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.
| Original language | English |
|---|---|
| Pages (from-to) | 269-283.e19 |
| Journal | Cell |
| Volume | 183 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Oct 1 2020 |
Bibliographical note
Publisher Copyright:© 2020 Elsevier Inc.
Funding
We acknowledge the GTEx donors and families for donating organs to the GTEx Consortium. We thank K. Ardlie for coordinating the distribution of tissue samples. We also thank A. Breschi for discussion on RNA-seq data analysis and R. Tibshirani for suggestions on statistical analysis. We thank H. Tang for her help with experimental design and data analysis. Funding was provided by the NIH eGTEx grant ( 1U01HG007611-01 ) and NIH Center for Personal Dynamic Regulomes grant ( 3P50HG007735-05S1 ). We acknowledge the GTEx donors and families for donating organs to the GTEx Consortium. We thank K. Ardlie for coordinating the distribution of tissue samples. We also thank A. Breschi for discussion on RNA-seq data analysis and R. Tibshirani for suggestions on statistical analysis. We thank H. Tang for her help with experimental design and data analysis. Funding was provided by the NIH eGTEx grant (1U01HG007611-01) and NIH Center for Personal Dynamic Regulomes grant (3P50HG007735-05S1). L.J. led this project in generating proteomics data, data analysis, and manuscript preparation. M.W. developed statistical methods for proteomics data analysis and integration. S.L. performed the initial data analysis and SNP database construction. R.J. and J.C. did proteomics sample preparation and mass spectrometry data acquisition and contributed to making figures. X.L. did the analysis on the association of tissue-enriched proteins to diseases. H.F. contributed to the discussion of data analysis. G.D. contributed to SNP and isoform early data analysis. A.R. did SNP peptide spectra library search. M.P.S. contributed to project supervision and manuscript review and revision. All the authors contributed to manuscript revision. M.P.S. is a cofounder and is on the scientific advisory board of Personalis, Filtircine, SensOmics, Qbio, January, Mirvie, Oralome, and Proteus. He is also on the scientific advisory board (SAB) of Genapsys and Jupiter. The other authors declare no competing interests.
| Funders | Funder number |
|---|---|
| GTEx | |
| GTEx Consortium | |
| National Institutes of Health | 1U01HG007611-01 |
| National Human Genome Research Institute | P50HG007735 |
ASJC Scopus Subject Areas
- General Biochemistry,Genetics and Molecular Biology
