Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs.Despite the potential for image-based phenotypi...Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs.Despite the potential for image-based phenotypic quantification of organoids,their complex 3D structure,and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges.In this work,we developed a virtual painting system,PhaseFIT(phase-fluorescent image transformation)utilizing customized and morphologically rich 2.5D intestinal organoids,which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images.This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects.The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images.The virtual painting results of nuclei,secretory cell markers,and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content.We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation,cell population,and cell stemness.PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids,enabling large-scale,informative,and efficient organoid phenotypic quantification.PhaseFIT would enable the use of organoids in high-throughput drug screening applications.展开更多
基金supported by funding from the National Institutes of Health(R01DK123219 and K01DK103947 to N.S.).
文摘Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs.Despite the potential for image-based phenotypic quantification of organoids,their complex 3D structure,and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges.In this work,we developed a virtual painting system,PhaseFIT(phase-fluorescent image transformation)utilizing customized and morphologically rich 2.5D intestinal organoids,which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images.This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects.The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images.The virtual painting results of nuclei,secretory cell markers,and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content.We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation,cell population,and cell stemness.PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids,enabling large-scale,informative,and efficient organoid phenotypic quantification.PhaseFIT would enable the use of organoids in high-throughput drug screening applications.
