Lipids play a pivotal role in biological processes and lipid analysis by mass spectrometry (MS) has significantly advanced lipidomic studies. While the structure specificity of lipid analysis proves to be critical for studying the biological functions of lipids, current mainstream methods for large-scale lipid analysis can only identify the lipid classes and fatty acyl chains, leaving the C=C location and sn-position unidentified. In this study, combining photochemistry and tandem MS we develop a simple but effective workflow to enable large-scale and near-complete lipid structure characterization with a powerful capability of identifying C=C location(s) and sn-position(s) simultaneously. Quantitation of lipid structure isomers at multiple levels of specificity is achieved and different subtypes of human breast cancer cells are successfully discriminated. Remarkably, human lung cancer tissues can only be distinguished from adjacent normal tissues using quantitative results of both lipid C=C location and sn-position isomers.

Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. To achieve highest spatial resolution, we reported a novel Spatial single nuclEar metAboloMics (SEAM) method, a scalable platform combining high resolution imaging mass spectrometry (IMS) and a series of computational algorithms, that can display multiscale/multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We firstly applied SEAM to a range of wild type mouse tissues, then delineate a consistent pattern of metabolic zonation in mouse liver. We further studied spatial metabolome in human fibrotic liver. Intriguingly, we discovered novel subpopulations of hepatocytes with special metabolic features associated with their proximity to fibrotic niche, which was further validated by spatial transcriptomics with Geo-seq. These demonstrations highlight how SEAM may be used to explore the spatial metabolome and tissue anatomy at single cell level, hence leading to a deeper understanding of the tissue metabolic organization.

Objectives The correlations between long non‐coding RNAs (lncRNAs) and diverse mammal diseases have been clarified by many researches, but the cognition about bovine mastitis‐related lncRNAs remains limited. This study aimed to investigate the potential role of lncRNA X‐inactive specific transcript (XIST) in the inflammatory response of bovine mammary epithelial cells. Materials and methods Two inflammatory bovine mammary alveolar cell‐T (MAC‐T) models were established by infecting the cells with Escherichia coli (E. coli) and Staphylococcus aureus ( S. aureus ). The expressions of pro‐inflammatory cytokines were measured, and the proliferation, viability and apoptosis of the inflammatory cells were evaluated after XIST was knocked down by an siRNA. The relationship among XIST, NF‐κB pathway and NOD‐like receptor protein 3 (NLRP3) inflammasome was investigated using an inhibitor of NF‐κB signal pathway. Results The expression of XIST was abnormally increased in bovine mastitic tissues and inflammatory MAC‐T cells. Silencing of XIST significantly increased the expression of E. coli or S. aureus ‐induced pro‐inflammatory cytokines. Additionally, knockdown of XIST could inhibit cell proliferation, suppress cell viability and promote cell apoptosis under inflammatory conditions. Furthermore, XIST inhibited E. coli or S. aureus ‐induced NF‐κB phosphorylation and the production of NLRP3 inflammasome. Conclusions The expression of XIST was promoted by activated NF‐κB pathway and, in turn, XIST generated a negative feedback loop to regulate NF‐κB/NLRP3 inflammasome pathway for mediating the process of inflammation.

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.

We have combined droplet extraction and a pulsed direct current electrospray ionization mass spectrometry method (Pico-ESI-MS) to obtain information-rich metabolite profiling from single cells. We studied normal human astrocyte cells and glioblastoma cancer cells. Over 600 tandem mass spectra (MS) of metabolites from a single cell were recorded, allowing the successful identification of more than 300 phospholipids. We found the ratios of unsaturated phosphatidylcholines (PCs) to saturated PCs were significantly higher in glioblastoma cells compared to normal cells. In addition, both isomeric PC (17:1) and (phosphatidylethanolamine) PE (20:1) were found in glioblastoma cells, whereas only PC (17:1) was observed in astrocyte cells. Our method paves the way to characterize the chemical contents of single cells, providing rich metabolome information. We suggest that this technique is general and can be applied to other life science studies such as differentiation and drug resistance of individual cells.