Evaluation of noninvasive biospecimens for transcriptome studies
Year: 2023
Type: article
Abstract: Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.
Source: BMC Genomics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: Characterizing genetic, longitudinal, and developmental effects on the transcriptome using a novel non-invasive RNA-sequencing method, Integration of omics data to improve interpretation of genetic risk variants in lung disease, Experimental and Computational Methods for Scaling-up Transcriptome Analyses and Improving Disease Risk Predictions, UNDERSTANDING CELLULAR AND TRANSCRIPTIONAL REGULATORY CHANGES IN HUMAN AGING, G7871733508, G8083334890
OpenAlex ID: W4389944062