A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.

中文翻译：

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SinCMat：基于单细胞的预测功能成熟转录因子的方法

再生医学的一个主要目标是产生组织特异性的成熟和功能细胞。然而，当前的细胞工程方案仍然无法系统地产生完全成熟的功能细胞。虽然现有的计算方法旨在预测细胞分化/重编程的转录因子（TF），但目前还没有专门考虑功能性细胞成熟过程的方法。为了应对这一挑战，我们开发了 SinCMat，一种基于单细胞 RNA 测序 (RNA-seq) 的计算方法，用于预测细胞成熟 TF。基于细胞成熟模型，SinCMat 识别了身份转录因子和信号依赖性转录因子对，它们共同靶向驱动功能成熟的基因。 SinCMat 在小鼠细胞图谱和白板中的大规模应用准确地概括了已知的成熟转录因子并预测了新的候选转录因子。我们期望 SinCMat 成为一种重要资源，补充现有的计算方法，用于旨在生产功能成熟细胞的研究。