Evidence map›Paper›PMID 30777892›Full record

ArticleMolecular systems biology2019

A deep proteome and transcriptome abundance atlas of 29 healthy human tissues.

Dongxue Wang, Basak Eraslan, Thomas Wieland, Björn Hallström, Thomas Hopf, Daniel Paul Zolg, Jana Zecha, Anna Asplund, Li-Hua Li, Chen Meng and 9 more

Abstract read
In one paragraph

Article in Molecular systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 442 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
442citing papers in PubMed, 1 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

442 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Type VIII collagen: advances in matrix biology and translational promise.Frontiers in bioengineering and biotechnology · 2025
    Pooled it
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382 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

19 authors.

Dongxue WangChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.ORCID 0000-0002-4402-0690
Basak EraslanComputational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany.
Thomas WielandOmicScouts GmbH, Freising, Germany.
Björn HallströmScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
Thomas HopfOmicScouts GmbH, Freising, Germany.
Daniel Paul ZolgChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
Jana ZechaChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
Anna AsplundScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Li-Hua LiChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
Chen MengChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
Martin FrejnoChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.ORCID 0000-0002-6651-1773
Tobias SchmidtChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
Karsten SchnatbaumJPT Peptide Technologies GmbH, Berlin, Germany.
Mathias WilhelmChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany.
Frederik PontenScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-0703-3940
Mathias UhlenScience for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.
Julien GagneurComputational Biology, Department of Informatics, Technical University of Munich, Garching bei München, Germany gagneur@in.tum.de hannes.hahne@omicscouts.com kuster@tum.de.ORCID 0000-0002-8924-8365
Hannes HahneOmicScouts GmbH, Freising, Germany gagneur@in.tum.de hannes.hahne@omicscouts.com kuster@tum.de.ORCID 0000-0003-3601-0051
Bernhard KusterChair of Proteomics and Bioanalytics, Technische Universität München, Freising, Germany gagneur@in.tum.de hannes.hahne@omicscouts.com kuster@tum.de.ORCID 0000-0002-9094-1677

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-, transcriptome- and proteome-wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein-level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue-specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.

Indexed as

Gene Expression RegulationGenome, HumanHumansMass SpectrometryProteomeProteomicsRNA, MessengerSequence Analysis, RNATissue DistributionTranscriptomeProteomeRNA, Messengerhuman proteomehuman transcriptomeproteogenomicsquantitative mass spectrometryRNA‐Seq

Identifiers

PMID30777892
PMCPMC6379049

What OpenQuestion holds

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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.