Gaussian-filtering of the scanned, inverted dot blot. The zip-file can be re-opened in Fiji by drag&drop: all selections will be listed in the ROI Manager after re-opening.ġ. For quality control purposes, the macro also saves the selections within the ROI Manager as a zip-file and a control-image with all spot selections overlaid over the inverted background-subtracted dot blot. The final output of the macro is a result table that lists intensity measurements for each spot. After this the macro performs a background subtraction and measures the intensities of each spot. The user has the possibility to manually adjust the selections by activating a selection within the ROI manager, adjusting it and pressing “update” to save the new position. After registration the macro creates selections for each spot. DotBlot_Analysis.ijm uses the user-set landmarks to register the mask image to the scanned blot in an iterative process. The user then needs to mark 3–5 corresponding dots (=landmarks) in the scanned dot blot and in the mask. After starting the macro, the user will be asked to select the names of the mask and dot blot images, as well as enter an expected spot size in pixel and a prominence value (discussed under Implementation). The mask image is a representation of the spot pattern. The macro expects two images: the scanned dot blot and a mask image. It runs within the free and open-source software Fiji 5. It then automatically searches for more landmarks and performs the final registration and measurements. The presented ImageJ Macro DotBlot_Analysis.ijm relies on a few (3–5) landmarks selected by the user to give a first estimate of the transformation between the scanned dot blot and a template pattern. As a result, open source tools currently available for analyzing dot blots either strongly rely on user input 3 or assume full regular patterns 4, which is not always the case. The intensity-based algorithm fails for similar reasons. For algorithms that depend simply on feature detection, the pattern of the mask is too dense and regular to be successfully registered to few sparse dots. A scanned dot blot can often not be aligned to the mask with either method, especially when only a few analytes in the dot blot give positive signals. Linear Stack Alignment with SIFT 2) 1, 2. StackReg 1, or rely on the automatic identification of prominent features present in both images (e.g. Common registration algorithms are either intensity-based, as in e.g. For automatic quantification of the dot intensities a mask reflecting the spot pattern needs to be registered to the blot. Dot blots typically contain ~200 spots to be analyzed. Binding of the analyte to the capture antibody is detected by a detection antibody and chemiluminescence signals are recorded by film or scanner, similar as in classical western blots. Commercially available dot blots contain a set of so-called capture antibodies spotted to a membrane in a given pattern, with each spot representing one specific antibody. A dot blot is a common technique in molecular biology to query the presence of an analyte in a solution.
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