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

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 via 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.