Framework for increasing proteomics throughput

Simultaneously achieving achieving deep proteome coverage, high data completeness, high throughput, and high sensitivity has been a long standing challenge in the field of proteomics. This challenge is particularly evident and relevant to single-cell proteomics, which has the potential to enable mechanistic investigations of key biological questions, such as signaling mechanisms based on protein binding, modifications, and degradation. Realizing this potential requires scaling up throughput and increasing the efficiency of ion sampling

This may be achieved by accumulating ions in parallel as supported by data independent acquisition (DIA). Ideally such parallel accumulation should be synergistically combined with sample (single cell) multiplexing strategies to support the high throughput needed by most single-cell applications. The mass offsets introduced by multiplexing with non isobaric mass tags, such as precisely known offsets in mass/charge space, may provide additional constraints on peptide identification. Towards achieving these aims, plexDIA introduced algorithms to acquire and analyze multiplexed DIA data.

The simultaneous multiplexing of peptides and samples allows for multiplicative increase in quantitative protein data points, both with bulk and with single-cell samples. These results point to a framework for multiplicative scaling of single-cell proteomics that has much potential for further advances, such as engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. 

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