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Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types

View ORCID ProfileLouise A. Huuki-Myers, View ORCID ProfileKelsey D. Montgomery, View ORCID ProfileSang Ho Kwon, View ORCID ProfileStephanie C. Page, View ORCID ProfileStephanie C. Hicks, View ORCID ProfileKristen R. Maynard, View ORCID ProfileLeonardo Collado-Torres
doi: https://doi.org/10.1101/2022.04.28.489923
Louise A. Huuki-Myers
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
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Kelsey D. Montgomery
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
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Sang Ho Kwon
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
2The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
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Stephanie C. Page
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
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Stephanie C. Hicks
3Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Kristen R. Maynard
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
4Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
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  • For correspondence: lcolladotor@gmail.com
Leonardo Collado-Torres
1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
5Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
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  • For correspondence: lcolladotor@gmail.com
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Abstract

Next-generation sequencing technologies have facilitated data-driven identification of gene sets with different features including genes with stable expression, cell-type specific expression, or spatially variable expression. Here, we aimed to define and identify a new class of “control” genes called Total RNA Expression Genes (TREGs), which correlate with total RNA abundance in heterogeneous cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single cell RNA-sequencing (RNA-seq) data, available as an R/Bioconductor package at https://bioconductor.org/packages/TREG. We demonstrated the utility of our method in the postmortem human brain using multiplex single molecule fluorescent in situ hybridization (smFISH) and compared candidate TREGs against classic housekeeping genes. We identified AKT3 as a top TREG across five brain regions, especially in the dorsolateral prefrontal cortex.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/LieberInstitute/TREG_paper

  • Abbreviations

    AMY
    amygdala
    Astro
    astrocytes
    DLPFC
    dorsolateral prefrontal cortex
    Excit
    excitatory neurons
    Expression Rank
    rank of the log normalized counts expression values for a given gene and nucleus, with high expression values translating into high rank values
    HPC
    hippocampus
    HK
    housekeeping
    Inhib
    inhibitory neurons
    Micro
    microglia
    NAc
    nucleus accumbens
    Oligo
    oligodendrocytes
    OPC
    oligodendrocyte progenitor cells
    Proportion Zero
    defined in Methods: Expression and Proportion Zero filtering
    RI
    Rank Invariance
    sACC
    subgenual anterior cingulate cortex
    smFISH
    single-molecule fluorescent in situ hybridization
    TREG
    total RNA expression gene
  • Copyright 
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    Posted April 29, 2022.
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    Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types
    Louise A. Huuki-Myers, Kelsey D. Montgomery, Sang Ho Kwon, Stephanie C. Page, Stephanie C. Hicks, Kristen R. Maynard, Leonardo Collado-Torres
    bioRxiv 2022.04.28.489923; doi: https://doi.org/10.1101/2022.04.28.489923
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    Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types
    Louise A. Huuki-Myers, Kelsey D. Montgomery, Sang Ho Kwon, Stephanie C. Page, Stephanie C. Hicks, Kristen R. Maynard, Leonardo Collado-Torres
    bioRxiv 2022.04.28.489923; doi: https://doi.org/10.1101/2022.04.28.489923

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