Stem Cell Transcriptome Responses and Corresponding Biomarkers That Indicate the Transition from Adaptive Responses to Cytotoxicity.
Tanja WaldmannMarianna GrinbergAndré KönigEugen RempelStefan SchildknechtMargit HenryAnna-Katharina HolzerNadine DreserVaibhav ShindeAgapios SachinidisJörg RahnenführerJan G HengstlerMarcel LeistPublished in: Chemical research in toxicology (2016)
Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.
Keyphrases
- genome wide
- cell death
- rna seq
- cell cycle arrest
- single cell
- gene expression
- stem cells
- induced apoptosis
- dna methylation
- pluripotent stem cells
- endothelial cells
- transcription factor
- magnetic resonance imaging
- high intensity
- magnetic resonance
- big data
- computed tomography
- deep learning
- social media
- cell proliferation
- health information
- genome wide analysis