COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.

Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.

An integrated microfabricated cell sorter.

We have developed an integrated microfabricated cell sorter using multilayer soft lithography. This integrated cell sorter is incorporated with various microfluidic functionalities, including peristaltic pumps, dampers, switch valves, and input and output wells, to perform cell sorting in a coordinated and automated fashion. The active volume of an actuated valve on this integrated cell sorter can be as small as 1 pL, and the volume of optical interrogation is approximately 100 fL. Different algorithms of cell manipulation, including cell trapping, were implemented in these devices. We have also demonstrated sorting and recovery of Escherichia coli cells on the chip.