基于数据驱动的全RNA表达基因鉴定，用于评估脑组织中突出显示的异质细胞类型中的RNA丰度。

Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue.

作者信息

Huuki-Myers Louise A, Montgomery Kelsey D, Kwon Sang Ho, Page Stephanie C, Hicks Stephanie C, Maynard Kristen R, Collado-Torres Leonardo

机构信息

Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.

The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.

出版信息

Genome Biol. 2023 Oct 16;24(1):233. doi: 10.1186/s13059-023-03066-w.

We define and identify a new class of control genes for next-generation sequencing called total RNA expression genes (TREGs), which correlate with total RNA abundance in cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single-cell RNA sequencing data, allowing the estimation of total amount of RNA when restricted to quantifying a limited number of genes. We demonstrate our method in postmortem human brain using multiplex single-molecule fluorescent in situ hybridization and compare candidate TREGs against classic housekeeping genes. We identify AKT3 as a top TREG across five brain regions.