小柯机器人

美国麻省理工学院David Gifford研究团队完成对细胞分化重编程因子的排序。相关论文于2022年6月16日在线发表在《自然—方法学》杂志上。

研究人员通过测试九种计算方法（CellNet、GarNet、EBseq、AME、DREME、HOMER、KMAC、diffTF和DeepAccess）对已知重编程方案的八种目标细胞类型发现和排列候选因子的能力来考察了分化的成功率。研究人员比较了使用基因表达、生物网络和染色质可及性数据的方法，并全面测试了输入数据的参数和预处理来优化性能。研究人员发现最好的因子识别方法可以在前十名候选中平均识别50-60%的重编程因子，而使用染色质可及性的方法表现最好。在染色质可及性方法中，复杂的方法DeepAccess和diffTF与分化的重编程协议内转录因子候选的排名意义有更高的相关性。

研究人员提供的证据表明，AME和diffTF是转录因子发现的最佳方法，这将使转录因子候选者的优先次序得到系统化，从而帮助设计新的重编程方案。

据介绍，转录因子的过表达是一种行之有效的方法，可将细胞重新编程为再生医学和治疗发现所需的细胞类型。然而，识别重编程因子以创建一个任意的细胞类型的一般方法是一个开放的问题。

附：英文原文

Title: Ranking reprogramming factors for cell differentiation

Author: Hammelman, Jennifer, Patel, Tulsi, Closser, Michael, Wichterle, Hynek, Gifford, David

Issue&Volume: 2022-06-16

Abstract: Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50–60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

DOI: 10.1038/s41592-022-01522-2

Source: https://www.nature.com/articles/s41592-022-01522-2

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex