Genome Biology (Oct 2023)

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
https://doi.org/10.1186/s13059-023-03066-w
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 21

Abstract

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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.

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