Our ability to study Chinese hamster ovary (CHO) cell biology has been revolutionised over the last decade following the development of next generation sequencing technology and publication of reference DNA sequences for CHO cells and the Chinese hamster. RNA sequencing has not only enabled the association of transcript expression with bioreactor conditions and desirable bioprocess phenotypes but played a key role in the characterisation of protein coding and small noncoding RNAs. The annotation of long noncoding RNAs, and therefore our understanding of their role in CHO cell biology, has been limited to date. In this manuscript, we use high‐resolution RNASeq data to more than double the number of annotated lncRNA transcripts for the CHO K1 genome. In addition, the utilisation of strand‐specific sequencing enabled the identification of more than 1,000 new antisense and divergent lncRNAs. The utility of monitoring lncRNA expression is demonstrated through an analysis of the transcriptomic response to a reduction of cell culture temperature and identification of simultaneous sense/antisense differential expression for the first time in CHO cells. To enable further studies of lncRNAs, the transcripts annotated in this study have been made available for the CHO cell biology community.
Single cell RNA-seq (scRNA-seq) has recently been shown to provide a powerful method for the analysis of transcriptional heterogeneity in Chinese hamster ovary (CHO) cells. A potential drawback of current scRNA-seq platforms is that the cost can limit the complexity of experimental design and therefore the utility of the approach. In this manuscript, we report the use of oligonucleotide barcoding to perform multiplexed CHO cell scRNA-seq to study the impact of tunicamycin (TM), an inducer of the unfolded protein response (UPR). For this experiment, we treated a CHO-K1 GS cell line with 10μg/ml tunicamycin and acquired samples at 1, 2, 4 and 8 hr post-treatment as well as a non-treated TM- control. We transfected cells with sample-specific polyadenylated ssDNA oligonucleotide barcodes enabling us to pool all cells for scRNA-seq. The sample from which each cell originated was subsequently determined by the oligonucleotide barcode sequence. Visualisation of the transcriptome data in a reduced dimensional space confirmed that cells were not only separable by sample but were also distributed according to time post-treatment. These data were subsequently utilised to perform weighted gene co-expression analysis (WGCNA) and uncovered groups of genes associated with TM treatment. For example, the expression of one group of coexpressed genes was found to increase over the time course and were enriched for biological processes associated with ER stress. The use of multiplexed single cell RNA-seq has the potential to reduce the cost associated with higher sample numbers and avoid batch effects for future studies of CHO cell biology.
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