Proteomics experiments are applied to large populations of cells, representing the average protein expression under given biological conditions. However, understanding the cellular heterogeneity provides insights that cannot be gained from bulk studies, such that the analysis of single-cell protein expression is of growing interest. Current LC-MS-based proteomics workflows have not been widely applicable to single cell analysis, mainly due to large sample losses during sample preparation, limited analytical sensitivity and low throughput. To address these challenges, we have leveraged proteoChip technology with or without tandem mass tag (TMT) isobaric labeling to analyze single mammalian cells containing ~0.2 ng total proteins on the new Orbitrap Ascend™ Tribrid™ mass spectrometer with real time search and the FAIMS Pro™ Interface to improve single cell proteome coverage and enhance quantitation accuracy.  Single cells were isolated and processed in an automated fashion using the celleONE instrument. Processed samples were then injected into an LCMS system equipped with µPAC™ HPLC Columns coupled to the Orbitrap Ascend. Proteome Discoverer™ 3.0 software was used for data analysis. Both label-free and isobaric labelling (TMT16plex)-based protein quantitation were evaluated with a focus on reproducibility and improved throughput. The FAIMS-enhanced label-free workflow resulted in the quantitation of an average over 2000 protein groups from single Hela and HEK cells. Furthermore, the workflow was evaluated with on dissociated tumor cells, as well as PBMCs derived from the tumors. This work demonstrates that single cell proteomes can be quantified using label-free or TMT workflows by combing automated sample preparation with ultra-high efficient separations, the Orbitrap Ascend Tribrid mass spectrometer, and the FAIMS Pro Interface, enabling researchers to investigate cell heterogeneity as well as rare cells in an ultra-sensitive, higher throughput LC-MS analysis

Ultra-sensitive LC/MS workflows for high-throughput single-cell proteomics

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