Scalable, compressed phenotypic screening using pooled perturbations
High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.
Authors
- Sergio Triana (ORCID: https://orcid.org/0000-0003-0370-7821)
- Thomas W. Cheng (ORCID: https://orcid.org/0000-0003-1779-9720)
- Kristen E. Lowder (ORCID: https://orcid.org/0000-0003-2615-4043)
- Walaa E. Kattan (ORCID: https://orcid.org/0000-0002-0599-9744)
- Paul C. Blainey (ORCID: https://orcid.org/0000-0002-4889-8783)
- Srivatsan Raghavan (ORCID: https://orcid.org/0000-0002-5374-9918)
- Jaime H. Cheah (ORCID: https://orcid.org/0000-0003-4455-4219)
- Joshua M. Peters (ORCID: https://orcid.org/0000-0001-9163-6706)
- Lorin Crawford (ORCID: https://orcid.org/0000-0003-0178-8242)
- Jane K. McIninch
- Conner Kummerlowe (ORCID: https://orcid.org/0000-0002-8641-2916)
- Benjamin E. Mead (ORCID: https://orcid.org/0000-0001-7258-7777)
- Manuel Guzmán (ORCID: https://orcid.org/0000-0001-7475-118X)
- Bryan D. Bryson (ORCID: https://orcid.org/0000-0003-1716-6712)
- William C. Hahn (ORCID: https://orcid.org/0000-0003-2840-9791)
- Peter Winter (ORCID: https://orcid.org/0000-0002-6557-3219)
- Tyler T. Dao (ORCID: https://orcid.org/0000-0003-1468-3896)
- Ava P. Amini (ORCID: https://orcid.org/0000-0002-8601-6040)
- Anita Vrcic (ORCID: https://orcid.org/0000-0002-2941-5459)
- Brian Cleary
- Christian K. Soule (ORCID: https://orcid.org/0000-0002-9145-4547)
- Alex K. Shalek (ORCID: https://orcid.org/0000-0001-5670-8778)
- Nuo Liu (ORCID: https://orcid.org/0000-0002-1334-422X)
- Sarah Ingabire
Institutions
- Broad Institute (US)
- Boston University (US)
- Microsoft (United States) (US)
- Harvard University (US)
- Brown University (US)
- Ragon Institute of MGH, MIT and Harvard (US)
- Dana-Farber Cancer Institute (US)
- Harvard Stem Cell Institute (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Nature Biotechnology
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1038/s41587-024-02403-z
- Citations
- 16
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 10.60
Funders
- Bill and Melinda Gates Foundation
- National Institute on Drug Abuse