Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression

Qing Yu, Xinyue Liu, Mark P. Keller, Jose Navarrete-Perea, Tian Zhang, Sipei Fu, Laura P. Vaites, Steven R. Shuken, Ernst Schmid, Gregory R. Keele, Jiaming Li, Edward L. Huttlin, Edrees H. Rashan, Judith Simcox, Gary A. Churchill, Devin K. Schweppe, Alan D. Attie, Joao A. Paulo and Steven P. Gygi ()
Additional contact information
Qing Yu: Harvard Medical School
Xinyue Liu: Harvard Medical School
Mark P. Keller: University of Wisconsin-Madison
Jose Navarrete-Perea: Harvard Medical School
Tian Zhang: Harvard Medical School
Sipei Fu: Harvard Medical School
Laura P. Vaites: Harvard Medical School
Steven R. Shuken: Harvard Medical School
Ernst Schmid: Harvard Medical School
Gregory R. Keele: The Jackson Laboratory
Jiaming Li: Harvard Medical School
Edward L. Huttlin: Harvard Medical School
Edrees H. Rashan: University of Wisconsin-Madison
Judith Simcox: University of Wisconsin-Madison
Gary A. Churchill: The Jackson Laboratory
Devin K. Schweppe: University of Washington
Alan D. Attie: University of Wisconsin-Madison
Joao A. Paulo: Harvard Medical School
Steven P. Gygi: Harvard Medical School

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.

Date: 2023
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DOI: 10.1038/s41467-023-36269-7

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