Cancer cells in vivo are coordinately influenced by an interactive 3D microenvironment. However, identification of drug targets and initial target validations are usually performed in 2D cell culture systems. The opportunity to design 3D co-culture models that reflect, at least in part, these heterotypic interactions, when coupled with RNA interference, would enable investigations on the phenotypic impact of gene function in a model that more closely resembles tumor growth in vivo. Here we describe a high-throughput-compatible method to discover cancer gene functions in a co-culture 3D tumor microtissue model system composed of human DLD1 colon cancer cells together with murine fibroblasts. Strikingly, DLD1 cells in this model failed to expand upon siRNA-mediated depletion of Kif11/Eg5, a member of the mitotic kinesin-like motor protein family. In contrast, these cancer cells proved to be more resistant to Kif11/Eg5 depletion when grown as a 2D monolayer. These results suggest that growth of certain cancer cells in 3D versus 2D can unveil differential dependencies on specific genes for their survival. Moreover, they denote that the high-throughput-compatible, hanging drop technology-based 3D co-culture model will enable the discovery, characterization, and validation of gene functions in key biological and pathological processes.

In the developing heart, a specific subset of endocardium undergoes an endothelial-to-mesenchymal transformation (EndMT) thus forming nascent valve leaflets. Extracellular matrix (ECM) proteins and growth factors (GFs) play important roles in regulating EndMT but the combinatorial effect of GFs with ECM proteins is less well understood. Here we use microscale engineering techniques to create single, binary, and tertiary component microenvironments to investigate the combinatorial effects of ECM proteins and GFs on the attachment and transformation of adult ovine mitral valve endothelial cells to a mesenchymal phenotype. With the combinatorial microenvironment microarrays, we utilized 60 different combinations of ECM proteins (Fibronectin, Collagen I, II, IV, Laminin) and GFs (TGF-β1, bFGF, VEGF) and were able to identify new microenvironmental conditions capable of modulating EndMT in MVECs. Experimental results indicated that TGF-β1 significantly upregulated the EndMT while either bFGF or VEGF downregulated EndMT process markedly. Also, ECM proteins could influence both the attachment of MVECs and the response of MVECs to GFs. In terms of attachment, fibronectin is significantly better for the adhesion of MVECs among the five tested proteins. Overall collagen IV and fibronectin appeared to play important roles in promoting EndMT process. Great consistency between macroscale and microarrayed experiments and present studies demonstrates that high-throughput cellular microarrays are a promising approach to study the regulation of EndMT in valvular endothelium. Biotechnol. Bioeng. 2016;113: 1403-1412. © 2015 Wiley Periodicals, Inc.

Cell-based microarrays are being increasingly used as a tool for combinatorial and high throughput screening of cellular microenvironments. Analysis of microarrays requires several steps, including microarray imaging, identification of cell spots, quality control, and data exploration. While high content image analysis, cell counting, and cell pattern recognition methods are established, there is a need for new post-processing and quality control methods for cell-based microarrays used to investigate combinatorial microenvironments. Previously, microarrayed cell spot identification and quality control were performed manually, leading to excessive processing time and potentially resulting in human bias. This work introduces an automated approach to identify cell-based microarray spots and spot quality control. The approach was used to analyze the adhesion of murine cardiac side population cells on combinatorial arrays of extracellular matrix proteins. Microarrays were imaged by automated fluorescence microscopy and cells were identified using open-source image analysis software (CellProfiler™). From these images, clusters of cells making up single cell spots were reliably identified by analyzing the distances between cells using a density-based clustering algorithm (OPTICS). Naïve Bayesian classifiers trained on manually scored training sets identified good and poor quality spots using spot size, number of cells per spot, and cell location as quality control criteria. Combined, the approach identified 78% of high quality spots and 87% of poor quality spots. Full factorial analysis of the resulting microarray data revealed that collagen IV exhibited the highest positive effect on cell attachment. This data processing approach allows for fast and unbiased analysis of cell-based microarray data.