Atlas of RNA sequencing profiles for normal human tissues

Abstract

Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed Oncobox Atlas of Normal Tissue Expression (ANTE) . Our data will be useful to all those working with the analysis of human gene expression.

Consistent RNA sequencing contamination in GTEx and other data sets

“…To determine whether highly expressed tissue-enriched contamination is a feature of sequencing in general, we identified two additional RNA-Seq data sets containing multiple organs/ tissues 19,23,24 . Neither study sequenced the pituitary, which is the organ with the highest levels of prolactin (PRL) expression ( Fig.…”

“…Using the R package recount version 1.8.2, we downloaded HPA RNA-Seq data, accession ERP003613 19 . The RNA Atlas was acquired by downloading their raw RNA-seq counts from Gene Expression Omnibus (GSE120795) 23 . The HPA RNA-Seq was performed across 27 tissues and the RNA Atlas was across 20 tissues.…”

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures

“…In this section, we use real data to show this ineffectiveness and justify the advantages of the proposed wTSD as a higher resolution method. For this purpose, we used the publicly available dataset in GEO GSE120795, also presented in Suntsova et al (2019), a comprehensive gene expression database of normal human tissues based on uniformly screened RNA-seq data. This database includes 142 tissue samples taken from 20 organs of healthy human donors of different ages, collected no later than 36 hours after death.…”

“…at various states (e.g., healthy, diseased, etc.) (Uhlen et al (2015(Uhlen et al ( , 2017; Yu et al (2015); Keen et al (2015); Mele et al (2015); Suntsova et al (2019); Sollner et al (2017)) using RNA-sequencing, a mature technology for quantifying gene transcripts in biological samples. A notable effort is the Human Protein Atlas (HPA) project (Uhlen et al (2015)), a Swedish-based program providing, among others, gene expression signatures for 37 healthy human organ tissues.…”

“…Such tools will not only help us understand the model capabilities and limitations but also reveal aspects we can improve in their design to optimize their physiological relevance and increase their value for precision medicine. Next-generation sequencing data (e.g., RNA-seq) is already utilized to determine the distance between organ tissue samples (Mele et al (2015); Suntsova et al (2019); Sollner et al (2017); Sudmant et al (2015)) in conjunction with classical mathematical tools such as the Euclidean distance, or dimensionality reduction techniques (e.g., Principal Component Analysis, Linear Discriminant Analysis, Uniform Manifold Approximation and Projection, etc.). However, all these methods exhibit significant limitations due to sensitivity to noisy measurements and outliers, inability to capture non-linear relations, etc.…”