Spot Identification and Quality Control in Cell-Based Microarrays

Abstract

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.

3D bioprinting for engineering complex tissues

“…Unfortunately, most of the glass slide substrates have poor contact angles. This problem can be solved by coating the substrates with a thin layer of material, such as 3-(Trimethoxysilyl)propyl methacrylate (TMSPMA) (Zhang et al 2008), to enhance their hydrophobicity before printing (Bauer et al 2012; Nikkhah et al 2012). …”

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

“…Arrays were fabricated as previously described (Bauer et al, 2012;Brafman et al, 2009;Flaim et al, 2005). The schematic of fabrication process is described in Figure 2A.…”

Context-explorer: Analysis of spatially organized protein expression in high-throughput screens

“…The CE software is meant to extend the functionality of currently available software solutions, both open source and commercial, for analyzing features of imaged cells (Carpenter et al, 2006, Jones et al, 2008, Misselwitz et al, 2010. While some implementations already exist that can be used to identify arrays of cells on glass slides (Bauer et al, 2012) or to study differential gene expression of cells in different spatial locations within the same colony (Gorman et al, 2014), our software aims to improve the HTP workflow for analysing cells in micropatterned multi-well plates by facilitating evaluation of patterning fidelity, enabling identification of colonies within a well, and improving spatial analyses of heterogeneous protein expression within colonies. As HTP technologies become more widespread, it is increasingly important to provide user friendly data analysis software targeted towards these platforms.…”