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

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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., 2023. "Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36269-7
    DOI: 10.1038/s41467-023-36269-7
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    Cited by:

    1. Heta Desai & Katrina H. Andrews & Kristina V. Bergersen & Samuel Ofori & Fengchao Yu & Flowreen Shikwana & Mark A. Arbing & Lisa M. Boatner & Miranda Villanueva & Nicholas Ung & Elaine F. Reed & Alexe, 2024. "Chemoproteogenomic stratification of the missense variant cysteinome," Nature Communications, Nature, vol. 15(1), pages 1-24, December.

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