Heritable non-genetic phenotypes are enriched for stress responses as a form of bet hedging
To produce humanized therapeutics, Chinese hamster ovary (CHO) cells have become the preferred host organism. During manufacturing batches, these cells encounter critical levels of environmental stressors such as ammonia, lactate, and osmolality accumulation that can significantly reduce cell health and productivity. It is therefore crucial that stress adaptation and resistance be factored into cell line development (CLD) considerations. An alternative to screening stress sensitivity during CLD, where the scaled-down micro-environment makes it difficult, is selecting for or engineering cell lines with a stress resistant phenotype. In this study, we employee population-based transcriptomic analysis and differential gene expression analysis on stress-induced CHO cells to identify biomarkers for engineering stress resistance. These biomarkers are identified as genes displaying heritable properties while being differentially expressed in the presence of manufacturing relevant stress levels. Using this workflow, 199 genes displayed transcriptional variability characteristic of a bistable system that formed six network communities of co-fluctuating genes. These communities were enriched in genes related to response to stimuli, regulation of apoptotic processes, and regulation of gene expression or metabolic pathways. Significant overlap with differentially expressed genes in stress-induced production fed-batch flasks reinforce the relevancy of these heritable genes states for stress adaptation. Assessing the overlap and the biological function, six genes were identified as promising biomarkers for engineering a stress-resistant phenotype. Cell line engineering methods may be employed in the future to bias clonal populations for higher stress tolerance and adaptability to manufacturing stress, therefore increasing cell health and productivity in at-scale bioreactors. Overall design: Population-based transcriptomic analysis, known as MemorySeq, in which single-cell derived mini-pools (40 samples) of CHOZN® GS -/- ZFN Clone 23 gene expression is measured in an attempt to correlate highly variable genes with those displaying heritable patterns. Samples compared to bulk RNA-Seq as a noise control to monitor innate biological variability. Collection of genes demonstrating heritable expression patterns compared to differentially expressed genes from day 5 of stress-induced production fed-batch flasks to identify heritable genes critical to stress recognition and adaptation Please note that only R1 raw data fastq file is provided for the 'MemorySeq_Sample26_Single-Cell-26' sample (as R2 file is corrupted).