Machine learning-driven single-cell phenotyping in size-controlled microenvironments via parallel deterministic droplet microfluidics

Abstract

Understanding how individual cells respond to distinct physical microenvironments is critical for mechanobiology, cell therapy, and tissue engineering. Current single-cell encapsulation methods are often limited by Poisson loading and fixed droplet sizes, preventing parallel generation of multiple, size-specific microenvironments and constraining high-resolution phenotypic analyses. Here, we present a droplet microfluidic platform that enables deterministic single-cell encapsulation within microgels of multiple sizes from a single precursor stream, achieved through parallelized flow-focusing combined with cell-selective gelation. This system produces distinct microgel size regimes simultaneously, minimizing empty compartments and enabling direct, side-by-side comparisons of cellular behavior under controlled yet variable confinement. Using machine learning to analyze 3D morphological and cytoskeletal features, we reveal heterogeneous, size-dependent phenotypic responses and demonstrate that cellular phenotypes alone can predict microgel confinement across time. Together, these results establish a data-driven framework for mapping single-cell responses across engineered microenvironments and provide a scalable platform for predictive studies of mechanosensitive behavior in heterogeneous niches.