De Novo Detection of Clonal Structure and Evolution in Single-Cell and Spatial Transcriptomes

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.