🆕Baseline expression
June 2026 version
Overview
Baseline (healthy, unstimulated) human RNA and protein expression data can help determine whether a target is expressed broadly across tissues and cell types or selectively in specific biological contexts. The presence of a target molecule in the tissue or cell type of interest is an important consideration throughout the drug discovery and development process.
The Open Targets Platform integrates baseline expression data generated using three complementary technologies:
Single-cell RNA sequencing (scRNA-seq)
Bulk RNA sequencing (RNA-seq)
Mass spectrometry (MS)-based proteomics
Data are collected from four sources:
Data source
Technology
Coverage
Tabula Sapiens
Single-cell RNA-seq
66 tissues, 180 annotated cell types
GTEx (v10)
Bulk RNA-seq
54 tissues
DICE
Bulk RNA-seq
12 flow-sorted immune cell types
PRIDE (OTAR3091)
Mass spectrometry proteomics
74 tissues
Data Sources
Tabula Sapiens
Tabula Sapiens v2 is a single-cell transcriptomic atlas containing over 1.1 million cells spanning multiple human organs and tissues. The atlas was generate
Genotype-Tissue Expression (GTEx)
Genotype-Tissue Expression (GTEx) aims to build a comprehensive public resource for studying tissue-specific gene expression and its relationship to genetic variation. The GTEx v10 dataset contains bulk RNA-seq data from 54 tissues collected from 946 human donors.
Database of Immune Cells (DICE)
The Database of Immune Cells (DICE) provides gene expression profiles from 12 flow-sorted immune cell types derived from peripheral blood samples from 91 healthy donors. Open Targets uses DICE build 2.23.2022. The data were generated using bulk RNA sequencing.
PRoteomics IDEntifications Database (PRIDE)
PRoteomics IDEntification Database (PRIDE) is a public repository for proteomics data. As part of an Open Targets project, the PRIDE team reanalysed four human proteomics datasets to provide protein expression measurements across multiple human tissues.
The following datasets were included:
Computational pipelines and datasets
Initial data processing
Single-cell RNA sequencing (scRNA-seq) data from Tabula Sapiens
Single-cell RNA sequencing data for tissues and cell types was downloaded from the Tabula Sapiens portal as AnnData files containing raw count matrices with the associated metadata. The data were then processed into pseudobulked expression profiles for each cell type, each tissue, and each cell type within each tissue. The pseudobulked expression profiles are generated with a dSum approach as described in Cuomo et al. 2021. In short, in order to obtain a pseudobulked sample we sum the raw counts of each gene across all cells within a donor for a given annotation (tissue, cell type or cell type within tissue). This results in one pseudobulked expression sample per donor per annotation. The summed counts are then normalised using the Counts Per Million (CPM) approach.
The result is three pseudobulked expression datasets: cell type, tissue, and cell type within tissue.
Bulk RNA sequencing (RNA-seq) data from GTEx and DICE
Bulk RNA sequencing data for tissues was downloaded in transcripts per million (TPM) units from GTEx and for cell types from the DICE portal.
Mass spectrometry (MS)-based proteomics data from PRIDE
Baseline mass spectrometry–based proteomics data were compiled from selected public PRIDE datasets curated using SDRF proteomics metadata guidelines. Raw files in these datasets had been reanalysed centrally using the open source tools MaxQuant (version 2.7.0.0) according to predefined dataset selection and curation guidelines, and the resulting protein-level quantification tables with associated SDRF metadata were downloaded from the PRIDE FTP resource.
Generating unaggregated expression profiles
All baseline expression data are processed into a unified set of unaggregated, donor-level expression profiles. We harmonise metadata and map tissue and cell type annotations to standardised ontologies: UBERON and the Cell Ontology. If a donor has multiple samples for the same annotation, we calculate the mean expression to produce a single value for each donor-annotation pair. The data structure is standardised, but expression values retain their original units: TPM for bulk RNA-seq, pseudobulk CPM (sum[counts]) for pseudobulked scRNA-seq, and PPB (iBAQ) for proteomics.
Generating aggregated expression profiles
We aggregate the donor-level data to produce a summary of expression for each target in each tissue or cell type. This includes:
Expression Quartiles: We calculate the minimum, 25th percentile, median, 75th percentile, and maximum expression values across all donors for a given annotation.
Expression Distribution Score: We compute a score representing the breadth of expression within a dataset, defined as the proportion of annotations in which the target is expressed above a threshold of 0.5 (unit) median expression. The threshold is chosen to minimise the impact of noise (e.g. from ambient RNA).
Expression Specificity Score: We use the tool CELLEX (Timshel et al. 2020) which combines four expression-specificity metrics into a single, ranked specificity score that reflects how specifically a gene is expressed in a given annotation relative to all other annotations in that dataset. Because ranking is done within each annotation, a gene expressed in a tissue expressing many genes must have stronger specificity to rank highly than it would in a tissue with fewer distinct markers. Consequently, the annotation with the highest expression is not always the one where the gene has the highest specificity score. For a given gene, specificity is denoted as “high” for annotations where the gene is scored amongst the top 25% of specifically expressed genes.
To enable comparisons across similar tissues and cell types from different data sources, we also assign a set of high-level, parental tissue and cell type categories to each annotation. The parental labels are derived from manual grouping of the ontology mappings, for example, the heart left ventricle from GTEx and cardiac atrium from Tabula Sapiens both share the parental label of heart. There are 37 parental tissue labels and 24 parental cell type labels.
Data Availability
These datasets can be accessed via the Platform web interface, download page and GraphQL API.
Example of new baseline expression widget for KRAS.
Publications
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369(6509):1318–1330.
The Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science. 2022;376(6594).
Ha B, et al. Database of Immune Cell eQTLs, Expression, Epigenomics. The Journal of Immunology. 2019;202(1 Supplement):131.18.
Perez-Riverol Y, et al. The PRIDE database at 20 years: 2025 update. Nucleic Acids Research. 2025;53(D1)–D553.
Tyanova S, et al. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nature Protocols. 2016;11(12):2301–2319.
Cuomo AS, et al. Optimizing expression quantitative trait locus mapping workflows for single-cell studies. Genome Biology. 2021;22(1):240.
Timshel P, et al. Genetic mapping of etiologic brain cell types for obesity. eLife. 2020;9.
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