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-generation platform, we will profile >10,000 cells to study acquired resistance to autophagy
inhibitors in the context of autophagy-dependent triple negative breast cancer, thus establishing an innovative
platform for advancing biomedical research and individualizing therapy.
