Sequencing thousands of single-cell genomes with combinatorial indexing

Area of Science:

• Genomics
• Molecular Biology
• Cancer Research

Background:

• Single-cell genome sequencing is crucial for studying tumor evolution and somatic variation.
• High costs of current technologies limit the number of cells analyzed, hindering heterogeneity assessment.

Purpose of the Study:

• To introduce single-cell combinatorial indexed sequencing (SCI-seq) for cost-effective, high-throughput single-cell library generation.
• To enable the detection of somatic copy-number variants in thousands of cells simultaneously.
• To provide a detailed assessment of subclonal variation in complex biological samples.

Main Methods:

• Development and application of single-cell combinatorial indexed sequencing (SCI-seq).
• Construction of low-pass single-cell libraries for 16,698 cells.
• Analysis of samples including cell lines, primate brain tissue, and human adenocarcinomas.

Main Results:

• Successfully generated libraries for a large number of single cells (16,698).
• Demonstrated the capability of SCI-seq to detect somatic copy-number variants.
• Provided a detailed assessment of subclonal variation within a pancreatic tumor sample.

Conclusions:

• SCI-seq offers a scalable and cost-effective solution for single-cell genome sequencing.
• The method significantly enhances the ability to measure cellular heterogeneity and subclonal architecture.
• SCI-seq is a valuable tool for advancing research in tumor evolution and other fields requiring single-cell analysis.