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Related Experiment Video

Updated: Dec 30, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.

Zilu Zhou^1,2, Bihui Xu³, Andy Minn⁴

¹Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.

|January 16, 2020

Summary

DENDRO reliably identifies cancer cell subclones and reconstructs their evolutionary history using single-cell RNA sequencing data. This method improves understanding of tumor evolution and treatment response.

Keywords:

Cancer genomics Intratumor heterogeneity Multi-omics analysis Phylogeny inference Single-cell RNA sequencing

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Last Updated: Dec 30, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

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Published on: May 29, 2014

Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Single-cell RNA sequencing (scRNA-seq) is widely used for studying intratumor heterogeneity.
Current methods for detecting somatic mutations and inferring clonal membership from scRNA-seq data are unreliable.
Accurate subclonal identification is crucial for understanding cancer evolution and treatment resistance.

Purpose of the Study:

To develop and validate DENDRO, a novel computational method for analyzing scRNA-seq data.
To accurately cluster single cells into genetically distinct subclones.
To reconstruct the phylogenetic tree representing the evolutionary relationships between cancer subclones.

Main Methods:

DENDRO utilizes transcribed point mutations detected in scRNA-seq data.
The method incorporates algorithms to account for technical noise and expression stochasticity inherent in scRNA-seq.
DENDRO clusters cells and infers phylogenetic relationships.

Main Results:

DENDRO was benchmarked using simulation data and applied to real-world data from three cancer types.
The method demonstrated reliable performance in identifying subclones and reconstructing phylogenies.
Application to a mouse melanoma model revealed the role of neoantigens in immunotherapy response.

Conclusions:

DENDRO provides a robust approach for analyzing scRNA-seq data to study tumor phylogenetics.
The method enhances the reliable detection of somatic mutations and clonal architecture in tumors.
DENDRO offers insights into cancer evolution, particularly in response to therapeutic interventions like immunotherapy.