High-speed cell partitioning through reactive machine learning-guided inkjet printing.

Journal: Lab on a chip
Published Date:

Abstract

Partitioning cells in open nanowells permits high confidence in single cell occupancy and enables flexibility in the development of different molecular assays. A challenge for this approach however is to print cells sufficiently quickly to enable experiments of adequate statistical power in a reasonable time. To address this, we developed a single cell dispensing instrument leveraging inkjet technology with continuous real-time optical feedback and machine learning algorithms for high-throughput single cell isolation. The Isolatrix enables rapid partitioning of cells into open substrates such as nanowell arrays, permitting high-throughput application of custom genomic assays such as direct-transposition single cell whole genome sequencing (scWGS). We trained the classifier on manually labelled data with a range of cell sizes and applied the instrument to generate scWGS profiles from cell lines and primary mouse tissue. Comparison to existing predictive workflows demonstrated that this reactive approach, featuring machine learning classification of events post-dispensing, gives up to a 9.69 times increase in isolation speed. Validation fluorescent imaging of cell lines confirmed a classification accuracy of 98.7%, at a rate of 0.52 seconds per single cell, under tuned spotting parameters. Genomic analysis showed low background contamination and high coverage uniformity across the genome, enabling detection of chromosomal copy number alterations. With data tracing capabilities and a convenient user interface, we expect the Isolatrix to enable large-scale profiling of a range of genomic data modalities.

Authors

  • Eric Cheng
    Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada. kcheung@ece.ubc.ca.
  • Glenn Chang
    Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, V5Z 1L3, Canada.
  • Haley MacDonald
    Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, V5Z 1L3, Canada.
  • Miguel Ramirez
    Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, V5Z 1L3, Canada.
  • Pamela A Hoodless
    Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, V5Z 1L3, Canada.
  • Robin Coope
    Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
  • Adi Steif
    Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, V5Z 1L3, Canada.
  • Karen C Cheung
    Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada. kcheung@ece.ubc.ca.

Keywords

No keywords available for this article.