Abstract: Quantifying Live Single-Cell Array Benefits with the PyAMA Refresh (DPG 2026)

Live single-cell array (LiSCA) chambers were introduced before PyAMA existed, and early analyses relied on Fiji macros that offered little evidence beyond anecdote. PyAMA automated the pipeline later, but its first generation still lacked built-in metrics to document why LiSCA outperforms flat substrates. We now deliver a refreshed PyAMA release that keeps the assay unchanged while modernizing the software stack: ND2 ingestion, LOG-STD segmentation, background-aware intensity extraction, IoU tracking, and QC filtering all run inside a reproducible UV environment with synchronized API/CLI interfaces and audit trails. In parallel, we developed a companion analysis notebook that taps PyAMA outputs to compute LiSCA-vs-flat metrics (edge proximity, frame persistence, migration speed, fluorescence residuals, cell-size stability) without bespoke Fiji scripting.

Processing matched datasets (~100 FOVs, ≈10⁴ cells, 180 frames at Δt=10 min) finally substantiates the LiSCA advantage. Cells on arrays remain in view for the full 30 h, while flat-substrate cells drop below 12 h; average speeds fall from >1 µm/min to near zero; fluorescence fit residuals halve; and cell-size coefficients of variation stay flat instead of drifting upward. By packaging these measurements alongside the refreshed pipeline—even as a side project—we transform LiSCA from a technique supported by intuition to a platform backed by quantitative, reproducible evidence. Our DPG 2026 contribution highlights how software stewardship plus lightweight comparative analytics can close documentation gaps without inventing new biology.