High-resolution time-lapse imaging of droplet-cell dynamics via optimal transport and contrastive learning

Abstract

Single-cell analysis is essential for uncovering heterogeneous biological functions that arise from intricate cellular responses. Here, microfluidic droplet arrays enable high-throughput data collection through cell encapsulation in picoliter volumes, and the time-lapse imaging of these arrays further reveal functional dynamics and changes. However, accurate tracking of cell identities across time frames with large intervals in between remains challenging when droplets move significantly. Specifically, existing machine learning methods often depend on labeled data or require neighboring cells as reference; without them, these methods struggle to track droplets and cells across long distances within images with complex movement patterns. To address these limitations, we developed a pipeline that combines visual object detection, feature extraction via contrastive learning, and optimal transport-based object matching, minimizing the reliance on labeled training data. We validated our approach across various experimental and simulated conditions and were able to track thousands of water-in-oil microfluidic droplets over large distances and long intervals between frames (>30 min). We achieved high precision in previously untraceable scenarios, tracking 50 pl droplets in images with small, medium and large movements (corresponding to ∼126, ∼800 and ∼10 000 μm, respectively) with a success rate of correctly tracked droplets of >90% for average movements within 2–12 droplet diameters, and >60% for average movements of >100 droplet diameters. This workflow lays the foundation for the tracking of droplets over time in these arrays when large and complex movement patterns are present and where the uniqueness of the sample makes repeated experiments infeasible.