Accelerated discovery of cell migration regulators using label-free deep learning-based automated tracking

Cell migration underlies immune surveillance, tissue repair, embryogenesis, and-when dysregulated-tumor metastasis. Yet unlike proliferation, which can be profiled at scale, migration studies remain limited by labor-intensive imaging and analysis. Existing assays often forfeit single-cell resolution, require phototoxic fluorescent labeling, or depend on tedious manual tracking, restricting the range of molecular perturbations and microenvironmental contexts that can be examined. We present Deep learning Brightfield Imaging and cell Tracking (DeepBIT), a high-throughput platform that captures live-cell behavior in multiwell plates and uses a convolutional neural network to detect and track individual cells in brightfield videos-without labels or user bias. Brightfield images are paired with nuclear fluorescence images to generate diverse ground-truth datasets, enabling automated training and eliminating manual annotation. This scalability supports a data-driven approach to systematically dissect the regulation of cell migration. Using breast cancer cells as a testbed, we tracked ~1500 cells per well across 840 conditions-including 96 FDA-approved drugs at multiple doses, a range of extracellular matrix and growth factor combinations, and CRISPR knockouts of cytoskeletal genes-yielding ~1.3 million trajectories in 30 hours (~2 minutes per condition). This dataset revealed previously unrecognized motility modulators among FDA-approved compounds and uncovered strong context dependence; for example, TNF-α and RhoA could either suppress or promote migration in the same cells depending on extracellular cues. Together, DeepBIT provides an unbiased, label-free platform for single-cell motility profiling at a scale compatible with modern drug libraries and genomic perturbation tools, enabling systematic exploration and therapeutic targeting of cell migration.