Technological advances led to the generation of large, often under-exploited collections of transcriptional profiles from single-cells, enabling the detection of transcriptional heterogeneity in seemingly homogeneous cell populations. Sparse evidence suggests that this is associated with development, ageing, and diseases, such as cancer. We thus hypothesize that transcriptional heterogeneity is regulated, subject to natural selection and exploited by cancer cells to increase their evolvability and plasticity, enhancing their chances of developing resistance to therapies. To address these hypotheses, we comprehensively mapped transcriptional heterogeneity across tissues, age groups, and diseases. We developed a computational pipeline for the re-analysis and robust estimation of transcriptional variability from over 1000000 published single-cell profiles. Our analyses revealed 6000 hyper-variably transcribed genes (HVG) in immune cells of the spleen, but almost none in pancreatic cells, emphasizing potential biological implications of this phenomenon. We further identified a switch from stable to variable transcription at 541 genes in aging hepatocytes. We are currently training random forest and regression models to identify the upstream regulators of such patterns, combining transcription factor binding, chromatin state, and alternative splicing data as predictors. Initial results are promising (AUC 0.67); we therefore aim to refine these models and extend them to detect cell type-, age-, and disease-specific drivers of heterogeneity. Preliminary models on HVG across different cell types also yielded promising results (AUC 0.72), highlighting general regulators of transcriptional variability. These results may advance our understanding of transcription regulation mechanisms, their correlation with age, as well as the evolvability and plasticity of cancer cells.

Co-authors: Helene Kaufmann, Eszter Söjtöry, Stefania Astrologo, Hans Westerhoff, Pernette Verschure, Iros Barozzi