Self-digitization chip for single-cell genotyping of cancer-related mutations

• Published in: PloS one Vol. 13; no. 5; p. e0196801
• Main Authors: Thompson, Alison M., Smith, Jordan L., Monroe, Luke D., Kreutz, Jason E., Schneider, Thomas, Fujimoto, Bryant S., Chiu, Daniel T., Radich, Jerald P., Paguirigan, Amy L.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.05.2018; Public Library of Science (PLoS)
• Subjects: Acute myelocytic leukemia; Acute myeloid leukemia; Alleles; Biology and Life Sciences; Biomedical microelectromechanical systems; Cancer; Digitization; DNA sequencing; Genetic aspects; Genetic variability; Genetic variation; Genotyping; Genotyping Techniques; Health aspects; Heterogeneity; Leukemia; Methods; Microfluidics; Mutation; Mutation - genetics; Myeloid leukemia; Neoplasms - genetics; Oligonucleotide Array Sequence Analysis; Physical Sciences; Quality assessment; Quality control; Research and Analysis Methods; Tumors; Variability; Zygosity
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0196801

Cancer is a heterogeneous disease, and patient-level genetic assessments can guide therapy choice and impact prognosis. However, little is known about the impact of genetic variability within a tumor, intratumoral heterogeneity (ITH), on disease progression or outcome. Current approaches using bulk tumor specimens can suggest the presence of ITH, but only single-cell genetic methods have the resolution to describe the underlying clonal structures themselves. Current techniques tend to be labor and resource intensive and challenging to characterize with respect to sources of biological and technical variability. We have developed a platform using a microfluidic self-digitization chip to partition cells in stationary volumes for cell imaging and allele-specific PCR. Genotyping data from only confirmed single-cell volumes is obtained and subject to a variety of relevant quality control assessments such as allele dropout, false positive, and false negative rates. We demonstrate single-cell genotyping of the NPM1 type A mutation, an important prognostic indicator in acute myeloid leukemia, on single cells of the cell line OCI-AML3, describing a more complex zygosity distribution than would be predicted via bulk analysis.