![]() Phalloidin stain is shown in red, while the nuclei are shown in blue. Cells within a cluster are identified with a red spot, while cells outside of the cluster are identified with a black spot (right). (F) Fluorescent image of a single microarrayed spot with adhered CSP cells (left) with outliers from the microarrayed spot circled in white. The brightness of image (E) has been increase so that the identified debris is clearly visible. (B–E) Selected images of microarrayed spots that are of high quality (B,C), low quality (D), and damaged or contain debris (E debris is circle in white). Sets of five replicates in each sub-array are randomly ordered. Each sub- array contains five replicates of 30 combinations of Fn, Lm, CI, CIII, and CIV (300 μg/mL total protein concentration) and five replicates of negative controls with no protein and five replicates containing 300 μg/mL of BSA. The mosaic image shows one of four sub-arrays per glass slide. Images were acquired at 10X magnification after 12 hours of culture and show phalloidin (red) and nuclear (blue) staining. (A) A mosaic of fluorescent images of a combinatorial ECM microarray seeded with 10,000 CSP cells per cm 2. This data processing approach allows for fast and unbiased analysis of cell-based microarray data.Ĭombinatorial ECM-cardiac side population (CSP) cell microarrays. Full factorial analysis of the resulting microarray data revealed that collagen IV exhibited the highest positive effect on cell attachment. Combined, the approach identified 78% of high quality spots and 87% of poor quality spots. 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. 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). Microarrays were imaged by automated fluorescence microscopy and cells were identified using open-source image analysis software (CellProfiler). The approach was used to analyze the adhesion of murine cardiac side population cells on combinatorial arrays of extracellular matrix proteins. ![]() This work introduces an automated approach to identify cell-based microarray spots and spot quality control. Previously, microarrayed cell spot identification and quality control were performed manually, leading to excessive processing time and potentially resulting in human bias. 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. Analysis of microarrays requires several steps, including microarray imaging, identification of cell spots, quality control, and data exploration. Cell-based microarrays are being increasingly used as a tool for combinatorial and high throughput screening of cellular microenvironments.
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