{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yu Q"],"funding":["NIDDK NIH HHS","NCI NIH HHS","NIGMS NIH HHS"],"pagination":["555"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9894840"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_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 cr"],"journal":["Nature communications"],"pubmed_title":["Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression."],"pmcid":["PMC9894840"],"funding_grant_id":["R01 GM132129","R01 DK101573","RC2 DK125961","K99 CA273170","R01 GM067945"],"pubmed_authors":["Keele GR","Attie AD","Fu S","Li J","Churchill GA","Schmid E","Yu Q","Simcox J","Huttlin EL","Keller MP","Liu X","Shuken SR","Navarrete-Perea J","Zhang T","Vaites LP","Paulo JA","Rashan EH","Gygi SP","Schweppe DK"],"additional_accession":[]},"is_claimable":false,"name":"Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression.","description":"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 cr","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-03-17T15:43:47.196Z","creation":"2025-04-06T14:06:35.511Z"},"accession":"S-EPMC9894840","cross_references":{"pubmed":["36732331"],"doi":["10.1038/s41467-023-36269-7"]}}