Department of Computer Science, University of Bristol, Bristol, UK.
Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore.
Nat Genet. 2016 Mar;48(3):331-5. doi: 10.1038/ng.3487. Epub 2016 Jan 18.
Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine. The identification of key transcription factors for reprogramming is currently limited by the cost of exhaustive experimental testing of plausible sets of factors, an approach that is inefficient and unscalable. Here we present a predictive system (Mogrify) that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce cell conversion. We have applied Mogrify to 173 human cell types and 134 tissues, defining an atlas of cellular reprogramming. Mogrify correctly predicts the transcription factors used in known transdifferentiations. Furthermore, we validated two new transdifferentiations predicted by Mogrify. We provide a practical and efficient mechanism for systematically implementing novel cell conversions, facilitating the generalization of reprogramming of human cells. Predictions are made available to help rapidly further the field of cell conversion.
转分化是指一种细胞类型在不经历多能状态的情况下转化为另一种细胞类型的过程，它在再生医学中有很大的应用前景。目前，重编程关键转录因子的鉴定受到详尽的实验测试合理因子集的成本限制，这种方法效率低下且不可扩展。在这里，我们提出了一个预测系统（Mogrify），它将基因表达数据与调控网络信息相结合，以预测诱导细胞转化所需的重编程因子。我们已经将 Mogrify 应用于 173 个人类细胞类型和 134 种组织，定义了一个细胞重编程图谱。Mogrify 可以正确预测已知转分化中使用的转录因子。此外，我们还验证了 Mogrify 预测的两种新的转分化。我们提供了一种系统实现新细胞转化的实用且高效的机制，促进了人类细胞的重编程推广。预测结果可供参考，以帮助快速推进细胞转化领域的发展。
