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A Quantitative Proteome Map of the Human Body
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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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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-citationTranscriptome variation in human tissues revealed by long-read sequencing
Glinos, D. A., Garborcauskas, G., Hoffman, P., Ehsan, N., Jiang, L., Gokden, A., Dai, X., Aguet, F., Brown, K. L., Garimella, K. V., et al. (2022) · Nature · doi:10.1038/s41586-022-05035-y · Cited by 318
Self-citationMarcu, A., Bichmann, L., Kuchenbecker, L., Kowalewski, D. J., Freudenmann, L. K., Backert, L., Mühlenbruch, L., Szolek, A., Lübke, M., Wagner, P., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-002071 · Cited by 250
Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
Jiang, Y., Balaya, R. D. A., Schuster, D., Neely, B. A., Rosano, G. L., Volkmar, N., Momenzadeh, A., Peters-Clarke, T. M., Egbert, S., Kreimer, S. D., et al. (2024) · ACS Measurement Science Au · doi:10.1021/acsmeasuresciau.3c00068 · Cited by 217
Oliva, M., Demanelis, K., Lu, Y., Chernoff, M., Jasmine, F., Ahsan, H., Kibriya, M. G., Chen, L., & Pierce, B. L. (2022) · Nature Genetics · doi:10.1038/s41588-022-01248-z · Cited by 202
Self-citationProteome-wide mapping of short-lived proteins in human cells
Li, J., Cai, Z., Vaites, L. P., Shen, N., Mitchell, D. C., Huttlin, E. L., Paulo, J. A., Harry, B. L., & Gygi, S. P. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.09.015 · Cited by 159
Self-citationShankar‐Hari, M., Calandra, T., Soares, M. P., Bauer, M. W., Wiersinga, W. J., Prescott, H. C., Knight, J. C., Baillie, J. K., Bos, L. D. J., Derde, L. P. G., et al. (2024) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(23)00468-x · Cited by 142
Hellinger, R., Sigurdsson, A., Wu, W., Romanova, E. V., Li, L., Sweedler, J. V., Süßmuth, R. D., & Gruber, C. W. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-023-00205-2 · Cited by 142
Małecki, J., Muszyński, S., & Sołowiej, B. G. (2021) · Polymers · doi:10.3390/polym13152506 · Cited by 151
“Protein is considered a key ingredient in the human diet for assessing the body’s needs due to the complex metabolic changes required to run two processes constantly, such as the synthesis and breakdown of the body’s proteins [5].”
BackgroundPaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts
Huang, Q. D., Szklarczyk, D., Wang, M., Simonovic, M., & von Mering, C. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100640 · Cited by 131
Self-citationDavies, J. P., Almasy, K. M., McDonald, E. F., & Plate, L. (2020) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.0c00500 · Cited by 128
SARS-CoV-2 viremia is associated with distinct proteomic pathways and predicts COVID-19 outcomes
Li, Y., Schneider, A. M., Mehta, A., Sade-Feldman, M., Kays, K. R., Gentili, M., Charland, N. C., Gonye, A. L. K., Gushterova, I., Khanna, H. K., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci148635 · Cited by 130
Self-citationThe roles of mitochondria in global and local intracellular calcium signalling
Cartes-Saavedra, B., Ghosh, A., & Hajnóczky, G. (2025) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00820-1 · Cited by 126
“MICU1 expression has a positive correlation with MCU expression in almost all tissues 109 , but the MICU1 to MCU ratio is relatively low, resulting in less effective MCU gating in cardiac muscle 110 .”
RNA splicing analysis using heterogeneous and large RNA-seq datasets
Vaquero-Garcia, J., Aicher, J. K., Jewell, S., Gazzara, M. R., Radens, C. M., Jha, A., Green, C. J., Norton, S., Lahens, N. F., Grant, G. R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36585-y · Cited by 129
Self-citationTargeted Protein Degradation via Lysosomes
Paudel, R. R., Lu, D., Chowdhury, S. R., Monroy, E. Y., & Wang, J. (2022) · Biochemistry · doi:10.1021/acs.biochem.2c00310 · Cited by 121
Wang, R., Maron, B. A., & Loscalzo, J. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318731 · Cited by 125
Shemesh, N., Jubran, J., Dror, S., Simonovsky, E., Basha, O., Argov, C. M., Hekselman, I., Abu‐Qarn, M., Vinogradov, E., Mauer, O., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22369-9 · Cited by 117
Self-citationProteomes Are of Proteoforms: Embracing the Complexity
Carbonara, K., Andonovski, M., & Coorssen, J. R. (2021) · Proteomes · doi:10.3390/proteomes9030038 · Cited by 121
Plasma proteomics links brain and immune system aging with healthspan and longevity
Oh, H. S., Guen, Y. L., Rappoport, N., Urey, D. Y., Farinas, A., Rutledge, J., Channappa, D., Wagner, A. D., Mormino, E. C., Brunet, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03798-1 · Cited by 164
“%) were secreted proteins produced by the liver; we called these proteins ‘liver-enriched’ based on RNA, but, based on Jiang et al. tissue proteomics, they were called either artery-enriched or non-enriched (Supplementary Fig.”
InfluentialCancer diagnostics based on plasma protein biomarkers: hard times but great expectations
Landegren, U. D., & Hammond, M. (2020) · Molecular Oncology · doi:10.1002/1878-0261.12809 · Cited by 111
Deep learning neural network tools for proteomics
Meyer, J. G. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100003 · Cited by 101
“Nearly all cells in an organism share one genome that acts as a library of instructions to produce diverse specialized cells and tissues, each of which have unique and dynamic proteomes (Jiang et al., 2020) that reflect the biological milieu of physiology or disease.”
BackgroundDowling, P., Gargan, S., Swandulla, D., & Ohlendieck, K. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032415 · Cited by 103
Multi-omics data integration methods and their applications in psychiatric disorders
Sathyanarayanan, A., Mueller, T. T., Moni, M. A., Schueler, K., Baune, B. T., Lió, P., Mehta, D., Baune, B. T., Dierssen, M., Ebert, B., et al. (2023) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2023.01.001 · Cited by 93
Upadhya, S. R., & Ryan, C. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100288 · Cited by 81
A “multi-omics” analysis of blood–brain barrier and synaptic dysfunction in APOE4 mice
Barisano, G., Kisler, K., Wilkinson, B., Nikolakopoulou, A. M., Sagare, A. P., Wang, Y., Gilliam, W., Huuskonen, M. T., Hung, S.-T., Ichida, J. K., et al. (2022) · Journal of Experimental Medicine · doi:10.1084/jem.20221137 · Cited by 78
“It is widely accepted that the correlation between transcripts and protein levels is generally weak (Jiang et al., 2020).”
InfluentialPrecision cancer medicine: Concepts, current practice, and future developments
Edsjö, A., Holmquist, L., Geoerger, B., Nowak, F., Gomon, G., Alix‐Panabières, C., Staaf, J., Ploeger, C., Lassen, U., Tourneau, C. L., et al. (2023) · Journal of Internal Medicine · doi:10.1111/joim.13709 · Cited by 91
Muraoka, S., Hirano, M., Isoyama, J., Nagayama, S., Tomonaga, T., & Adachi, J. (2022) · iScience · doi:10.1016/j.isci.2022.104012 · Cited by 82
“Proteome studies of human tissue have successfully quantified 13,000 proteins (Jiang et al., 2020; Wang et al., 2019), but there were fewer tissue-specific proteins than with HPA dataset.”
BackgroundThe EN-TEx resource of multi-tissue personal epigenomes & variant-impact models
Rozowsky, J. S., Gao, J., Borsari, B., Yang, Y., Galeev, T. R., Gürsoy, G., Epstein, C. B., Xiong, K., Xu, J., Li, T., et al. (2023) · Cell · doi:10.1016/j.cell.2023.02.018 · Cited by 79
Self-citationToward an Integrated Machine Learning Model of a Proteomics Experiment
Neely, B. A., Dorfer, V., Martens, L., Bludau, I., Bouwmeester, R., Degroeve, S., Deutsch, E. W., Gessulat, S., Käll, L., Palczynski, P., et al. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00711 · Cited by 79
Genetic basis of mitochondrial diseases
Gušić, M., & Prokisch, H. (2021) · FEBS Letters · doi:10.1002/1873-3468.14068 · Cited by 79
Emergence of co-expression in gene regulatory networks
Yin, W., Mendoza, L., Monzón‐Sandoval, J., Urrutia, A. O., & Gutiérrez, H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247671 · Cited by 69
The elegant complexity of mammalian ecto-5′-nucleotidase (CD73)
Alcedo, K. P., Bowser, J. L., & Snider, N. T. (2021) · Trends in Cell Biology · doi:10.1016/j.tcb.2021.05.008 · Cited by 72
Human UFSP1 is an active protease that regulates UFM1 maturation and UFMylation
Millrine, D., Cummings, T., Matthews, S. P., Peter, J. J., Magnussen, H. M., Lange, S. M., Macartney, T., Lamoliatte, F., Knebel, A., & Kulathu, Y. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111168 · Cited by 71
Senabouth, A., Daniszewski, M., Lidgerwood, G. E., Liang, H. H., Hernández, D., Mirzaei, M., Keenan, S. N., Zhang, R., Han, X., Neavin, D. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31707-4 · Cited by 75
Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues
Qie, J., Liu, Y., Wang, Y., Zhang, F., Qin, Z., Tian, S., Liu, M., Li, K., Shi, W., Song, L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35095-7 · Cited by 75
A large-scale proteogenomic atlas of pear
Wang, P., Wu, X. B., Shi, Z., Tao, S., Liu, Z., Qi, K., Xie, Z., Qiao, X., Gu, C., Yin, H., et al. (2023) · Molecular Plant · doi:10.1016/j.molp.2023.01.011 · Cited by 68
Human proteome distribution atlas for tissue-specific plasma proteome dynamics
Malmström, E., Malmström, L., Hauri, S., Mohanty, T., Scott, A. M., Karlsson, C. A. Q., Gueto-Tettay, C. A., Åhrman, E., Nozohoor, S., Tingstedt, B., et al. (2025) · Cell · doi:10.1016/j.cell.2025.03.013 · Cited by 72
Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease
Kolic, J., Sun, W. G., Cen, H. H., Ewald, J., Rogalski, J. C., Sasaki, S., Sun, H., Rajesh, V., Xia, Y. H., Moravcova, R., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.06.001 · Cited by 65
Proteotoxic stress response drives T cell exhaustion and immune evasion
Wang, Y., Ma, A., Song, N.-J., Shannon, A. E., Amankwah, Y. S., Chen, X., Wu, W., Wang, Z., Saadey, A. A., Yousif, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09539-1 · Cited by 68
Fernandes, P. M., Kinkead, J., McNae, I. W., Michels, P. A. M., & Walkinshaw, M. D. (2020) · Biochemical Journal · doi:10.1042/bcj20200656 · Cited by 70
The genetics of mitochondrial disease: dissecting mitochondrial pathology using multi‐omic pipelines
Alston, C. L., Stenton, S. L., Hudson, G., Prokisch, H., & Taylor, R. W. (2021) · The Journal of Pathology · doi:10.1002/path.5641 · Cited by 60
“The encoded proteins of approximately 65–75% of the mitochondrial disease genes and 40–50% of all OMIM disease genes can be quantified in muscle and fibroblasts, respectively [82] (Figure 4A).”
A stem cell roadmap of ribosome heterogeneity reveals a function for RPL10A in mesoderm production
Genuth, N. R., Shi, Z., Kunimoto, K., Hung, V., Xu, A., Kerr, C. H., Tiu, G. C., Osés-Prieto, J. A., Salomon, R. E. A., Axelrod, J. D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33263-3 · Cited by 62
Spatial proteomics for understanding the tissue microenvironment
Mao, Y., Wang, X., Huang, P., & Tian, R. (2021) · The Analyst · doi:10.1039/d1an00472g · Cited by 55
Knol, J. C., Lyu, M., Böttger, F., Monteiro, M. N., Pham, T. V., Rolfs, F., Vallés‐Martí, A., Schelfhorst, T., Haas, R. R. D. G., Bijnsdorp, I. V., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.05.003 · Cited by 65
Pan-cancer analysis of antibody-drug conjugate targets and putative predictors of treatment response
Bosi, C., Bartha, Á., Galbardi, B., Notini, G., Naldini, M. M., Licata, L., Viale, G., Mariani, M., Pistilli, B., Ali, H. R., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.113379 · Cited by 57
de Haan, N., Pučić‐Baković, M., Novokmet, M., Falck, D., Lageveen‐Kammeijer, G. S. M., Razdorov, G., Vučković, F., Trbojević‐Akmačić, I., Gornik, O., Hanić, M., et al. (2022) · Glycobiology · doi:10.1093/glycob/cwac026 · Cited by 54
TheMarker: a comprehensive database of therapeutic biomarkers
Zhang, Y., Zhou, Y., Zhou, Y., Yu, X., Shen, X., Hong, Y., Zhang, Y., Wang, S., Mou, M., Zhang, J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad862 · Cited by 54
“First , the expression data for 182 transcriptomic and 201 proteomic samples across 32 major human tissues were collected ( 87 ).”
Zhang, W., Roy Burman, S. S., Chen, J., Donovan, K. A., Cao, Y., Shu, C., Zhang, B., Zeng, Z., Gu, S., Zhang, Y., et al. (2022) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.008 · Cited by 51
Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade
Lill, J. R., Mathews, W. R., Rose, C. M., & Schirle, M. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.1962300 · Cited by 57
Wu, D., Li, H., Liu, M., Qin, J., & Sun, Y. (2022) · Nature Communications · doi:10.1038/s41467-022-30707-8 · Cited by 55
Targeting RNA splicing modulation: new perspectives for anticancer strategy?
Lv, X., Sun, X., Gao, Y., Song, X., Hu, X., Gong, L., Li, H., He, M., & Wei, M. (2025) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-025-03279-w · Cited by 57
Assessment of risks, implications, and opportunities of waterborne neurotoxic pesticides
Dara, D., & Drabovich, A. P. (2022) · Journal of Environmental Sciences · doi:10.1016/j.jes.2022.03.033 · Cited by 53
Mitochondrial genome copy number variation across tissues in mice and humans
Rath, S. P., Gupta, R., Todres, E. Z., Wang, H., Jourdain, A. A., Ardlie, K., Calvo, S. E., & Mootha, V. K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2402291121 · Cited by 52
Self-citationTranscription factor regulation of eQTL activity across individuals and tissues
Flynn, E. D., Tsu, A. L., Kasela, S., Kim-Hellmuth, S., Aguet, F., Ardlie, K., Bussemaker, H. J., Mohammadi, P., & Lappalainen, T. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1009719 · Cited by 43
Self-citationExtracellular Vesicle ITGAM and ITGB2 Mediate Severe Acute Pancreatitis-Related Acute Lung Injury
Hu, Q., Zhang, S., Yang, Y., Li, J., Kang, H., Tang, W., Lyon, C. J., & Wan, M. (2023) · ACS Nano · doi:10.1021/acsnano.2c12722 · Cited by 47
Self-citationProteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms
Yang, K., Kalloger, S. E., Aird, J. J., Lee, M. K. C., Rushton, C. K., Mungall, K. L., Mungall, A. J. J., Gao, D., Chow, C., Xu, J., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109817 · Cited by 48
Illuminating the dark protein-protein interactome
Tabar, M. S., Parsania, C., Chen, H., Su, X., Bailey, C. G., & Rasko, J. E. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100275 · Cited by 45
ALPL-1 is a target for chimeric antigen receptor therapy in osteosarcoma
Mensali, N., Köksal, H., Joaquina, S., Wernhoff, P., Casey, N. P., Romecín, P. A., Panisello, C., Rodrı́guez, R., Vimeux, L., Juzeniene, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39097-x · Cited by 46
Psychosocial experiences are associated with human brain mitochondrial biology
Trumpff, C., Monzel, A. S., Sandi, C., Menon, V., Klein, H., Fujita, M., Lee, A., Petyuk, V., Hurst, C. D., Duong, D. M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317673121 · Cited by 46
Neuronal autophagy in the control of synapse function
Karpova, A., Hiesinger, P. R., Kuijpers, M., Albrecht, A., Kirstein, J., Andres‐Alonso, M., Biermeier, A. J., Eickholt, B. J., Mikhaylova, M., Maglione, M., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.01.019 · Cited by 49
“101 All other factors being equal, autophagosomes are more likely to engulf proteins and subcellular structures that are more abundant.”
Wang, Q. S., Hasegawa, T., Namkoong, H., Saiki, R., Edahiro, R., Sonehara, K., Tanaka, H., Azekawa, S., Chubachi, S., Takahashi, Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01896-3 · Cited by 43
“The testis and most brain-related tissues were significantly depleted for colocalization (for example, 0.33-fold, P = 1 × 10 −3 for the testis), which is consistent with the previous literature 29 .”
InfluentialNext-generation in vitro blood–brain barrier models: benchmarking and improving model accuracy
Linville, R. M., & Searson, P. C. (2021) · Fluids and Barriers of the CNS · doi:10.1186/s12987-021-00291-y · Cited by 43
The proteomic characterization of the peritumor microenvironment in human hepatocellular carcinoma
Gu, Y., Guo, Y., Gao, N., Fang, Y., Xu, C., Hu, G., Guo, M., Ma, Y., Zhang, Y., Zhou, J., et al. (2022) · Oncogene · doi:10.1038/s41388-022-02264-3 · Cited by 43
Cellular control of protein levels: A systems biology perspective
Munro, V., Kelly, V. C., Messner, C. B., & Kustatscher, G. (2023) · PROTEOMICS · doi:10.1002/pmic.202200220 · Cited by 45
“Depending on the study, median Spearman correlations across tissues are only 0.35 [11] and 0.46 [16], respectively.”
BackgroundMethodInfluentialCieri, N., Maurer, K., & Wu, C. J. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-0301 · Cited by 41
Integrative omics approaches to advance rare disease diagnostics
Smirnov, D., Konstantinovskiy, N., & Prokisch, H. (2023) · Journal of Inherited Metabolic Disease · doi:10.1002/jimd.12663 · Cited by 47
Huang, R., Hua, J., Ru, M., Meng, Y., Wang, L., Huang, Y., Yan, S., Zhang, Q., & Xu, W. (2024) · ACS Nano · doi:10.1021/acsnano.4c05017 · Cited by 41
Translational remodeling by RNA ‐binding proteins and noncoding RNAs
Ho, J. J. D., Man, J. H. S., Schatz, J. H., & Marsden, P. A. (2021) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1647 · Cited by 45
“In particular, a high throughput quantitative comparison of protein versus RNA tissue-specificity across human tissues revealed extensive discordance between the two readouts (Jiang et al., 2020).”
BackgroundResultTeyssonnière, E. M., Trébulle, P., Muenzner, J., Loegler, V., Ludwig, D., Amari, F., Mülleder, M., Friedrich, A., Hou, J., Ralser, M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319211121 · Cited by 40
Imaging the future: the emerging era of single‐cell spatial proteomics
Paul, I., White, C. A., Turcinovic, I., & Emili, A. (2020) · FEBS Journal · doi:10.1111/febs.15685 · Cited by 41
Chromatin accessibility associates with protein-RNA correlation in human cancer
Sanghi, A., Gruber, J. J., Metwally, A. A., Jiang, L., Reynolds, W. D., Sunwoo, J. B., Orloff, L. A., Chang, H. Y., Kasowski, M. M., & Snyder, M. P. (2021) · Nature Communications · doi:10.1038/s41467-021-25872-1 · Cited by 42
Self-citationSurfaceome Profiling of Rhabdomyosarcoma Reveals B7-H3 as a Mediator of Immune Evasion
Lavoie, R. R., Gargollo, P. C., Ahmed, M. E., Kim, Y., Baer, E., Phelps, D. A., Charlesworth, C. M., Madden, B. J., Wang, L., Houghton, P. J., et al. (2021) · Cancers · doi:10.3390/cancers13184528 · Cited by 38
“The gene-level raw read counts matrix file was downloaded from the GEO database, and differential expression analyses were performed using DESeq2 [18].”
Circadian biology to advance therapeutics for mood disorders
Bhatnagar, A., Murray, G., & Ray, S. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.07.008 · Cited by 41
Single-cell mapping of combinatorial target antigens for CAR switches using logic gates
Kwon, J., Kang, J., Jo, A., Seo, K., An, D., Baykan, M. Y., Lee, J. H., Kim, N., Eum, H. H., Hwang, S., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01686-y · Cited by 38
“44,45 ).”
Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease
Hou, L., Xiong, X., Park, Y., Boix, C. A., James, B. T., Sun, N., He, L., Patel, A., Zhang, Z., Molinié, B., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01509-5 · Cited by 44
Self-citationAnalysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition
Le, T. H., Winsnes, C. F., Axelsson, U., Xu, H., Kaimal, J. M., Mahdessian, D., Dai, S., Makarov, I. S., Ostankovich, V., Xu, Y. J., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01606-z · Cited by 38
“4,5 and Supplementary Note 1).”
BackgroundGil, R. S., Pascovici, D., Venturato, J., Brown-Wright, H., Mehta, P. M., Martin, L. M. S., Wu, J. X., Luan, W., Chui, Y. K., Bademosi, A. T., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45646-9 · Cited by 37
“This is likely a brain-speci fi c feature given that a quantitative proteome map of the human body revealed that brain tissue shows the lowest correlation between RNA and protein levels compared to all other tissues 74 .”
A proteomic meta-analysis refinement of plasma extracellular vesicles
Vallejo, M. C., Sarkar, S., Elliott, E. C., Henry, H. R., Powell, S. M., Ludovico, I. D., You, Y., Huang, F., Payne, S., Ramanadham, S., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02748-1 · Cited by 37
“Specific tissue markers were mapped by comparing the data against a previously published human tissue proteomics dataset 25 .”
MethodReitz, C. J., Tavassoli, M., Kim, D. H., Shah, S., Lakin, R., Teng, A. C. T., Zhou, Y., Li, W., Hadipour‐Lakmehsari, S., Backx, P. H., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2212118120 · Cited by 37
“…protein/gene expression likely largely attributable to inter-patient variability, variability in sample collection time at transplant/autopsy, and collection methodology across different research centers, as well as established discordance between RNA and protein levels across human tissues (22).”
Multi‐omic analyses and network biology in cardiovascular disease
Reitz, C. J., Kuzmanov, U., & Gramolini, A. O. (2023) · PROTEOMICS · doi:10.1002/pmic.202200289 · Cited by 35
High-Resolved Near-Field Sensing by Means of Dielectric Grating With a Box-Like Resonance Shape
di Toma, A., Brunetti, G., Colapietro, P., & Ciminelli, C. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3349948 · Cited by 35
“Monitoring proteins helps in understanding human health conditions and disease progress [1].”
BackgroundLi, S., Ke, Y., Zhu, Y., Zhu, T.-Y., Huang, H., Li, L., Hou, Z., Zhang, X., Li, Y., Liu, C., et al. (2024) · Nature Plants · doi:10.1038/s41477-024-01745-5 · Cited by 35
“Like the observations in recent proteomic maps of humans, mouse and Arabidopsis 6–8 , samples from spatially related tissues, such as the stem and SN, were clustered.”
InfluentialSelf-citationNovel approaches leading towards peptide GPCR de‐orphanisation
Hauser, A. S., Gloriam, D. E. I., Bräuner‐Osborne, H., & Foster, S. R. (2019) · British Journal of Pharmacology · doi:10.1111/bph.14950 · Cited by 35
“Interestingly, new large-scale transcriptome and proteome studies now have improved coverage of human peptides and proteins, which may also lead to the discovery of previously unappreciated protein products (Jiang et al., 2019).”
BackgroundSong, Q., Ye, Y., Jiang, D., Qin, Z., Xu, C., Wang, H., Huang, J., Chen, L., Luo, R., Zhang, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31719-0 · Cited by 35
“(Years) (55-65] (65-93] High Medium Low None High Medium Low Single Small Medium Large Male Female (26-55] Gender Age Differentiation Keratinization Cell Nest Size Cell Size”
BackgroundSelf-citationProteomic profiling of carbonic anhydrase CA3 in skeletal muscle
Dowling, P., Gargan, S., Zweyer, M., Sabir, H., Swandulla, D., & Ohlendieck, K. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.2017776 · Cited by 35
“Most human skeletal muscles were shown to contain proteoforms of CA1, CA2 and CA3 [19,52,55,66] and exhibit considerably higher Table 2.”
BackgroundSrivastava, H., Lippincott, M. J., Currie, J., Canfield, R., Lam, M. P. Y., & Lau, E. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010702 · Cited by 36
Imai, K., & Nakai, K. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.607812 · Cited by 38
“Recently, relative protein levels of more than 12,000 genes across 32 normal human tissues were quantified and tissue-specific or tissue-enriched proteins were identified (Jiang et al., 2020).”
BackgroundMultiomics Analyses Reveal Sex Differences in Mouse Renal Proximal Subsegments
Chen, L., Chou, C., Yang, C., & Knepper, M. A. (2023) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000089 · Cited by 32
Self-citationNg, R. H., Lee, J. W., Baloni, P., Diener, C., Heath, J. R., & Su, Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.914594 · Cited by 31
“Emerging evidence shows that newly developed quantitative proteome may better explain genetic disease and metabolism (191), emphasizing the advantage of using proteome evidence for metabolic model reconstruction.”
BackgroundTang, S., Meng, J., Tan, J., Liu, X., Zhou, H., Li, N., & Hou, S. (2022) · Clinical Immunology · doi:10.1016/j.clim.2022.109080 · Cited by 29
Enhanced Multiplexing Technology for Proteomics
Bowser, B. L., & Robinson, R. A. S. (2023) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-091622-092353 · Cited by 32
Using protein-per-mRNA differences among human tissues in codon optimization
Hernandez‐Alias, X., Benisty, H., Radusky, L., Serrano, L. G., & Schaefer, M. H. (2023) · Genome biology · doi:10.1186/s13059-023-02868-2 · Cited by 30
“A Proteomics and mRNA‑seq data included in this study contains samples from the GTEx project [19] and Human Protein Atlas [20].”
InfluentialTranscriptomics for Clinical and Experimental Biology Research: Hang on a Seq
Stokes, T., Cen, H. H., Kapranov, P., Gallagher, I. J., Pitsillides, A. A., Volmar, C., Kraus, W. E., Johnson, J. D., Phillips, S. M., Wahlestedt, C., et al. (2023) · Advanced Genetics · doi:10.1002/ggn2.202200024 · Cited by 28
The coming era of proteomics-driven precision medicine
Jiang, Y., Wang, J., Sun, A., Zhang, H., Yu, X., Qin, W., Ying, W., Li, Y., Chang, C., Wang, X., et al. (2025) · National Science Review · doi:10.1093/nsr/nwaf278 · Cited by 34
“Progress in the HPP has significantly advanced our understanding of the human proteome [21 ,22 ,101 ,102 ].”
Self-citationYu, X., Zeng, X., Xiao, F., Chen, R., Sinharoy, P., & Gross, E. R. (2022) · Redox Biology · doi:10.1016/j.redox.2022.102369 · Cited by 29
“Similar to our findings, a recent non-biased approach to map the proteome of the human body for 32 different organs measured from 14 individuals revealed the liver had ~2-fold higher ALDH2 protein expression relative to the heart [52].”
ResultComprehensive Analysis of N6-Methyladenosine (m6A) Writers, Erasers, and Readers in Cervical Cancer
Condic, M., Ralser, D. J., Klümper, N., Ellinger, J., Qureischi, M., EGGER, E. K., Kristiansen, G., Mustea, A., & Thiesler, T. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23137165 · Cited by 29
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
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