Here we introduce three intelligent data acquisition approaches for use in the analysis of single cells. The first two utilize a carrier proteome labeled with an offset mass chemical tag (i.e., TMTPro-SH) to enable peptide detection, selection, and quantification. An offset mass carrier proteome is advantageous because it separates the carrier proteome peptide cluster from the multiplexed single cell proteomes and enables quantification of single cells in the absence of carrier proteome reporter ions. An offset mass carrier proteome also enables longer accumulation of endogenous single cell ions without the associated accumulation of carrier proteome ions. The first approach, SureQuant-TMT, utilizes the standard instrument method editor to enable the monitoring of thousands of peptides derived from a TMT-SH labeled proteome. This technique has the potential to improve the depth and reproducibility of single cell proteomics by targeting the most abundant peptides from proteins within the sample. The second approach introduces a novel API programming platform, called inSeqAPI, that enables facile construction of custom real-time data analysis methods. We demonstrate its utility by constructing a method that performs a real-time search on a TMTPro-SH labeled carrier proteome and triggers quantitative MS2 or MS3 analysis on endogenous TMTPro labeled single cell proteomes only when a carrier proteome peptide is identified. This unbiased approach enables deep proteomic analysis and maintains accuracy of single cell proteome quantification even when high levels of carrier proteome are used. The last approach called PairQuant utilizes inSeqAPI to improve the quantification of pairs in hyperplexed samples which is a potential path towards increasing the number of samples simultaneously measured with multiplexed single cell analysis.

Intelligent data acquisition approaches to single cell proteomics

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