The development of effective therapies to advance human health requires an in-depth molecular-level understanding of cellular processes and dynamic interactions between individual cells. Conventional population- based biochemical measurements provide limited utility, as contributions from individual cells are averaged and crucial information is lost. Direct measurements of the biochemical makeup of single cells are thus needed to characterize cellular transitions, regulatory mechanisms and the contribution of the microenvironment. Single- cell RNA sequencing is making a tremendous impact on biological research, but proteins mediate the bulk of cellular function and the correlation between RNA and protein abundance is often poor. In addition, RNA measurements are unable to inform on important posttranslational modifications that are readily measured by mass spectrometry. Current efforts to directly quantify targeted proteins in single cells such as CyTOF and immunohistochemistry share common shortcomings in that only a limited number of proteins can be analyzed. There is thus an urgent unmet need for technologies capable of directly generating unbiased and in-depth single- cell protein profiles to provide a more complete picture of cellular processes. We recently developed a proof-of- concept platform termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples) that effectively downscales sample processing volumes to the nanoliter scale to reduce sample losses. In combination with ultrasensitive liquid chromatography-mass spectrometry (LC-MS), nanoPOTS enables global proteome profiling of ~1000 protein groups in individual dissociated cells isolated by cell sorting or small regions of tissue sections isolated by microdissection. Building upon this proof-of-concept platform, our overall objective is to develop a fully automated prototype that yields far greater proteome coverage and throughput than is currently achievable, 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 fully automating sample preparation and decreasing sample processing volumes at least tenfold to further reduce sample losses and increase proteome coverage.
Aim 2 will automate sample transfer to the analytical platform and develop a fully automated and ultrasensitive LC-MS workflow with 100% MS utilization efficiency.
Aim 3 will extend these advances in sensitivity, throughput and automation to the multiplexed analysis of single cells based on barcoding with unique isobaric labels. We will combine two distinct multiplexing approaches to enable simultaneous analysis of up to 32 samples in a single run. The completed platform will be fully automated, capable of highly quantitative label-free and multiplexed single cell proteome profiling to a depth of >3000 proteins per cell, and will achieve an unprecedented measurement throughput of >300 single cells per day for multiplexed analyses. This will constitute a unique and broadly enabling technology for the acquisition of basic biomedical knowledge.
Advancing human health requires an in-depth molecular understanding of dynamic cellular processes, particularly for the proteins that mediate the bulk of cellular function. Conventional population-level proteome measurements are unable to resolve contributions from individual cells, and current approaches to profile protein expression in single cells are lacking in molecular depth, robustness, quantitative accuracy and measurement throughput. The proposed effort will provide a fully automated sample processing and analysis platform that is capable of direct and unbiased single-cell proteome profiling with unprecedented throughput and sensitivity, thus providing a unique and broadly enabling technology for large-scale, in-depth profiling of thousands of cells.
