Human Protein Atlas

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Human Protein Atlas
DescriptionThe Human Protein Atlas portal is a publicly available database with millions of high-resolution images showing the spatial distribution of proteins in normal human tissues and different cancer types, as well the sub cellular localisation in single cells.
Research centerKTH, UU, SciLifeLab, Sweden
Primary citationUhlén M, et al. (January 2015). "Proteomics. Tissue-based map of the human proteome". Science. 347 (6220): 1260419. doi:10.1126/science.1260419. PMID 25613900. S2CID 802377.
WebAdvanced search, bulk retrieval/download
Data release
12 months
Curation policyYes – manual
Yes – both individual protein entries and searches

The Human Protein Atlas (HPA) is a Swedish-based program started in 2003 with the aim to map all the human proteins in cells, tissues and organs using integration of various omics technologies, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics and systems biology. All the data in the knowledge resource is open access to allow scientists both in academia and industry to freely access the data for exploration of the human proteome. In June 2023, version 23 was launched where a new Interaction section was introduced containing human protein-protein interaction networks for more than 11,000 genes that will add new aspects in terms of protein function.

The resource now includes twelve separate sections with complementary information about all human proteins. All data has been updated on the approximately 5 million individual web pages. The Human Protein Atlas program has already contributed to several thousands of publications in the field of human biology and disease and was selected by the organization ELIXIR as a European core resource due to its fundamental importance for a wider life science community. The HPA consortium is funded by the Knut and Alice Wallenberg Foundation.

Twelve sections[edit]

The Human Protein Atlas consists of twelve sections:

  • The Tissue[1] section of the Human Protein Atlas focuses on the expression profiles in human tissues of genes both on the mRNA and protein level. The protein expression data from 44 normal human tissue types is derived from antibody-based protein profiling using conventional and multiplex immunohistochemistry. All underlying images of immunohistochemistry stained normal tissues are available together with knowledge-based annotation of protein expression levels.
  • The Brain[2] section provides comprehensive spatial profiling of the brain, including overview of protein expression in the mammalian brain based on integration of data from human, pig and mouse. Transcriptomics data combined with affinity-based protein in situ localization down to single cell detail is available in this brain-centric sub atlas of the Human Protein Atlas. The data presented are for human genes and their one-to-one orthologues in pig and mouse. Gene summary pages provide the hierarchical expression landscape form 13 main regions of the brain to individual nuclei and subfields for every protein coding gene. For selected proteins, high content images are available to explore the cellular and subcellular protein distribution. In addition, the Brain section contains lists of genes with elevated expression in one or a group of regions to help the user identify unique protein expression profiles linked to physiology and function.
  • The Single Cell Type[3] section contains information based on single cell RNA sequencing (scRNAseq) data from 31 human tissues including peripheral blood mononuclear cells (PBMCs). The data is linked to in-house generated immunohistochemically stained tissue sections presented in the Tissue section in order to visualize the corresponding spatial protein expression patterns. The scRNAseq analysis was based on publicly available genome-wide expression data and comprises all protein-coding genes in 557 individual cell type clusters corresponding to 15 different cell type groups. A specificity classification was performed to determine the number of genes elevated in these single cell types. The genes expressed in each of the cell types can be explored in interactive UMAP plots and bar charts, with links to corresponding immunohistochemical stainings in human tissues.
  • The Tissue Cell Type section contains cell type expression specificity predictions for all human protein coding genes, generated using integrated network analysis of publicly available bulk RNAseq data. A specificity classification is used to predict which genes are enriched in each constituent cell type within an individual tissue. The data can be explored on a tissue-by-tissue basis, together with in-house generated immunohistochemically stained tissue sections. In addition, a core cell type analysis focuses on the cell types found in all, or the majority, of the profiled tissues, e.g., endothelial cells or macrophages. Here, genes with predicted specificity in these core cell types in multiple tissues are detailed.
  • The Pathology[4] section contains information based on mRNA and protein expression data from 17 different forms of human cancer, together with millions of in-house generated immunohistochemically stained tissue sections images and Kaplan-Meier plots showing the correlation between mRNA expression of each human protein gene and cancer patient survival.
  • The Disease section contains information on protein levels in blood in patients with different diseases and highlights proteins associated with these diseases using differential expression analysis and a disease prediction strategy based on machine-learning.
  • The Immune Cell[5] section contains single cell information on genome-wide RNA expression profiles of human protein-coding genes covering various B- and T-cells, monocytes, granulocytes and dendritic cells. The transcriptomics analysis covers 18 cell types isolated with cell sorting and includes classification based on specificity, distribution and expression cluster across all immune cells.
  • The Blood Protein[6] section presents estimated plasma concentrations of the proteins detected in human blood from mass spectrometry-based proteomics studies, published immune assay data and a longitudinal study based on proximity extension assay (PEA). Further, an analysis of the “human secretome” is presented including annotation of the genes predicted to be actively secreted to human blood, as well as to other compartments or organ systems of the human body such as the digestive tract or the brain.
  • The Subcellular[7] section of the Human Protein Atlas provides high-resolution insights into the expression and spatiotemporal distribution of proteins encoded by 13147 genes (65% of the human protein-coding genes). For each gene, the subcellular distribution of the protein has been investigated by immunofluorescence (ICC-IF) and confocal microscopy in up to three different cell lines, selected from a panel of 37 cell lines used in the subcellular section. Upon image analysis, the subcellular localization of the protein has been classified into one or more of 35 different organelles and fine subcellular structures. In addition, the section includes an annotation of genes that display single-cell variation in protein expression levels and/or subcellular distribution, as well as an extended analysis of cell cycle dependency of such variations.
  • The Cell Line section contains information on genome-wide RNA expression profiles of human protein-coding genes in 1206 human cell lines, including 1132 cancer cell lines. The transcriptomics analysis includes classification based on specificity analysis across 28 cancer types, distribution and expression cluster analysis across all cell lines and for selected cancer types also analysis of similarity of the cell lines to their corresponding cancer type.
  • The Structure section contains information about the three-dimensional structure of human proteins.
  • The Interaction section presents data on interaction networks based on protein-protein interactions from the IntAct database[8] and metabolic pathways from the Metabolic Atlas.[9]

Additional features[edit]

In addition to the twelve sections of HPA, exploring gene and protein expression, there are various features available at the HPA website to assist the research community, including integrated external resources, such as Metabolic Atlas, educational material and free downloadable data.

  • The “Learn” section of HPA includes educational resources, including information regarding antibody-based applications and techniques, a histology dictionary and educational 3D videos. The dictionary is an interactive tool for free full-screen exploration of whole slide images of normal human organs and tissues, cancer tissues and cell structures, guided with detailed annotations of all major structural elements. Educational videos have been produced by HPA, depicting the exploration of the human body in 3D, using antibody-based profiling of tissues and light sheet microscopy. The movies are available at the HPA website as well as on a YouTube channel.
  • Datasets used in HPA are made freely available to encourage further studies within the research community. Access to the extensive datasets is given through the downloadable data page of HPA, wherein 29 different downloadable files are available, containing genome‐wide data across various assays.


The Human Protein Atlas program was started in 2003 and funded by the non-profit organization Knut and Alice Wallenberg Foundation (KAW). The main site of the project is the Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health (Stockholm, Sweden). Additionally, the project involves research groups at Uppsala University, Karolinska Institutet, Chalmers University of Technology and Lund University, as well as several present and past international collaborations initiated with research groups in Europe, the United States, South Korea, China, and India. Professor Mathias Uhlén is the director of the program.

The research underpinning the start of the exploration of the whole human proteome in the Human Protein Atlas program was carried out in the late 1990s and early 2000s. A pilot study employing an affinity proteomics strategy using affinity-purified antibodies raised against recombinant human protein fragments was carried out for a chromosome-wide protein profiling of chromosome 21.[10] Other projects were also carried out to establish processes for parallel and automated affinity purification of mono-specific antibodies and their validation.[11][12]


Antibodies and antigens, produced in the Human Protein Atlas workflow, are used in research projects to study potential biomarkers in various diseases, such as breast cancer, prostate cancer, colon cancer, diabetes, autoimmune diseases, ovarian cancer and renal failure.[13][14][15][16][17][18]

Researchers involved with Human Protein Atlas projects, are sharing protocols and method details in an open-access group on[19] A large effort is put into validating the antibody reagents used for profiling of tissues and cells, and the HPA has implemented stringent antibody validation criteria as suggested by the International Working Group for Antibody Validation (IWGAV).[20][21][22]


The Human Protein Atlas program has participated in 9 EU research projects ENGAGE, PROSPECTS, BIO_NMD, AFFINOMICS, CAGEKID, EURATRANS, ITFoM, DIRECT and PRIMES.

See also[edit]


  1. ^ Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, et al. (January 2015). "Proteomics. Tissue-based map of the human proteome". Science. 347 (6220): 1260419. doi:10.1126/science.1260419. PMID 25613900. S2CID 802377.
  2. ^ Sjöstedt, E; Zhong, W; Fagerberg, L; Karlsson, M; Mitsios, N; Adori, C; Oksvold, P; Edfors, F; Limiszewska, A; Hikmet, F; Huang, J; Du, Y; Lin, L; Dong, Z; Yang, L; Liu, X; Jiang, H; Xu, X; Wang, J; Yang, H; Bolund, L; Mardinoglu, A; Zhang, C; von Feilitzen, K; Lindskog, C; Pontén, F; Luo, Y; Hökfelt, T; Uhlén, M; Mulder, J (2020). "An atlas of the protein-coding genes in the human, pig, and mouse brain". Science. 367 (6482). doi:10.1126/science.aay5947. PMID 32139519. S2CID 212560645.
  3. ^ Karlsson, M; Zhang, C; Méar, L; Zhong, W; Digre, A; Katona, B; Sjöstedt, E; Butler, L; Odeberg, J; Dusart, P; Edfors, F; Oksvold, P; von Feilitzen, K; Zwahlen, M; Arif, M; Altay, O; Li, X; Ozcan, M; Mardinoglu, A; Fagerberg, L; Mulder, J; Luo, Y; Ponten, F; Uhlén, M; Lindskog, C (July 2021). "A single-cell type transcriptomics map of human tissues". Science Advances. 7 (31). Bibcode:2021SciA....7.2169K. doi:10.1126/sciadv.abh2169. PMC 8318366. PMID 34321199.
  4. ^ Uhlen, M; Zhang, C; Lee, S; Sjöstedt, E; Fagerberg, L; Bidkhori, G; Benfeitas, R; Arif, M; Liu, Z; Edfors, F; Sanli, K; von Feilitzen, K; Oksvold, P; Lundberg, E; Hober, S; Nilsson, P; Mattsson, J; Schwenk, JM; Brunnström, H; Glimelius, B; Sjöblom, T; Edqvist, PH; Djureinovic, D; Micke, P; Lindskog, C; Mardinoglu, A; Ponten, F (18 August 2017). "A pathology atlas of the human cancer transcriptome". Science. 357 (6352). doi:10.1126/science.aan2507. PMID 28818916. S2CID 206659235.
  5. ^ Uhlen, M; Karlsson, MJ; Zhong, W; Tebani, A; Pou, C; Mikes, J; Lakshmikanth, T; Forsström, B; Edfors, F; Odeberg, J; Mardinoglu, A; Zhang, C; von Feilitzen, K; Mulder, J; Sjöstedt, E; Hober, A; Oksvold, P; Zwahlen, M; Ponten, F; Lindskog, C; Sivertsson, Å; Fagerberg, L; Brodin, P (20 December 2019). "A genome-wide transcriptomic analysis of protein-coding genes in human blood cells". Science. 366 (6472). doi:10.1126/science.aax9198. PMID 31857451. S2CID 209424418.
  6. ^ Uhlén, M; Karlsson, MJ; Hober, A; Svensson, AS; Scheffel, J; Kotol, D; Zhong, W; Tebani, A; Strandberg, L; Edfors, F; Sjöstedt, E; Mulder, J; Mardinoglu, A; Berling, A; Ekblad, S; Dannemeyer, M; Kanje, S; Rockberg, J; Lundqvist, M; Malm, M; Volk, AL; Nilsson, P; Månberg, A; Dodig-Crnkovic, T; Pin, E; Zwahlen, M; Oksvold, P; von Feilitzen, K; Häussler, RS; Hong, MG; Lindskog, C; Ponten, F; Katona, B; Vuu, J; Lindström, E; Nielsen, J; Robinson, J; Ayoglu, B; Mahdessian, D; Sullivan, D; Thul, P; Danielsson, F; Stadler, C; Lundberg, E; Bergström, G; Gummesson, A; Voldborg, BG; Tegel, H; Hober, S; Forsström, B; Schwenk, JM; Fagerberg, L; Sivertsson, Å (26 November 2019). "The human secretome". Science Signaling. 12 (609). doi:10.1126/scisignal.aaz0274. PMID 31772123. S2CID 208321549.
  7. ^ Thul, PJ; Åkesson, L; Wiking, M; Mahdessian, D; Geladaki, A; Ait Blal, H; Alm, T; Asplund, A; Björk, L; Breckels, LM; Bäckström, A; Danielsson, F; Fagerberg, L; Fall, J; Gatto, L; Gnann, C; Hober, S; Hjelmare, M; Johansson, F; Lee, S; Lindskog, C; Mulder, J; Mulvey, CM; Nilsson, P; Oksvold, P; Rockberg, J; Schutten, R; Schwenk, JM; Sivertsson, Å; Sjöstedt, E; Skogs, M; Stadler, C; Sullivan, DP; Tegel, H; Winsnes, C; Zhang, C; Zwahlen, M; Mardinoglu, A; Pontén, F; von Feilitzen, K; Lilley, KS; Uhlén, M; Lundberg, E (26 May 2017). "A subcellular map of the human proteome". Science. 356 (6340). doi:10.1126/science.aal3321. PMID 28495876. S2CID 10744558.
  8. ^ Orchard, S; Ammari, M; Aranda, B; Breuza, L; Briganti, L; Broackes-Carter, F; Campbell, NH; Chavali, G; Chen, C; del-Toro, N; Duesbury, M; Dumousseau, M; Galeota, E; Hinz, U; Iannuccelli, M; Jagannathan, S; Jimenez, R; Khadake, J; Lagreid, A; Licata, L; Lovering, RC; Meldal, B; Melidoni, AN; Milagros, M; Peluso, D; Perfetto, L; Porras, P; Raghunath, A; Ricard-Blum, S; Roechert, B; Stutz, A; Tognolli, M; van Roey, K; Cesareni, G; Hermjakob, H (January 2014). "The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases". Nucleic Acids Research. 42 (Database issue): D358-63. doi:10.1093/nar/gkt1115. PMC 3965093. PMID 24234451.
  9. ^ Robinson, JL; Kocabaş, P; Wang, H; Cholley, PE; Cook, D; Nilsson, A; Anton, M; Ferreira, R; Domenzain, I; Billa, V; Limeta, A; Hedin, A; Gustafsson, J; Kerkhoven, EJ; Svensson, LT; Palsson, BO; Mardinoglu, A; Hansson, L; Uhlén, M; Nielsen, J (24 March 2020). "An atlas of human metabolism". Science Signaling. 13 (624). doi:10.1126/scisignal.aaz1482. PMC 7331181. PMID 32209698.
  10. ^ Agaton C, Galli J, Höidén Guthenberg I, Janzon L, Hansson M, Asplund A, Brundell E, Lindberg S, Ruthberg I, Wester K, Wurtz D, Höög C, Lundeberg J, Ståhl S, Pontén F, Uhlén M (Jun 2003). "Affinity proteomics for systematic protein profiling of chromosome 21 gene products in human tissues". Molecular & Cellular Proteomics. 2 (6): 405–14. doi:10.1074/mcp.M300022-MCP200. PMID 12796447.
  11. ^ Falk R, Agaton C, Kiesler E, Jin S, Wieslander L, Visa N, Hober S, Ståhl S (Dec 2003). "An improved dual-expression concept, generating high-quality antibodies for proteomics research". Biotechnology and Applied Biochemistry. 38 (Pt 3): 231–9. doi:10.1042/BA20030091. PMID 12875650. S2CID 43820440.
  12. ^ Uhlén M, Björling E, Agaton C, Szigyarto CA, Amini B, Andersen E, et al. (Dec 2005). "A human protein atlas for normal and cancer tissues based on antibody proteomics". Molecular & Cellular Proteomics. 4 (12): 1920–32. doi:10.1074/mcp.M500279-MCP200. PMID 16127175.
  13. ^ Jonsson L, Gaber A, Ulmert D, Uhlén M, Bjartell A, Jirström K (2011). "High RBM3 expression in prostate cancer independently predicts a reduced risk of biochemical recurrence and disease progression". Diagnostic Pathology. 6: 91. doi:10.1186/1746-1596-6-91. PMC 3195697. PMID 21955582.
  14. ^ Larsson A, Fridberg M, Gaber A, Nodin B, Levéen P, Jönsson G, Uhlén M, Birgisson H, Jirström K (2012). "Validation of podocalyxin-like protein as a biomarker of poor prognosis in colorectal cancer". BMC Cancer. 12: 282. doi:10.1186/1471-2407-12-282. PMC 3492217. PMID 22769594.
  15. ^ Lindskog C, Asplund A, Engkvist M, Uhlen M, Korsgren O, Ponten F (Jun 2010). "Antibody-based proteomics for discovery and exploration of proteins expressed in pancreatic islets". Discovery Medicine. 9 (49): 565–78. PMID 20587347.
  16. ^ Neiman M, Hedberg JJ, Dönnes PR, Schuppe-Koistinen I, Hanschke S, Schindler R, Uhlén M, Schwenk JM, Nilsson P (Nov 2011). "Plasma profiling reveals human fibulin-1 as candidate marker for renal impairment". Journal of Proteome Research. 10 (11): 4925–34. doi:10.1021/pr200286c. PMID 21888404.
  17. ^ Nodin B, Fridberg M, Jonsson L, Bergman J, Uhlén M, Jirström K (2012). "High MCM3 expression is an independent biomarker of poor prognosis and correlates with reduced RBM3 expression in a prospective cohort of malignant melanoma". Diagnostic Pathology. 7: 82. doi:10.1186/1746-1596-7-82. PMC 3433373. PMID 22805320.
  18. ^ Schwenk JM, Igel U, Neiman M, Langen H, Becker C, Bjartell A, Ponten F, Wiklund F, Grönberg H, Nilsson P, Uhlen M (Nov 2010). "Toward next generation plasma profiling via heat-induced epitope retrieval and array-based assays". Molecular & Cellular Proteomics. 9 (11): 2497–507. doi:10.1074/mcp.M110.001560. PMC 2984230. PMID 20682762.
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