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A Quantitative Proteome Map of the Human Body
Open accessPublished version of a preprint
Abstract
SUMMARY 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.
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“Although transcriptomic profiling has provided insights into Tex cell biology, mRNA abundance is not always a faithful proxy of protein expression across various organisms18–22.”
From Proteotoxic stress response drives T cell exhaustion and immune evasion (2025) · cites this paper as [21]
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“Correlation between the mRNAs for the paired RNA-Seq relative to the global proteomics datasets showed lower positive Pearson correlation values ranging from 0.333 – 0.378 for all genotypes tested which is consistent with prior studies on transcriptome / proteome correlation82–84.”
From EXOSC3 G191 Variants Trigger System-Wide Recalibration of RNA Processing Machinery (2026) · cites this paper as [83]
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BackgroundSOD1 Protein Content in Human Central Nervous System and Peripheral Tissues
Leykam, L., Jonsson, P. A., Forsberg, K., Andersen, P. M., Brännström, T., Marklund, S. L., & Zetterström, P. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70136 · Cited by 6
“Since the human genome consists of ~20 000 genes with around 12 000 quantifiable proteins in different tissues (Jiang et al. 2020) SOD1 is still expressed at a rate ~2000 times higher than if all 12 000 cellular proteins were to be equally expressed.”
A multi-omics molecular landscape of 30 tissues in aging female rhesus macaques
Li, G., Zhu, X., Xiao, F., Zhao, X., Lv, L., Yin, F.-Q., Chang, L., Ge, M., Wang, Q., Zhao, J., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02912-y · Cited by 5
“Consistent with previous studies 20–22 , samples from the same tissue were clustered together by transcriptome, proteome or metabolome, respectively (Fig.”
Self-citationPrakash, A., Collins, A., Haim-Vilmovsky, L., Fexova, S. K., Jones, A. R., & Vizcaíno, J. A. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00788 · Cited by 5
“Additionally, we made a comparison of the expression values coming from DIA data sets and the protein abundance results from previous DDA studies including human baseline tissues, 18,19 for instance, using ProteomicsDB data and also our previous combined analysis of DDA public human data sets.”
Xu, X., Qin, W., Fu, H., Li, R., Chen, C., Sun, B., Pang, D., Shao, X., & Li, G. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c06292 · Cited by 5
Self-citationProtein hormone fragmentation in intercellular signaling: hormones as nested information systems
Campbell, K. L., Haspel, N., Gath, C., Kurniatash, N., Akkiraju, I., Stuffers, N., & Vadher, U. S. (2021) · Biology of Reproduction · doi:10.1093/biolre/ioaa234 · Cited by 5
Becerra, D., Calixto, A., & Orio, P. (2023) · International journal of psychological research · doi:10.21500/20112084.6487 · Cited by 5
Niazi, S. K., & Magoola, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252312797 · Cited by 5
“The human body produces thousands of proteins [12], which are responsible for cellular and extracellular functions vital to life.”
Ayidh, A. A., Abbas, M. A. E., Parayangat, M., & Ijyas, T. (2025) · Current Cancer Drug Targets · doi:10.2174/0115680096362452250301054711 · Cited by 6
Perplexity as a metric for isoform diversity in the human transcriptome
Schertzer, M. D., Park, S. H., Su, J., Reese, F., Sheynkman, G. M., & Knowles, D. A. (2026) · Genome biology · doi:10.1186/s13059-026-04260-2 · Cited by 9
Yang, M., Zhang, L., Ding, X., Wei, P., Zhang, H., Chen, J., Zhang, Y., Li, D., Chen, Z., Lin, D., et al. (2025) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5c00122 · Cited by 4
Proteomic aging clocks in epidemiological studies: advances, applications and prospects
Xiao, H., Lau, C. E., Dehghan, A., & Robinson, O. (2026) · Nature Aging · doi:10.1038/s43587-026-01118-x · Cited by 6
“Mass spectrometry provides high-resolution detection of proteoforms (post-translational modifications and protein variants), which is ideal in pilot studies and is used in various tissues 40,42 .”
Slováčková, J., Bernatík, O., Čimborová, K., Barák, M., Hendrych, M., Kocourkova, K., Sulcova, M., Oľha, J., Černá, K. A., Hodný, Z., et al. (2026) · Brain Pathology · doi:10.1111/bpa.70125 · Cited by 4
“Mass spectrometry-based proteomics offers a powerful approach to address these questions by directly quantifying functional effectors, including ECM and secreted proteins that are often poorly represented at the transcript level [17, 18].”
Antibody reliability influences observed mRNA–protein correlations in tumour samples
Upadhya, S. R., & Ryan, C. J. (2023) · Life Science Alliance · doi:10.26508/lsa.202201885 · Cited by 5
Zhang, D., Zhang, H., Lu, J., & Hu, X. (2023) · Transplantation · doi:10.1097/tp.0000000000004754 · Cited by 4
“According to previous large-scale studies, 53,54 the divergence in levels between proteome and transcriptome broadly exists, which could be caused by posttranscriptional regulation, posttranslational regulation, protein degradation, and so on.”
MethodJin, H., Wang, X., Li, L., Rui, C., Gan, H., Wang, Q., Tao, F., & Zhu, Y. (2024) · iScience · doi:10.1016/j.isci.2024.111423 · Cited by 5
Barrio‐Alonso, C., Nieto‐Valle, A., Barandalla-Revilla, L., Avilés-Izquierdo, J. A., Parra‐Blanco, V., Sánchez‐Mateos, P., & Samaniego, R. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1587545 · Cited by 4
Chen, X., Liu, Q., Wu, J., Zhou, P., Zhao, M., & Song, J. (2025) · Nature Communications · doi:10.1038/s41467-025-62135-9 · Cited by 6
MICU2 controls mitochondrial calcium signaling and migration in neurons during development
Berezhnaya, E. V., Cartes-Saavedra, B., Singh, R., Rodríguez-Prados, M., Reiner, O., Alkuraya, F. S., & Hajnóczky, G. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116583 · Cited by 4
“26,28 The most abundant MICU isoforms in the brain are MICU1 and MICU3, 20,29 which raises a question of how the MICU2 null mutation in patients causes severe neurodevelopmental disorders without major effects seen in other tissues.”
Proteomic Analysis of Mucosal and Systemic Responses to SARS-CoV-2 Antigen
Martinson, N. A., Gordhan, B. G., Petkov, S., Pillay, A.-D. A. P., Seiphetlo, T. B., Singh, N., Otwombe, K. N., Lebina, L., Fredolini, C., Chiodi, F., et al. (2023) · Vaccines · doi:10.3390/vaccines11020334 · Cited by 4
“Hence, new tools are required to further characterize the protein content of secretions, and these biological fluids might not fully recapitulate the proteomic profile of mucosal tissues [14,15].”
BackgroundResultQuantitative Aspects of the Human Cell Proteome
Naryzhny, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24108524 · Cited by 4
“Several publications were considered as sources of information about the abundance of proteins or proteoforms in different tissues or cell lines [26,27,44–47, 50 , 52 ].”
BackgroundMethodErgin, E. K., Myung, J. J. K., & Lange, P. F. (2024) · Journal of Proteome Research · doi:10.1021/acs.jproteome.4c00131 · Cited by 4
OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft
Zhang, Y., Lian, X., Xu, H., Zhu, S., Zhang, H., Ni, Z., Fu, T., Liu, S., Tao, L., Zhou, Y., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae861 · Cited by 4
“The data for tissue-specific RNA and protein expressions were collected from Jiang ’s study, which quantified a large number of proteins across 32 normal human tissues ( 75 ).”
A community-led initiative to de-risk and advance Parkinson’s disease therapeutic targets
Vaiana, A., Behr, J., Birol, R., Blauwendraat, C., Casey, B. H., Chowdhury, K., Citron, M., Crapser, J. D., Dardov, V. J., Ducotterd, F., et al. (2025) · npj Parkinson s Disease · doi:10.1038/s41531-025-01039-3 · Cited by 4
MIMIC: an optimization method to identify cell type-specific marker panel for cell sorting
Zou, M., Duren, Z., Yuan, Q., Li, Y. H., Hutchins, A. P., Wong, W. H., & Wang, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab235 · Cited by 4
“46 across 32 normal human tissues [34].”
Buscà, R., Onesto, C., Egensperger, M., Pouysségur, J., Pagès, G., & Lenormand, P. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad528 · Cited by 6
IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection
Korchak, J. A., Jeffery, E. D., Bandyopadhyay, S., Jordan, B. T., Lehe, M. D., Watts, E. F., Fenix, A. M., Wilhelm, M., & Sheynkman, G. (2024) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.4c00119 · Cited by 5
Chen, S., Wang, G., Shen, X., Hornburg, D., Rego, S. M., Hoffman, R., Nevins, S. A., Cheng, X., & Snyder, M. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.28.446235 · Cited by 4
Self-citationDavis, E. T., Raman, R., Byrne, S. R., Ghanegolmohammadi, F., Mathur, C., Begley, U., Dedon, P. C., & Begley, T. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.03.636209 · Cited by 4
Nazli, S. A., Zimmerman, K. D., Riojas, A. M., Cox, L. A., & Olivier, M. (2023) · Biomolecules · doi:10.3390/biom13020215 · Cited by 3
“In fact, analysis of GTEx data suggests that some proteins are significantly enriched in particular tissues, but very few are exclusively expressed in one or only few tissues [24].”
Background
Metadata
- Authors
- Jiang, L., Wang, M., Lin, S., Jian, R., Li, X., Chan, J., Dong, G., Fang, H., Robinson, A. E., Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., Kashin, S., MacArthur, D. G., Meier, S. R., Nedzel, J. L., Nguyen, D. Y., Segrè, A. V., Todres, E., Balliu, B., Barbeira, A. N., Battle, A., Bonazzola, R., Brown, A., Brown, C. D., Castel, S. E., Conrad, D., Cotter, D. J., Cox, N., Das, S., de Goede, O. M., Dermitzakis, E. T., Engelhardt, B. E., Eskin, E., Eulalio, T. Y., Ferraro, N. M., Flynn, E., Fresard, L., Gamazon, E. R., Garrido-Martín, D., Gay, N. R., Guigó, R., Hamel, A. R., He, Y., Hoffman, P. J., Hormozdiari, F., Hou, L., Im, H. K., Jo, B., Kasela, S., Kellis, M., Kim-Hellmuth, S., Kwong, A., Lappalainen, T., Li, X., Liang, Y., Mangul, S., Mohammadi, P., Montgomery, S. B., Muñoz-Aguirre, M., Nachun, D. C., Nobel, A. B., Oliva, M., Park, Y., Park, Y., Parsana, P., Reverter, F., Rouhana, J. M., Sabatti, C., Saha, A., Skol, A. D., Stephens, M., Stranger, B. E., Strober, B. J., Teran, N. A., Viñuela, A., Wang, G., Wen, X., Wright, F., Wucher, V., Zou, Y., Ferreira, P. G., Li, G., Melé, M., Yeger-Lotem, E., Barcus, M. E., Bradbury, D., Krubit, T., McLean, J. A., Qi, L., Robinson, K., Roche, N. V., Smith, A. M., Sobin, L., Tabor, D. E., Undale, A., Bridge, J., Brigham, L. E., Foster, B. A., Gillard, B. M., Hasz, R., Hunter, M., Johns, C., Johnson, M., Karasik, E., Kopen, G., Leinweber, W. F., McDonald, A., Moser, M. T., Myer, K., Ramsey, K. D., Roe, B., Shad, S., Thomas, J. A., Walters, G., Washington, M., Wheeler, J., Jewell, S. D., Rohrer, D. C., Valley, D. R., Davis, D. A., Mash, D. C., Branton, P. A., Barker, L. K., Gardiner, H. M., Mosavel, M., Siminoff, L. A., Flicek, P., Haeussler, M., Juettemann, T., Kent, W. J., Lee, C. M., Powell, C. C., Rosenbloom, K. R., Ruffier, M., Sheppard, D., Taylor, K., Trevanion, S. J., Zerbino, D. R., Abell, N. S., Akey, J., Chen, L., Demanelis, K., Doherty, J. A., Feinberg, A. P., Hansen, K. D., Hickey, P. F., Jasmine, F., Kaul, R., Kibriya, M. G., Li, J. B., Li, Q., Linder, S. E., Pierce, B. L., Rizzardi, L. F., Smith, K. S., Stamatoyannopoulos, J., Tang, H., Carithers, L. J., Guan, P., Koester, S. E., Little, A. R., Moore, H. M., Nierras, C. R., Rao, A. K., Vaught, J. B., Volpi, S., & Snyder, M. P.
- Affiliations
- Stanford Medicine (US)
- University of Washington (US)
- Thermo Fisher Scientific (United States) (US)
- Corresponding author
- Snyder, Michael P.
- ORCID
- Jiang, Lihua
- Wang, Meng
- Lin, Shin
- Jian, Ruiqi
- Li, Xiao
- Chan, Joanne
- Dong, Guanlan
- Fang, Huaying
- Robinson, Aaron E.
- Aguet, François
- Anand, Shankara
- Ardlie, Kristin G.
- Gabriel, Stacey
- Getz, Gad
- Graubert, Aaron
- Hadley, Kane
- Handsaker, Robert E.
- Huang, Katherine H.
- Kashin, Seva
- MacArthur, Daniel G.
- Meier, Samuel R.
- Nedzel, Jared L.
- Segrè, Ayellet V.
- Todres, Ellen
- Balliu, Brunilda
- Barbeira, Alvaro N.
- Battle, Alexis
- Bonazzola, Rodrigo
- Brown, Andrew
- Brown, Christopher D.
- Castel, Stephane E.
- Cotter, Daniel J.
- Cox, Nancy
- Das, Sayantan
- Dermitzakis, Emmanouil T.
- Engelhardt, Barbara E.
- Eskin, Eleazar
- Eulalio, Tiffany Y.
- Ferraro, Nicole M.
- Flynn, Elise
- Gamazon, Eric R.
- Garrido-Martín, Diego
- Gay, Nicole R.
- Guigó, Roderic
- Hamel, Andrew R.
- He, Yuan
- Hormozdiari, Farhad
- Hou, Lei
- Im, Hae Kyung
- Jo, Brian
- Kasela, Silva
- Kellis, Manolis
- Kim-Hellmuth, Sarah
- Kwong, Alan
- Lappalainen, Tuuli
- Li, Xin
- Liang, Yanyu
- Mangul, Serghei
- Mohammadi, Pejman
- Montgomery, Stephen B.
- Muñoz-Aguirre, Manuel
- Nachun, Daniel C.
- Nobel, Andrew B.
- Oliva, Meritxell
- Park, YoSon
- Park, Yongjin
- Parsana, Princy
- Rouhana, John M.
- Sabatti, Chiara
- Saha, Ashis
- Skol, Andrew D.
- Stephens, Matthew
- Stranger, Barbara E.
- Strober, Benjamin J.
- Teran, Nicole A.
- Viñuela, Ana
- Wang, Gao
- Wen, Xiaoquan
- Wright, Fred
- Wucher, Valentin
- Zou, Yuxin
- Ferreira, Pedro G.
- Li, Gen
- Melé, Marta
- Smith, Anna M.
- Tabor, David E.
- Foster, Barbara A.
- Gillard, Bryan M.
- Hasz, Richard
- Johns, Christopher
- Johnson, Mark
- Moser, Michael T.
- Jewell, Scott D.
- Rohrer, Daniel C.
- Davis, David A.
- Mash, Deborah C.
- Branton, Philip A.
- Gardiner, Heather M.
- Mosavel, Maghboeba
- Siminoff, Laura A.
- Flicek, Paul
- Haeussler, Maximilian
- Juettemann, Thomas
- Lee, Christopher M.
- Rosenbloom, Kate R.
- Ruffier, Magali
- Sheppard, Dan
- Taylor, Kieron
- Trevanion, Stephen J.
- Zerbino, Daniel R.
- Abell, Nathan S.
- Akey, Joshua
- Chen, Lin
- Demanelis, Kathryn
- Doherty, Jennifer A.
- Feinberg, Andrew P.
- Hansen, Kasper D.
- Hickey, Peter F.
- Jasmine, Farzana
- Kaul, Rajinder
- Kibriya, Muhammad G.
- Li, Jin Billy
- Li, Qin
- Pierce, Brandon L.
- Rizzardi, Lindsay F.
- Smith, Kevin S.
- Stamatoyannopoulos, John
- Tang, Hua
- Guan, Ping
- Little, A. Roger
- Moore, Helen M.
- Volpi, Simona
- Snyder, Michael P.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 183
- Issue
- 1
- Pages
- 269-283.e19
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC7575058
- OpenAlex
- W3083820674
- Semantic Scholar
- 433ccfe1fc4a69e669d65b7624ab4341f13fa1a9
- Cited by
- 458
- References
- 72
- Citations by year
- 2026: 56 · 2025: 105 · 2024: 83 · 2023: 96 · 2022: 61 · 2021: 46 · 2020: 10
- FWCI
- 20.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- human proteome, tissue-specific proteins, protein abundance, transcriptome, RNA-protein correlation, secretome
- MeSH terms
- Humans, RNA, RNA, Messenger, Gene Expression, Proteome, Gene Expression Profiling, Proteomics, Transcriptome
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