Spot Identification and Quality Control in Cell-Based Microarrays

• Published in: ACS combinatorial science Vol. 14; no. 8; pp. 471 - 477
• Main Authors: Bauer, Michael, Kim, Keekyoung, Qiu, Yiling, Calpe, Blaise, Khademhosseini, Ali, Liao, Ronglih, Wheeldon, Ian
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 13.08.2012
• Subjects: Algorithms; Animals; Arrays; Automated; Cell Adhesion; Cell Separation; Cellular; Combinatorial analysis; Combinatorial Chemistry Techniques; Extracellular Matrix - chemistry; Humans; Image analysis; Mice; Quality Control; Source code; Tissue Array Analysis - methods; Tissue Array Analysis - standards; cell-based microarrays; cellular microenvironments; spot identification
• ISSN: 2156-8952; 2156-8944; 2156-8944
• DOI: 10.1021/co300039w

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. 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.