Data-driven identification of total RNA expression genes for estimation of RNA abundance in heterogeneous cell types highlighted in brain tissue.
Louise A Huuki-MyersKelsey D MontgomerySang Ho KwonStephanie C PageStephanie C HicksKristen R MaynardLeonardo Collado-TorresPublished in: Genome biology (2023)
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.
Keyphrases
- single cell
- bioinformatics analysis
- genome wide
- single molecule
- rna seq
- genome wide identification
- poor prognosis
- high throughput
- genome wide analysis
- resting state
- nucleic acid
- white matter
- cell therapy
- dna methylation
- transcription factor
- signaling pathway
- stem cells
- multiple sclerosis
- functional connectivity
- quantum dots
- machine learning
- antibiotic resistance genes
- atomic force microscopy
- blood brain barrier
- artificial intelligence
- heat shock protein
- cerebral ischemia
- anaerobic digestion