Accumulating evidence suggests that probing spatial distribution of biomolecules within tissues at the single-cell level provides critical information to decipher molecular heterogeneity and reveal individual cell dynamics. Here, we utilized trapped ion mobility separation coupled with mass spectrometry imaging (MSI) to enable high throughput <i>in situ</i> profiling of single cell (SC) lipidomes at subcellular resolution. Multimodal SC imaging, in which dual-polarity-mode MSI was used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage and sample analysis throughput. Subcellular resolution SC-MSI identified both inter- and intra-cellular lipid heterogeneity; this heterogeneity was further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterized SC lipidome alteration in response to stearoyl-CoA desaturase 1 (SCD1) inhibition and revealed SC lipidome remodeling induced by pharmacological intervention.  Furthermore, this integrated multimodal SC-MSI technology also enabled identification of new layer-specific lipid distribution patterns in mouse cerebellar cortex, facilitating lipidomic profiling in the brain regions of a mouse model of Alzheimer's disease. <br/>In addition to single-cell lipidomic analysis, we are also developing a set of novel multiplexed isobaric tags to facilitate glycomics analysis at the single-cell level.  By employing mass-defect phenomenon and a high-resolution orbitrap instrument, 6-plex mass-defect SUGAR tags have been developed and enabled MS1-level quantitation of glycans released from glycoproteins and single tumor cells.  Collectively, we have developed sampling tools and tagging reagents that enable multi-omic analysis of single cells with spatial and quantitative information.

Single-cell multiomic analyses enabled by high-throughput MALDI mass spectrometry imaging (MSI) and isobaric tagging approaches

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