Significance: Unsaturated lipids constitute a significant portion of total lipids in mammalian cells. They fulfill different biological roles and often exist in isomeric structures that differ only in the locations of carbon-carbon double (C=C) bonds. A long-standing challenge in lipidomics is the identification and quantitation of lipid C=C location isomers at adequate sensitivity. In this study, we have developed a solution to this challenge by implementing online photochemical derivatization (Paternò–Büchi reaction) with tandem mass spectrometry into a shotgun lipidomics workflow. The method is widely applicable for different classes of lipids and is sensitive, fast, and compatible with different MS platforms. It shall serve as an enabling tool for advancing studies in lipid biology and biomarker discovery.

In this paper, we have constructed a low temperature plasma (LTP) probe using dielectric barrier discharge (DBD) and employed it for the detection of explosives on a variety of substrates under ambient conditions. Upon discharge, a transient, low-temperature non-equilibrium plasma comprising ions, electrons and metastable atoms are generated between the electrodes. Three common explosives, 2,4,6-trinitrotoluene (TNT), hexahydro-1,3,5-trinitro-1,3,5-trinitro-1,3,5-triazine (RDX), and pentaerythritol tetranitrate (PETN), were directly desorbed and ionized from solid surfaces, followed by subsequent analysis using the mass spectrometer in the negative ion mode. Limits of detection (LODs) were 500 fg for TNT, 1 pg for RDX, and 500 fg for PETN. The reliability of the method was characterized by a successful analysis of a mixture of the three explosives. The ion source also allowed direct detection of trace explosives on both conductive and non-conductive substrates, thus expanding the applicability of low temperature plasma desorption mass spectrometry.

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 andsn-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) andsn-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 andsn-position isomers.

Single-cell analysis is critical to revealing cell-to-cell heterogeneity that would otherwise be lost in ensemble analysis. Detailed lipidome characterization for single cells is still far from mature, especially when considering the highly complex structural diversity of lipids and the limited sample amounts available from a single cell. We report the development of a general strategy enabling single-cell lipidomic analysis with high structural specificity. Cell fixation is applied to retain lipids in the cell during batch treatments prior to single-cell analysis. In addition to tandem mass spectrometry analysis revealing the class and fatty acyl-chain for lipids, batch photochemical derivatization and single-cell droplet treatment are performed to identify the C=C locations and sn-positions of lipids, respectively. Electro-migration combined with droplet-assisted electrospray ionization enables single-cell mass spectrometry analysis with easy operation but high efficiency in sample usage. Four subtypes of human breast cancer cells are correctly classified through quantitative analysis of lipid C=C location or sn-position isomers in ~160 cells. Most importantly, the single-cell deep lipidomics strategy successfully discriminates gefitinib-resistant cells from a population of wild-type human lung cancer cells (HCC827), highlighting its unique capability to promote precision medicine.