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

  • Biology
  • Cancer
  • Chemistry
  • Computational Methods
  • Genomics
  • R&D
  • Statistics
  • Technology
  • Ben Mead
  • Conner Kummerlowe
  • Ivy Liu
  • Walaa Kattan
  • Thomas Nok Hin Cheng
  • Peter Winter
  • Alex K. Shalek
  • 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.§
  • bioRxiv
  • March, 2023
Biology
Cancer
Chemistry
Computational Methods
Genomics
R&D
Statistics
Technology
Ben Mead
Conner Kummerlowe
Ivy Liu
Walaa Kattan
Thomas Nok Hin Cheng
Peter Winter
Alex K. Shalek

Abstract

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.

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