Human Protein Atlas

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 November 2020, version 20 was launched along with a celebratory publication of the 20 year history of HPA. Version 20 introduces an additional sub-atlas, the Single Cell Type Atlas, showing expression of protein-coding genes in single human cell types, in addition to the existing sub-atlases; the Tissue Atlas[1] showing the distribution of the proteins across all major tissues and organs in the human body, the Pathology Atlas[2] showing the impact of protein levels for survival of patients with cancer, the Blood Atlas[3] showing the expression profiles of blood cells and actively secreted proteins, the Brain Atlas[4] showing the distribution of proteins in human, mouse, and pig brain, and the Cell Atlas[5] showing the subcellular localization of proteins in single cells. Version 20 also included updates of the preexisting sub-atlases, a major update of the Dictionary tool, eleven educational 3D videos and a section regarding SARS-CoV-2 interacting human proteins. 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.

Human Protein Atlas
Content
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.
OrganismsHuman
Contact
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.
Access
Websitewww.proteinatlas.org
Download URLwww.proteinatlas.org/about/download
Tools
WebAdvanced search, bulk retrieval/download
Miscellaneous
VersioningYes
Data release
frequency		12 months
Version20.0
Curation policyYes – manual
Bookmarkable
entities		Yes – both individual protein entries and searches

Six major projects

The Human Protein Atlas consists of six sub-atlases:

  • The Tissue Atlas contains information regarding the expression profiles of human genes both on the mRNA and protein level. The protein expression data is derived from antibody-based protein profiling using immunohistochemistry. Altogether 76 different cell types, corresponding to 44 normal human tissue types, have been analyzed and the data is presented as pathology-based annotation of protein expression levels. All underlying images of immunohistochemistry stained normal tissues are available as high-resolution images in the normal tissue atlas.
  • The Single Cell Type Atlas shows single cell RNA sequencing (scRNAseq) data from 13 different human tissues, together with immunohistochemically stained tissue sections visualizing 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 192 individual single cell clusters. These clusters have been annotated as 51 cell types using >500 well-known cell type-specific markers. 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 Pathology Atlas is based on the analysis of 17 main cancer types using data from 8,000 patients. In addition, a new concept for showing patient survival data is introduced, called Interactive Survival Scatter plots, and the atlas includes more than 400,000 such plots. A national supercomputer center was used to analyze more than 2.5 petabytes of underlying publicly available data from the Cancer Genome Atlas (TCGA) to generate more than 900,000 survival plots describing the consequence of RNA and protein levels on clinical survival. The Pathology Atlas also contains 5 million pathology-based images generated by the Human Protein Atlas consortium.
  • The Brain Atlas explores the protein expression in the mammalian brain by visualization and integration of data from three mammalian species (human, pig and mouse). Transcriptomics data combined with affinity-based protein in situ localization down to single cell detail is here available in a brain-centric sub-atlas of the Human Protein Atlas. The data focuses on human genes and one-to-one orthologues in pig and mouse. Each gene is provided with a summary page, showing available expression data (mRNA) for summarized regions of the brain as well as protein location for selected targets. High resolution staining images as well as expression data for the individual sub regions are all available for exploring the brain, the most complex organ.
  • The Blood Atlas contains single cell type information on genome-wide RNA expression profiles of human protein-coding genes covering various B- and T-cells, monocytes, granulocytes and dendritic cells. The single cell transcriptomics analysis covers 18 cell types isolated with cell sorting followed by RNA-seq analysis. In addition, an analysis of the “human secretome” is presented including annotation of the genes predicted to be actively secreted to human blood, as well as the annotation of proteins predicted to be secreted to other parts of the human body, such as the gastric tract and local compartments. An analysis of the proteins detected in human blood is also presented with an estimation of the respective protein concentrations determined either with mass spectrometry-based proteomics or antibody-based immunoassays.
  • The Cell Atlas provides high-resolution insights in the spatial distribution of proteins within cells. The Cell Atlas contains mRNA expression profiles for a diverse panel of human-derived cell lines (n=69) representing different cell types, tissues and organs in the human body. Also, the Cell Atlas contains high-resolution, multi-colour immunofluorescence images of cells that detail the subcellular distribution pattern of proteins encoded by 12813 genes (65% of the human protein-coding genes). By default, U-2 OS and 2 other cell lines, selected based on gene expression, are probed with each antibody. The cells are stained in a standardized way where the antibody of interest is visualized in green, microtubules red, the endoplasmic reticulum yellow, and the nucleus counterstained in blue. The images are manually annotated in terms of spatial distribution to 35 different subcellular structures, representing 14 major organelles. The annotated locations for each protein are classified as main and additional, and assigned a reliability score.

Additional features

In addition to the six sub-atlases 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.

  • Metabolic Atlas[6][7] is an external atlas, which is partly integrated into the Tissue Atlas portion of HPA, enabling visual exploration of protein function and tissue-specific gene expression in the context of the human metabolic network. For proteins involved in metabolism, a metabolic summary is provided that describes the metabolic subsystems/pathways, cellular compartments, and number of reactions associated with the protein. Over 120 manually curated metabolic pathway maps facilitate the visualization of each protein's participation in different metabolic processes. Each pathway map is accompanied by a heatmap detailing the mRNA levels across 37 different tissue types for all proteins involved in the metabolic pathway.
  • 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 28 different downloadable files are available, containing genome‐wide data across various assays.

History

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.[8] Other projects were also carried out to establish processes for parallel and automated affinity purification of mono-specific antibodies and their validation.[9][10]

Research

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.[11][12][13][14][15][16]

Researchers involved with Human Protein Atlas projects, are sharing protocols and method details in an open-access group on protocols.io.[17] 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).[18][19][20]

Collaborations

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

References
  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. Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, et al. (August 2017). "A pathology atlas of the human cancer transcriptome". Science. 357 (6352): eaan2507. doi:10.1126/science.aan2507. PMID 28818916.
  3. 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): eaax9198. doi:10.1126/science.aax9198. PMID 31857451. S2CID 209424418.
  4. 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 (6 March 2020). "An atlas of the protein-coding genes in the human, pig, and mouse brain". Science. 367 (6482): eaay5947. doi:10.1126/science.aay5947. PMID 32139519. S2CID 212560645.
  5. Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, et al. (May 2017). "A subcellular map of the human proteome". Science. 356 (6340): eaal3321. doi:10.1126/science.aal3321. PMID 28495876. S2CID 10744558.
  6. "Metabolic Atlas". Retrieved 2021-01-31.
  7. 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): eaaz1482. doi:10.1126/scisignal.aaz1482. PMC 7331181. PMID 32209698.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. "Human Protein Atlas - research group on protocols.io". protocols.io. Retrieved 2019-12-12.
  18. Uhlen, M; Bandrowski, A; Carr, S; Edwards, A; Ellenberg, J; Lundberg, E; Rimm, DL; Rodriguez, H; Hiltke, T; Snyder, M; Yamamoto, T (October 2016). "A proposal for validation of antibodies". Nature Methods. 13 (10): 823–7. doi:10.1038/nmeth.3995. PMID 27595404. S2CID 34259132.
  19. Edfors, F; Hober, A; Linderbäck, K; Maddalo, G; Azimi, A; Sivertsson, Å; Tegel, H; Hober, S; Szigyarto, CA; Fagerberg, L; von Feilitzen, K; Oksvold, P; Lindskog, C; Forsström, B; Uhlen, M (8 October 2018). "Enhanced validation of antibodies for research applications". Nature Communications. 9 (1): 4130. Bibcode:2018NatCo...9.4130E. doi:10.1038/s41467-018-06642-y. PMC 6175901. PMID 30297845.
  20. Sivertsson, Å; Lindström, E; Oksvold, P; Katona, B; Hikmet, F; Vuu, J; Gustavsson, J; Sjöstedt, E; von Feilitzen, K; Kampf, C; Schwenk, JM; Uhlén, M; Lindskog, C (10 November 2020). "Enhanced Validation of Antibodies Enables the Discovery of Missing Proteins". Journal of Proteome Research. 19 (12): 4766–4781. doi:10.1021/acs.jproteome.0c00486. PMC 7723238. PMID 33170010.
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