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

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

doi:10.1186/s13059-023-03066-w

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

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

Authors

Louise A Huuki-Myers¹, Kelsey D Montgomery¹, Sang Ho Kwon^{1

2}, Stephanie C Page¹, Stephanie C Hicks³, Kristen R Maynard^{4

5}, Leonardo Collado-Torres^{6

7}

Affiliations

¹ Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
² The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
³ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
⁴ Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA. [email protected].
⁵ Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].
⁶ Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA. [email protected].
⁷ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. [email protected].

Abstract

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.

Keywords: Bioconductor; Deconvolution; RNA abundance; RNAscope; TREG; snRNA-seq.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Brain* / metabolism
Humans
In Situ Hybridization, Fluorescence
RNA* / genetics
RNA* / metabolism
Sequence Analysis, RNA / methods

Substances

RNA

Grants and funding

R01 MH123183/MH/NIMH NIH HHS/United States