Immunohistochemistry - tissues

The Human Protein Atlas contains images of histological sections from normal and cancer tissues obtained by immunohistochemistry. Antibodies are labeled with DAB (3,3'-diaminobenzidine) and the resulting brown staining indicates where an antibody has bound to its corresponding antigen. The section is furthermore counterstained with hematoxylin to enable visualization of microscopical features. Tissue microarrays are used to show antibody staining in samples from 144 individuals corresponding to 44 different normal tissue types, and samples from 216 cancer patients corresponding to 20 different types of cancer (movie about tissue microarray production and immunohistochemical staining). Each sample is represented by 1 mm tissue cores, resulting in a total number of 576 images for each antibody. Normal tissues are represented by samples from three individuals each, one core per individual, except for endometrium, skin, soft tissue and stomach, which are represented by samples from six individuals each and parathyroid gland, which is represented by one sample. Protein expression is annotated in 76 different normal cell types present in these tissue samples. For cancer tissues, two cores are sampled from each individual and protein expression is annotated in tumor cells. A small fraction of the 576 images are missing for most antibodies due to technical issues. Specimens containing normal and cancer tissue have been collected and sampled from anonymized paraffin embedded material of surgical specimens, in accordance with approval from the local ethics committee. For selected proteins extended tissue profiling is performed in addition to standard tissue microarrays. Examined tissues include mouse brain, human lactating breast, eye, thymus and extended samples of adrenal gland, skin and brain.
Since specimens are derived from surgical material, normal is here defined as non-neoplastic and morphologically normal. It is not always possible to obtain fully normal tissues and thus several of the tissues denoted as normal will include alterations due to inflammation, degeneration and tissue remodeling. In rare tissues, hyperplasia or benign proliferations are included as exceptions. It should also be noted that within normal morphology there may exist interindividual differences and variations due to primary diseases, age, sex etc. Such differences may also affect protein expression and thereby immunohistochemical staining patterns. Samples from cancer are also derived from surgical material. Due to subgroups and heterogeneity of tumors within each cancer type, included cases represent a typical mix of specimens from surgical pathology. The inclusion of tumors is based on availability and representativity, however, an effort has been made to include high and low grade malignancies where such is applicable. In certain tumor groups, subtypes have been included, e.g. breast cancer includes both ductal and lobular cancer, lung cancer includes both squamous cell carcinoma and adenocarcinoma and liver cancer includes both hepatocellular and cholangiocellular carcinoma etc. Tumor heterogeneity and interindividual differences may be reflected in diverse expression of proteins resulting in variable immunohistochemical staining patterns.


In order to provide an overview of protein expression patterns, all images of tissues stained by immunohistochemistry are manually annotated by a specialist followed by verification by a second specialist. Annotation of each different normal and cancer tissue is performed using fixed guidelines for classification of immunohistochemical results. Each tissue is examined for representability, and subsequently immunoreactivity in the different cell types present in normal or cancer tissues was annotated. Basic annotation parameters include an evaluation of i) staining intensity (negative, weak, moderate or strong), ii) fraction of stained cells (<25%, 25-75% or >75%) and iii) subcellular localization (nuclear and/or cytoplasmic/membranous). The manual annotation also provides two summarizing texts describing the staining pattern for each antibody in normal tissues and in cancer tissues.
The terminology and ontology used is compliant with standards used in pathology and medical science. SNOMED classification is used for assignment of topography and morphology. SNOMED classification also underlies the given original diagnosis from which normal as well as cancer samples were collected.
A histological dictionary used in the annotation is available as a PDF-document, containing images stained by immunohistochemistry using antibodies included in the Human Protein Atlas. The dictionary displays subtypes of cells distinguishable from each other and also shows specific expression patterns in different intracellular structures. Annotation dictionary: screen usage (15 MB), printing (95 MB).

Knowledge-based annotation

Knowledge-based annotation aims to create a comprehensive overview of protein expression patterns in normal human tissues. This is achieved by stringent evaluation of immunohistochemical staining pattern, RNA-seq data from internal and external sources and available protein/gene characterization data, with special emphasis on RNA-seq. Annotated protein expression profiles are performed using single antibodies as well as independent antibodies (two or more independent antibodies directed against different, non-overlapping epitopes on the same protein). For independent antibodies, the immunohistochemical data from all the different antibodies are taken into consideration. The immunohistochemical staining pattern in normal tissues is subjectively annotated according to strict guidelines. It is based on the experienced evaluation of positive immunohistochemical signals in the 76 normal cell types analyzed. The review also takes suboptimal experimental procedures and interindividual variations into consideration.
The final annotated protein expression is considered a best estimate and as such reflects the most probable histological distribution and relative expression level for each protein. To enable a protein expression profile, one or several of the following additional data sources is necessary; i) an independent antibody targeting another epitope of the same protein ii) RNA-seq data, and iii) available protein/gene characterization data. The result of the knowledge-based annotation is considered inconclusive when the information available at the time of analysis is evaluated as not sufficient for verification of the staining pattern and an estimation of the expected protein expression. The knowledge-based protein expression profiles are performed using fixed guidelines on evaluation and presentation of the resulting expression profiles. Standardized explanatory sentences are used when necessary to provide additional information required for full understanding of the expression profile. A reliability score, set as Enhanced, Supported, Approved, or Uncertain is set for each annotated protein expression profile based on evaluation of all available data.

Reliability score

A reliability score is manually set for all genes and indicates the level of reliability of the analyzed protein expression pattern based on knowledge-based evaluation of available RNA-seq data, protein/gene characterization data and immunohistochemical data from one or several antibodies designed towards non-overlapping sequences of the same gene. The reliability score is based on the 44 normal tissues analyzed, and is displayed on both the Tissue Atlas and the Pathology Atlas.

The reliability score is divided into Enhanced, Supported, Approved, or Uncertain. If there is available data from more than one antibody, the staining patterns of all antibodies are taken into consideration during the evaluation of the reliability score.

One or several antibodies targeting non-overlapping sequences of the same gene have obtained enhanced validation based on either orthogonal or independent antibody validation methods.

If one of the following criteria is fulfilled:

  • At least one antibody shows high or medium consistency between RNA levels and staining pattern, but the antibody does not qualify for Orthogonal validation and staining pattern is consistent with valid literature, or there is no valid literature available
  • At least one antibody has RNA consistency defined as “Cannot be evaluated” and staining pattern is consistent with valid literature
  • Paired antibodies (several antibodies targeting non-overlapping sequences) show similar staining pattern, but the antibodies do not qualify for Independent antibody validation and staining pattern is consistent with valid literature, or there is no valid literature availa

If one of the following criteria is fulfilled:

  • At least one antibody shows high or medium consistency between RNA levels and staining pattern and staining pattern is inconsistent with valid literature
  • At least one antibody shows low consistency between RNA levels and staining pattern and staining pattern is consistent with valid literature
  • At least one antibody has RNA consistency defined as “Cannot be evaluated” and staining pattern is partly consistent with valid literature, or consistent with limited literature
  • Paired antibodies show partly similar expression patterns

If one of the following criteria is fulfilled:

  • Only multi-targeting antibodies are available. Multi-targeting antibodies are used for genes where it was not possible to generate single-targeting antibodies due to high sequence identity among proteins belonging to different genes. These genes are in many cases closely related and belong to known gene families, and in these cases a multi-targeting antibody was produced that has >80% sequence identity to transcripts of the genes belonging to the family and low sequence identity to the transcripts of all other human genes.
  • At least one antibody shows low or very low consistency between RNA and staining pattern, or RNA consistency is defined as “Cannot be evaluated” and staining pattern is inconsistent with valid literature, or there is no valid literature available
  • Paired antibodies show dissimilar expression patterns

Multiplex immunohistochemistry/IF - tissues

As part of the Tissue Atlas section, the multiplex immunohistochemistry(mIHC)/IF data was generated by staining tissue microarrays obtained from histological sections from normal tissues. The mIHC/IF tissue data displays high-resolution, 6-plex images of proteins labeled by indirect mIHC and in addition to conventional IHC, provides spatial information on protein expression patterns related to distinct single cells and cell types, or even cellular states and histological and biological structures embedded in the tissue.

Similarly to conventional IHC, in mIHC/IF, primary antibodies are first labeled with secondary antibodies coupled with horseradish peroxidase (HRP) (or similar). Further, the method utilizes tyramide signal amplification (TSA) where fluorescent tyramide molecules are catalyzed by HRP which creates a fluorescent precipitate on and proximal to the binding site. The ability to run several staining-stripping-cycles allows for tissue sections with up to 6 labeled proteins per slide. Lastly, the slides are counterstained with DAPI (4′,6-diamidino-2-phenylindole). In this setup, tissue microarrays consisting of doublet 1 mm cores from three patients are used to profile each protein.


The protein localization is manually annotated by assessing the target of interest by estimating the fraction of cells that overlap with the panel antibodies and annotating their subceullular localization. For each slide, the tissue cores are examined for representability as well. The annotation parameters include an evaluation of i) fraction of cells with expression of unknown protein that overlap with panel markers (<25%, 25-75% or >75%), and ii) subcellular localization (nuclear and/or cytoplasmic/plasma membrane/membrane) of the staining. The manual annotation also provides two summarizing texts describing the staining pattern for each antibody.

Testis panels

For testis, two panels have been developed where the aim was i) to capture the transition of spermatogonial stem cells to preleptotene spermatocytes (Spermatogonia panel), and ii) to identify the expression of proteins during spermatocyte differentiation and meiosis (Spermatocyte panel). For each unknown protein, the antibody targeting the protein is labeled with the available TSA-flourophore (OPAL 520) not occupied by the marker proteins.

Spermatogonia panel

Cell type Marker protein Antibody Fluorescent label Pseudo-color
State 0 UTF1 CAB022384 OPAL480 Yellow
State 1 IRF2BPL HPA050862 OPAL650 White
State 2-3 DMRT1 HPA027850 OPAL690 Cyan
State 4 CTCFL HPA001472 OPAL780 Magenta
Preleptotene spermatocytes BEND2 HPA013142 OPAL570 Red
Empty slot Unknown protein of interest - OPAL520 Green

Spermatocyte panel

Cell type Marker protein Antibody Fluorescent label Pseudo-color
Preleptotene spermatocytes HELLS HPA063242 OPAL480 Yellow
Leptotene spermatocytes SCML1 HPA035270 OPAL690 Cyan
Pachytene/diplotene spermatocytes TCFL5 HPA076419 OPAL780 Magenta
Early spermatids SUN5 HPA048529 OPAL620 White
Late spermatids PRM1 HPA055150 OPAL570 Red
Empty slot Unknown protein of interest - OPAL520 Green

Kidney panel

For kidney, a kidney tubules panel was developed to characterize the spatial localization of kidney proteins mainly in renal tubules but also in podocytes. An endothelial cell marker was also added to distinguish non-podocytes in the glomerular compartment. For each unknown protein, the antibody targeting the protein is labeled with the available TSA-flourophore (OPAL 520) not occupied by the marker proteins.

Kidney tubules panel

Cell type Marker protein Antibody Fluorescent label Pseudo-color
Collecting ducts AQP2 HPA046834 OPAL690 Cyan
Distal tubules CASR HPA039686 OPAL570 Red
Proximal tubules ACSM2A/B HPA057699 OPAL620 White
Podocytes PTPRO HPA034525 OPAL480 Yellow
Endothelial cells CD34 HPA036722 OPAL780 Magenta
Empty slot Unknown protein of interest - OPAL520 Green

Data reliability

For each antibody and protein, an internal reliability assessment is performed to ensure high quality data before release. The antibody staining pattern of the unknown protein is always reviewed against its corresponding conventional IHC staining pattern for reproducibility, and against available tissue and single-cell RNA-seq data, and protein/gene characterization data. This assessment should not be confused with the Reliability scoring performed for the tissue-wide analysis. The reproducibility of the panel the panel marker proteins are also assessed to ensure high quality of the annotation.

Immunohistochemistry/IF - mouse brain

As a complement to the immunohistochemically stained tissues, the protein atlas also includes the mouse brain atlas as a sub compartment of the normal tissue atlas. In which comprehensive profiles are available in mouse brain. A selected set of targets have been analyzed by using the antibodies in serial sections of mouse brain which covers 129 areas and subfields of the brain, several of these regions difficult to cover in the human brain. In addition pituitary, retina and trigeminal ganglions are included in recent and future image series but not annotated yet.

The tissue microarray method used within the human protein atlas enabled the global mapping of proteins in the human body, including the brain. Currently, the human tissue atlas covers four areas of the human brain: cerebral cortex, hippocampus, caudate and cerebellum. Due to the heterogeneous structure of the brain, with many nuclei and cell-types organized in complex networks, it is difficult to achieve a comprehensive overview in a 1 mm tissue sample. Analysis of more human brain samples, including smaller brain nuclei, is thus desirable in order to generate a more detailed map of protein distribution in the brain. Therefore, we here complemented the human brain atlas effort with a more comprehensive analysis of the mouse brain. A series of mouse brain sections is explored for protein expression and distribution in a large number of brain regions.

Antibodies are selected against protein involved in normal brain physiology, brain development and neuropathological processes. A limit of 60% homology (human vs mouse) is used as cut off when comparing the PrEST sequence for the antibody targets.

Selected antibodies are applied to test-sections containing brain regions or cell types with known expression based on in situ hybridization (Allen Brain Atlas) and single cell RNAseq data (Linnarsson Lab and Barres Lab). Staining patterns are evaluated based on consistency between staining patterns of multiple antibodies against the same target and match to transcriptomics data. Antibody immunoreactivity is visualized using tyramid signal amplification shown in green. A nuclear reference staining (DAPI) is visualized in blue. The immunofluorescence protocol is standardized through antibody concentration and incubation time are variable depending on protein abundance and antibody affinity determined during the test staining. The complete mouse brain profile is represented by serial coronal sections of adult mouse brain, 16 µm thick. Stained slides are then scanned and digitalized before further processing.

Table 1. Brain regions. Abbreviations are based on The Mouse Brain in Stereotaxic Coordinates, Third Edition: The coronal plates and diagrams (ISBN: 9780123742445)

Region Abbreviation Allen Brain Atlas
cerebral cortex cerebral cortex frontal association cortex fra FRP
cerebral cortex cerebral cortex motor cortex m MO
cerebral cortex cerebral cortex cingulate cortex cg ACA
cerebral cortex cerebral cortex piriform cortex, L1 pirl1 PIR1
cerebral cortex cerebral cortex piriform cortex, L2 pirl2 PIR2
cerebral cortex cerebral cortex piriform cortex, L3 pirl3 PIR3
cerebral cortex cerebral cortex insular cortex i AI
cerebral cortex cerebral cortex somatosensory cortex s SS
cerebral cortex cerebral cortex retrosplenial granular cortex rsg RSP
cerebral cortex cerebral cortex parietal association cortex p PTLp
cerebral cortex cerebral cortex entorhinal cortex ent ENT
cerebral cortex cerebral cortex visual cortex v VIS
olfactory bulb olfactory bulb anterior olfactory nucleus aon AON
olfactory bulb olfactory bulb granule cell layer gro MOBgr
olfactory bulb olfactory bulb internal plexiform layer ipl MOBipl
olfactory bulb olfactory bulb mitral cell layer mi MOBmi
olfactory bulb olfactory bulb glomerular layer gl MOBgl
olfactory bulb olfactory bulb rostral migratory stream rms SEZ
olfactory bulb olfactory bulb external plexiform layer epl MOBopl
olfactory bulb olfactory bulb external plexiform layer of the accessory OB epla
olfactory bulb olfactory bulb granule cell layer of the accessory OB gra AOBgr
olfactory bulb olfactory bulb glomerular layer of the accessory OB gla AOBgl
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The digitalized images are processed (axel-adjusted and tissue edges defined) and regions of interest (ROIs) are then marked according to the table above. These ROIs are then used for image analysis and the relative fluorescence intensity is listed for each region. The relative fluorescence is defined intensity of the annotated region relative to the intensity of the region with highest intensity.

The overview and preserved orientation in the mouse brain has enabled us to annotate additional cell classes (ependymal), glial subpopulations (microglia, oligodendrocytes, and astrocytes), and additional brain specific subcellular locations (axon, dendrite, synapse, and glia endfeet) for each investigated protein.

All images of immunofluorescence stained sections were manually annotated by specially educated personnel followed by review and verification by a second qualified member of the staff. The cellular and subcellular location of the immunoreactivity is defined and a summarizing text is provided describing the general staining pattern.

Specificity is validated by comparing the data with in situ hybridization data (Allen brain atlas) and/or available literature; support from other data leads to a supportive reliability score, while more unknown targets are viewed as uncertain and awaits further validation.

Reliability score

A reliability score is set for all genes and indicates the level of reliability of the analyzed protein expression pattern based on available protein/RNA/gene characterization data.

The reliability score of the antibodies in mouse brain atlas is scored as Supported or Uncertain depending on support from in situ hybridization data (Allen brain atlas) and/or previous published data, UniProtKB/Swiss-Prot database.

Immunocytochemistry/IF - cells

The subcellular section revolves around high-resolution, multicolor images of proteins labeled by indirect immunocytochemistry/immunofluorescence (ICC-IF). This provides spatial information on protein localization in terms of the subcellular distribution of the protein in organelles and subcellular structures at single cell level.

Originally three cell lines, U2OS, A-431 and U-251 MG, originating from different human tissues were chosen to be included in the analysis of protein subcellular localization by ICC-IF. The cell line panel has since been expanded to cover more cell types and lineages, e.g. tumor cell lines from mesenchymal, epithelial and glial tumors, as well as cell lines that have immortalized by introduction of telomerase. The selection was furthermore based on morphological characteristics and widespread use of these cell lines. Information regarding sex and age of the donor, cellular origin and source is listed here. In order to localize the whole human proteome on a subcellular level in one specific cell line, most proteins are stained in U2OS. Two additional cell lines are selected based on mRNA expression data. In addition to the human cell lines, many proteins have been stained in the mouse cell line NIH 3T3, given that the human and mouse genes are orthologous.

The standard immunostaining protocol for ICC can be found on the open access repository for science methods at For the great majority of antibodies, fixation is achieved with paraformaldehyde (PFA), but for a few antibodies, this is replaced by methanol in order to better preserve the morphology of certain cellular structures. For each gene, the use of PFA or methanol, as well as dilution factors for the antibodies, are stated in the Antibodies and Validation section. In order to facilitate the annotation of the subcellular localization of the protein targeted by the HPA antibody, the cells are also stained with reference markers: (i) DAPI for the nucleus, (ii) anti-tubulin antibody for microtubules, and (iii) anti-calreticulin or anti-KDEL for the endoplasmic reticulum (ER).

The resulting confocal images are single slice images representing one optical section of the cells. The microscope settings are standardized, but the detector gain is optimized for each sample. The different organelle probes are displayed as different channels in the multicolor images, with the HPA antibody stainingshown in green, nucleus in blue, microtubules in red and ER in yellow.


In order to provide an interpretation of the staining patterns, all images generated by ICC-IF are manually annotated. For each cell line and antibody, the staining is described in terms of subcellular location(s) and single-cell variability (SCV). The table below lists the subcellular locations used for annotation, with links to the cell structure dictionary entry and corresponding GO terms. SCVs within an immunofluorescence image are classified as intensity variation (variation in their expression level) or as spatial variation (variation in the spatial distribution).

Subcellular location GO term
Actin filaments GO:0015629
Aggresome GO:0016235
Cell Junctions GO:0030054
Centriolar satellite GO:0034451
Centrosome GO:0005813
Cleavage furrow GO:0032154
Cytokinetic bridge GO:0045171
Cytoplasmic bodies GO:0036464
Cytosol GO:0005829
Endoplasmic reticulum GO:0005783
Endosomes GO:0005768
Focal adhesion sites GO:0005925
Golgi apparatus GO:0005794
Intermediate filaments GO:0045111
Kinetochore GO:0000776
Lipid droplets GO:0005811
Lysosomes GO:0005764
Microtubule ends GO:1990752
Microtubules GO:0015630
Midbody GO:0030496
Midbody ring GO:0090543
Mitochondria GO:0005739
Mitotic chromosome GO:0005694
Mitotic spindle GO:0072686
Nuclear bodies GO:0016604
Nuclear membrane GO:0031965
Nuclear speckles GO:0016607
Nucleoli GO:0005730
Nucleoli fibrillar center GO:0001650
Nucleoli rim GO:0005730
Nucleoplasm GO:0005654
Peroxisomes GO:0005777
Plasma membrane GO:0005886
Rods & Rings
Vesicles GO:0043231

Knowledge-based annotation

The knowledge-based annotation aims to provide an interpretation of the detected subcellular localization of a protein. In the first step, stainings in different cell lines with the same antibody are reviewed and the results are compared with external experimental protein/gene characterization data for subcellular localization, available in the UniProtKB/Swiss-Prot database. In the second step, all antibodies targeting the same protein are taken in consideration for a final annotation of the subcellular distribution of the protein.

Reliability score

Each location is separately given one of the four reliability scores (Enhanced, Supported, Approved, or Uncertain) based on available protein/RNA/gene characterization data from both HPA and the UniProtKB/Swiss-Prot database. The reliability score also encompass several additional factors, including reproducibility of the antibody staining in different cell lines, correlation between staining intensity and RNA expression levels, and assays for enhanced antibody validation. Enhanced validation in achieved by using antibodies binding to different epitopes on the same target protein (independent antibody validation), by assessing staining intensity upon knockdown/knockout of the target protein (genetic validation) and/or by matching of the signal with a GFP-tagged protein (recombinant expression validation), and experimental evidence for subcellular location described in literature. The individual location relibility scores are summarized in an overall gene reliability score.

There are four different reliability scores:

  • Enhanced - The antibody has enhanced validation and there is no contradicting data, such as literature describing experimental evidence for a different location.
  • Supported - There is no enhanced validation of the antibody, but the annotated localization is reported in literature.
  • Approved - The localization of the protein has not been previously described and was detected by only one antibody without additional antibody validation.
  • Uncertain - The antibody-staining pattern contradicts experimental data or expression is not detected at RNA level.

Protein array

All purified antibodies are analyzed on antigen microarrays. The specificity profile for each antibody is determined based on the interaction with 384 different antigens including its own target. The antigens present on the arrays are consecutively exchanged in order to correspond to the next set of 384 purified antibodies. Each microarray is divided into 21 replicated subarrays, enabling the analysis of 21 antibodies simultaneously. The antibodies are detected through a fluorescently labeled secondary antibody and a dual color system is used in order to verify the presence of the spotted proteins. A specificity profile plot is generated for each antibody, where the signal from the binding to its own antigen is compared to the eventual off target interactions to all the other antigens. The vast majority (86%) of antibodies are given a pass and the remaining are failed either due to low signal or low specificity.

Western blot

Western blot analysis of antibody specificity has been done using a routine sample setup composed of IgG/HSA-depleted human plasma and protein lysates from a limited number of human tissues and cell lines. Antibodies with an uncertain routine WB have been revalidated using an over-expression lysate (VERIFY Tagged Antigen(TM), OriGene Technologies, Rockville, MD) as a positive control. Antibody binding was visualized by chemiluminescence detection in a CCD-camera system using a peroxidase (HRP) labeled secondary antibody.

Antibodies included in the Human Protein Atlas have been analyzed without further efforts to optimize the procedure and therefore it cannot be excluded that certain observed binding properties are due to technical rather than biological reasons and that further optimization could result in a different outcome.


HPA RNA-seq data

In total, 1055 cell lines, 51 human tissues and 18 blood cell types as well as total peripheral blood mononuclear cells (PBMC) have been analyzed by RNA-seq to estimate the transcript abundance of each protein-coding gene. Additionally, 19 mouse tissue samples and 32 pig tissue samples collected from the brain and retina of the animals were sampled and analyzed by RNA-seq.

For normal tissue and blood samples, specimens were collected with consent from patients and all samples were anonymized in accordance with approval from the local ethics committee (ref #2011/473 and ref #2015/1552-32) and Swedish rules and legislation. All tissues were collected from the Uppsala Biobank and RNA samples were extracted from frozen tissue sections. Blood samples were enriched for PBMC and granulocytes, labeled with antibodies and separated into subpopulation by flow sorting. For cell lines, early-split samples were used as duplicates and total RNA was extracted using Qiagen RNeasy mini kit. Information regarding cellular origin and the source of each cell line is listed here.

For mouse tissue, samples were collected and handled in accordance with Swedish laws and regulation, and all experiments were approved by the local ethical committee (Stockholms Norra Djurförsöksetiska Nämd N183/14). The animal experiments conformed to the European Communities Council Directive (86/609/EEC), and all efforts were made to minimize the suffering and the number of animals used. WT male (n = 2) and female (n = 2) C57BL/6J mice (2 month old) were obtained from Charles River Laboratories and maintained under standard conditions on a 12-hour day/night cycle, with water and food ad libitum. After washing out the blood, brains, pituitary gland, and spinal cord were quickly removed from the skull and spine and placed in ice-cold sterile PBS to make the tissue stiff and easier to dissect. The entire brain was carefully dissected into 17 sub-regions on an ice-cold surface. Retina samples were collected by separating the retina from the pigment layer in warm (37°C) PBS, pH 7.4. All dissected regions were placed in a 1.5 ml Eppendorf tube and snap-frozen in liquid nitrogen. Samples were stored at -80°C until further processing for the RNA extraction. Transcript expression of all brain regions, pituitary and retina were analysed. Tissue was homogenized mechanically using a TissueLyser LT (Qiagen) and total RNA was prepared using the RNeasy Mini isolation kit (Qiagen). This generated high-quality RNA, with 84% of the samples having RNA Integrity Number (RIN) values higher than 8.0 and only one sample removed due to a very low RIN value (less than 6.0). In total, 75 samples were subsequently used for library construction with Illumina TruSeq Stranded mRNA reagents. The Illumina HiSeq2500 platform was used for sequencing at approximately 20 million reads depth.

For a total number of 141 HPA cell line samples, 375 normal tissue samples, and 109 blood samples, mRNA sequencing was performed on Illumina HiSeq2000 and 2500 machines (Illumina, San Diego, CA, USA) using the standard Illumina RNA-seq protocol with a read length of 2x100 bases. The RNA seq data for the remaining cell lines was imported from the Cancer Cell Line Encyclopedia (CCLE). More information about the cell line data can be found here. Blood cells mRNA sequencing was performed on an Illumina NovaSeq 6000 System in four S4 lanes with a read length of 2x150 bases. Transcript abundance estimation was performed using Kallisto v0.46.2. The 18 blood cell types are classified into six different lineages including B-cells, T-cells, NK-cells, monocytes, granulocytes and dendritic cells. More information can be found here.

The HPA Human brain sample set contains 1324 samples of >200 regions of the human brain. The analysis is a collaboration with Human Brain Tissue Bank (HBTB; Semmelweis University, Budapest) in accordance with approval from the Committee of Science and Research Ethic of the Ministry of Health Hungary (ETT TUKEB: 189/KO/02.6008/2002/ETT) and the Semmelweis University Regional Committee of Science and Research Ethic (No. 32/1992/TUKEB) to remove human brain tissue samples, collect, store and use them for research. Samples were collected by Prof. Palkovits and RNA was extracted from frozen brain punches. The human prefrontal cortex dataset includes 165 samples from 3 male and 3 female donors providing a detailed overview of protein expression in 17 subregions of the prefrontal cortex and 3 reference cortical regions was analyzed using the Illumina sequencing platform, all other samples were analyzed using the MGI DNBSEQ-T7 platform.

The pig tissue samples were collected and analyzed in collaboration with BGI. Pig brain used for mRNA analysis were collected and handled in accordance with national guidance for large experimental animals and under permission of the local ethical committee (ethical permission numbers No.44410500000078 and BGI-IRB18135) as well as conducted in line with European directives and regulations. The experimental minipigs (Chinese Bama Minipig) were provided by the Peral Lab Animal Sci & Tech Co.,Ltd (Permit number SYXK2017-0123). Male (n = 2) and female (n = 2) Chinese Bama minipigs (1 year old), were housed in a specific pathogen-free stable facility under standard conditions. The brain was cut in coronal slabs at the level of 1) frontal lobe/olfactory tract, 2) optic chiasm and 3) between hypothalamus and cerebral peduncle. Slabs were divided in 2 hemispheres exposing all main brain structures. For mRNA analysis, pieces of cerebral cortex and cerebellum were collected, based on a sampling strategy collecting a representative sample that contained all cell layers. All other regions were dissected and collected completely. Two samples (somatosensory cortex and periaqueductal gray) are missing from female 1 due to the fact that these two regions could not be identified with 100% certainty, and thus were excluded. Duplicate samples were taken from olfactory bulb from female 2, resulting in totally 119 brain samples and additional 8 samples (retina and pituitary gland), all in all 127 samples. All samples were stored at -80° C until RNA was extracted within one month.

GTEx RNA-seq data

The Genotype-Tissue Expression (GTEx) project collects and analyzes multiple human post mortem tissues. RNA-seq data from 36 of their tissue types was mapped based on RSEMv1.3.0 (v8) and the resulting TPM values have been included in the Human Protein Atlas for all corresponding genes that could be mapped from Gencode v26 to Ensembl version 103.38. The GTEx retina data are based on EyeGEx data from Ratnapriya et al., Nature Genetics 2019 and transcript abundance estimation was performed using Kallisto v0.46.2 using Ensembl version 103.38 as reference genome.

Tissue GTEx tissue Number of samples
Adipose tissue Adipose - Subcutaneous 663
Adipose - Visceral (Omentum) 541
Adrenal gland Adrenal Gland 258
Amygdala Brain - Amygdala 152
Breast Breast - Mammary Tissue 459
Caudate Brain - Caudate (basal ganglia) 246
Cerebellum Brain - Cerebellar Hemisphere 215
Brain - Cerebellum 241
Cerebral cortex Brain - Anterior cingulate cortex (BA24) 176
Brain - Cortex 255
Brain - Frontal Cortex (BA9) 209
Cervix Cervix - Ectocervix 9
Cervix - Endocervix 10
Colon Colon - Sigmoid 373
Colon - Transverse 406
Endometrium Uterus - Endometrium 16
Esophagus Esophagus - Mucosa 555
Fallopian tube Fallopian Tube 9
Heart muscle Heart - Atrial Appendage 429
Heart - Left Ventricle 432
Hippocampus Brain - Hippocampus 197
Hypothalamus Brain - Hypothalamus 202
Kidney Kidney - Cortex 85
Kidney - Medulla 4
Liver Liver 226
Lung Lung 578
Nucleus accumbens Brain - Nucleus accumbens (basal ganglia) 246
Ovary Ovary 180
Pancreas Pancreas 328
Pituitary gland Pituitary 283
Prostate Prostate 245
Putamen Brain - Putamen (basal ganglia) 205
Retina Retina 105
Salivary gland Minor Salivary Gland 162
Skeletal muscle Muscle - Skeletal 803
Skin Skin - Not Sun Exposed (Suprapubic) 604
Skin - Sun Exposed (Lower leg) 701
Small intestine Small Intestine - Terminal Ileum 187
Spinal cord Brain - Spinal cord (cervical c-1) 159
Spleen Spleen 241
Stomach Stomach 359
Substantia nigra Brain - Substantia nigra 139
Testis Testis 361
Thyroid gland Thyroid 653
Urinary bladder Bladder 21
Vagina Vagina 156


The Functional Annotation of Mammalian Genomes 5 (FANTOM5) project provides comprehensive expression profiles and functional annotation of mammalian cell-type specific transcriptomes using Cap Analysis of Gene Expression (CAGE) (Takahashi H et al. (2012)), which is based on a series of full-length cDNA technologies developed in RIKEN. CAGE data for 60 of their tissues was obtained from the FANTOM5 repository and mapped to Ensembl version 103.38.

Tissue FANTOM5 tissue Sample description FANTOM5 sample id
Adipose tissue Adipose tissue 65,65,76 years, mixed FF:10010-101C1
Amygdala Amygdala 76 years, female FF:10151-102I7
Appendix Appendix 29 years, male FF:10189-103D9
Breast Breast 77 years, female FF:10080-102A8
Caudate Caudate nucleus 76 years, female FF:10164-103B2
Cerebellum Cerebellum 22-68 years, mixed FF:10083-102B2
Cerebellum 76 years, female FF:10166-103B4
Cervix Cervix 40,46,57,65 years, female FF:10013-101C4
Colon Colon 62,83,84 years, mixed FF:10014-101C5
Corpus callosum Corpus callosum 24-68 years, mixed FF:10042-101F6
Ductus deferens Ductus deferens 24 years, male FF:10196-103E7
Endometrium Uterus 23-63 years, female FF:10100-102D1
Epididymis Epididymis 24 years, male FF:10197-103E8
Esophagus Esophagus 68,74,75 years, mixed FF:10015-101C6
Frontal lobe Frontal lobe 32-61 years, mixed FF:10040-101F4
Gallbladder Gall bladder 57 years, male FF:10198-103E9
Globus pallidus Globus pallidus 76 years, female FF:10161-103A8
Globus pallidus 60 years, female FF:10175-103C4
Heart muscle Heart 70,73,74 years, mixed FF:10016-101C7
Left ventricle 73 years, female FF:10078-102A6
Left atrium 40 years, male FF:10079-102A7
Hippocampus Hippocampus 76 years, female FF:10153-102I9
Hippocampus 60 years, female FF:10169-103B7
Insular cortex Insula 20-68 years, mixed FF:10039-101F3
Kidney Kidney 60,62,63 years, female FF:10017-101C8
Liver Liver 64,69,70 years, mixed FF:10018-101C9
Locus coeruleus Locus coeruleus 76 years, female FF:10165-103B3
Locus coeruleus 60 years, female FF:10182-103D2
Lung Lung 46,65,94 years, mixed FF:10019-101D1
Lung - right lower lobe 29 years, male FF:10075-102A3
Lymph node Lymph node 30 years, male FF:10077-102A5
Medial frontal gyrus Medial frontal gyrus 76 years, female FF:10150-102I6
Medial temporal gyrus Medial temporal gyrus 76 years, female FF:10156-103A3
Medial temporal gyrus 60 years, female FF:10183-103D3
Medulla oblongata Medulla oblongata 18-64 years, mixed FF:10038-101F2
Medulla oblongata 76 years, female FF:10155-103A2
Medulla oblongata 60 years, female FF:10174-103C3
Nucleus accumbens Nucleus accumbens 23-56 years, mixed FF:10037-101F1
Occipital cortex Occipital cortex 76 years, female FF:10163-103B1
Occipital lobe Occipital lobe 27 years, male FF:10076-102A4
Occipital pole Occipital pole 22-68 years, mixed FF:10036-101E9
Olfactory bulb Olfactory region 87 years, female FF:10195-103E6
Ovary Ovary 47,75,84 years, female FF:10020-101D2
Pancreas Pancreas 52 years, male FF:10049-101G4
Paracentral gyrus Paracentral gyrus 22-69 years, mixed FF:10035-101E8
Parietal lobe Parietal lobe 35-89 years, mixed FF:10034-101E7
Parietal lobe 76 years, female FF:10157-103A4
Parietal lobe 60 years, female FF:10171-103B9
Pituitary gland Pituitary gland 76 years, female FF:10162-103A9
Placenta Placenta female FF:10021-101D3
Pons Pons 18-54 years, mixed FF:10033-101E6
Postcentral gyrus Postcentral gyrus 44-52 years, mixed FF:10032-101E5
Prostate Prostate 73,79,93 years, male FF:10022-101D4
Putamen Putamen 60 years, female FF:10176-103C5
Retina Retina 24-65 years, mixed FF:10030-101E3
Salivary gland Salivary gland 16-60 years, mixed FF:10093-102C3
Parotid gland 23 years, male FF:10199-103F1
Submaxillary gland 24 years, male FF:10202-103F4
Seminal vesicle Seminal vesicle 24 years, male FF:10201-103F3
Skeletal muscle Skeletal muscle 55,79,79 years, mixed FF:10023-101D5
Skeletal muscle - soleus muscle male FF:10282-104F3
Small intestine Small intestine 15,40,85 years, mixed FF:10024-101D6
Smooth muscle Smooth muscle 20-68 years, male FF:10048-101G3
Spinal cord Spinal cord 76 years, female FF:10159-103A6
Spinal cord 60 years, female FF:10181-103D1
Spleen Spleen 39,50,70 years, male FF:10025-101D7
Substantia nigra Substantia nigra 76 years, female FF:10158-103A5
Temporal cortex Temporal lobe 32-61 years, mixed FF:10031-101E4
Testis Testis 34,53,86 years, male FF:10026-101D8
Testis 14-64 years, male FF:10096-102C6
Thalamus Thalamus 76 years, female FF:10154-103A1
Thymus Thymus 0.5,0.5,0.83 years old infant years, male FF:10027-101D9
Thyroid gland Thyroid 67,68,78 years, mixed FF:10028-101E1
Tongue Tongue 28 years, male FF:10203-103F5
Tonsil Tonsil 22-61 years, mixed FF:10047-101G2
Urinary bladder Bladder 55,58,79 years, mixed FF:10011-101C2
Vagina Vagina 68 years, female FF:10204-103F6

Tissue Cell Type: Using GTEx bulk RNAseq data to profile gene cell type specificity

GTEx data was used in an integrative network analysis to determine the cell type specificity of all protein coding genes within a given tissue type. For more details on this analysis and the classifications, see the Tissue Cell Type section Methods Summary.

scRNA-seq data

Inclusion criteria

The single cell RNA sequencing dataset is based on meta-analysis of literature on single cell RNA sequencing and single cell databases that include healthy human tissue. To avoid technical bias and to ensure that the single cell dataset can best represent the corresponding tissue, the following data selection criteria were applied: (1) Single cell transcriptomic datasets were limited to those based on the Chromium single cell gene expression platform from 10X Genomics (version 2 or 3); (2) Single cell RNA sequencing was performed on single cell suspension from tissues without pre-enrichment of cell types; (3) Only studies with >4,000 cells and 20 million read counts were included, (4) Only dataset whose pseudo-bulk transcriptomic expression profile is highly correlated with the transcriptomic expression profile of the corresponding HPA tissue bulk sample were included. It should be noted that exceptions were made for lung (~7.3 million reads), pancreas (3,719 cells) and rectum (3,898 cells) to include various cell types in the analysis.

Single cell transcriptomics datasets

In total, 30 different datasets were analyzed. These datasets were respectively retrieved from the Single Cell Expression Atlas, the Human Cell Atlas, the Gene Expression Omnibus, the Allen Brain Map, European Genome-phenome Archive and the Tabula Sapiens. The complete list of references is shown in the table below.

Tissue Data source No. of M reads No. of cells Reference
Colon GSE116222 47.1 5302 Parikh K et al. (2019)
Heart muscle GSE109816 55.9 6012 Wang L et al. (2020)
Small intestine GSE125970 45.8 5460 Wang Y et al. (2020)
Kidney GSE131685 36 18365 Liao J et al. (2020)
Liver GSE115469 32.8 11175 MacParland SA et al. (2018)
Placenta E-MTAB-6701 326 25615 Vento-Tormo R et al. (2018)
Rectum GSE125970 44.2 2638 Wang Y et al. (2020)
PBMC GSE112845 18.9 5274 Chen J et al. (2018)
Pancreas GSE131886 110.2 5313 Qadir MMF et al. (2020)
Skin GSE130973 57.5 22335 Solé-Boldo L et al. (2020)
Brain Allen brain 1404.4 76533 Allen brain map
Bronchus 88 26676 Lukassen S et al. (2020)
Skeletal muscle GSE143704 61.1 24579 De Micheli AJ et al. (2020)
Adipose tissue GSE155960 351.4 80083 Hildreth AD et al. (2021)
Esophagus GSE159929-GSM4850580 33 10441 He S et al. (2020)
Lymph node GSE159929-GSM4850583 16.4 9076 He S et al. (2020)
Bone marrow GSE159929-GSM4850584 9.8 3484 He S et al. (2020)
Spleen GSE159929-GSM4850589 15.6 4492 He S et al. (2020)
Stomach GSE159929-GSM4850590 20.4 5701 He S et al. (2020)
Thymus Tabula sapiens 197 23618 Tabula Sapiens Consortium* et al. (2022)
Tongue Tabula sapiens 283.7 18331 Tabula Sapiens Consortium* et al. (2022)
Prostate Tabula sapiens 90.7 19009 Tabula Sapiens Consortium* et al. (2022)
Lung Tabula sapiens 349.7 27756 Tabula Sapiens Consortium* et al. (2022)
Vascular Tabula sapiens 172.6 9172 Tabula Sapiens Consortium* et al. (2022)
Salivary gland Tabula sapiens 231.9 28809 Tabula Sapiens Consortium* et al. (2022)
Eye GSE137537 12.6 9555 Menon M et al. (2019)
Breast GSE164898 262.2 46126 Bhat-Nakshatri P et al. (2021)
Ovary GSE146512 292.2 44316 Man L et al. (2020)
Testis GSE120508 65.2 6459 Guo J et al. (2018)
Endometrium GSE111976 285 52594 Wang W et al. (2020)

Clustering of single cell transcriptomics data

For each of the single cell transcriptomics datasets, the quantified raw sequencing data were downloaded from the corresponding depository database based on the accession number provided by the corresponding study in the available format (total cells, read, and feature counts, or count tables). Unfiltered data were used as input for downstream analysis with an in-house pipeline using Scanpy (version 1.4.4.post1) in Python 3.7.3 for the 13 tissues and PBMC published in HPA v20 and Scanpy (version 1.7.2) in Python 3.8.5 for the 12 tissues published in HPA v21. In the pipeline, the data were filtered using two criteria: a cell is considered as valid if at least 200 genes are detected and a gene is considered as valid if it is expressed in at least 10% of the cells. Specially, in the HPA v21, tissues which contain more than 10,000 cells used 1000 cells as their cutoff. Subsequently, the cell counts were normalized to have a total count per cell of 10000. The valid cells were then clustered using Louvain clustering function within Single-Cell Analysis in Python (Scanpy). Default values of parameters were used in clustering. More in detail, the features of cells were projected into a PCA space with 50 components using UMAP, and a k-nearest neighbours (KNN) graph was generated. 15 neighbours were used in the network for Louvain, while the resolution of clustering was set as 1.0. The total read counts for all genes in each cluster was calculated by adding up the read counts of each gene in all cells belonging to the corresponding cluster. Finally, the read counts were normalized to transcripts per million protein coding genes (pTPM) for each of the single cell clusters. When calculating the expression profile for pseudo-bulk samples based on single cell transcriptomics, we added the read counts for all genes from all cells of the sample, and normalized it to pTPM in the same way as for the cluster ones.

Defining cell types

Each of the 536 different cell type clusters were manually annotated based on an extensive survey of >500 well-known tissue and cell type-specific markers, including both markers from the original publications, and additional markers used in pathology diagnostics. For each cluster, one main cell type was chosen by taking into consideration the expression of different markers. For a few clusters, no main cell type could be selected, and these clusters were not used for gene classification. The most relevant markers are presented in a heatmap on the Cell Type Atlas, in order to clarify cluster annotation to visitors.

Cell type dendrogram

The cell type dendrogram presented on the Single Cell Type section shows the relationship between the single cell types based on genome-wide expression. The dendrogram is based on agglomerative clustering of 1 - Spearman's rho between cell types using Ward's criterion. The dendrogram was then transformed into a hierarchical graph, and link distances were normalized to emphasize graph connections rather than link distances. Link width is proportional to the distance from the root, and links are colored according to cell type group if only one cell type group is present among connected leaves.

Normalization of transcriptomics data

For both the HPA and GTEx transcriptomics datasets, the average TPM value of all individual samples for each human tissue or human cell type was used to estimate the gene expression level. To be able to combine the datasets into consensus transcript expression levels, a pipeline was set up to normalize the data for all samples. In brief, all TPM values per sample were scaled to a sum of 1 million TPM (denoted pTPM) to compensate for the non-coding transcripts that had been previously removed. Next, all TPM values of all samples within each data source (HPA + GTEx human tissues, HPA immune cell types, HPA cell lines) were normalized separately using Trimmed mean of M values (TMM) to allow for between-sample comparisons. The resulting normalized transcript expression values, denoted nTPM, were calculated for each gene in every sample. nTPM values below 0.1 are not visualized on the Atlas sections.

For the brain dataset, an additional normalization was performed using linear regression to do the correction for inter-individual variation using the removeBatchEffect in the R package Limma with subject as a batch parameter. To reduce the technical variation between MGI and illumina platforms, 19 reference samples were included and run on both platforms. Intensity normalization based on reference samples was conducted to minimize technical variation between two platforms.

Consensus transcript expression levels for each gene were summarized in 54 human tissues based on transcriptomics data from the two sources HPA and GTEx. The consensus nTPM value for each gene and tissue type represents the maximum nTPM value based on HPA and GTEx. For tissues with multiple sub-tissues (brain regions, blood cells, lymphoid tissues and intestine) the maximum of all sub-tissues is used for the tissue type and the total number of tissue types in the human tissue consensus set is 36.

The FANTOM5 dataset was normalized separately on the sample level using TMM. The normalized Tags Per Million for each gene were calculated based on the average of all individual samples for each human tissue.

Mouse and pig transcriptomic data generated by the HPA in collaboration with BGI, were normalized separately, according to the same procedure used for human tissues and cell types, no Limma adjustment was performed on the mouse and pig data. Consensus transcript expression levels is summarized into 13 brain regions for mouse brain and 15 regions for pig brain, where sub-regional samples were combined and the maximum of sub-regions used for the brain region.

Single cell type clusters were normalized separately from other transcriptomics datasets using TMM. To generate expression values per cell type, clusters were aggregated per cell type by first calculating the mean nTPM in all cells with the same cluster annotation within a dataset. The values for the same cell types in different data sets were then mean averaged to a single aggregated value.

Classification of transcriptomics data

The consensus transcriptomics data was used to classify all genes according to their tissue-specific, single cell type-specific, brain region-specific, blood cell-specific or cell line-specific expression into two different schemas: specificity category and distribution category. These are defined based on the total set of all nTPM values in 256 tissues, 79 single cell types, 13 main regions of each mammalian brain,18 immune cell types or 1055 cell lines and using a cutoff value of 1 nTPM as a limit for detection across all tissues or cell types.

Explanation of the specificity category

Category Description
Enriched nTPM in a particular tissue/region/cell type at least four times any other tissue/region/cell type
Group enriched nTPM in a group (of 2-5 tissues, brain regions, single cell types or cell lines, or 2-10 blood cell types) at least four times any other tissue/region/cell line/blood cell type/cell type
Enhanced nTPM in a one or several (1-5 tissues, brain regions or cell lines, or 1-10 immune cell types or single cell types) at least four times the mean of other tissue/region/cell types
Low specificity nTPM ≥ 1 in at least one tissue/region/cell type but not elevated in any tissue/region/cell type
Not detected nTPM < 1 in all tissue/region/cell types

An additional category "elevated", containing all genes in the first three categories (tissue/cell line/cell type enriched, group enriched and tissue/cell line/cell type enhanced), has been used for some parts of the analysis. TS/CS-score (Tissue Specificity/Cell Specificity score) is calculated for “elevated” tissues/cell lines. TS/CS-score is calculated as the fold change from the tissue/cell line with highest RNA to the tissue/cell line with second highest RNA.

Explanation of the distribution category

Category Description
Detected in single Detected in a single tissue/region/cell type
Detected in some Detected in more than one but less than one third of tissues/regions/cell types
Detected in many Detected in at least a third but not all tissues/regions/cell types
Detected in all Detected in all tissues/regions/cell types
Not detected nTPM < 1 in all tissues/regions/cell types

External blood RNA-seq data

In addition to the immune cell type data from blood, generated within the Human Protein Atlas project, data from 15 immune cell types by Schmiedel et al. and 29 immune cell types as well as total PBMC by Monaco et al. have been incorporated into the Blood Atlas.

The Schmiedel dataset is available at the DICE (Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics) database, which was established to address how genetic variants associated with risk for human diseases affect gene expression in various cell types. The TPM values per gene for 15 immune cell types were mapped to the corresponding genes in the Ensembl version used in the Human Protein Atlas.

The Monaco dataset contains data for 29 immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA-seq and flow cytometry. Raw data for 29 immune cells as well as total PBMC were analyzed using the same pipeline as for HPA-generated RNA-seq data and also normalized using TMM to allow for between-sample comparisons. Normalized gene expression values are reported as nTPM values.

Gene expression clustering of transcriptomics data

The RNA expression data has been used to classify protein-coding genes into expression clusters for tissues, single cell types, immune cells, and cell lines.

Clustering Number of tissues, cell types or cell lines Sample aggregation level
Tissue 53 Averaged expression per tissue type
Single cell 444 Averaged expression per cell type cluster
Cell lines 69 Expression of individual cell line samples
Immune cells 103 Averaged expression per immune cell sample

Pre-processing the data for clustering

For each dataset, genes detected at nTPM > 1 in at least one of the samples were selected, and the data was genewise scaled to z-scores to account for differences in dynamic ranges between genes across samples. After scaling, the expression data was projected into a lower dimensional space using Principal Component Analysis (PCA), where a number of components were selected to satisfy Kaiser’s rule and at least 80% of variance explained.

Gene clustering

Gene to gene distances were calculated as the Spearman correlation of gene expression across samples, and transformed to Spearman distance (1 - Spearman correlation). The distances were transformed into a shared nearest neighbor graph and used for Louvain clustering to find clusters of genes with similar expression profiles within the graph. To account for stochasticity in the clustering process each clustering was run 100 times, and consequently collapsed into a single consensus clustering. Confidence of the gene-to-cluster assignment was calculated as the fraction of times that the gene was assigned to the cluster.

Cluster annotation

The clustering generated for each of the datasets is manually annotated to assign a specificity and function to each cluster. The annotation is based on overrepresentation analysis towards biological databases, including Gene Ontology, Reactome, PanglaoDB, TRRUST, and KEGG, as well as HPA classifications including subcellular location, protein class, secretion location and classification, and specificity toward tissues, single cell types, immune cells, brain regions, and cell lines. A reliability score is manually set for each cluster indicating the confidence of specificity and function assignment.

Clustering visualization

The clustering results are visualized in a UMAP. Colored polygons were generated to represent the main contiguous masses of genes corresponding to the same cluster. First, for each cluster, the two-dimensional density was estimated in the UMAP, and an area enveloping 95% of the total density was determined. The areas were moderated to include contiguous areas corresponding to at least 5% of the total area in the UMAP space. Finally, contiguous areas were converted to two-dimensional polygons per each cluster.

TCGA RNA-seq data

The Cancer Genome Atlas (TCGA) project of Genomic Data Commons (GDC) collects and analyzes multiple human cancer samples. RNA-seq data from 17 cancer types representing 21 cancer subtypes with a corresponding major cancer type in the Human Pathology Atlas were included to allow for comparisons between the protein staining data from the Human Protein Atlas and RNA-seq from TCGA data.

The TCGA RNA-seq data was mapped using the Ensembl gene id available from TCGA, and the FPKMs (number Fragments Per Kilobase of exon per Million reads) for each gene were subsequently used for quantification of expression with a detection threshold of 1 FPKM. Genes were categorized using the same classification as described above.

HPA cancer type TCGA cancer No. of samples in TCGA
Breast cancer Breast Invasive Carcinoma (BRCA) 1075
Cervical cancer Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) 291
Colorectal cancer Colon Adenocarcinoma (COAD) 438
Rectum Adenocarcinoma (READ) 159
Endometrial cancer Uterine Corpus Endometrial Carcinoma (UCEC) 541
Glioma Glioblastoma Multiforme (GBM) 153
Head and neck cancer Head and Neck Squamous Cell Carcinoma (HNSC) 499
Liver cancer Liver Hepatocellular Carcinoma (LIHC) 365
Lung cancer Lung Adenocarcinoma (LUAD) 500
Lung Squamous Cell Carcinoma (LUSC) 494
Melanoma Skin Cuteneous Melanoma (SKCM) 102
Ovarian cancer Ovary Serous Cystadenocarcinoma (OV) 373
Pancreatic cancer Pancreatic Adenocarcinoma (PAAD) 176
Prostate cancer Prostate Adenocarcinoma (PRAD) 494
Renal cancer Kidney Chromophobe (KICH) 64
Kidney Renal Clear Cell Carcinoma (KIRC) 528
Kidney Renal Papillary Cell Carcinoma (KIRP) 285
Stomach cancer Stomach Adenocarcinoma (STAD) 354
Testis cancer Testicular Germ Cell Tumor (TGCT) 134
Thyroid cancer Thyroid Carcinoma (THCA) 501
Urothelial cancer Bladder Urothelial Carcinoma (BLCA) 406

TCGA survival

Based on the FPKM value of each gene, patients were classified into two expression groups and the correlation between expression level and patient survival was examined. The prognosis of each group of patients was examined by Kaplan-Meier survival estimators, and the survival outcomes of the two groups were compared by log-rank tests. Both median and maximally separated Kaplan-Meier plots are presented in the Human Protein Atlas, and genes with log rank P values less than 0.001 in maximally separated Kaplan-Meier analysis were defined as prognostic genes. If the group of patients with high expression of a selected prognostic gene has a higher observed event than expected event, it is an unfavorable prognostic gene; otherwise, it is a favorable prognostic gene. Genes with a median expression less than FPKM 1 were lowly expressed, and classified as unprognostic in the database even if they exhibited significant prognostic effect in survival analysis

Allen Mouse brain ISH dataset

The Allen Brain Atlas (ABA) is an open access database focusing on the brain, and includes both human and mouse expression data. The ABA is a part of the Allen Institute for Brain Science, which is one of the three branches of the Allen Institute. The Mouse brain In situ hybridization (ISH) data provides information on where in the adult mouse brain each gene is expressed (Lein ES et al. (2007)). We have imported the expression values available through the ABA API (© 2004 Allen Institute for Brain Science, Allen Mouse Brain Atlas) and show the regional expression grouped in the same manner as the other datasets visualized on the HPA Brain Atlas.

The Allen mouse brain ISH data was mapped to the mouse gene annotation of Ensembl version 103.38 using the probe nucleotide sequences provided through the Allen mouse brain API together with the blast program package. The mouse genes where then mapped to human genes using Ensembl orthologue data with a one-to-one restriction.


Protein evidence is calculated for each gene based on three different sources: UniProt protein existence (UniProt evidence); a Human Protein Atlas antibody- or RNA based score (HPA evidence); and evidence based on PeptideAtlas (MS evidence). In addition, for each gene, a protein evidence summary score is based on the maximum level of evidence in all three independent evidence scores (Evidence summary).

All scores are classified into the following categories:

  • Evidence at protein level
  • Evidence at transcript level
  • No evidence
  • Not available

UniProt evidence is based on UniProt protein existence data, which uses five types of evidence for the existence of a protein. All genes in the classes "Experimental evidence at protein level" or "Experimental evidence at transcript level" are classified into the first two evidence categories, whereas genes from the "Inferred from homology", "Predicted", or "Uncertain" classes are classified as "No evidence". Genes where the gene identifier could not be mapped to UniProt from Ensembl version 103.38 are classified as "Not available".

The HPA evidence is calculated based on the manual curation of Western blot, tissue profiling and subcellular location as well as transcript profiling. All genes with Data reliability "Supported" in one or both of the two methods immunohistochemistry and immunofluorescence, or standard validation "Supported" for the Western blot application (assays using over-expression lysates not included) are classified as "Evidence at protein level". For the remaining genes, all genes detected at NX > 1 in at least one of the tissue types, cell type or cell lines used in the RNA-seq analysis based on HPA, GTEx or FANTOM5 are classified as "Evidence at transcript level". The remaining genes are classified as "No evidence".

The MS evidence displays protein evidence based on PeptideAtlas for all proteins with protein evidence in NextProt and with a minimum of two distinct peptides. Each gene detected on the protein level according to PeptideAtlas is classified as "Evidence at protein level" and all remaining genes as "Not available".