A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development.

data-independent acquisition mass spectrometry phosphopeptide phosphorylation proteomics stable isotope label targeted

Journal

Molecular & cellular proteomics : MCP
ISSN: 1535-9484
Titre abrégé: Mol Cell Proteomics
Pays: United States
ID NLM: 101125647

Informations de publication

Date de publication:
Oct 2023
Historique:
received: 28 03 2023
revised: 22 08 2023
accepted: 24 08 2023
pubmed: 2 9 2023
medline: 2 9 2023
entrez: 1 9 2023
Statut: ppublish

Résumé

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

Identifiants

pubmed: 37657519
pii: S1535-9476(23)00150-0
doi: 10.1016/j.mcpro.2023.100639
pmc: PMC10561125
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

100639

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflict of interest B. C. S. is a founder and shareholder in Proteome Software, which operates in the field of proteomics. A. J. N. and J. M. R. are employees of Cell Signaling Technology. A. W. H. and B. P. are employees of Thermo Fisher Scientific.

Auteurs

Brian C Searle (BC)

Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA. Electronic address: brian.searle@osumc.edu.

Allis Chien (A)

Mass Spectrometry Center, Stanford University, Stanford, California, USA.

Antonius Koller (A)

YatiriBio, San Diego, California, USA.

David Hawke (D)

BreakBio Corp, New York, New York, USA.

Anthony W Herren (AW)

UC Davis Genome Center, Proteomics Core, University of California Davis, Davis California, USA.

Jenny Kim Kim (J)

Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA.

Kimberly A Lee (KA)

Cell Signaling Technology, Inc, Danvers, Massachusetts, USA.

Ryan D Leib (RD)

Mass Spectrometry Center, Stanford University, Stanford, California, USA.

Alissa J Nelson (AJ)

Cell Signaling Technology, Inc, Danvers, Massachusetts, USA.

Purvi Patel (P)

Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA.

Jian Min Ren (JM)

Cell Signaling Technology, Inc, Danvers, Massachusetts, USA.

Paul M Stemmer (PM)

Department of Pharmaceutical Sciences, Wayne State University, Detroit, Michigan, USA.

Yiying Zhu (Y)

Cell Signaling Technology, Inc, Danvers, Massachusetts, USA.

Benjamin A Neely (BA)

National Institute of Standards and Technology, Charleston, South Carolina, USA.

Bhavin Patel (B)

Thermo Fisher Scientific, Rockford, Illinois, USA.

Classifications MeSH