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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number233
JournalGenome Biology
Volume24
Issue number1
DOIs
StatePublished - Dec 2023
Externally publishedYes

Keywords

  • Bioconductor
  • Deconvolution
  • RNA abundance
  • RNAscope
  • TREG
  • snRNA-seq

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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