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

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...

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Autores principales: Huuki-Myers, Louise A., Montgomery, Kelsey D., Kwon, Sang Ho, Page, Stephanie C., Hicks, Stephanie C., Maynard, Kristen R., Collado-Torres, Leonardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578035/
https://www.ncbi.nlm.nih.gov/pubmed/37845779
http://dx.doi.org/10.1186/s13059-023-03066-w
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author Huuki-Myers, Louise A.
Montgomery, Kelsey D.
Kwon, Sang Ho
Page, Stephanie C.
Hicks, Stephanie C.
Maynard, Kristen R.
Collado-Torres, Leonardo
author_facet Huuki-Myers, Louise A.
Montgomery, Kelsey D.
Kwon, Sang Ho
Page, Stephanie C.
Hicks, Stephanie C.
Maynard, Kristen R.
Collado-Torres, Leonardo
author_sort Huuki-Myers, Louise A.
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03066-w.
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spelling pubmed-105780352023-10-17 Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue Huuki-Myers, Louise A. Montgomery, Kelsey D. Kwon, Sang Ho Page, Stephanie C. Hicks, Stephanie C. Maynard, Kristen R. Collado-Torres, Leonardo Genome Biol Software 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03066-w. BioMed Central 2023-10-16 /pmc/articles/PMC10578035/ /pubmed/37845779 http://dx.doi.org/10.1186/s13059-023-03066-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Huuki-Myers, Louise A.
Montgomery, Kelsey D.
Kwon, Sang Ho
Page, Stephanie C.
Hicks, Stephanie C.
Maynard, Kristen R.
Collado-Torres, Leonardo
Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue
title Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue
title_full Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue
title_fullStr Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue
title_full_unstemmed Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue
title_short Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue
title_sort data-driven identification of total rna expression genes for estimation of rna abundance in heterogeneous cell types highlighted in brain tissue
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578035/
https://www.ncbi.nlm.nih.gov/pubmed/37845779
http://dx.doi.org/10.1186/s13059-023-03066-w
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