in cancer immunotherapy. Due to the heterogeneity of cells, individual cancer cells show different resistance to NK cytotoxicity, which has been revealed by flow cytometry. Here we used label-free mass cytometry (CyESI-MS) as a new tool to analyze the metabolites in Human Hepatocellular Carcinoma (HepG2) cells at the single-cell level after the interaction with different numbers of NK92 MI cells. A large amount of chemical information from individual HepG2 cells was obtained showing the process of cell apoptosis induced by NK cells. Nineteen metabolites which consecutively change during cell apoptosis were revealed by calculating their average relative intensity. Four metabolic pathways were impacted during cell apoptosis which hit 4 metabolites including glutathione (GSH), creatine, glutamic acid and taurine. We found that the HepG2 cells could be divided into two phenotypes after co-culturing with NK cells according to the bimodal distribution of concentration of these 4 metabolites. The correlation between metabolites and different apoptotic pathways in the early apoptosis cell group was established by the 4 metabolites at the single-cell level. This is a new idea of using single-cell specific metabolites to reveal the metabolic heterogeneity in cell apoptosis which would be a powerful means for evaluating the cytotoxicity of NK cells.

Label-free mass cytometry is utilized to study the dynamic metabolic change during apoptosis in HepG2 cells induced by NK92 MI cells at the single-cell level. The metabolic heterogeneity of individual HepG2 cells during apoptosis was revealed.

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.

Recent progress of inertial microfluidic-based cell separation

Cell separation has consistently been a pivotal technology of sample preparation in biomedical research. Compared with conventional bulky cell separation technologies applied in the clinic, cell separation based on microfluidics can accurately manipulate the displacement of liquid or cells at the microscale, which has great potential in point-of-care testing (POCT) applications due to small device size, low cost, low sample consumption, and high operating accuracy. Among various microfluidic cell separation technologies, inertial microfluidics has attracted great attention due to its simple structure and high throughput. In recent years, many researchers have explored the principles and applications of inertial microfluidics and developed different channel structures, including straight channels, curved channels, and multistage channels. However, the recently developed multistage channels have not been discussed and classified in detail compared with more widely discussed straight and curved channels. Therefore, in this review, a comprehensive and detailed review of recent progress in the multistage channel is presented. According to the channel structure, the inertial microfluidic separation technology is divided into (i) straight channel, (ii) curved channel, (iii) composite channel, and (iv) integrated device. The structural development of straight and curved channels is discussed in detail. And based on straight and curved channels, the multistage cell separation structures are reviewed, with a special focus on a variety of latest structures and related innovations of composite and integrated channels. Finally, the future prospects for the existing challenges in the development of inertial microfluidic cell separation technology are presented.

Inertial microfluidics for high-throughput cell analysis and detection: a review

Since it was first proposed in 2007, inertial microfluidics has been extensively studied in terms of theory, design, fabrication, and application. In recent years, with the rapid development of microfabrication technologies, a variety of channel structures that can focus, concentrate, separate, and capture bioparticles or fluids have been designed and manufactured to extend the range of potential biomedical applications of inertial microfluidics. Due to the advantages of high throughput, simplicity, and low device cost, inertial microfluidics is a promising candidate for rapid sample processing, especially for large-volume samples with low-abundance targets. As an approach to cellular sample pretreatment, inertial microfluidics has been widely employed to ensure downstream cell analysis and detection. In this review, a comprehensive summary of the application of inertial microfluidics for high-throughput cell analysis and detection is presented. According to application areas, the recent advances can be sorted into label-free cell mechanical phenotyping, sheathless flow cytometric counting, electrical impedance cytometer, high-throughput cellular image analysis, and other methods. Finally, the challenges and prospects of inertial microfluidics for cell analysis and detection are summarized.

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.

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.

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.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Multi-Dimensional Organic Mass Cytometry: Simultaneous Analysis of Proteins and Metabolites on Single Cells

Mass cytometry is attracting significant attention for enabling spatiotemporal high-throughput single-cell analysis. As the first demonstration of the simultaneous detection of single-cell proteins and untargeted metabolites, a multi-dimensional organic mass-cytometry system was established by a simple microfluidic chip connected to a nanoelectrospray mass spectrometer, providing useful heterogeneous information about the cells. A series of mass probes with online-dissociated mass tags were developed, ensuring the semi-quantification of cell-surface proteins and the compatibility of endogenous metabolite detection at the single-cell level. Six cell surface antigens and ≈100 metabolites from three ovarian-cancer cell types and two breast-cancer cell types were successfully monitored and contributed to highly sensitive and specific cell typing. Doxorubicin-resistant cancer-cell analysis confirmed the applications in distinguishing rare cell phenotypes. The proposed system is simple, extensible, and promising for cell typing, drug-resistance analysis of tumor cells, and clinical diagnosis and therapy at the single-cell level.

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

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

Microfluidic Probe for In-Situ Extraction of Adherent Cancer Cells to Detect Heterogeneity Difference by Electrospray Ionization Mass Spectrometry

The pathological studies of cancer tissues and cell molecules could provide an early diagnosis for the treatment of cancer. In this work, we have designed a microfluidic surface extractor (MSE). The MSE has been coupled with electrospray mass spectrometry (extraction reagent, methanol; optimum flow rate, 0.5 mL/h) to analyze the phospholipid content of different tumor cells. Three types of cancer cell lines, including A549 cells, HepG2 cells, and U87 cells, were investigated, and the principle component analysis (PCA: linear discriminant analysis (LDA), PC1 97.2%; PC2, 2.8%) was carried out to analyze the difference in the lipid contents. The LDA revealed heterogeneity among the cancer cells. The designed MSE could have potential applications in the clinical analysis of cancer tissues.

The up-to-date strategies for the isolation and manipulation of single cells

Due to the large cellular heterogeneity, the strategies for the isolation and manipulation of single cells have been pronounced indispensable in the fields of disease diagnostics, drug delivery, and cancer biology at the single-cell resolution. Herein, an overview of the up-to-date techniques for precise manipulation/separation and analysis of single-cell is accomplished, these include the various approaches for the isolation and detection of individual cells in flow cytometry, microfluidic systems, micromodule systems, and others. In addition, the advanced application of these protocols is discussed. In particular, a few designs are highlighted for visualization, non-invasion, and intelligentization in single cell analysis, i.e., imaging flow cytometry, label-free microfluidic platform, single-cell capillary probe, and other related techniques. At the present, the main barriers in the various schemes for single cell manipulation which limited their practical applications are their cumbersome construction and single-functionality. The future opportunities and outstanding challenges in the isolation/manipulation of single cells are depicted.

Inertial-Force-Assisted, High-Throughput, Droplet-Free, Single-Cell Sampling Coupled with ICP-MS for Real-Time Cell Analysis

Single-cell analysis facilitates perception into the most essential processes in life's mysteries. While it is highly challenging to quantify them at the single-cell level, where precise single-cell sampling is the prerequisite. Herein, a real-time single-cell quantitative platform was established for high-throughput droplet-free single-cell sampling into time-resolved (TRA) ICP-MS and real-time quantification of intracellular target elements. The concentrated cells (2 × 10⁶ cells mL^-1) were spontaneously and orderly aligned in a spiral microchannel with 104 periodic dimensional confined micropillars. The quantification is conducted simultaneously by internal standard inducing from another branch channel in the chip. The flow-rate-independent feature of single-cell focusing into an aligned stream within a wide range of fluidic velocities (100-800 μL min^-1) facilitates high-throughput, oil-free, single-cell introduction into TRA-ICP-MS. The system was used for real-time exploration of intracellular antagonism of Cu²⁺ against Cd²⁺. an obvious antagonistic effect was observed for the MCF-7 cell by culturing for 3, 6, 9, and 12 h with 100 μg L^-1 Cd²⁺ and 100 μg L^-1 Cu²⁺, and a rivalry rate of 12.8% was achieved at 12 h. At identical experimental conditions, however, limited antagonistic effect was encountered for a bEnd3 cell within the same incubation time period, with a rivalry rate of 4.81%. On the contrary, an antagonistic effect was not observed for the HepG2 cell by culturing for 6 h, while an obvious antagonistic effect was found by further culturing to 12 h, with a rivalry rate of 10.43%. For all three cell lines, significant heterogeneity was observed among individual cells.

Microfluidic Single-Cell Omics Analysis

The commonly existing cellular heterogeneity plays a critical role in biological processes such as embryonic development, cell differentiation, and disease progress. Single-cell omics-based heterogeneous studies have great significance for identifying different cell populations, discovering new cell types, revealing informative cell features, and uncovering significant interrelationships between cells. Recently, microfluidics has evolved to be a powerful technology for single-cell omics analysis due to its merits of throughput, sensitivity, and accuracy. Herein, the recent advances of microfluidic single-cell omics analysis, including different microfluidic platform designs, lysis strategies, and omics analysis techniques, are reviewed. Representative applications of microfluidic single-cell omics analysis in complex biological studies are then summarized. Finally, a few perspectives on the future challenges and development trends of microfluidic-assisted single-cell omics analysis are discussed.

Concentrating Single Cells in Picoliter Droplets for Phospholipid Profiling on a Microfluidic System

Cellular membranes are composed of a variety of lipids in different amounts and proportions, and alterations of them are usually closely related to various diseases. To reveal the intercellular heterogeneity of the lipid variation, an integrated microfluidic system is designed, which consists of droplet-based inkjet printing, dielectrophoretic electrodes, and de-emulsification interface to achieve on-line single-cell encapsulation, manipulation, and mass spectrometry (MS) detection. This integrated system effectively improves the single-cell encapsulation rate, and meanwhile reduces the matrix interference and continuous oil phase interference to the MS detection. Using this system, the heterogeneities between the normal and cancer cells are compared, and the heterogeneity of the same cells before and after the drug treatment changed obviously, indicating that this system can be used as a promising tool for studying the link between the alterations of lipid homeostasis and various diseases.

Aminophenylboronic Acid-Functionalized Thorny-Trap-Shaped Monolayer Microarray for Efficient Capture and Release of Circulating Tumor Cells

An aminophenylboronic acid (APBA)-functionalized thorny-trap-shaped monolayer microarray as a 3D fractal structures' substrate was fabricated to capture and release circulating tumor cells (CTCs) efficiently. The microarray can not only trap cells inside by microbowls but also enhance the interaction between cell and substrate by providing more binding sites and facilitating the spread of cell filopodias via the growth of nanorods. Modification of APBA enhanced the interaction further by binding with sialic acid of CTCs surface. The special topological structure achieved a high capture efficiency of 79.5%. The captured cancer cells were released without introducing any affinity molecules by a ligand exchange reaction with up to 70% efficiency and good proliferation. This substrate can isolate 33 tumor cells from a mimic blood sample even at a low spiked number of 50 cancer cells. This study provides valuable guidance for isolation and release of CTCs and is significant for the further study of tumors.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Single-cell identification by microfluidic-based in situ extracting and online mass spectrometric analysis of phospholipids expression

This work describes a microfluidic system for in situ extraction of a single-cell and its phosphatidylcholine analysis through mass spectrometry. This approach uncovered cellular heterogeneity among seemingly identical cells and provided a new platform for identification and classification of cells.

Real-Time Imaging of Ammonia Release from Single Live Cells via Liquid Crystal Droplets Immobilized on the Cell Membrane

Tumor cells exhibit prominent metabolic alterations through which they acclimatize to their stressful microenvironment. These cells have a high rate of glutaminolysis and release ammonia (NH3) as a byproduct, which may function as a diffusible signal among cancer cells and can reveal cellular heterogeneity. E7, a nematic liquid crystal (LC), is doped with 4-pentyl-4'-biphenyl carboxylic acid (PBA) and encapsulated in polymeric microcapsules (P-E7PBA), which are then immobilized on cells in a microfluidic channel. Normal human umbilical vein endothelial cells (HUVECs) and myeloma, human primary glioblastoma (U87), human colon carcinoma (Caco-2), and human breast adenocarcinoma (MCF-7) cells are investigated for the release of NH3. The P-E7PBA is able to visualize NH3 release from the cell via a radial-to-bipolar (R-B) orientation change, observed through a polarized optical microscope. The various cell lines significantly differ in their response time required for an R-B change. The mean response times for Caco-2, U87, and MCF-7 cells are 277, 155, and 121 s, respectively. NH3 release from a single cell captured in a microwell flow chip shows a similar R-B change. The P-E7PBA droplets technology could be applied to other multiple targets by functionalizing LCs with different probes.

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

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

Label-free Mass Cytometry for Unveiling Cellular Metabolic Heterogeneity

Comprehensive analysis of single-cell metabolites is critical since differences in cellular chemical compositions give rise to specialized biological functions. Herein, we propose a label-free mass cytometry by coupling flow cytometry to ESI-MS (named CyESI-MS) for high-coverage and high-throughput detection of cellular metabolites. Cells in suspension were isolated, online extracted by sheath fluid, and lysed during gas-assisted electrospray, followed by real-time MS analysis. Hundreds of metabolites, including nucleotides, amino acids, peptides, carbohydrates, fatty acyls, glycerolipids, glycerophospholipids, and sphingolipids, were detected and identified from one single cell. Discrimination of four types of cancer cell lines and even three subtypes of breast cancer cells was readily achieved using their distinct metabolic profiles. Furthermore, we screened out 102 characteristic ions from 615 detected peak signals for distinguishing breast cancer cell subtypes and identified 40 characteristic molecules which exhibited significant differences among these subtypes and would be potential metabolic markers for clinical diagnosis. CyESI-MS is expected to be a new-generation mass cytometry for studying cell heterogeneity on the metabolic level.

Mass Spectrometry Measurement of Single Suspended Cells Using a Combined Cell Manipulation System and a Single-Probe Device

Existing single cell mass spectrometry (SCMS) sampling platforms are largely designed to work only with immobilized cells and not the suspended cells isolated from patient samples. Here, we present a novel method that integrates a commercially available cell manipulation system commonly used for in vitro fertilization with the Single-probe SCMS sampling technology. The combined Single-probe SCMS/cell manipulating platform is capable of rapidly analyzing intracellular species in real time from a suspension leukemia cell line. A broad range of molecular species was detected, and species of interest were verified using tandem MS (MS/MS). Experimental results were analyzed utilizing statistical analyses such as principle component analysis (PCA) and  t-tests. The developed SCMS/cell manipulation system is a versatile tool to provide rapid single cell analysis of broad types of patient cell samples.

Single-cell assay on microfluidic devices

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

Advances in mass spectrometry based single-cell metabolomics

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