Tissue - Methods summary
The Tissue section of the Atlas contains immunohistochemistry-based protein expression profiles covering 44 normal tissues and mRNA expression data from 56 tissues derived mainly from deep sequencing of mRNA.
Key publication: Uhlén M et al. (2015) “Tissue-based map of the human proteome.” Science 347(6220):1260419.
What can you learn from the Tissue section?
How has the data been generated?
Immunohistochemistry on tissue microarrays
The protein expression data covering 44 normal human tissue types was derived from antibody-based protein profiling using immunohistochemistry (IHC). Tissue microarrays of 1 mm samples were stained with DAB (3,3'-diaminobenzidine)-labeled antibodies and counterstained with hematoxylin. Each tissue type is represented by samples from three individuals, with the exception of endometrium, skin, soft tissue and stomach, each represented by samples from six individuals. For selected proteins, additional tissues were stained, including mouse brain, human lactating breast, eye, thymus and extended samples of adrenal gland, skin and brain. Immunohistochemically stained sections from tissue microarrays were scanned to allow for subsequent analysis and presentation at the HPA web portal. All tissue samples were collected and handled in accordance with Swedish laws and regulation and obtained from the Department of Clinical Pathology, Uppsala University Hospital, Uppsala, Sweden as part of the sample collection governed by the Uppsala Biobank. All tissue samples were anonymized in accordance with an approval and advisory report from the Uppsala Ethical Review Board.
RNA expression data
The transcriptomics expression data was derived from three different sources. The HPA dataset was generated in-house based on mRNA samples from 51 normal tissues extracted from frozen tissue sections, which were obtained and handled as described above for immunohistochemistry. The GTEx dataset was imported from the Genotype-Tissue Expression consortium and consists of samples from 37 normal tissues. Both of these datasets were produced by deep sequencing of mRNA (RNA-seq). In addition, the transcriptomics dataset from the FANTOM5 Consortium, based on Cap Analysis of Gene Expression (CAGE) in 60 normal tissues, was also imported.
How has the data been analyzed?
Images of the IHC-stained tissue samples were manually annotated with regard to staining intensity (negative, weak, moderate or strong), fraction of stained cells, defined of relevance for each tissue type (<25%, 25-75% or >75%) and subcellular localization (nuclear and/or cytoplasmic/membranous) in the annotated cell types. For a number of proteins (7260), an in-depth characterization of the spatial distribution of protein expression in selected tissues of the standard TMA (testis, cerebellum, bronchus, nasopharynx, fallopian tube, placenta, kidney, intestine and skin) was performed, where in total 57 new cell types or cell structures were annotated.
Knowledge-based annotation of protein expression
To create a comprehensive, knowledge-based, overview of protein expression in normal tissues for each gene, the primary annotation of IHC images from one or several available antibodies were stringently evaluated together with RNA-seq data from internal and external sources and available protein/gene characterization data (with special emphasis on RNA-seq data). Based on this evaluation, a reliability score for each annotated protein expression profile was set as either Enhanced, Supported, Approved, or Uncertain. “Enhanced” reliability score was assigned to genes where at least one antibody has been validated using orthogonal or independent enhanced validation methods.
RNA expression data
The RNA expression values included in each RNA dataset (HPA, GTEx and FANTOM5) were mapped to corresponding genes in the Ensembl version used in the Human Protein Atlas. The HPA and GTEx datasets were processed in a normalization pipeline in order to be combined into a consensus dataset, allowing for a clear comparison of gene expression across 54 human tissues. The FANTOM5 dataset is normalized using a separate pipeline and also presented separately.
What is presented in the section?
How has the classification of all protein-coding genes been done?
A genome-wide classification of the protein-coding genes with regard to tissue distribution as well as specificity has been performed using between-sample normalized data. The results can serve as a reference for researchers interested in expression profiles in any of all the major tissues and organs. The genes were classified according to specificity into (i) tissue-enriched genes with at least fourfold higher expression levels in one tissue type as compared with any other analysed tissue; (ii) group-enriched genes with enriched expression in a small number of tissues (2 to 5); and (iii) tissue-enhanced genes with only moderately elevated expression. In addition, all genes were classified according to distribution in which each gene is scored according to the presence (expression levels higher than a cut-off) in the normal tissues. Finally, a new classification based on expression clusters has recently been introduced in which each gene is scored based on similarity of expression across all normal tissues. The results are presented as an UMAP cluster plot (see below figure). By clicking on any clusters, the visitor can access an interactive version of the UMAP and details about all clusters .
In the below figure, the number of tissue-enriched and group-enriched genes are shown, in red and orange, respectively.