Research Paper Volume 16, Issue 13 pp 10943—10971

Unfolding the mysteries of heterogeneity from a high-resolution perspective: integration analysis of single-cell multi-omics and spatial omics revealed functionally heterogeneous cancer cells in ccRCC

Jie Zheng1,4, *, , Wenhao Lu1,4, *, , Chengbang Wang1,4, *, , Shaohua Chen3,4, , Qingyun Zhang3,4, , Cheng Su2,4, ,

  • 1 Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
  • 2 Department of Pediatric Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
  • 3 Department of Urology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
  • 4 Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
* Equal contribution

Received: September 19, 2023       Accepted: May 16, 2024       Published: June 26, 2024
How to Cite

Copyright: © 2024 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


The genomic landscape of clear cell renal cell carcinoma (ccRCC) has a considerable intra-tumor heterogeneity, which is a significant obstacle in the field of precision oncology and plays a pivotal role in metastasis, recurrence, and therapeutic resistance of cancer. The mechanisms of intra-tumor heterogeneity in ccRCC have yet to be fully established. We integrated single-cell RNA sequencing (scRNA-seq) and transposase-accessible chromatin sequencing (scATAC-seq) data from a single-cell multi-omics perspective. Based on consensus non-negative matrix factorization (cNMF) algorithm, functionally heterogeneous cancer cells were classified into metabolism, inflammatory, and EMT meta programs, with spatial transcriptomics sequencing (stRNA-seq) providing spatial information of such disparate meta programs of cancer cells. The bulk RNA sequencing (RNA-seq) data revealed high clinical prognostic values of functionally heterogeneous cancer cells of three meta programs, with transcription factor regulatory network and motif activities revealing the key transcription factors that regulate functionally heterogeneous ccRCC cells. The interactions between varying meta programs and other cell subpopulations in the microenvironment were investigated. Finally, we assessed the sensitivity of cancer cells of disparate meta programs to different anti-cancer agents. Our findings inform on the intra-tumor heterogeneity of ccRCC and its regulatory networks and offers new perspectives to facilitate the designs of rational therapeutic strategies.


ccRCC: clear cell renal cell carcinoma; cNMF: consensus non-negative matrix factorization; GEO: Gene Expression Omnibus; OS: overall survival; ssGSEA: single-sample gene set enrichment analysis; scATAC-seq: transposase-accessible chromatin sequencing; scRNA-seq: single-cell RNA sequencing; stRNA-seq: spatial transcriptomics sequencing; TME: tumor microenvironment; TCGA: The Cancer Genome Atlas.