High-throughput screening in multicellular spheroids for target discovery in the tumor microenvironment.

Introduction: Solid tumors are highly influenced by a complex tumor microenvironment (TME) that cannot be modeled with conventional two-dimensional (2D) cell culture. In addition, monolayer culture conditions tend to induce undesirable molecular and phenotypic cellular changes. The discrepancy between and is an important factor accounting for the high failure rate in drug development.

High-throughput investigation of endothelial-to-mesenchymal transformation (EndMT) with combinatorial cellular microarrays.

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.

A high-throughput-compatible 3D microtissue co-culture system for phenotypic RNAi screening applications.

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.

Spot identification and quality control in cell-based microarrays.

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 postprocessing and quality control methods for cell-based microarrays used to investigate combinatorial microenvironments.