Variation in transcriptome size: are we getting the message?

Abstract

The number of RNA molecules per cell (transcriptome size) is highly variable, differing among and within cell types depending on cell size, stage of the cell cycle, ploidy level, age, disease state, and growth condition. Such variation has been observed at the level of total RNA, ribosomal RNA, messenger RNA (mRNA), and the polyadenylated fraction of mRNA, and these distinct RNA species can also vary in abundance with respect to each other. This variation in transcriptome size has been largely ignored or overlooked, and in fact, standard data normalization procedures for transcript profiling experiments implicitly assume that mRNA transcriptome size is constant. Consequently, variation in transcriptome size has important technical implications for such experiments, as well as profound biological implications for the affected cells and underlying genomes. Here, we review what is known about transcriptome size variation, explore how ignoring this variation introduces systematic bias into standard expression profiling experiments, and present examples of how such biases have led to erroneous conclusions in expression studies of sex chromosome dosage compensation, cancer, Rett syndrome, embryonic development, aging, and polyploidy. We also discuss how quantifying transcriptome size will help to elucidate the selective forces underlying patterns of gene and genome evolution and review the evidence that cells exert tight control over transcriptome size in order to maintain cell size homeostasis and to optimize chemical reactions within the cell, such that loss of control over transcriptome size is associated with cancer and aging. Thus, transcriptome size is an important phenotype in its own right. Finally, we discuss strategies for quantifying transcriptome size and individual gene dosage responses in order to account for and better understand this important biological phenomenon.

Dosage compensation of the sex chromosomes and autosomes

“…However, it is inherently difficult to measure absolute amounts of transcripts in cells. Global studies of gene expression make specific assumptions about normalization to the total transcriptome that may not always be correct and lead to aberrant conclusions when comparing samples [77, 78]. Co-isolation of RNA and genomic DNA together with counting the number of cells from which the nucleic acids are extracted are helpful, as well as spiking reactions with exogenous RNA.…”

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

“…Transcriptome size and overall variation have been documented across cell cycle stages, tissue types, developmental stages, diurnal cycles, sexes, and environment [50]. Despite the ubiquity of transcriptome size variation, its potential to introduce systematic bias into expression profiling has been largely overlooked and this study uncovers responses of the transcriptome to stress.…”

The “Polyploid Hop”: Shifting Challenges and Opportunities Over the Evolutionary Lifespan of Genome Duplications

“…Methods commonly used to evaluate genome-wide expression patterns (microarrays and RNAseq) rely on extensive normalization of the RNA input and therefore are perfectly appropriate to detect relative changes in gene expression but not at all to measure variations in absolute transcriptome size (Lovén et al, 2012;Coate and Doyle, 2015). Only recently has a directed investigation of expression differences between diploids and autotetraploids been reported, using three normalization procedures to take into account transcriptome size, biomass, and cell density (Visger et al, 2017).…”