  Uncovering the heterogeneity of cell populations is a long-standing goal in
fields ranging from antimicrobial resistance to cancer research. Emerging
technology platforms such as droplet microfluidics hold the promise to decipher
cellular heterogeneity at ultra-high-throughput. However, there is a lack of
methods able to rapidly identify and isolate single cells or 3D cell cultures.
Here we demonstrate that deep neural networks can accurately classify single
droplet images in real-time based on the presence and number of micro-objects
including single mammalian cells and multicellular spheroids. This approach
also enables the identification of specific objects within mixtures of objects
of different types and sizes. The training sets for the neural networks
consisted of several hundred images manually picked and augmented to up to
thousands of images per training class. Training required less than 10 minutes
using a single GPU, and yielded accuracies of over 90% for single mammalian
cell identification. Crucially, the same model could be used to classify
different types of objects such as polystyrene spheres, polyacrylamide beads
and MCF-7 cells. We applied the developed method to the selection of 3D cell
cultures generated with Hek293FT cells encapsulated in agarose gel beads,
highlighting the potential of the technology for the selection of objects with
high diversity of visual appearance. The real-time sorting of single droplets
was in-line with droplet generation and occurred at rates up to 40 per second
independently of image size up to 480 x 480 pixels. The presented microfluidic
device also enabled storage of sorted droplets to allow for downstream
analyses.

Image and Video Processing;Quantitative Methods