Department of Biology, Reed College, Portland, OR, USA.
Methods Mol Biol. 2023;2545:227-250. doi: 10.1007/978-1-0716-2561-3_12.
RNA-seq has been used extensively to study expression responses to polyploidy. Most current methods for normalizing RNA-seq data yield estimates of transcript concentrations (transcripts per transcriptome). The implicit assumption of these normalization methods is that transcriptome size is equivalent between the samples being compared such that transcript concentrations are equivalent to transcripts per cell. In recent years, however, evidence has mounted that transcriptome size can vary dramatically in response to a range of factors including polyploidy and that such variation is ubiquitous. Where such variation exists, transcript concentration is often a poor or even misleading proxy for expression responses at other biologically relevant scales (e.g., expression per cell). Thus, it is important that transcriptomic studies of polyploids move beyond simply comparing transcript concentrations if we are to gain a complete understanding of how genome multiplication affects gene expression. I discuss this issue in more detail and summarize a suite of approaches that can leverage RNA-seq to quantify expression responses per genome, per cell, and per unit of biomass.
RNA-seq 已被广泛用于研究多倍体对表达的响应。目前大多数用于 RNA-seq 数据标准化的方法都能得出转录本浓度(转录本每转录组)的估计值。这些标准化方法的隐含假设是,在比较的样本之间转录组大小是等效的,因此转录本浓度相当于每个细胞的转录本。然而,近年来有越来越多的证据表明,转录组大小可以根据多种因素(包括多倍体)而发生显著变化,并且这种变化无处不在。在存在这种变化的情况下,转录本浓度往往是其他生物学相关尺度(例如,每个细胞的表达)上表达响应的一个很差甚至误导性的替代指标。因此,如果我们要全面了解基因组倍增如何影响基因表达,那么对多倍体的转录组研究就不能仅仅停留在比较转录本浓度上。我将更详细地讨论这个问题,并总结一套可以利用 RNA-seq 来定量基因组、细胞和生物量单位上的表达响应的方法。