Digital microfluidic isolation of single cells for -Omics

Julian Lamanna, Erica Y. Scott, Harrison S. Edwards, M. Dean Chamberlain, Michael D. M. Dryden, Jiaxi Peng, Barbara Mair, Adam Lee, Calvin Chan, Alexandros A. Sklavounos, Austin Heffernan, Farhana Abbas, Charis Lam, Maxwell E. Olson, Jason Moffat and Aaron R. Wheeler ()
Additional contact information
Julian Lamanna: University of Toronto
Erica Y. Scott: University of Toronto
Harrison S. Edwards: University of Toronto
M. Dean Chamberlain: University of Toronto
Michael D. M. Dryden: University of Toronto
Jiaxi Peng: University of Toronto
Barbara Mair: University of Toronto
Adam Lee: University of Toronto
Calvin Chan: University of Toronto
Alexandros A. Sklavounos: University of Toronto
Austin Heffernan: University of Toronto
Farhana Abbas: University of Toronto
Charis Lam: University of Toronto
Maxwell E. Olson: University of Toronto
Jason Moffat: University of Toronto
Aaron R. Wheeler: University of Toronto

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract We introduce Digital microfluidic Isolation of Single Cells for -Omics (DISCO), a platform that allows users to select particular cells of interest from a limited initial sample size and connects single-cell sequencing data to their immunofluorescence-based phenotypes. Specifically, DISCO combines digital microfluidics, laser cell lysis, and artificial intelligence-driven image processing to collect the contents of single cells from heterogeneous populations, followed by analysis of single-cell genomes and transcriptomes by next-generation sequencing, and proteomes by nanoflow liquid chromatography and tandem mass spectrometry. The results described herein confirm the utility of DISCO for sequencing at levels that are equivalent to or enhanced relative to the state of the art, capable of identifying features at the level of single nucleotide variations. The unique levels of selectivity, context, and accountability of DISCO suggest potential utility for deep analysis of any rare cell population with contextual dependencies.

Date: 2020
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DOI: 10.1038/s41467-020-19394-5

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