סמינר מיוחד בכימיה פיזיקלית: Leveraging Twin Information for Inference of Gene Regulatory Networks

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Abstract:

Determining the architecture of gene regulatory networks is essential for understanding how cells maintain their phenotype, and how they respond to stimuli. Single-cell transcriptomics provides the high-throughput data required for such inference en masse, but at the cost of losing dynamical information. Regulatory correlations can still be observed through intrinsic variation of transcriptional levels across isogenic single cells, albeit heterogeneity in cell states precludes accurate inference. Here, we overcome these challenges by developing TwINFER, a framework that utilizes information obtained from recently divided sister cells, identifiable with modern barcoding techniques. Twin information resolves regulatory from non-regulatory correlations. Moreover, separating twins and measuring their transcriptome at different time points enables inference of the direction and sign of regulatory interactions. As a proof-of-principle, we use an extensive set of simulations, covering common network motifs and large-scale networks. As a case study, we focus on two ubiquitous motifs, fan-out and feed-forward loop, where most inference schemes perform poorly. Twin information resolves the false positive problem in these systems. We then apply TwINFER to a well-recognized lineage-barcoded single-cell transcriptomic dataset tracing hematopoiesis. We use the twin information stored in this dataset to refine the inference of the underlying gene regulatory network, flag multi-state genes and determine causal relations.

מארגן הסמינר: פרופ' איליה קמינקר