Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA

Abstract

Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include 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. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.

Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics

“…24,41,42 important role in achieving the high sample throughput required for single-cell applications. 11,13 We do not find empirical evidence of such a relationship in SCP data, especially for multiplexed data sets (Figure 2). Future efforts should consider cell-to-cell heterogeneity and batch effects when deciphering the missing data mechanisms in the SCP data.…”

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

“…LCM has been used for spatially resolved extraction and subsequent MS analysis of tissue regions 31 . The application of plexDIA and isotopologous carriers 7,32 are showing promise to extend this analysis to single cells extracted by LCM 33 . We recommend avoiding the use of protocols that require cleanup from detergents for tissue disruption and instead prefer methods using only MS-compatible reagents.…”

“…These tend to be more prevalent in single-cell proteomics than in typical bulk experiments as some proteins may be below the limit of detection (especially in smaller cells) or may not be sent for MS 2 analysis in every single cell. The latter problems can be fundamentally resolved by using DIA or prioritized data acquisition, and such methods substantially increase data completeness 7,18,32 .…”

“…Indeed, current single-cell proteomic MS methods are capable of measuring tens of thousands of peptide-like features; however, only a small fraction (between 1% and 10%) of these features are assigned sequences at 1% FDR 20,56,77 . Anticipated models that successfully address these unique challenges will enable identification rates to approach those of bulk experiments and extend the utility of single-cell proteomics in biomedical research 32,77 .…”

Great Gains in Mass Spectrometry Data Interpretation

“…This individual example highlights a general point: MS collects large data sets of highly accurate and precise measurements, and improvements in their interpretation offer opportunities for great gains. These opportunities are particularly prominent in challenging contexts, such as single-cell proteomics, intact protein analysis, and plexDIA; indeed, almost any MS data analysis workflow can be substantially improved, sometimes by over 100% as demonstrated by CIDS …”