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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
Published on: May 23, 2018
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Single-Cell Genetic Analysis Using Automated Microfluidics to Resolve Somatic Mosaicism.
Keith E Szulwach1, Peilin Chen1, Xiaohui Wang1
1Fluidigm Corporation, South San Francisco, California, United States of America.
Plos One
|August 25, 2015
Summary
This study introduces an automated microfluidic method for single-cell whole genome amplification (WGA), improving genome accessibility and uniformity. This technique effectively resolves somatic mutation patterns in individual cancer cells, aiding in understanding tumor heterogeneity.
Area of Science:
- Genomics
- Cancer Biology
- Biotechnology
Background:
- Somatic mosaicism and intratumor genetic heterogeneity are significant in disease and cancer development.
- Bulk DNA analysis masks crucial subclonal phylogenetic architectures, hindering effective cancer treatment.
- Accurate characterization of cancers necessitates single-cell genetic analysis, yet methods are limited.
Purpose of the Study:
- To present an automated microfluidic workflow for efficient single-cell capture, lysis, and whole genome amplification (WGA).
- To evaluate the performance of this new WGA method in terms of genome accessibility, uniformity, allelic dropout (ADO), and variant false discovery rates (SNV FDR).
- To demonstrate the application of this platform in resolving somatic mutation patterns and classifying cancer cells.
Main Methods:
- Development of an automated microfluidic workflow for single-cell processing.
- Whole genome amplification (WGA) of DNA from single cells.
- Analysis of genomic data including mutation identification, allelic dropout rates, and variant false discovery rates.
- Application to ER-/PR-/HER2+ breast cancer cells and matched normal controls.
Main Results:
- The workflow achieved ~90% genome accessibility with improved uniformity compared to existing single-cell WGA methods.
- Low allelic dropout (ADO) rates of 13.75% and variant false discovery rates (SNV FDR) of 4.11x10(-6) were observed.
- Novel mutations in subpopulations of breast cancer cells were identified, and segregation of known cancer-related mutations was resolved at single-cell resolution.
- Effective cell classification was demonstrated using mutation profiles with 10X average exome coverage depth per cell.
Conclusions:
- The developed automated microfluidic platform provides an efficient method for single-cell WGA.
- This platform enables high-resolution analysis of somatic mutation patterns within single cells.
- The technology is valuable for characterizing cancer heterogeneity and potentially improving therapeutic strategies.
