Detect changes in cell viability with bulk RNA-seq?
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6 days ago
txema.heredia ▴ 200

Hi,

I'm reanalyzing some old bulk RNA-seq data. It is a comparison of 2 treatments in cancer cell lines. More like, a main treatment + an extra drug.

In the wet lab, they observed a ~20% decrease in cell viability after 3 days. However, when looking at the RNA-seq data I can only detect <40 up/down DEG, even with very lax thresholds. No, the bioinformatics analysis is not wrong because I detect more than 1,000 DEG when comparing either treatment vs controls.

I suspect this might be due because the "extra drug" has an effect mostly at the protein level, with very little trace/interaction between treatments at the RNA-level.

The apoptosis pathways, cell cycle arrest, yada yada, appear already highlighted when doing pathway enrichment analysis vs controls, but there aren't enough differences to show up enriched when comparing the 2 treatments.

Is there any (more or less reliable) method to detect changes in cell viability between two groups when looking at bulk-RNA data?

Thanks

DEG viability RNA • 236 views
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Why would you need RNA evidence if you already know that viability is different? The problem in my head with measuring "death" on RNA level at the point where the "death" is already happening is always that the apoptotic process itself, such as cleavage of Casp3 and its downstream consequences are not transcriptional. The magic so to say on RNA level when you observe cell death has already happened, so the effector proteins are already present, hence the transcription of course has happened even earlier. Maybe show some critical genes, it doesn't need to be an enriched pathway since the pathway action happens mostly on protein level.

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Thanks for your reply.

The point of my question is that the response to the treatment can be very heterogeneous from cell to cell. Some cells get DNA damage, others not. Some cells can repair the damage, others not.

What I want to measure is any sort of proxy for "a higher fraction of cells are affected by the treatment in group A vs group B". Just going by DEG and pathway enrichment is giving me nothing. I have applied before with some success a custom adaptation of Seurat's single-cell cell-cyle scoring on bulk RNA samples. This showed results that matched what we would expect from the lab results. I was wondering if there is any similar method adapted to viability/cell death/damage response able to quantify "there is a higher % of cells messed up somehow in this group".

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