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Abstract Variability in T cell performance presents a major challenge to adoptive cellular immunotherapy (ACT). This includes expansion of a small starting population into therapeutically effective numbers, which can fail due to differences between individuals and disease states. Intriguingly, modulating the mechanical stiffness of materials used to activate T cells can rescue subsequent expansion. However, the magnitude of this effect and the optimal stiffnesses differ between individuals, complicating the use of mechanosensing to improve cell production. The ability to predict this long‐term, substrate‐dependent expansion from a short‐term assay would accelerate the deployment of immunotherapy. Here, it is demonstrated that short‐term cell spreading predicts subsequent, mechanosensitive expansion. As an initial task, cell spreading is used to identify whether a sample of cells came from a healthy donor or a Chronic Lymphocytic Leukemia (CLL) patient. Notably, a deep learning (DL) model outperforms morphometric approaches to this classification task. This system also successfully predicts the long‐term expansion potential of cells as a function of both source and mechanical stiffness of the activating substrate. By predicting long‐term T cell function from small, diagnostic samples, this approach will improve the reliability and efficacy of cell production and immunotherapy. 

A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility—a highly coordinated and rapid movement of bacteria powered by flagella. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye swarm patterns, to ‘write’ external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we separately show that growing colonies can record dynamic environmental changes. We decode the resulting multicondition patterns with deep classification and segmentation models. Finally, we engineer a strain that records the presence of aqueous copper. This work creates an approach for building macroscale bacterial recorders, expanding the framework for engineering emergent microbial behaviors.