High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics
- Maurizio Pellegrino1,5,
- Adam Sciambi1,5,
- Sebastian Treusch1,5,
- Robert Durruthy-Durruthy1,
- Kaustubh Gokhale1,
- Jose Jacob1,
- Tina X. Chen1,
- Jennifer A. Geis1,
- William Oldham1,
- Jairo Matthews2,
- Hagop Kantarjian2,
- P. Andrew Futreal3,
- Keyur Patel4,
- Keith W. Jones1,
- Koichi Takahashi2,3 and
- Dennis J. Eastburn1
- 1Mission Bio, Incorporated, South San Francisco, California 94080, USA;
- 2Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA;
- 3Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA;
- 4Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
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↵5 These authors contributed equally to this work.
Abstract
To enable the characterization of genetic heterogeneity in tumor cell populations, we developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cancer cells confined to droplets. The barcodes are then used to reassemble the genetic profiles of cells from next-generation sequencing data. By using this approach, we sequenced longitudinally collected acute myeloid leukemia (AML) tumor populations from two patients and genotyped up to 62 disease relevant loci across more than 16,000 individual cells. Targeted single-cell sequencing was able to sensitively identify cells harboring pathogenic mutations during complete remission and uncovered complex clonal evolution within AML tumors that was not observable with bulk sequencing. We anticipate that this approach will make feasible the routine analysis of AML heterogeneity, leading to improved stratification and therapy selection for the disease.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.232272.117.
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Freely available online through the Genome Research Open Access option.
- Received November 9, 2017.
- Accepted June 25, 2018.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
