Understanding vascular structures and dysfunction is a fundamental challenge. This task has been approached by using traditional methodologies such as microscopic computed tomography and magnetic resonance imaging. Both techniques are not only expensive but also time-consuming. Here, we present a new method for visualizing vascular structures in different organs in an efficient manner. A cationic near infrared (NIR) fluorescent dye was developed with attractive features to specifically stain blood vessels. Furthermore, we refined the process of organ staining and harvesting by retrograde perfusion and optimized the subsequent dehydration and clearing process by the use of an automatic tissue processor and a non-toxic substance, ethyl-cinnamate. Using this approach, the time interval between organ harvesting and microscopic analysis can be reduced from day(s) or weeks to 4 hours. Finally, we have demonstrated that the new NIR fluorescent agent in combination with confocal or light-sheet microscopy can be efficiently used for visualization of vascular structures, such as the blood vessels in different organs e.g. glomeruli in kidneys, with an extremely high resolution. Our approach facilitates the development of automatic image processing and the quantitative analysis to study vascular and kidney diseases.
In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.
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