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Uncovering the heterogeneity of cell populations is a long-standing goal infields ranging from antimicrobial resistance to cancer research. Emergingtechnology platforms such as droplet microfluidics hold the promise to deciphercellular heterogeneity at ultra-high-throughput. However, there is a lack ofmethods able to rapidly identify and isolate single cells or 3D cell cultures.Here we demonstrate that deep neural networks can accurately classify singledroplet images in real-time based on the presence and number of micro-objectsincluding single mammalian cells and multicellular spheroids. This approachalso enables the identification of specific objects within mixtures of objectsof different types and sizes. The training sets for the neural networksconsisted of several hundred images manually picked and augmented to up tothousands of images per training class. Training required less than 10 minutesusing a single GPU, and yielded accuracies of over 90% for single mammaliancell identification. Crucially, the same model could be used to classifydifferent types of objects such as polystyrene spheres, polyacrylamide beadsand MCF-7 cells. We applied the developed method to the selection of 3D cellcultures generated with Hek293FT cells encapsulated in agarose gel beads,highlighting the potential of the technology for the selection of objects withhigh diversity of visual appearance. The real-time sorting of single dropletswas in-line with droplet generation and occurred at rates up to 40 per secondindependently of image size up to 480 x 480 pixels. The presented microfluidicdevice also enabled storage of sorted droplets to allow for downstreamanalyses.

Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures