Evidence map›Paper›PMID 32916130›Full record

ArticleCell2020

A Quantitative Proteome Map of the Human Body.

Lihua Jiang, Meng Wang, Shin Lin, Ruiqi Jian, Xiao Li, Joanne Chan, Guanlan Dong, Huaying Fang, Aaron E Robinson, GTEx Consortium and 1 more

Abstract read
In one paragraph

Article in Cell, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 308 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
308citing 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

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

  1. Brain Proteomic Responses to Glucocorticoids and Their Relationship With Transcriptome: A Systematic Meta-Analysis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Pooled it
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248 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

11 authors.

Lihua JiangDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Meng WangDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Shin LinDivision of Cardiology, University of Washington, Seattle, WA 98195, USA.
Ruiqi JianDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Xiao LiDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Joanne ChanDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Guanlan DongDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Huaying FangDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Aaron E RobinsonDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
GTEx Consortium
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: mpsnyder@stanford.edu.

Funding

Pilot and Feasibility ProgramP30DK020595 · NIDDK · UNIVERSITY OF CHICAGO · PI Raghavendra G Mirmira · 2013 to 2026
$20.9M
Special EquipmentP50HG007735 · NHGRI · STANFORD UNIVERSITY · PI CHANG, HOWARD Y · 2014 to 2018
$15.2M
Center for Personal Dynamic RegulomesRM1HG007735 · NHGRI · STANFORD UNIVERSITY · PI CHANG, HOWARD Y · 2019 to 2023
$13.8M
A portal and integrative collaborative analysis platform for GTExU41HG009494 · NHGRI · BROAD INSTITUTE, INC. · PI ARDLIE, KRISTIN · 2017 to 2021
$5.2M
Machine Learning for Decoding the Genetic Architecture of Complex Traits and Diseases Across PopulationsR35GM127063 · NIGMS · STANFORD UNIVERSITY · PI Hua Tang · 2018 to 2026
$2.2M
Genotype-Tissue-Protein: proteomic variation and quantitative trait loci (pQTL)U01HG007611 · NHGRI · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P., TANG, HUA · 2014 to 2014
$1.7M
NHGRI NIH HHS P50 HG007735NHGRI NIH HHS RM1 HG007735NHGRI NIH HHS U01 HG007611NHGRI NIH HHS U41 HG009494NIDDK NIH HHS P30 DK020595NIGMS NIH HHS R35 GM127063
6 · The paper itself

Abstract

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.

Indexed as

Gene ExpressionGene Expression ProfilingHumansProteomeProteomicsRNARNA, MessengerTranscriptomeProteomeRNARNA, MessengerBBS syndromebranched-chain amino acid metabolismLeigh syndromemass spectrometryprotein and RNA correlationquantitative proteomicssecretiontissue-enriched or -specific proteinsTMT

Identifiers

PMID32916130
PMCPMC7575058

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.