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Updated: Jan 3, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing.

Emma Laks¹, Andrew McPherson², Hans Zahn³

¹Department of Molecular Oncology, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada; Genome Science and Technology Graduate Program, University of British Columbia, Vancouver, BC, Canada.

|November 16, 2019

Summary

This summary is machine-generated.

Researchers developed a scalable single-cell whole-genome sequencing platform (DLP+) to analyze tumor evolution. This platform enables precise measurement of clonal genotypes and mutational processes, advancing our understanding of cancer development.

Keywords:

DNA sequencing aneuploidy cancer genomics cell cycle copy number genomic instability single cell tumor evolution tumor heterogeneity

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Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

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Characterizing Mutational Load and Clonal Composition of Human Blood

Characterizing Mutational Load and Clonal Composition of Human Blood

Published on: July 11, 2019

Area of Science:

Genomics
Cancer Biology
Bioinformatics

Background:

Understanding tumor evolution requires accurate analysis of individual tumor cell genomes.
Existing methods face limitations in resolving clonal genotypes and mutational processes at the single-cell level.

Purpose of the Study:

To develop and validate a scalable single-cell whole-genome sequencing platform (DLP+) for comprehensive tumor analysis.
To investigate variations in mitotic mis-segregation rates and their correlation with cellular morphology and ploidy states.
To enable high-resolution inference of clonal genotypes, phylogenies, and chromosomal aneuploidies in polyclonal tumor populations.

Main Methods:

Development of DLP+ platform using commodity instruments, image-based object recognition, and open-source computational tools.
Generation of a large dataset of 51,926 single-cell genomes and matched cell images from various sample types.
Analysis of genomic and image data to correlate cellular morphology with genome ploidy and infer clonal structures.

Main Results:

Successful generation of a large-scale single-cell genome resource.
Identification of variations in mitotic mis-segregation rates across different tissue types and genotypes.
Correlation established between cellular morphology and genome ploidy states.
High-resolution clonal genotypes and phylogenies inferred by aggregating cells with shared copy number profiles, overcoming bulk deconvolution limitations.
Definition of clone-specific chromosomal aneuploidy in polyclonal populations through joint analysis.

Conclusions:

DLP+ provides a scalable and effective platform for single-cell whole-genome sequencing, crucial for studying tumor evolution.
The platform facilitates novel insights into mitotic errors, cellular morphology-genome relationships, and complex clonal architectures within tumors.
This approach enables precise characterization of tumor heterogeneity and evolutionary trajectories at an unprecedented resolution.