Gene expression measurements normalized to cell number reveal large scale differences due to cell size changes, transcriptional amplification and transcriptional repression in CHO cells

•Spike-in normalization strategy is applied to gene expression analysis of CHO cells.•Cell cycle inhibition increases cell size and total cellular RNA content.•mTOR inhibition decreases total cellular RNA content without impacting cell size.•High culture osmolarity increases cell size and total cellular RNA content.•Cell size should be factored into specific productivity calculations.

Conventional approaches to differential gene expression comparisons assume equal cellular RNA content among experimental conditions. We demonstrate that this assumption should not be universally applied because total RNA yield from a set number of cells varies among experimental treatments of the same Chinese Hamster Ovary (CHO) cell line and among different CHO cell lines expressing recombinant proteins. Conventional normalization strategies mask these differences in cellular RNA content and, consequently, skew biological interpretation of differential expression results. On the contrary, normalization to synthetic spike-in RNA standards added proportional to cell numbers reveals these differences and allows detection of global transcriptional amplification/repression. We apply this normalization method to assess differential gene expression in cell lines of different sizes, as well as cells treated with a cell cycle inhibitor (CCI), an mTOR inhibitor (mTORI), or subjected to high osmolarity conditions. CCI treatment of CHO cells results in a cellular volume increase and global transcriptional amplification, while mTORI treatment causes global transcriptional repression without affecting cellular volume. Similarly to CCI treatment, high osmolarity increases cell size, total RNA content and antibody expression. Furthermore, we show the importance of spike-in normalization for studies involving multiple CHO cell lines and advocate normalization to spike-in controls prior to correlating gene expression to specific productivity (qP). Overall, our data support the need for cell number specific spike-in controls for all gene expression studies where cellular RNA content differs among experimental conditions.