Probing cell identity hierarchies by fate titration and collision during direct reprogramming

Abstract

Despite the therapeutic promise of direct reprogramming, basic principles concerning fate erasure and the mechanisms to resolve cell identity conflicts remain unclear. To tackle these fundamental questions, we established a single‐cell protocol for the simultaneous analysis of multiple cell fate conversion events based on combinatorial and traceable reprogramming factor expression: Collide‐seq. Collide‐seq revealed the lack of a common mechanism through which fibroblast‐specific gene expression loss is initiated. Moreover, we found that the transcriptome of converting cells abruptly changes when a critical level of each reprogramming factor is attained, with higher or lower levels not contributing to major changes. By simultaneously inducing multiple competing reprogramming factors, we also found a deterministic system, in which titration of fates against each other yields dominant or colliding fates. By investigating one collision in detail, we show that reprogramming factors can disturb cell identity programs independent of their ability to bind their target genes. Taken together, Collide‐seq has shed light on several fundamental principles of fate conversion that may aid in improving current reprogramming paradigms.

Deterministic and probabilistic fate decisions co‐exist in a single retinal lineage

“…In the model, noise came from the base levels of transcription factor expression (Elowitz et al , 2002; Johnston & Desplan, 2008; Raj & van Oudenaarden, 2008; Raj et al , 2010) and the inhibitions were modelled considering the presence of a threshold level of expression of each factor. If the expression level of a TF was above the threshold, then effective inhibition took place on its target TFs (Huang et al , 2007; Hersbach et al , 2022). To model the temporal evolution of fate probabilities from the experimental data, we considered that TF levels change during development, as previously shown (Jusuf & Harris, 2009).…”

Dissecting and improving gene regulatory network inference using single-cell transcriptome data

“…Furthermore, we compared inferred GRNs using both methods from data sets in which exogenously induced TFs were used to program cell fates, such that we should be able to recover the TFs being induced in the inferred network without needing to rely on GRN databases as the ground truth. By analyzing public data sets taken at two different time points postinduction ( Hersbach et al 2022 ), we found that whereas both methods identified exogenously induced TFs as hub nodes in the GRN, only the pre-mRNA-based method appeared to capture the temporal behaviors of the TFs ( Supplemental Fig. S6C,D ; Supplemental Note S3 ).…”

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions

“…Furthermore, researchers transferred specific TFs into donor cells to reconfigure the intracellular GRNs for acquiring cell types of interest ( Hammelman et al, 2022 ; Mazid et al, 2022 ). Although some studies suggested that perturbation of a single TF is sufficient to transform certain cell fates ( Ng et al, 2021 ), a large number of TFs are inevitably involved in most differentiation/reprogramming processes ( Hersbach et al, 2022 ). In particular to orchestrate decisions among multiple cell fates, it is necessary for TFs to regulate target genes cooperatively ( Trojanowski and Rippe, 2022 ).…”