Taiji-reprogram: a framework to uncover cell-type specific regulators and predict cellular reprogramming cocktails

Abstract

Cellular reprogramming is a promising technology to develop disease models and cell-based therapies. Identification of the key regulators defining the cell type specificity is pivotal to devising reprogramming cocktails for successful cell conversion but remains a great challenge. Here, we present a systems biology approach called Taiji-reprogram to efficiently uncover transcription factor (TF) combinations for conversion between 154 diverse cell types or tissues. This method integrates the transcriptomic and epigenomic data to construct cell-type specific genetic networks and assess the global importance of TFs in the network. Comparative analysis across cell types revealed TFs that are specifically important in a particular cell type and often tightly associated with cell-type specific functions. A systematic search of TFs with differential importance in the source and target cell types uncovered TF combinations for desired cell conversion. We have shown that Taiji-reprogram outperformed the existing methods to better recover the TFs in the experimentally validated reprogramming cocktails. This work not only provides a comprehensive catalog of TFs defining cell specialization but also suggests TF combinations for direct cell conversion.

Neural network facilitated ab initio derivation of linear formula: A case study on formulating the relationship between DNA motifs and gene expression

“…We next examined whether the top 300 motifs define a parts list of TF binding motifs in the promoters and whether the linear relationship remains valid on predicting cell-type specific gene expressions. We collected the RNA-seq data of 154 cell types/tissues 45 from Epigenomic Roadmap Project 46 and ENCODE 47 . We trained and tested the model using the same division of train, validate and test set as in the previous section.…”

Multi-Omics Atlas-Assisted Discovery of Transcription Factors for Selective T Cell State Programming

“…We used a framework we recently developed called Taiji-reprogram( 22 ) to identify differentiation-step-specific TFs, we first calculated PageRank ratios between target and source cell types. We then defined ratio.abs as the reciprocal of ratio if the ratio was smaller than 1, or otherwise, as the ratio itself.…”

“…While our knowledge of individual T cell states has sharply increased and comparative analysis between exhaustion versus memory has been done ( 20 ), systems-level understanding of T cell state landscape and cell-state specifying TFs network remains minimal. We previously performed a global analysis of TF specification for hard-wired cell types in embryonic development ( 21, 22 ) and hematopoietic lineage ( 23 ). As different cell states share a large number of same cell type-defining TFs ( 24 ), an advanced and precise bioinformatics approach is needed to identify TFs that specify cell states.…”

Computational tools for inferring transcription factor activity

“…Applying the PageRank algorithm on the network gives the overall importance of a TF. In an extension called Taiji‐reprogram the method predicts the top TFs whose differential activity explains the transcriptional differences between two conditions [58].…”

“…Hence, developing computational methods capable of accurately predicting effective TF sets is crucial for advancing this field. While many of the established approaches are built on the identification of TFs that show differential activity between two types of cells [80, 87–89], others are specifically tailored to infer new reprogramming strategies, like Taiji‐reprogram [58]. Hammelman et al.…”