SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes

IF 16.6 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Nucleic Acids Research Pub Date : 2025-05-28 DOI:10.1093/nar/gkaf431

Kulandaisamy Arulsamy, Bo Xia, Yang Yu, Hong Chen, William T Pu, Lili Zhang, Kaifu Chen

{"title":"SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes","authors":"Kulandaisamy Arulsamy, Bo Xia, Yang Yu, Hong Chen, William T Pu, Lili Zhang, Kaifu Chen","doi":"10.1093/nar/gkaf431","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":19471,"journal":{"name":"Nucleic Acids Research","volume":"45 1","pages":""},"PeriodicalIF":16.6000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nucleic Acids Research","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/nar/gkaf431","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

SCIG：机器学习通过基因序列密码揭示单细胞中的细胞识别基因

破译细胞身份基因对于理解细胞分化、发育和涉及疾病的细胞身份失调至关重要。在这里，我们介绍SCIG，一种机器学习方法来揭示单细胞中的细胞识别基因。根据最近的报道，细胞识别基因（CIGs）受到独特的表观遗传特征的调控，我们发现CIGs具有独特的基因序列特征，例如独特的顺式调控元件富集模式。利用这些基因序列特征，以及来自单细胞RNA-seq数据的基因表达信息，SCIG发现了一个细胞的身份基因，而不需要与其他细胞进行比较。通过网络分析，SCIG定义的CIG评分超过了表达值，揭示了调控细胞身份的主转录因子（TFs）。将SCIG应用于人内皮细胞图谱表明，组织微环境是掌握tf的关键补充，可以用于细胞身份的改进。SCIG可在https://doi.org/10.5281/zenodo.14726426公开获取，为推进细胞分化、发育和再生医学研究提供了有价值的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Nucleic Acids Research 生物-生化与分子生物学

CiteScore

27.10

自引率

4.70%

发文量

1057

审稿时长

2 months

期刊介绍： Nucleic Acids Research (NAR) is a scientific journal that publishes research on various aspects of nucleic acids and proteins involved in nucleic acid metabolism and interactions. It covers areas such as chemistry and synthetic biology, computational biology, gene regulation, chromatin and epigenetics, genome integrity, repair and replication, genomics, molecular biology, nucleic acid enzymes, RNA, and structural biology. The journal also includes a Survey and Summary section for brief reviews. Additionally, each year, the first issue is dedicated to biological databases, and an issue in July focuses on web-based software resources for the biological community. Nucleic Acids Research is indexed by several services including Abstracts on Hygiene and Communicable Diseases, Animal Breeding Abstracts, Agricultural Engineering Abstracts, Agbiotech News and Information, BIOSIS Previews, CAB Abstracts, and EMBASE.