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Related Experiment Video

Updated: Jan 9, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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De Novo Detection of Clonal Structure and Evolution in Single-Cell and Spatial Transcriptomes.

Shihao Bai1, Xianbin Su1, Ziyao Chen1

  • 1Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.

International Journal of Molecular Sciences
|December 11, 2025
PubMed
Summary

scClone is a new computational toolkit that analyzes single-cell RNA sequencing data to reveal tumor clonal evolution. It overcomes technical challenges, providing insights into tumor heterogeneity and genotype-phenotype associations.

Keywords:
clone inferencemutational signaturesingle-cell mutationsingle-cell transcriptomesspatial transcriptomes

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Tumors exhibit dynamic clonal evolution, characterized by distinct cellular genotypes and phenotypes.
  • Traditional bulk sequencing struggles to resolve fine-scale clonal structures.
  • Single-cell genome sequencing is costly and limited in cell throughput for reconstructing tumor evolution.

Purpose of the Study:

  • To develop a computational toolkit for reconstructing tumor clonal evolution using single-cell transcriptomic data.
  • To address limitations of single-cell RNA-seq (scRNA-seq) such as drop-out and allelic imbalance.
  • To enable comprehensive profiling of tumor heterogeneity and genotype-phenotype associations.

Main Methods:

  • Developed scClone, a toolkit integrating variant detection and genotype inference for scRNA-seq and spatial transcriptomic data.
  • Incorporated cell type annotation and mutational signature analysis.
  • Enabled interactive visualization of clonal structure and dynamic evolution.

Main Results:

  • scClone demonstrated robust performance across diverse datasets (myeloma, hepatocellular carcinoma, pancreatic cancer).
  • Accurately reproduced mutation profiles and resolved clonal mixtures.
  • Successfully applied to spatial transcriptomics for ovarian and cutaneous squamous cell carcinoma, delineating clonal structures within histological sections.

Conclusions:

  • scClone effectively extracts genetic information from scRNA-seq data.
  • Establishes genotype-phenotype associations at the single-cell level.
  • Provides valuable insights into tumor clonal evolution and heterogeneity.