Evidence map›Paper›PMID 29988108›Full record

ArticleNature protocols2018

Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry.

Philipp Mertins, Lauren C Tang, Karsten Krug, David J Clark, Marina A Gritsenko, Lijun Chen, Karl R Clauser, Therese R Clauss, Punit Shah, Michael A Gillette and 20 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 312 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
312citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

312 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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252 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

30 authors.

Philipp MertinsProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Lauren C TangProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-8786-5418
Karsten KrugProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
David J ClarkDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Marina A GritsenkoBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.
Lijun ChenDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Karl R ClauserProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Therese R ClaussBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.
Punit ShahDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Michael A GilletteProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Vladislav A PetyukBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.
Stefani N ThomasDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-1679-5453
D R ManiProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Filip MundtProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Ronald J MooreBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.
Yingwei HuDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Rui ZhaoBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.
Michael SchnaubeltDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Hasmik KeshishianProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Matthew E MonroeBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.
Zhen ZhangDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Namrata D UdeshiProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-5312-1402
Deepak ManiProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Sherri R DaviesDepartment of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
R Reid TownsendDepartment of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
Daniel W ChanDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Richard D SmithBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.
Hui ZhangDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Tao LiuBiological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, USA.ORCID http://orcid.org/0000-0001-9529-6550
Steven A CarrProteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA. scarr@broad.mit.edu.ORCID http://orcid.org/0000-0002-7203-4299

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
YOGA LIFESTYLE INTERVENTION REDUCES BLOOD PRESSURE IN HIV-INFECTED ADULTSP41RR000954 · NCRR · WASHINGTON UNIVERSITY · PI GROSS, MICHAEL L · 1985 to 2011
$16.0M
Cancer Proteome Center at Washington Univ, Univ of North Carolina & Boise StateU24CA160035 · NCI · WASHINGTON UNIVERSITY · PI CHEN, XIAN, ELLIS, MATTHEW J · 2011 to 2016
$11.8M
Biological Breast Cancer Classification by qRT-PCRU01CA114722 · NCI · WASHINGTON UNIVERSITY · PI ELLIS, MATTHEW J · 2005 to 2009
$8.7M
MICROSCALED PROTEOGENOMICS FOR CANCER CLINICAL TRIALSU01CA214125 · NCI · BAYLOR COLLEGE OF MEDICINE · PI ANURAG, MEENAKSHI, CARR, STEVEN A · 2017 to 2021
$7.0M
Center of Excellence for High Throughput Proteogenomic CharacterizationU24CA210986 · NCI · BROAD INSTITUTE, INC. · PI CARR, STEVEN A, GILLETTE, MICHAEL A · 2016 to 2020
$6.4M
NOVEL BIOMARKERS FOR AROMATASE INHIBITOR THERAPYR01CA095614 · NCI · WASHINGTON UNIVERSITY · PI ELLIS, MATTHEW J, LUO, JINGQIN · 2003 to 2013
$5.6M
The Comprehensive Proteome Characterization Center at Johns Hopkins: High Precision Discovery and Confirmation of Genoproteomic TargetsU24CA210985 · NCI · JOHNS HOPKINS UNIVERSITY · PI CHAN, DANIEL WANYUI, ZHANG, HUI · 2016 to 2020
$5.3M
PNNL Proteome Characterization CenterU24CA210955 · NCI · BATTELLE PACIFIC NORTHWEST LABORATORIES · PI LIU, TAO, SMITH, RICHARD D · 2016 to 2020
$5.3M
Integrated Translational Genoproteomics Center at Washington UniversityU10CA180860 · NCI · WASHINGTON UNIVERSITY · PI CHUNG, CHRISTINE H, ELLIS, MATTHEW J · 2014 to 2018
$2.6M
NCATS NIH HHS UL1 TR000448NCATS NIH HHS UL1 TR002345NCI NIH HHS P30 CA091842NCI NIH HHS R01 CA095614NCI NIH HHS U01 CA114722NCI NIH HHS U24 CA160035NCI NIH HHS U24 CA210955NCI NIH HHS U24 CA210985NCI NIH HHS U24 CA210986NCRR NIH HHS P41 RR000954
6 · The paper itself

Abstract

Here we present an optimized workflow for global proteome and phosphoproteome analysis of tissues or cell lines that uses isobaric tags (TMT (tandem mass tags)-10) for multiplexed analysis and relative quantification, and provides 3× higher throughput than iTRAQ (isobaric tags for absolute and relative quantification)-4-based methods with high intra- and inter-laboratory reproducibility. The workflow was systematically characterized and benchmarked across three independent laboratories using two distinct breast cancer subtypes from patient-derived xenograft models to enable assessment of proteome and phosphoproteome depth and quantitative reproducibility. Each plex consisted of ten samples, each being 300 μg of peptide derived from <50 mg of wet-weight tissue. Of the 10,000 proteins quantified per sample, we could distinguish 7,700 human proteins derived from tumor cells and 3100 mouse proteins derived from the surrounding stroma and blood. The maximum deviation across replicates and laboratories was <7%, and the inter-laboratory correlation for TMT ratio-based comparison of the two breast cancer subtypes was r > 0.88. The maximum deviation for the phosphoproteome coverage was <24% across laboratories, with an average of >37,000 quantified phosphosites per sample and differential quantification correlations of r > 0.72. The full procedure, including sample processing and data generation, can be completed within 10 d for ten tissue samples, and 100 samples can be analyzed in ~4 months using a single LC-MS/MS instrument. The high quality, depth, and reproducibility of the data obtained both within and across laboratories should enable new biological insights to be obtained from mass spectrometry-based proteomics analyses of cells and tissues together with proteogenomic data integration.

Indexed as

AnimalsBenchmarkingBreast NeoplasmsChromatography, LiquidDisease Models, AnimalFemaleHeterograftsHigh-Throughput Screening AssaysHumansMass SpectrometryMiceNeoplasm TransplantationPhosphoproteinsProteomeProteomicsWorkflowPhosphoproteinsProteome

Identifiers

PMID29988108
PMCPMC6211289

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.