ACS Applied Materials & InterfacesCancer BiologyPeer-reviewed journal article
Automated High-Content, High-Throughput Spatial Analysis Pipeline for Drug Screening in 3D Tumor Spheroid Inverted Colloidal Crystal Arrays
Hyunsu JeoniD, Gaeun KimiD, James Carpenter, Yamil J. ColóniD, Yichun WangiD
Abstract
High-content, high-throughput (HCHT) screening platforms are essential for drug discovery, yet conventional 2D assays lack physiological relevance, and current 3D spheroid systems often face challenges to scalability, uniformity, and the analytical efficiency required for statistically robust screening. Here, we present a fully integrated 3D HCHT platform that synergizes tumor spheroid arrays generated from a bioinert inverted colloidal crystal (iCC) hydrogel frame-work with an automated, high-speed image analysis pipeline for rapid and spatially resolved therapeutic profiling. The iCC framework enables spontaneous self-assembly of highly-ordered tumor spheroid array at high spheroid density ([~]79.8 spheroids{middle dot}mm-2) with tight size uniformity (<10% standard deviation), supporting reproducible and high-content imaging ([~]40 spheroids{middle dot}image-1). The automated image-processing algorithm achieves robust region-of-interest segmentation, fluorescence-weighted centroiding, and multi-parametric spatial analysis in <5 seconds per image--markedly faster than conventional well-plate (5 min per 96 throughput) or single-spheroid analysis workflows methods ([~]1 sec per spheroid). Using this platform, we capture dose-dependent diffusion of doxorubicin, tumor penetration profile of small extracellular vesicles, and cell-type-specific infiltration behaviors of monocytes versus macrophages. Comparative viability profiling across chemotherapeutics further reveals distinct spatial toxicity signatures, highlighting the importance of spatial context in drug response assessment. Collectively, this platform enables rapid, reproducible, and spatially informative screening in 3D, offering a powerful tool for drug discovery, tumor modeling, and immunotherapeutic development.
- organism: human
- assay: imaging
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 7
- FWCI
- 1.70
- Field percentile
- top 17%
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