multiplexed DIA: plexDIA

Increasing the throughput of sensitive proteomics by plexDIA

 

plexDIA Preprint plexDIA code on GitHub


Current mass-spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. We aimed to increase throughput for analyzing limited samples while achieving high proteome coverage and quantitative accuracy. We developed a general experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using using 3-plex nonisobaric mass tags, plexDIA enables quantifying 3-fold more protein ratios among nanogram-level samples. Using 1 hour active gradients and first-generation Q Exactive, plexDIA quantified about 8,000 proteins in each sample of labeled 3-plex sets. Furthermore, plexDIA increases the consistency of protein quantification, resulting in over 2-fold reduction of missing data across samples. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content. The high sensitivity and accuracy of plexDIA detected many classical cell cycle proteins and discovered new ones. These results establish a general framework for increasing the throughput of highly sensitive and quantitative protein analysis.


plexDIA: Multiplexed data-independent acquisition for increasing proteomics throughput


Perspectives on high-throughput multiplexed proteomics

About the project

plexDIA is a project developed in the Slavov Laboratory at Northeastern University in collaboration with Demichev and Rasler Laboratories at Charité, Universitätsmedizin. It was authored by Jason Derks, Andrew Leduc, Harrison Specht, R. Gray Huffman, Markus Ralser, Vadim Demichev and Nikolai Slavov.

Contact the authors by email: nslavov{at}northeastern.edu.

This project was supported by funding from the NIH Director’s Award and by an Allen Distinguished Investigator Award from the Paul G. Allen Frontiers Group.