The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
(2008) · Journal of Machine Learning Research · Cited by 33,051
Tissue-based map of the human proteome
Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015) · Science · doi:10.1126/science.1260419 · Cited by 15,286
ggplot2: Elegant Graphics for Data Analysis
Ginestet, C. E. (2011) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00676_9.x · Cited by 14,511
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447
Robust Regression and Outlier Detection
Piepel, G. F., Rousseeuw, P. J., & Leroy, A. M. (1989) · Technometrics · doi:10.2307/1268828 · Cited by 6,183
Robust Regression and Outlier Detection
Rousseeuw, P. J., & Leroy, A. M. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/0471725382 · Cited by 5,609
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Self-citationGENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585
Self-citationSTRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398
Flagged first, then by citations
Research works printed but not linked: 1
- Wang, 2019, RobNorm: Model-Based Robust Normalization for High-Throughput Proteomics from Mass Spectrometry Platform, bioRxiv
Cited by
Sorted by citations
Circular RNAs: Characterization, cellular roles, and applications
Liu, C., & Chen, L. (2022) · Cell · doi:10.1016/j.cell.2022.04.021 · Cited by 1,094
Self-citationTTD: Therapeutic Target Database describing target druggability information
Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., Wang, S., Qiu, Y., Chen, Y., & Zhu, F. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad751 · Cited by 683
“Considering the substantial discordance in target’s expression at the levels of proteins and RNAs ( 57 ), the distributions of TTD targets among various tissues / organs were determined.”
BackgroundZhou, N., Yuan, X., Du, Q., Zhang, Z., Shi, X., Bao, J., Ning, Y., & Peng, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac935 · Cited by 514
Multi-Omics Profiling for Health
Babu, M., & Snyder, M. P. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100561 · Cited by 434
“Furthermore, we demonstrated how understanding protein level patterns can provide insights into gene regulation, the secretome, metabolism, and human diseases (137).”
BackgroundSelf-citationStructured elements drive extensive circular RNA translation
Chen, C., Cheng, R., Demeter, J., Chen, J., Weingarten-Gabbay, S., Jiang, L., Snyder, M. P., Weissman, J. S., Segal, E., Jackson, P. K., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.07.042 · Cited by 323
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
