Scholarlink

Background <p>Accurately identifying functionally distinct tumor cell subpopulations remains a critical challenge in cancer research. While single-cell epigenomics assays provide powerful insights into tumor heterogeneity beyond gene expression, computational limitations have hindered their application.</p> Methods <p>We introduce Multimodal-based Analysis of scATAC-Seq data (MAAS), a method that integrates chromatin accessibility, copy number variations (CNVs), and single-nucleotide variants (SNVs) to identify functional tumor cell subpopulations. MAAS employs a self-expressive multimodal matrix factorization approach with rigorous coverage normalization and data denoising. We applied MAAS to simulated datasets and multiple real-world tumor scATAC-seq datasets, including pediatric ependymoma, B-cell lymphoma, and glioblastoma, and benchmarked its performance against existing integration methods. Functional relevance of subpopulation-specific genes was experimentally validated using gene knockdown and overexpression assays. Furthermore, we constructed subpopulation-specific gene regulatory networks and developed a prognostic signature from the key regulatory genes.</p> Results <p>MAAS demonstrated superior accuracy in detecting clinically relevant subpopulations, particularly in tumors with limited CNV heterogeneity, such as pediatric ependymoma and B-cell lymphoma. In glioblastoma, MAAS uncovered a previously unrecognized subpopulation with temozolomide resistance and further experimentally validated the effects of its signature genes through gene knockdown and overexpression. The MAAS-derived prognostic signature, MAASig, outperformed traditional clinicopathologic features across multiple cancer types when applied to independent validation cohorts.</p> Conclusions <p>By integrating multimodal information from scATAC-seq data, MAAS provides the robust identification of functionally distinct tumor cell subpopulations, facilitating the discovery of potential therapeutic targets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multimodal-based analysis of single-cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月13日

查看全文
  • Kewei Xiong,
  • Wei Wang,
  • Ruofan Ding,
  • Dinglin Luo,
  • Yangmei Qin,
  • Xudong Zou,
  • Jiguang Wang,
  • Chen Yu,
  • Lei Li

摘要

Background

Accurately identifying functionally distinct tumor cell subpopulations remains a critical challenge in cancer research. While single-cell epigenomics assays provide powerful insights into tumor heterogeneity beyond gene expression, computational limitations have hindered their application.

Methods

We introduce Multimodal-based Analysis of scATAC-Seq data (MAAS), a method that integrates chromatin accessibility, copy number variations (CNVs), and single-nucleotide variants (SNVs) to identify functional tumor cell subpopulations. MAAS employs a self-expressive multimodal matrix factorization approach with rigorous coverage normalization and data denoising. We applied MAAS to simulated datasets and multiple real-world tumor scATAC-seq datasets, including pediatric ependymoma, B-cell lymphoma, and glioblastoma, and benchmarked its performance against existing integration methods. Functional relevance of subpopulation-specific genes was experimentally validated using gene knockdown and overexpression assays. Furthermore, we constructed subpopulation-specific gene regulatory networks and developed a prognostic signature from the key regulatory genes.

Results

MAAS demonstrated superior accuracy in detecting clinically relevant subpopulations, particularly in tumors with limited CNV heterogeneity, such as pediatric ependymoma and B-cell lymphoma. In glioblastoma, MAAS uncovered a previously unrecognized subpopulation with temozolomide resistance and further experimentally validated the effects of its signature genes through gene knockdown and overexpression. The MAAS-derived prognostic signature, MAASig, outperformed traditional clinicopathologic features across multiple cancer types when applied to independent validation cohorts.

Conclusions

By integrating multimodal information from scATAC-seq data, MAAS provides the robust identification of functionally distinct tumor cell subpopulations, facilitating the discovery of potential therapeutic targets.