Drug screening at single-organoid resolution via bioprinting and interferometry.

Autor: Tebon PJ; Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA., Wang B; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.; Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA., Markowitz AL; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA.; Institute for Precision Health, University of California Los Angeles, Los Angeles, CA, USA., Davarifar A; Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA., Tsai BL; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA.; Institute for Precision Health, University of California Los Angeles, Los Angeles, CA, USA., Krawczuk P; Information Sciences Institute, University of Southern California, Marina Del Rey, CA, USA., Gonzalez AE; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA.; Institute for Precision Health, University of California Los Angeles, Los Angeles, CA, USA.; Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA., Sartini S; Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA., Murray GF; Department of Physics, Virginia Commonwealth University, Richmond, VA, USA., Nguyen HTL; Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA., Tavanaie N; Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA., Nguyen TL; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.; Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA., Boutros PC; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA.; Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA.; Institute for Precision Health, University of California Los Angeles, Los Angeles, CA, USA.; Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA.; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA.; California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA.; Department of Urology, University of California Los Angeles, Los Angeles, CA, USA., Teitell MA; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA. mteitell@mednet.ucla.edu.; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA. mteitell@mednet.ucla.edu.; Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. mteitell@mednet.ucla.edu.; Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA. mteitell@mednet.ucla.edu.; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA. mteitell@mednet.ucla.edu.; California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA. mteitell@mednet.ucla.edu.; Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. mteitell@mednet.ucla.edu., Soragni A; Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. alices@mednet.ucla.edu.; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA. alices@mednet.ucla.edu.; Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA. alices@mednet.ucla.edu.; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA. alices@mednet.ucla.edu.; California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA. alices@mednet.ucla.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2023 Jun 06; Vol. 14 (1), pp. 3168. Date of Electronic Publication: 2023 Jun 06.
DOI: 10.1038/s41467-023-38832-8
Abstrakt: High throughput drug screening is an established approach to investigate tumor biology and identify therapeutic leads. Traditional platforms use two-dimensional cultures which do not accurately reflect the biology of human tumors. More clinically relevant model systems such as three-dimensional tumor organoids can be difficult to scale and screen. Manually seeded organoids coupled to destructive endpoint assays allow for the characterization of treatment response, but do not capture transitory changes and intra-sample heterogeneity underlying clinically observed resistance to therapy. We present a pipeline to generate bioprinted tumor organoids linked to label-free, time-resolved imaging via high-speed live cell interferometry (HSLCI) and machine learning-based quantitation of individual organoids. Bioprinting cells gives rise to 3D structures with unaltered tumor histology and gene expression profiles. HSLCI imaging in tandem with machine learning-based segmentation and classification tools enables accurate, label-free parallel mass measurements for thousands of organoids. We demonstrate that this strategy identifies organoids transiently or persistently sensitive or resistant to specific therapies, information that could be used to guide rapid therapy selection.
(© 2023. The Author(s).)
Databáze: MEDLINE
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