Increasing the throughput of sensitive proteomics by plexDIA.

Area of Science:

• Proteomics
• Mass Spectrometry
• Biotechnology

Background:

• High-throughput proteomics methods are limited for low sample amounts.
• Existing techniques face challenges in depth and throughput for trace samples.

Purpose of the Study:

• To develop an experimental and computational framework for multiplexed proteomics.
• To increase the throughput and sensitivity of protein analysis in low-input samples.

Main Methods:

• Developed plexDIA, a framework for simultaneous multiplexing of peptide and sample analysis.
• Utilized three-plex non-isobaric mass tags for enhanced quantification.
• Employed 1-hour active gradients for high-throughput analysis.

Main Results:

• plexDIA increases throughput multiplicatively with the number of labels without compromising proteome coverage or quantitative accuracy.
• Quantified threefold more protein ratios in nanogram-level samples using three-plex labeling.
• ~8,000 proteins quantified per sample with reduced missing data.
• Quantified ~1,000 proteins per single human cell with 98% data completeness using minimal chromatography time.

Conclusions:

• plexDIA establishes a general framework for boosting the throughput of sensitive, quantitative protein analysis.
• The method significantly improves data completeness and reduces missing values in complex proteomic datasets.
• Achieved unprecedented protein quantification in single-cell proteomics.