Computational approaches for predicting key transcription factors in targeted cell reprogramming (Review)

Epigenetic reprogramming of cell identity: lessons from development for regenerative medicine

Epigenetic mechanisms are known to define cell-type identity and function. Hence, reprogramming of one cell type into another essentially requires a rewiring of the underlying epigenome. Cellular reprogramming can convert somatic cells to induced pluripotent stem cells (iPSCs) that can be directed to differentiate to specific cell types. Trans-differentiation or direct reprogramming, on the other hand, involves the direct conversion of one cell type into another. In this review, we highlight how gene regulatory mechanisms identified to be critical for developmental processes were successfully used for cellular reprogramming of various cell types. We also discuss how the therapeutic use of the reprogrammed cells is beginning to revolutionize the field of regenerative medicine particularly in the repair and regeneration of damaged tissue and organs arising from pathological conditions or accidents. Lastly, we highlight some key challenges hindering the application of cellular reprogramming for therapeutic purposes.

Molecular mechanisms of transcription factor mediated cell reprogramming: conversion of liver to pancreas

Transdifferentiation is a type of cellular reprogramming involving the conversion of one differentiated cell type to another. This remarkable phenomenon holds enormous promise for the field of regenerative medicine. Over the last 20 years techniques used to reprogram cells to alternative identities have advanced dramatically. Cellular identity is determined by the transcriptional profile which comprises the subset of mRNAs, and therefore proteins, being expressed by a cell at a given point in time. A better understanding of the levers governing transcription factor activity benefits our ability to generate therapeutic cell types at will. One well-established example of transdifferentiation is the conversion of hepatocytes to pancreatic β-cells. This cell type conversion potentially represents a novel therapy in T1D treatment. The identification of key master regulator transcription factors (which distinguish one body part from another) during embryonic development has been central in developing transdifferentiation protocols. Pdx1 is one such example of a master regulator. Ectopic expression of vector-delivered transcription factors (particularly the triumvirate of Pdx1, Ngn3 and MafA) induces reprogramming through broad transcriptional remodelling. Increasingly, complimentary cell culture techniques, which recapitulate the developmental microenvironment, are employed to coax cells to adopt new identities by indirectly regulating transcription factor activity via intracellular signalling pathways. Both transcription factor-based reprogramming and directed differentiation approaches ultimately exploit transcription factors to influence cellular identity. Here, we explore the evolution of reprogramming and directed differentiation approaches within the context of hepatocyte to β-cell transdifferentiation focussing on how the introduction of new techniques has improved our ability to generate β-cells.

Experimental and Computational Approaches to Direct Cell Reprogramming: Recent Advancement and Future Challenges

The process of direct cell reprogramming, also named transdifferentiation, permits for the conversion of one mature cell type directly into another, without returning to a dedifferentiated state. This makes direct reprogramming a promising approach for the development of several cellular and tissue engineering therapies. To achieve the change in the cell identity, direct reprogramming requires an arsenal of tools that combine experimental and computational techniques. In the recent years, several methods of transdifferentiation have been developed. In this review, we will introduce the concept of direct cell reprogramming and its background, and cover the recent developments in the experimental and computational prediction techniques with their applications. We also discuss the challenges of translating this technology to clinical setting, accompanied with potential solutions.