Accelerated Discovery of Cell Migration Regulators Using Label-Free Deep Learning-Based Automated Tracking

Cell migration plays a key role in normal developmental programs and in disease, including immune responses, tissue repair, and metastasis. Unlike other cell functions, such as proliferation which can be studied using high-throughput assays, cell migration requires more sophisticated instruments and analysis, which decreases throughput and has led to more limited mechanistic advances in our understanding of cell migration. Current assays either preclude single-cell level analysis, require tedious manual tracking, or use fluorescently labeled cells, which greatly limit the number of extracellular conditions and molecular manipulations that can be studied in a reasonable amount of time. Using the migration of cancer cells as a testbed, we established a workflow that images large numbers of cells in real time, using a 96-well plate format. We developed and validated a machine-vision and deep-learning analysis method, DeepBIT, to automatically detect and track the migration of individual cells from time-lapsed videos without cell labeling and user bias. We demonstrate that our assay can examine cancer cell motility behavior in many conditions, using different small-molecule inhibitors of known and potential regulators of migration, different extracellular conditions such as different contents in extracellular matrix and growth factors, and different CRISPR-mediated knockouts. About 1500 cells per well were tracked in 840 different conditions, for a total of ~1.3M tracked cells, in 70h (5 min per condition). Manual tracking of these cells by a trained user would take ~5.5 years. This demonstration reveals previously unidentified molecular regulators of cancer cell migration and suggests that collagen content can change the sign of how cytoskeletal molecules can regulate cell migration.