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.
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
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?
mRNA abundance is not always a faithful proxy of protein expression across various organisms.
“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]
The Introduction says protein levels often correlate poorly with transcript levels, and the Results (Correlation between RNA and Protein Levels) report a median RNA–protein correlation of 0.46 across 32 human tissues. This supports the point that mRNA is not always a faithful proxy for protein expression, but the paper’s own analysis is of human tissues, not various organisms. (LLM, conf=0.84)
Experimental — accuracy not yet measuredCorrelation 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.
“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.
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 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
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 · see citing works
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 14,484 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447 · see citing works
Robust Regression and Outlier Detection
Piepel, G. F., Rousseeuw, P. J., & Leroy, A. M. (1989) · Technometrics · doi:10.2307/1268828 · Cited by 6,183 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses
Vogel, C., & Marcotte, E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3185 · Cited by 4,195 · see citing works
Gene Ontology Consortium: going forward
The Gene Ontology Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1179 · Cited by 3,492 · see citing works
On the Dependency of Cellular Protein Levels on mRNA Abundance
Liu, Y., Beyer, A., & Aebersold, R. (2016) · Cell · doi:10.1016/j.cell.2016.03.014 · Cited by 3,221 · see citing works
Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F., & Hamosh, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1205 · Cited by 2,691 · see citing works
A draft map of the human proteome
Kim, M.-S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., et al. (2014) · Nature · doi:10.1038/nature13302 · Cited by 2,198 · see citing works
Mass-spectrometry-based draft of the human proteome
Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al. (2014) · Nature · doi:10.1038/nature13319 · Cited by 1,820 · see citing works
Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N., & Mann, M. (2014) · Nature Methods · doi:10.1038/nmeth.2834 · Cited by 1,719 · see citing works
The human transcriptome across tissues and individuals
Melé, M., Ferreira, P. G., Reverter, F., DeLuca, D. S., Monlong, J., Sammeth, M., Young, T. R., Goldmann, J. M., Pervouchine, D. D., Sullivan, T. J., et al. (2015) · Science · doi:10.1126/science.aaa0355 · Cited by 1,304 · see citing works
Self-citationMcAlister, G. C., Nusinow, D. P., Jedrychowski, M. P., Wühr, M., Huttlin, E. L., Erickson, B. K., Rad, R., Haas, W., & Gygi, S. P. (2014) · Analytical Chemistry · doi:10.1021/ac502040v · Cited by 1,263 · see citing works
Metabolism disrupting chemicals and metabolic disorders
Heindel, J. J., Blumberg, B., Cave, M., Machtinger, R., Mantovani, A., Mendez, M. A., Nadal, A., Palanza, P., Panzica, G., Sargis, R., et al. (2017) · Reproductive Toxicology · doi:10.1016/j.reprotox.2016.10.001 · Cited by 1,101 · see citing works
Deep proteome and transcriptome mapping of a human cancer cell line
Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Pääbo, S., & Mann, M. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.81 · Cited by 979 · see citing works
Encyclopedia of Food and Health
(2016) · Elsevier eBooks · doi:10.1016/c2015-0-00355-0 · Cited by 953 · see citing works
A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project
Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., et al. (2015) · Biopreservation and Biobanking · doi:10.1089/bio.2015.0032 · Cited by 833 · see citing works
Self-citationThe quantitative proteome of a human cell line
Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., Herzog, F., Rinner, O., Ellenberg, J., & Aebersold, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.82 · Cited by 816 · see citing works
A deep proteome and transcriptome abundance atlas of 29 healthy human tissues
Wang, D., Eraslan, B., Wieland, T., Hallström, B., Hopf, T., Zolg, D. P., Zecha, J., Asplund, A., Li, L., Meng, C., et al. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188503 · Cited by 750 · see citing works
Holeček, M. (2018) · Nutrition & Metabolism · doi:10.1186/s12986-018-0271-1 · Cited by 736 · see citing works
Mayers, J. R., Wu, C. H., Clish, C. B., Kraft, P., Torrence, M. E., Fiske, B. P., Yuan, C., Bao, Y., Townsend, M. K., Tworoger, S. S., et al. (2014) · Nature Medicine · doi:10.1038/nm.3686 · Cited by 652 · see citing works
Wang, Z., Ying, Z., Bosy-Westphal, A., Zhang, J., Schautz, B., Later, W., Heymsfield, S. B., & Müller, M. J. (2010) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.2010.29885 · Cited by 546 · see citing works
Molecular and Therapeutic Potential and Toxicity of Valproic Acid
Chateauvieux, S., Morceau, F., Dicato, M., & Diederich, M. (2010) · Journal of Biomedicine and Biotechnology · doi:10.1155/2010/479364 · Cited by 526 · see citing works
Leigh syndrome: One disorder, more than 75 monogenic causes
Lake, N. J., Compton, A. G., Rahman, S., & Thorburn, D. R. (2015) · Annals of Neurology · doi:10.1002/ana.24551 · Cited by 499 · see citing works
Uhlén, M., Karlsson, M. J., Hober, A., Svensson, A.-S., Scheffel, J., Kotol, D., Zhong, W., Tebani, A., Strandberg, L., Edfors, F., et al. (2019) · Science Signaling · doi:10.1126/scisignal.aaz0274 · Cited by 495 · see citing works
White, P. J., McGarrah, R. W., Grimsrud, P. A., Tso, S.-C., Yang, W.-H., Haldeman, J. M., Grenier-Larouche, T., An, J., Lapworth, A. L., Astapova, I., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.04.015 · Cited by 365 · see citing works
Variation and genetic control of protein abundance in humans
Wu, L., Candille, S. I., Choi, Y., Xie, D., Jiang, L., Li-Pook-Than, J., Tang, H., & Snyder, M. (2013) · Nature · doi:10.1038/nature12223 · Cited by 364 · see citing works
Self-citationAlternative Splicing May Not Be the Key to Proteome Complexity
Tress, M. L., Abascal, F., & Valencia, A. (2016) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2016.08.008 · Cited by 341 · see citing works
Li, Z., Adams, R. M., Chourey, K., Hurst, G. B., Hettich, R. L., & Pan, C. (2011) · Journal of Proteome Research · doi:10.1021/pr200748h · Cited by 302 · see citing works
The effects of death and post-mortem cold ischemia on human tissue transcriptomes
Ferreira, P. G., Muñoz-Aguirre, M., Reverter, F., Godinho, C. D. S., Sousa, A., Amadoz, A., Sodaei, R., Hidalgo, M. R., Pervouchine, D. D., Carbonell‐Caballero, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02772-x · Cited by 300 · see citing works
Self-citationThe impact of rare variation on gene expression across tissues
Li, X., Kim, Y., Tsang, E. K., Davis, J. R., Damani, F. N., Chiang, C., Hess, G. T., Zappala, Z., Strober, B. J., Scott, A. J., et al. (2017) · Nature · doi:10.1038/nature24267 · Cited by 247 · see citing works
Self-citationThe utility of protein and mRNA correlation
Payne, S. (2014) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2014.10.010 · Cited by 236 · see citing works
Initial Quantitative Proteomic Map of 28 Mouse Tissues Using the SILAC Mouse
Geiger, T., Velic, A., Macek, B., Lundberg, E., Kampf, C., Nagaraj, N., Uhlen, M., Cox, J., & Mann, M. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.024919 · Cited by 235 · see citing works
Roles of oxidative stress in stomach disorders
Suzuki, H., Nishizawa, T., Tsugawa, H., Mogami, S., & Hibi∥, T. (2011) · Journal of Clinical Biochemistry and Nutrition · doi:10.3164/jcbn.11-115sr · Cited by 218 · see citing works
Branched-chain amino acid metabolism in heart disease: an epiphenomenon or a real culprit?
Huang, Y., Zhou, M., Sun, H., & Wang, Y. (2011) · Cardiovascular Research · doi:10.1093/cvr/cvr070 · Cited by 204 · see citing works
Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease The eGTEx Project
Stranger, B., Brigham, L., Hasz, R., Hunter, M., Johns, C., Johnson, M. D., Kopen, G., Leinweber, W. F., Lonsdale, J., McDonald, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3969 · Cited by 189 · see citing works
Branched Chain Amino Acids in Metabolic Disease
Arany, Z. P., & Neinast, M. D. (2018) · Current Diabetes Reports · doi:10.1007/s11892-018-1048-7 · Cited by 184 · see citing works
Genetic heterogeneity in leigh syndrome
DiMauro, S., & Vivo, D. C. D. (1996) · Annals of Neurology · doi:10.1002/ana.410400104 · Cited by 157 · see citing works
Genetics of human Bardet–Biedl syndrome, an updates
Khan, S. A., Muhammad, N., Khan, M. A., Kamal, A., Rehman, Z. U., & Khan, S. A. (2016) · Clinical Genetics · doi:10.1111/cge.12737 · Cited by 152 · see citing works
Innate Immunity of the Lung: From Basic Mechanisms to Translational Medicine
Hartl, D., Tirouvanziam, R. M., Laval, J., Greene, C. M., Habiel, D. M., Sharma, L., Yildirim, A. Ö., Cruz, C. S. D., & Hogaboam, C. M. (2018) · Journal of Innate Immunity · doi:10.1159/000487057 · Cited by 134 · see citing works
Lloyd, R. V., Cano, M., Rosa, P., Hille‐Rehfeld, A., & Huttner, W. Β. (1988) · PubMed Central · Cited by 133 · see citing works
Most Highly Expressed Protein-Coding Genes Have a Single Dominant Isoform
Ezkurdia, I., Rodrı́guez, J. M., Pau, E. C. D. S., Vázquez, J., Valencia, A., & Tress, M. L. (2015) · Journal of Proteome Research · doi:10.1021/pr501286b · Cited by 121 · see citing works
Bodzęta, A., Kahms, M., & Klingauf, J. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.07.040 · Cited by 103 · see citing works
Accelerating the search for the missing proteins in the human proteome
Baker, M. S., Ahn, S. B., Mohamedali, A., Islam, M. T., Cantor, D., Verhaert, P. D., Fanayan, S., Sharma, S., Nice, E. C., Connor, M., et al. (2017) · Nature Communications · doi:10.1038/ncomms14271 · Cited by 100 · see citing works
Katagiri, R., Goto, A., Nakagawa, T., Nishiumi, S., Kobayashi, T., Hidaka, A., Budhathoki, S., Yamaji, T., Sawada, N., Shimazu, T., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.07.033 · Cited by 89 · see citing works
A Statistical Model for iTRAQ Data Analysis
Hill, E. G., Schwacke, J. H., Comte‐Walters, S., Slate, E. H., Oberg, A. L., Eckel‐Passow, J. E., Therneau, T. M., & Schey, K. L. (2008) · Journal of Proteome Research · doi:10.1021/pr070520u · Cited by 88 · see citing works
Angione, C. (2019) · BioMed Research International · doi:10.1155/2019/8304260 · Cited by 88 · see citing works
De novo mutations of the ATP6V1A gene cause developmental encephalopathy with epilepsy
Fassio, A., Esposito, A., Kato, M., Saitsu, H., Mei, D., Marini, C., Conti, V., Nakashima, M., Okamoto, N., Turker, A. O., et al. (2018) · Brain · doi:10.1093/brain/awy092 · Cited by 85 · see citing works
Self-citationUsing BiblioSpec for Creating and Searching Tandem MS Peptide Libraries
Frewen, B. E., & MacCoss, M. J. (2007) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1307s20 · Cited by 84 · see citing works
Du, X., Li, Y., Wang, Y., You, H., Peng, H., Zheng, Y., & Du, J. (2018) · Life Sciences · doi:10.1016/j.lfs.2018.08.011 · Cited by 63 · see citing works
Pancreatitis Associated with Valproic Acid: A Review of the Literature
Chapman, S. A., Wacksman, G. P., & Patterson, B. D. (2001) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1592/phco.21.20.1549.34480 · Cited by 46 · see citing works
Gayam, V. B. R., Mandal, A., Khalid, M. O., Shrestha, B., Garlapati, P. R., & Khalid, M. (2018) · Journal of Community Hospital Internal Medicine Perspectives · doi:10.1080/20009666.2018.1514933 · Cited by 42 · see citing works
Feizi, A., Gatto, F., Uhlén, M., & Nielsen, J. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0021-4 · Cited by 39 · see citing works
Branched Chain Amino Acids in Clinical Nutrition
Patel, V. B., Preedy, V. R., & Rajendram, R. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1923-9 · Cited by 31 · see citing works
Loss of Bardet-Biedl syndrome proteins causes synaptic aberrations in principal neurons
Haq, N., Schmidt‐Hieber, C., Sialana, F. J., Ciani, L., Heller, J. P., Stewart, M. E., Bentley, L., Wells, S. E., Rodenburg, R. J., Nolan, P. M., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000414 · Cited by 25 · see citing works
Wang, M., Jiang, L., Jian, R., Chan, J., Liu, Q., Snyder, M. P., & Tang, H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa904 · Cited by 15 · see citing works
Self-citationIpsen, A. B. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b02446 · Cited by 12 · see citing works
AdaTiSS: a novel data- Ada ptive robust method for identifying Ti ssue S pecificity S cores
Wang, M., Jiang, L., & Snyder, M. P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab460 · Cited by 11 · see citing works
Self-citationPituitary Gland: Pituitary Hormones
Emerald, M. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-384947-2.00548-1 · Cited by 10 · see citing works
Development of Liver and Pancreas
Eşrefoğlu, M., Taşlıdere, E., & Çetin, A. (2016) · Bezmialem Science · doi:10.14235/bs.2016.841 · Cited by 7 · see citing works
Hormonale und metabolische Funktionen des Dünndarms
Wittenburg, H., Tennert, U., & Mössner, J. (2010) · Der Internist · doi:10.1007/s00108-009-2564-y · Cited by 6 · see citing works
The Cytosolic and Mitochondrial Branched Chain Aminotransferase
Conway, M. E., & Hutson, S. M. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1923-9_3 · Cited by 4 · see citing works
Bardet–Biedl syndrome and other ciliopathies
Foggensteiner, L., & Beales, P. L. (2015) · Oxford University Press eBooks · doi:10.1093/med/9780199592548.003.0314 · Cited by 4 · see citing works
Wang, M., Jiang, L., Jian, R., Chan, J., Liu, Q., Snyder, M. P., & Tang, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/770115 · Cited by 3 · see citing works
Self-citationWang, M., Jiang, L., & Snyder, M. P. (2021) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2020-0042 · Cited by 3 · see citing works
Self-citationThe Exocrine and Endocrine Pancreas: Embryology and Histology
Dubois, P. M. (1999) · Medical radiology · doi:10.1007/978-3-642-58380-3_1 · Cited by 2 · see citing works
AdaTiSS: A Novel Data-Adaptive Robust Method for Quantifying Tissue Specificity ScoresPreprint
Wang, M., Jiang, L., & Snyder, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/869404 · Cited by 2 · see citing works
Self-citationWang, M., Jiang, L., & Snyder, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/869362 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
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 1,081 · see citing works
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 667 · see citing works
Zhou, 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 505 · see citing works
Multi-Omics Profiling for Health
Babu, M., & Snyder, M. P. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100561 · Cited by 419 · see citing works
Self-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 316 · see citing works
Self-citationTranscriptome variation in human tissues revealed by long-read sequencing
Glinos, D. A., Garborcauskas, G., Hoffman, P. J., Ehsan, N., Jiang, L., Gokden, A., Dai, X.-G., Aguet, F., Brown, K. L., Garimella, K., et al. (2021) · Nature · doi:10.1038/s41586-022-05035-y · Cited by 278 · see citing works
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 · see citing works
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 200 · see citing works
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 189 · see citing works
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 154 · see citing works
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 · see citing works
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 134 · see citing works
Małecki, J., Muszyński, S., & Sołowiej, B. G. (2021) · Polymers · doi:10.3390/polym13152506 · Cited by 132 · see citing works
PaxDb 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 132 · see citing works
Self-citationDavies, J. P., Almasy, K. M., McDonald, E. F., & Plate, L. (2020) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.0c00500 · Cited by 129 · see citing works
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 127 · see citing works
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 · see citing works
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 124 · see citing works
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 120 · see citing works
Wang, R., Maron, B. A., & Loscalzo, J. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318731 · Cited by 117 · see citing works
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 114 · see citing works
Self-citationProteomes Are of Proteoforms: Embracing the Complexity
Carbonara, K., Andonovski, M., & Coorssen, J. (2021) · Proteomes · doi:10.3390/proteomes9030038 · Cited by 87 · see citing works
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 138 · see citing works
Cancer 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 108 · see citing works
Deep learning neural network tools for proteomics
Meyer, J. G. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100003 · Cited by 95 · see citing works
Dowling, P., Gargan, S., Swandulla, D., & Ohlendieck, K. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032415 · Cited by 95 · see citing works
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 90 · see citing works
Upadhya, S. R., & Ryan, C. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100288 · Cited by 81 · see citing works
A “multi-omics” analysis of blood–brain barrier and synaptic dysfunction in <i>APOE4</i> 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 · see citing works
Precision 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 86 · see citing works
Muraoka, S., Hirano, M., Isoyama, J., Nagayama, S., Tomonaga, T., & Adachi, J. (2022) · iScience · doi:10.1016/j.isci.2022.104012 · Cited by 78 · see citing works
The 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 77 · see citing works
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 74 · see citing works
Genetic basis of mitochondrial diseases
Gušić, M., & Prokisch, H. (2021) · FEBS Letters · doi:10.1002/1873-3468.14068 · Cited by 76 · see citing works
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 72 · see citing works
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 70 · see citing works
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 · see citing works
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 72 · see citing works
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 68 · see citing works
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 67 · see citing works
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 66 · see citing works
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 66 · see citing works
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 · see citing works
Fernandes, P. M., Kinkead, J., McNae, I. W., Michels, P. A. M., & Walkinshaw, M. D. (2020) · Biochemical Journal · doi:10.1042/bcj20200656 · Cited by 62 · see citing works
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 58 · see citing works
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 60 · see citing works
Spatial proteomics for understanding the tissue microenvironment
Mao, Y., Wang, X., Huang, P., & Tian, R. (2021) · The Analyst · doi:10.1039/d1an00472g · Cited by 53 · see citing works
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 59 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 54 · see citing works
Wu, D., Li, H., Liu, M., Qin, J., & Sun, Y. (2022) · Nature Communications · doi:10.1038/s41467-022-30707-8 · Cited by 53 · see citing works
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 52 · see citing works
Assessment of risks, implications, and opportunities of waterborne neurotoxic pesticides.
Dara, D., & Drabovich, A. (2022) · Journal of Environmental Science · doi:10.1016/j.jes.2022.03.033 · Cited by 35 · see citing works
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 47 · see citing works
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 47 · see citing works
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 · see citing works
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 46 · see citing works
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 43 · see citing works
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 · see citing works
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 45 · see citing works
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 46 · see citing works
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 · see citing works
Next-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 · see citing works
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 42 · see citing works
Cellular control of protein levels: A systems biology perspective
Munro, V., Kelly, V., Messner, C., & Kustatscher, G. (2023) · Proteomics · doi:10.1002/pmic.202200220 · Cited by 43 · see citing works
Cieri, N., Maurer, K., & Wu, C. J. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-0301 · Cited by 42 · see citing works
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 43 · see citing works
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 · see citing works
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 40 · see citing works
Teyssonnière, 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 · see citing works
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 40 · see citing works
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 41 · see citing works
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 · see citing works
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 39 · see citing works
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 · see citing works
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 37 · see citing works
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 36 · see citing works
Gil, 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 · see citing works
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 · see citing works
Reitz, 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 · see citing works
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 · see citing works
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 · see citing works
Li, 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 · see citing works
Self-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 · see citing works
Song, 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 · see citing works
Self-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 34 · see citing works
Srivastava, 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 34 · see citing works
Imai, K., & Nakai, K. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.607812 · Cited by 33 · see citing works
Multiomics 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 33 · see citing works
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 32 · see citing works
Tang, 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 31 · see citing works
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 31 · see citing works
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 · see citing works
Transcriptomics 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 26 · see citing works
The coming era of proteomics-driven precision medicine
Jiang, Y., Wang, J., Sun, A., Zhang, H., Yu, X.-B., Qin, W., Ying, W.-T., Li, Y., Chang, C., Wang, X., et al. (2025) · National Science Review · doi:10.1093/nsr/nwaf278 · Cited by 27 · see citing works
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 · see citing works
Comprehensive 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 · see citing works
- 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 · 2019: 1
- 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
