Arulsamy Kulandaisamy, Xia Bo, Yu Yang, Chen Hong, Pu William T, Zhang Lili, Chen Kaifu
Basic and Translational Research Division, Department of Cardiology, Boston Children's Hospital, Boston, MA 02115, United States.
Department of Pediatrics, Harvard Medical School, Boston, MA 02115, United States.
Nucleic Acids Res. 2025 May 22;53(10). doi: 10.1093/nar/gkaf431.
Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and cell identity dysregulation involving diseases. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes (CIGs) are regulated with unique epigenetic signatures, we found CIGs exhibit distinctive genetic sequence signatures, e.g. unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, SCIG uncovers the identity genes of a cell without a need for comparison to other cells. CIG score defined by SCIG surpassed expression value in network analysis to reveal the master transcription factors (TFs) regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master TFs for cell identity refinement. SCIG is publicly available at https://doi.org/10.5281/zenodo.14726426 , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.
破译细胞身份基因对于理解细胞分化、发育以及涉及疾病的细胞身份失调至关重要。在此，我们介绍了SCIG，这是一种用于在单细胞中揭示细胞身份基因的机器学习方法。与最近关于细胞身份基因（CIGs）受独特表观遗传特征调控的报道一致，我们发现CIGs表现出独特的遗传序列特征，例如顺式调控元件的独特富集模式。利用这些遗传序列特征，结合来自单细胞RNA测序数据的基因表达信息，SCIG无需与其他细胞进行比较就能揭示细胞的身份基因。由SCIG定义的CIG评分在网络分析中超过了表达值，从而揭示了调控细胞身份的主要转录因子（TFs）。将SCIG应用于人类内皮细胞图谱表明，组织微环境是对主要TFs进行细胞身份细化的关键补充。SCIG可在https://doi.org/10.5281/zenodo.14726426上公开获取，为推进细胞分化、发育和再生医学研究提供了一个有价值的工具。
