A novel co-culture model of human prostate epithelial and stromal cells for androgenic and antiandrogenic screening

A functional 3D full-thickness model for comprehending the interaction between airway epithelium and connective tissue in cystic fibrosis

Patients with cystic fibrosis (CF) experience severe lung disease, including persistent infections, inflammation, and irreversible fibrotic remodeling of the airways. Although therapy with transmembrane conductance regulator (CFTR) protein modulators reached optimal results in terms of CFTR rescue, lung transplant remains the best line of care for patients in an advanced stage of CF. Indeed, chronic inflammation and tissue remodeling still represent stumbling blocks during treatment, and underlying mechanisms are still unclear. Nowadays, animal models are not able to fully replicate clinical features of the human disease and the conventional in vitro models lack a stromal compartment undergoing fibrotic remodeling. To address this gap, we show the development of a 3D full-thickness model of CF with a human bronchial epithelium differentiated on a connective airway tissue. We demonstrated that the epithelial cells not only underwent mucociliary differentiation but also migrated in the connective tissue and formed gland-like structures. The presence of the connective tissue stimulated the pro-inflammatory behaviour of the epithelium, which activated the fibroblasts embedded into their own extracellular matrix (ECM). By varying the composition of the model with CF epithelial cells and a CF or healthy connective tissue, it was possible to replicate different moments of CF disease, as demonstrated by the differences in the transcriptome of the CF epithelium in the different conditions. The possibility to faithfully represent the crosstalk between epithelial and connective in CF through the full thickness model, along with inflammation and stromal activation, makes the model suitable to better understand mechanisms of disease genesis, progression, and response to therapy.

Machine learning-assisted high-content imaging analysis of 3D MCF7 microtissues for estrogenic effect prediction

Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was established to extract quantitative image features from estrogen-exposed microtissues. Moreover, a machine-learning classification model was built using estrogenic-associated differential imaging features. Based on 140 common differential image features found between the E2 and PPT group, the classification model predicted E2 and PPT exposure with AUC-ROC at 0.9528 and 0.9513, respectively. Deep learning-assisted analysis software was developed to characterize microtissue gland lumen formation. The fully automated tool can accurately characterize the number of identified lumens and the total luminal volume of each microtissue. Overall, the current study established an integrated approach by combining non-supervised image feature profiling and supervised luminal volume characterization, which reflected the complexity of functional ER signaling and highlighted a promising conceptual framework for estrogenic EDC risk assessment.

Microfluidic-based prostate cancer model for investigating the secretion of prostate-specific antigen and microRNAs in vitro

The study of prostate cancer in vitro relies on established cell lines that lack important physiological characteristics, such as proper polarization and expression of relevant biomarkers. Microphysiological systems (MPS) can replicate cancer microenvironments and lead to cellular phenotypic changes that better represent organ physiology in vitro. In this study, we developed an MPS model comprising conventional prostate cancer cells to evaluate their activity under dynamic culture conditions. Androgen-sensitive (LNCaP) and androgen-insensitive (PC3) cells were grown in conventional and 3D cultures, both static and dynamic. Cell morphology, the secretion of prostate-specific antigen, and the expression of key prostate markers and microRNAs were analyzed. LNCaP formed spheroids in 3D and MPS cultures, with morphological changes supported by the upregulation of cytokeratins and adhesion proteins. LNCaP also maintained a constant prostate-specific antigen secretion in MPS. PC3 cells did not develop complex structures in 3D and MPS cultures. PSA expression at the gene level was downregulated in LNCaP-MPS and considerably upregulated in PC3-MPS. MicroRNA expression was altered by the 3D static and dynamic culture, both intra- and extracellularly. MicroRNAs associated with prostate cancer progression were mostly upregulated in LNCaP-MPS. Overall dynamic cell culture substantially altered the morphology and expression of LNCaP cells, arguably augmenting their prostate cancer phenotype. This novel approach demonstrates that microRNA expression in prostate cancer cells is sensitive to external stimuli and that MPS can effectively promote important physiological changes in conventional prostate cancer models.