Single-cell metabolite analysis by electrospray ionization mass spectrometry

Single-Cell Mass Spectrometry Studies of Secondary Drug Resistance of Tumor Cells

Patients with epidermal growth factor receptor mutant nonsmall cell lung cancer (NSCLC) often fail to treat gefitinib because of secondary drug resistance. The development of tumor drug resistance is closely related to variations in cancer cell metabolism. Single-cell metabolomics analysis can provide unique information about tumor drug resistance. Herein, we constructed a platform to study the secondary resistance of tumor cells based on single-cell metabolomics (sSRTC-scM). A gefitinib-resistant NSCLC cell line (PC9GR) was constructed by increasing the dose step by step. The metabolic profiles of parental PC9 cells and PC9GR cells with different drug resistance levels were detected by intact living-cell electrolaunching ionization mass spectrometry at the single-cell level. The data were analyzed by statistical methods such as t-SNE, variance, volcano plot, heat map, and metabolic pathway analysis. Using this platform, we found that the metabolic fingerprints of PC9GR cells can evaluate drug resistance degrees. The metabolic fingerprints continue to be altered with the increase of drug resistance. We revealed 19 metabolic markers of secondary resistance by variance analysis and clarified that the glycerophospholipid metabolic pathway of PC9GR cells changed significantly. In addition, we found that with the increase in drug resistance levels, the heterogeneity of single-cell metabolism became greater and the number of cells with weak drug resistance gradually decreased. This phenomenon can be utilized to illustrate the drug resistance degrees of PC9GR cells. This study provides diagnostic markers for evaluating the drug resistance of tumors and gives new insight into overcoming the secondary resistance of tumors.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

SCMeTA: a pipeline for single-cell metabolic analysis data processing

SUMMARY

To address the challenges in single-cell metabolomics (SCM) research, we have developed an open-source Python-based modular library, named SCMeTA, for SCM data processing. We designed standardized pipeline and inter-container communication format and have developed modular components to adapt to the diverse needs of SCM studies. The validation was carried out on multiple SCM experiment data. The results demonstrated significant improvements in batch effects, accuracy of results, metabolic extraction rate, cell matching rate, as well as processing speed. This library is of great significance in advancing the practical application of SCM analysis and makes a foundation for wide-scale adoption in biological studies.

AVAILABILITY AND IMPLEMENTATION

SCMeTA is freely available on https://github.com/SCMeTA/SCMeTA and https://doi.org/10.5281/zenodo.13569643.

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

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

Cellular nucleus image-based smarter microscope system for single cell analysis

Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.

Integrating aptasensor with an explosive mass-tag signal amplification strategy for ultrasensitive and multiplexed analysis using a miniature mass spectrometer

Mass probes attached with aptamers and mass tags offer excellent specificity and sensitivity for multiplexed detection, wherein the dissociation of mass tags from the mass probes is as important as their labeling. Herein, aggregation-induced emission luminogen (AIEgen)-tagged mass probes (AIEMPs) were established to analyze estrogens, which integrated aptasensor with an explosive mass-tag signal amplification strategy via a simple ultrasound-assisted emulsification of nanoliposomes. The AIEMPs were assembled by the hybridization of aptamer-modified Fe3O4 nanoparticles (Fe NPs@Apt) and nanoliposomes loaded with massive AIEgen mass tags and partially complementary DNA strands (AIE NLs@cDNA). The aptamer was preferentially and specifically bound to estrogen, resulting in the detachment of AIE NLs from AIEMPs. Subsequently, the AIEMPs were deposited with electrospray solvents for explosive release of mass tags. Using nanoelectrospray ionization mass spectrometry (nanoESI-MS), the AIEMP-based aptasensor achieved ultrasensitive analysis of estrogens with limits of detection of 0.168-0.543 pg/mL and accuracies in the range of 87.9-114.0%. Compared to direct nanoESI-MS detection, the AIEMP-based aptasensor provides a signal amplification of four orders of magnitude. Furthermore, the utilization of different AIEMPs enables multiplexed detection of three estrogens with a miniature mass spectrometer, showing promising potential for on-site detection. This work expands the diversity of mass-tagging strategy and provides a versatile mass probe-based aptasensor platform for routine MS detection of trace analytes.

A polymer dot-based NADH-sensitive electrochemiluminescence biosensor for analysis of metabolites in serum

Nicotinamide adenine dinucleotide (NADH) plays a pivotal role in metabolism. Convenient detection of NADH and its related metabolites has the pursuit of point-of-care and clinical analysis. Here, we propose a polymer dots (Pdots)-based NADH-sensitive electrochemiluminescence (ECL) biosensor for detection of NADH and three metabolites. Pdots acted as the efficient ECL emitters without additional modification to construct this biosensor. Specially, NADH both acted as the final detection target and at the same time as the bio-coreactants to sensitively influence the ECL intensities, in which NADH was generated or consumed in the presence of the target analyte and their specific enzyme. For glucose and lactic acid detection, NAD+ was reduced to NADH to generate an enhanced ECL signal. Conversely, for pyruvate detection, NADH was consumed to further decrease the ECL. The designed Pdots-based ECL biosensor showed wide detection ranges, high selectivity and low limits of detection of 4.6 μM, 0.7 μM and 0.5 μM for the analysis of three analytes, respectively. This strategy was successfully applied in quantifying the concentrations of glucose, lactic acid and pyruvate in human serum, which also has the potential to be implemented as a powerful and fast tool for ECL sensing of NADH and other related metabolites for point-of-care use and disease monitoring.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Quantification of Nitric Oxide in Single Cells Using the Single-Probe Mass Spectrometry Technique

Nitric oxide (NO) is a small molecule that plays important roles in biological systems and human diseases. The abundance of intracellular NO is tightly related to numerous biological processes. Due to cell heterogeneity, the intracellular NO amounts significantly vary from cell to cell, and therefore, any meaningful studies need to be conducted at the single-cell level. However, measuring NO in single cells is very challenging, primarily due to the extremely small size of single cells and reactive nature of NO. In the current studies, the quantitative reaction between NO and amlodipine, a compound containing the Hantzsch ester group, was performed in live cells. The product dehydro amlodipine was then detected by the Single-probe single-cell mass spectrometry technique to quantify NO in single cells. The experimental results indicated heterogeneous distributions of intracellular NO amounts in single cells with the existence of subpopulations.

Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells

In this work, we demonstrate the development and first application of nanocapillary sampling followed by analytical flow liquid chromatography-mass spectrometry for single-cell lipidomics. Around 260 lipids were tentatively identified in a single cell, demonstrating remarkable sensitivity. Human pancreatic ductal adenocarcinoma cells (PANC-1) treated with the chemotherapeutic drug gemcitabine can be distinguished from controls solely on the basis of their single-cell lipid profiles. Notably, the relative abundance of LPC(0:0/16:0) was significantly affected in gemcitabine-treated cells, in agreement with previous work in bulk. This work serves as a proof of concept that live cells can be sampled selectively and then characterized using automated and widely available analytical workflows, providing biologically relevant outputs.

Single-cell metabolomics by mass spectrometry: ready for primetime?

Single-cell metabolomics (SCMs) is a powerful tool for studying cellular heterogeneity by providing insight into the differences between individual cells. With the development of a set of promising SCMs pipelines, this maturing technology is expected to be widely used in biomedical research. However, before SCMs is ready for primetime, there are some challenges to overcome. In this review, we summarize the trends and challenges in the development of SCMs. We also highlight the latest methodologies, applications, and sketch the perspective for integration with other omics and imaging approaches.

Desorption electrospray ionization and matrix-assisted laser desorption/ionization as imaging approaches for biological samples analysis

The imaging of biological tissues can offer valuable information about the sample composition, which improves the understanding of analyte distribution in such complex samples. Different approaches using mass spectrometry imaging (MSI), also known as imaging mass spectrometry (IMS), enabled the visualization of the distribution of numerous metabolites, drugs, lipids, and glycans in biological samples. The high sensitivity and multiple analyte evaluation/visualization in a single sample provided by MSI methods lead to various advantages and overcome drawbacks of classical microscopy techniques. In this context, the application of MSI methods, such as desorption electrospray ionization-MSI (DESI-MSI) and matrix-assisted laser desorption/ionization-MSI (MALDI-MSI), has significantly contributed to this field. This review discusses the evaluation of exogenous and endogenous molecules in biological samples using DESI and MALDI imaging. It offers rare technical insights not commonly found in the literature (scanning speed and geometric parameters), making it a comprehensive guide for applying these techniques step-by-step. Furthermore, we provide an in-depth discussion of recent research findings on using these methods to study biological tissues.

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

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

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

Analyzing macromolecular composition of E. Coli O157:H7 using Raman-stable isotope probing

Metabolic dynamics of bacterial cells is needed for understanding the correlation between changes in environmental conditions and cell metabolic activity. In this study, Raman spectroscopy combined with deuterium labelling was used to analyze the metabolic activity of a single Escherichia coli O157:H7 cell. The incorporation of deuterium from heavy water into cellular biomolecules resulted in the formation of carbon-deuterium (CD) peaks in the Raman spectra, indicating the cell metabolic activity. The broad vibrational peaks corresponding to CD and CH peaks encompassed different specific shifts of macromolecules such as protein, lipids, and nucleic acid. The utilization of tryptophan and oleic acid by the cell as the sole carbon source led to changes in cell lipid composition, as indicated by new peaks in the second derivative spectra. Thus, the proposed method could semi-quantitatively determine total metabolic activity, macromolecule specific identification, and lipid and protein metabolism in a single cell.

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

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

Computational Methods for Single-Cell Imaging and Omics Data Integration

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