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

Dongxue Wang(Technical University of Munich), Basak Eraslan(Quantitative BioSciences), Thomas Wieland, Björn M. Hallström(Science for Life Laboratory), Thomas A. Hopf, Daniel P. Zolg(Technical University of Munich), Jana Zecha(Technical University of Munich), Anna Asplund(Uppsala University), Lihua Li(Technical University of Munich), Chen Meng(Technical University of Munich), Martin Frejno(Technical University of Munich), Tobias Schmidt(Technical University of Munich), Karsten Schnatbaum(JPT Peptide Technologies (Germany)), Mathias Wilhelm(Technical University of Munich), Fredrik Pontén(Uppsala University), Mathias Uhlén(Science for Life Laboratory), Julien Gagneur(Technical University of Munich), Hannes Hahne(Tumkur University), Bernhard Küster(Center for Integrated Protein Science Munich)
Molecular Systems Biology
February 1, 2019
Cited by 793Open Access
Full Text

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.


Related Papers

No related papers found

Powered by citation graph analysis