Kattunga, V. M.; Wrobel, S. A.; Lerner, C. A.; Derycz, V. M.; Stephens, E. B.; Brown, I. S.; Cheng, H.; Taghizadeh, S.; Byrne, J.; Gross, S.; Schneider, S.; Senadheera, C.; Davis-Castillo, A.; Vistalli-Alvarado, S.; Goncharova, E.; Newman, J. C.; Stubbs, B. J.; Melov, S.; Lithgow, G.; Ellerby, L. M.; Andersen, J. K.; Gerencser, A. A.

Acoustic droplet ejection (ADE) enables nanoliter-scale liquid handling for complex microplate assays, yet translating experimental designs into validated, instrument-ready instructions remains a bottleneck. We present PickliPy, an open-source framework that converts spreadsheet-based assay designs into validated ADE picklists. PickliPy.Assay supports combinatorial, dose-response, and multi-addition time-course dispensing, while PickliPy.Screen extends to high-throughput workflows, including library reformatting and shortlisting. Across diverse biological contexts, the framework generated reproducible, assay-ready plates and standardized execution in human cohort studies. Acoustic pre-dispensing deepened bioenergetic phenotyping of isolated human skeletal muscle mitochondria, capturing substrate switching, and sharpened dose-response precision in human pancreatic {beta}-cells, revealing an age-associated change in succinate dehydrogenase kinetics. We benchmarked a wash-free, live-cell screen of mitochondrial function and morphology, in which deep-learning image analysis widened the assay window and ADE enabled integrative dose-response co-response analysis. Together, these tools make complex ADE experiments easier to design, reproduce, and scale from single benches to screening campaigns.