Mass Spectrometry-based Single-Cell Lipidomics: Advancements, Challenges, and the Path Forward

Mass spectrometry-based metabolomic as a powerful tool to unravel the component and mechanism in TCM

Mass spectrometry (MS)-based metabolomics has emerged as a transformative tool to unraveling components and their mechanisms in traditional Chinese medicine (TCM). The integration of advanced analytical platforms, such as LC-MS and GC-MS, coupled with metabolomics, has propelled the qualitative and quantitative characterization of TCM's complex components. This review comprehensively examines the applications of MS-based metabolomics in elucidating TCM efficacy, spanning chemical composition analysis, molecular target identification, mechanism-of-action studies, and syndrome differentiation. Recent innovations in functional metabolomics, spatial metabolomics, single-cell metabolomics, and metabolic flux analysis have further expanded TCM research horizons. Artificial intelligence (AI) and bioinformatics integration offer promising avenues for overcoming analytical bottlenecks, enhancing database standardization, and driving interdisciplinary breakthroughs. However, challenges remain, including the need for improved data processing standardization, database expansion, and understanding of metabolite-gene-protein interactions. By addressing these gaps, metabolomics can bridge traditional practices and modern biomedical research, fostering global acceptance of TCM. This review highlights the synergy of advanced MS techniques, computational tools, and TCM's holistic philosophy, presenting a forward-looking perspective on its clinical translation and internationalization.

Single-cell lipidomics: protocol development for reliable cellular profiling using capillary sampling

Single-cell lipidomics enables detailed analysis of the lipidomes of cells, but is challenged by small sample volumes, the risk of background interference and a lack of validation data. In this study, we explore the effect of different sampling variables on the lipid profiles of single pancreatic cancer cells, detected using liquid chromatography-mass spectrometry (LC-MS). We use automated and manual capillary sampling methods to isolate living single cells and evaluate different sampling media, capillary tips, aspiration volume, and temperature and humidity control. We demonstrate that automated and manual capillary sampling yield comparable lipid profiles when key parameters are controlled. Our findings highlight that appropriate blank correction, capillary tip type, and the control of aspiration volumes are all critical to preserving detection sensitivity. Conversely, choice of sampling medium does not affect lipidomics results. We also set out suggested best practices for these methodological variables, laying a foundation for robust, adaptable workflows in single-cell lipidomics for applications such as biomarker discovery and metabolic research.

Glial Biologist's Guide to Mass Spectrometry-Based Lipidomics: A Tutorial From Sample Preparation to Data Analysis

Neurological diseases are associated with disruptions in the brain lipidome that are becoming central to disease pathogenesis. Traditionally perceived as static structural support in membranes, lipids are now known to be actively involved in cellular signaling, energy metabolism, and other cellular activities involving membrane curvature, fluidity, fusion or fission. Glia are critical in the development, health, and function of the brain, and glial regulation plays a major role in disease. The major pathways of glial dysregulation related to function are associated with downstream products of metabolism including lipids. Taking advantage of significant innovations and technical advancements in instrumentation, lipidomics has emerged as a popular omics discipline, serving as the prevailing approach to comprehensively define metabolic alterations associated with organismal development, damage or disease. A key technological platform for lipidomics studies is mass spectrometry (MS), as it affords large-scale profiling of complex biological samples. However, as MS-based techniques are often refined and advanced, the relative comfort level among biologists with this instrumentation has not followed suit. In this review, we aim to highlight the importance of the study of glial lipids and to provide a concise record of best practices and steps for MS-based lipidomics. Specifically, we outline procedures for glia lipidomics workflows ranging from sample collection and extraction to mass spectrometric analysis to data interpretation. To ensure these approaches are more accessible, this tutorial aims to familiarize glia biologists with sample handling and analysis techniques for MS-based lipidomics, and to guide non-experts toward generating high quality lipidomics data.

Lipids: Emerging Players of Microglial Biology

Lipids are small molecule immunomodulators that play critical roles in maintaining cellular health and function. Microglia, the resident immune cells of the central nervous system, regulate lipid metabolism both in the extracellular environment and within intracellular compartments through various mechanisms. For instance, glycerophospholipids and fatty acids interact with protein receptors on the microglial surface, such as the Triggering Receptor Expressed on Myeloid Cells 2, influencing cellular functions like phagocytosis and migration. Moreover, cholesterol is essential not only for microglial survival but, along with other lipids such as fatty acids, is crucial for the formation, function, and accumulation of lipid droplets, which modulate microglial activity in inflammatory diseases. Other lipids, including acylcarnitines and ceramides, participate in various signaling pathways within microglia. Despite the complexity of the microglial lipidome, only a few studies have investigated the effects of specific lipid classes on microglial biology. In this review, we focus on major lipid classes and their roles in modulating microglial function. We also discuss novel analytical techniques for characterizing the microglial lipidome and highlight gaps in current knowledge, suggesting new directions for future research on microglial lipid biology.

Characterization of Key Lipid Components in the Cell Membrane of Freeze-Drying Resistant Lacticaseibacillus paracasei Strains Using Nontargeted Lipidomics

Lactic acid bacteria (LAB) are usually freeze-dried into powder for transportation and storage, with the bacterial membrane playing a crucial role in this process. However, different strains exhibit different levels of freeze-drying resistance in their cell membranes. In this study, Lacticaseibacillus paracasei (L. paracasei) strains 1F20, K56, and J5, demonstrating survival rates of 59.51, 25.86, and 4.05% after freeze-drying, respectively, were selected. The membrane structure and composition of these strains were subsequently analyzed. Bacterial live/dead staining results indicated that strain 1F20 maintained the highest membrane integrity after drying. Nontargeted lipidomics analysis revealed six differential lipid species that differed in membrane lipid compositions. KEGG functional enrichment analysis revealed 13 significantly different pathways, with glycerophospholipid metabolism being the most critical. This study explored the membrane composition of L. paracasei at the cellular level and identified key lipid species associated with freeze-drying resistance, providing a reference for screening highly resistant strains.

Single cell lipid biology

Lipids are major cell constituents endowed with astonishing structural diversity. The pathways responsible for the assembly and disposal of different lipid species are energetically demanding, and genes encoding lipid metabolic factors and lipid-related proteins comprise a sizable fraction of our coding genome. Despite the importance of lipids, the biological significance of lipid structural diversity remains largely obscure. Recent technological developments have enabled extensive lipid analysis at the single cell level, revealing unexpected cell-cell variability in lipid composition. This new evidence suggests that lipid diversity is exploited in multicellularity and that lipids have a role in the establishment and maintenance of cell identity. In this review, we highlight the emerging concepts and technologies in single cell lipid analysis and the implications of this research for future studies.

MALDI-2-Enabled Oversampling for the Mass Spectrometry Imaging of Metabolites at Single-Cell Resolution

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can provide valuable insights into the metabolome of complex biological systems such as organ tissues and cells. However, obtaining metabolite data at single-cell spatial resolutions presents a few technological challenges. Generally, spatial resolution is defined by the increment the sample stage moves between laser ablation spots. Stage movements less than the diameter of the focused laser beam (i.e., oversampling) can improve spatial resolution; however, such oversampling conditions result in a reduction in sensitivity. To overcome this, we combine an oversampling approach with laser postionization (MALDI-2), which allows for both higher spatial resolution and improved analyte ionization efficiencies. This approach provides significant enhancements to sensitivity for various metabolite classes (e.g., amino acids, purines, carbohydrates etc.), with mass spectral intensities from 6 to 8 μm pixel sizes (from a laser spot size of ∼13 μm) being commensurate with or higher than those obtained by conventional MALDI at 20 μm pixel sizes for many different metabolites. This technique has been used to map the distribution of metabolites throughout mouse spinal cord tissue to observe how metabolite localizations change throughout specific anatomical regions, such as those distributed to the somatosensory area of the dorsal horn, white matter, gray matter, and ventral horn. Furthermore, this method is utilized for single-cell metabolomics of human iPSC-derived astrocytes at 10 μm pixel sizes whereby many different metabolites, including nucleotides, were detected from individual cells while providing insight into cellular localizations.

Photoinduced Online Enrichment-Deglycosylation of Glycolipids for Enhancing Lipid Coverage and Identification in Single-Cell Mass Spectrometry

Single-cell lipidomics provides important information for molecular mechanisms of living processes and diseases at the individual cell level. However, single-cell lipidomic mass spectrometry (MS) techniques suffer from low lipid coverage and incomplete structural elucidation, especially for poorly ionizable glycosphingolipids (GSLs). Herein, a photoinduced enrichment-deglycosylation method of GSLs was developed and introduced into an ambient liquid extraction MS system for enhancing detection coverage and identification accuracy of GSLs in single-cell MS. GSL standards were selectively adsorbed on TiO2 in ammonia-added protic solvents. Under UV irradiation, the adsorbed GSLs would lose one hexosyl group (deglycosylation), and the products (>70% conversion efficiency) were desorbed from TiO2. By coating porous TiO2 into the capillary of the ambient liquid extraction MS system, online adsorption of GSLs and their separation from high-abundance phospholipids were achieved, largely reducing ion suppression. By UV irradiation, captured GSLs were rapidly deglycosylated and photodesorbed from TiO2 coating without solvent switching, resulting in 6-fold enrichment. With the new method, the detection coverage of GSLs was enhanced 9-fold without losing other lipidomes, compared with the conventional method. Moreover, deglycosylated GSLs from photodesorption had more MS/MS fragments than intact GSLs, facilitating detailed fatty acyl and sphingosine chain elucidation. Seven deglycosylated GSL peaks were identified with the confirmed hydroxyl group location in the fatty acyl chain, while only 1 was identified for intact GSL. The new method was applied to the single-cell lipidomics study of two types of nerve cells. Totally, 31 lipids including 11 GSLs were identified in a single cell, and 5 hexosylceramides were found significantly altered after neuron injury.

Recent Developments in Machine Learning for Mass Spectrometry

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement

Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.