Although cell morphology is often the gold standard for diagnosis and prognosis of many diseases, and conditions, it has seen limited application in combination with comprehensive molecular and functional characterization. This is largely due to the manual and subjective process of collecting cell morphology information and limited methods for sorting that do not perturb the cells. Here, we introduce the COSMOS platform, which is capable of high-throughput cell imaging and sorting, based on deep learning interpretation of high-content morphology information in realtime, yielding populations of cells that are label-free, viable, and minimally perturbed. We demonstrate the utility of the platform by enriching tumor cells and performing gene expression analysis on sorted cells showing concordance between morphological and molecular assessments.

No abstract

Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We have 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 deep learning interpretation of high-resolution brightfield images captured in real-time. 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 need for biomarker labels and stains/dyes. We demonstrated 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 ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images, rather than limited biophysical parameters or reconstructed images.

No abstract