MorphoNavigator-3D: Generalizable single-cell phenotyping of cancer spheroids using Bayesian-optimized deep-learning workflows
Mogollon, I.; Feodoroff, M.; Nylund, A.; Montedeoca, A.; Atarsaikhan, G.; Neto, P.; Horvath, P.; Rannikko, A.; Cerullo, V.; Pietiainen, V.; Paavolainen, L. · cancer biology · 2026-09-06 · 原文
DOI：10.1101/2024.09.08.611898作者：11 位
Accurate quantification of drug responses in 3D tumor-immune co-cultures remains challenging because complex spatial architecture and cellular heterogeneity limit the interpretability of bulk viability assays. Here, we present MorphoNavigator-3D ('Morphological Navigator in 3D';MoNa-3D), an automated framework for high-resolution, annotation-free single-cell analysis in complex 3D co-cultures. The approach integrates optimized live-cell staining, deep learning-based segmentation, and Bayesian optimization (BO) to adapt end-to-end image-analysis workflows across diverse experimental conditions. MoNa-3D was applied to clear cell renal cell carcinoma (ccRCC)-immune cell 3D-spheroid co-cultures, exposed to PI3K/mTOR pathway inhibitors and immunomodulatory compounds in a high-content imaging-based drug screen. The pipeline was used to extract multiscale phenotypic features encompassing ATP-based cell viability, morphology, nuclear remodeling, spatial dispersion, and immune infiltration. This analysis resolved distinct drug-induced phenotypes: PI3K/mTOR inhibitors promoted spheroid disintegration, nuclear enlargement, and immune exclusion, whereas immunomodulators preserved spheroid arch
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1. 人话版
Accurate quantification of drug responses in 3D tumor-immune co-cultures remains challenging because complex spatial architecture and cellular heterogeneity limit the interpretability of bulk viability assays.
Here, we present MorphoNavigator-3D ('Morphological Navigator in 3D';MoNa-3D), an automated framework for high-resolution, annotation-free single-cell analysis in complex 3D co-cultures.
2. 领域脉络
本文类目：cancer biology，属于其所在研究脉络的最新进展。
3. 机制拆解
The approach integrates optimized live-cell staining, deep learning-based segmentation, and Bayesian optimization (BO) to adapt end-to-end image-analysis workflows across diverse experimental conditions.
The pipeline was used to extract multiscale phenotypic features encompassing ATP-based cell viability, morphology, nuclear remodeling, spatial dispersion, and immune infiltration.
4. 证据与数字
MoNa-3D was applied to clear cell renal cell carcinoma (ccRCC)-immune cell 3D-spheroid co-cultures, exposed to PI3K/mTOR pathway inhibitors and immunomodulatory compounds in a high-content imaging-based drug screen.
This analysis resolved distinct drug-induced phenotypes: PI3K/mTOR inhibitors promoted spheroid disintegration, nuclear enlargement, and immune exclusion, whereas immunomodulators preserved spheroid arch
5. 反例与边界
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