Compressed phenotypic screens for complex multicellular models and high-content assays

Biology Biology
Cancer Cancer
Chemistry Chemistry
Computational Methods Computational Methods
Genomics Genomics
Statistics Statistics
Technology Technology
Alex K. Shalek Alex K. Shalek
Ben Mead Ben Mead
Conner Kummerlowe Conner Kummerlowe
Ivy Liu Ivy Liu
Peter Winter Peter Winter
Thomas Nok Hin Cheng Thomas Nok Hin Cheng
Walaa Kattan Walaa Kattan

Mead et al.▾ Mead, B.E.*, Kummerlowe, C.*, Liu, N.#, Kattan, W.E.#, Cheng, T., Cheah, J.H., Soule, C.K., Peters, J., Lowder, K.E., 3,6, Blainey, P.C., Hahn, W.C., Cleary, B., Bryson, B., Winter, P.S.§, Raghavan, S.§, Shalek, A.K.§


March, 2023


High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.