Abstract
Non-arbitrary and non-biased quantification of fluorescent images is an essential tool for the data-centric approach to biological systems. Typical application is high-content analysis, where various phenotypic changes in cellular components and/or morphology are measured from fluorescent image data. A standard protocol to detect cellular phenotypes is cell-segmentation, in which boundaries of cellular components, such as cell nucleus and plasma membrane, are first identified to define cell segments, then acquiring various phenotypic data of each segment. To achieve reliable outcome, cell-segmentation requires manual adjustments of many parameters; this requirement could hamper automated image processing in high-throughput workflow, whose quantification must be non-arbitrary and non-biased. As a practical alternative to the segmentation-based method, we developed GBIQ (Grid Based Image Quantification), which allows comparison of cellular information without identification of single cells. GBIQ divides an image with tiles of fixed size grids and records statistics of the grids with their location coordinates, minimizing arbitrary intervenes. GBIQ requires only one parameter (size of grid) to be set; nonetheless it robustly produces results suitable for further statistical evaluation. The simplicity of GBIQ allows it to be readily implemented in an automated high-throughput image analysis workflow.
Highlights
We report the process of GBIQ in comparison to the cell-segmentation based method, verifying GBIQ is a practical alternative to the segmentation-based method
GBIQ well performs in cases the cell density of subject is high, where the segmentation method would require elaborative fine-tunings of parameter-sets to identify each cell, as exampled here applying the both methods on same image datasets obtained from dense colonies of mouse ESCs
TQ indicates that EGFP(+)mouse ESCs (mESCs) express Oct4 approximately 22% (× 1.218115 over EGFP(−)) higher than EGFP(−) mESCs. These results strongly indicate that GBIQ can draw information paralleled to a cell segmentation based method
Summary
These arbitrary procedures inevitably take our labour and time, in this situation, a quick, easy and ideally parameter-free quantification method is in great demand. A solution to the problem would be a non-arbitrary and non-biased quantification method that processes fluorescent images under minimum set of rules. One possible way to attain this could be abandoning the cell-segmentation from the process. GBIQ well performs in cases the cell density of subject is high, where the segmentation method would require elaborative fine-tunings of parameter-sets to identify each cell, as exampled here applying the both methods on same image datasets obtained from dense colonies of mouse ESCs (mESCs). We apply GBIQ on various tissue sections from developing mouse embryos to elucidate a gene regulatory network
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