PROJECT SUMMARY/ABSTRACT Cancer tissues exhibit a high degree of phenotypic heterogeneity and plasticity and contain numerous subpopulations of cells in various states. Quantifying this heterogeneity at the single-cell level and with molecular depth across large numbers of cells provides information that cannot be obtained at the bulk scale and will ultimately lead to improved diagnostics and more effective treatments. While single-cell nucleic acid sequencing approaches are having a significant impact on cancer research, proteins mediate the bulk of cellular function and are the targets of most therapeutics. There is thus an urgent need to develop new technologies for large- scale direct proteome profiling at the single-cell level. To fill this gap, mass spectrometry (MS)-based profiling of protein expression in single cells has recently been demonstrated through the implementation of more efficient sample processing workflows, novel experimental designs and improved instrument sensitivity. Label-free MS- based proteomics can now quantify >2,000 protein groups per cell across >4 orders of magnitude of dynamic range, but efforts to profile more than a few dozen cells per day have resulted in significantly reduced proteome coverage. This low throughput is insufficient for the large-scale statistically powered studies required to characterize heterogeneity in cancer cell populations. To increase measurement throughput, multiplexed workflows based on isobaric tandem mass tags (TMTs) enable up to 18 single cells to be measured in an LC- MS analysis, but these have still been limited to ~100 cells/day and, as generally implemented, suffer from a large proportion of missing values and other issues affecting quantitative performance. Our overall objective is to develop a platform that combines simplified pipette-free high-throughput sample preparation with rapid, multicolumn liquid chromatography separations and ‘greedy’ data-dependent acquisition to profile >2000 proteins per cell with a measurement throughput of >1000 single cells per day. We hypothesize that the advanced sample preparation and separation, combined with a far more efficient MS acquisition workflow, will achieve in-depth SCP with a 10× throughput gain, thus providing a capability for direct, in-depth and large-scale protein quantification that is analogous to single-cell RNA-seq. Studies in Aim 1 will focus on developing massively parallel centrifugal nanoliter dispensing to prepare >10,000 single-cells per day at a total reagent and consumables cost of <$0.40/cell. In Aim 2, we will develop rapid, robust and high-peak-capacity 20-min nanoLC separations with 100% duty cycle. In Aim 3, we will develop a novel ‘greedy’ data acquisition strategy in which only proteotypic peptides are selected for fragmentation, and with custom automatic gain control settings and fragmentation energy for each peptide, providing an unprecedented combination of sensitivity and throughput. With this next-gene...