Finally, save the processed nuclear images with the name "KTR_cellmask_" to start at 1, in order to distinguish them from the original images from the folder.Then click "Apply", and deselect "Calculate threshold for each image". Apply a manual threshold from 300 to 65535: Image > Adjust > Threshold > Set (type 300 for the lower threshold level).Apply a Gaussian blur with sigma 2 to smoothen the borders and irregularities by: Process > Filters > Gaussian Blur.Open the data for the green channel (KTR_Green-Channel.tif).To determine the ROIs for the cell body, we use the data from the green channel, because whole cells can be clearly distinguished from the background. The result is 27 sequentially numbered TIF images, starting at KTR_nucleus_0001.tif.Save the result as individual images in a new folder (‘processed’), File > Save as > Image Sequence (start at 1) and use the name ‘KTR_nucleus_’.Subtract a background value to eliminate any cytoplasmic fluorescence, here we subtract 300 counts: Process > Math > Subtract and enter 300.Open the TIF stack of the red (nuclear) channel in ImageJ.Therefore, a background subtraction is performed. To facilitate segmentation of the nuclei and determine the individual ROIs, it is necessary to process the images so only the nuclei are visible, and there is no background or counts outside the nuclei. The result of this step is stored in the folder /Images/processed/ Image processing for nuclear segmentation It is necessary to stick to the filenames as these wil be used later on to identify the data by CellProfiler. The processing steps are specific for this dataset and probably need adjustment when the data is acquired in another way (different cell line, different sensor, different microscope). The purpose of the image processing is to prepare the images, by performing background subtraction, thresholding and saving the data with the right name. The raw datafiles are in /Images/raw or click here: A dynamic close-up of the data as an animated GIF is available here. The green and cyan channels display images of two different biosensors, which is the data that needs to be analyzed. The red channel displays nuclear mScarlet-I, which is used for nuclear segmentation. Here, we have data from three channels, a red, green and cyan channel. To follow the tutorial with your own data, it is recommended to convert your data to (TIF) stacks in ImageJ before you start.These will be split in individual images using ImageJ and saved with specific names that are recognized by CellProfiler. In this example, we start from three TIF stacks (see below for a description of the data).Install ImageJ ( ) and the Bio-Formats Importer ( ).The end-result is a plot that shows the nuclear to cytoplasmic ratio for individual cells over time. In this example, two reporters are present. This procedure assumes that there is a single fluorescence channel that is used for nuclear segmentation and at least one other channel that is used to measure a reporter. The main purpose is to analyze data from translocation reporters that shuttle between the cytoplasm and nucleus. Step-by-step instruction to quantify the cytoplasmic to nuclear fluorescence ratio (C/N ratio) from confocal timelapse imaging experiments. ilastik documentation: exporting output.Single cell analysis of nuclear translocation Authors: Sergei Chavez-Abiega and Joachim Goedhart (University of Amsterdam).ilastik documentation: exporting output.ilastik documentation: installation guide.Haubold C et al (2016) Segmenting and tracking multiple dividing targets using ilastik.Chenouard N et al (2014) Objective comparison of particle tracking methods.Meijering E, Dzyubachyk O, Smal I (2012) Methods for cell and particle tracking.Hilsenbeck O et al (2016) Software tools for single-cell tracking and quantification of cellular and molecular properties.Schindelin J, Arganda-Carreras I, Frise E (2012) Fiji: an open-source platform for biological-image analysis.Sbalzarini IF, Koumoutsakos P (2005) Feature point tracking and trajectory analysis for video imaging in cell biology.Sbalzarini I F (2006) Particle Tracker Fiji plugin.Tinevez J, Perry N, Schindelin J et al (2017) TrackMate: an open and extensible platform for single-particle tracking.Tinevez J (2016) TrackMate Fiji plugin.Stuurman N (2003) MTrack2 Fiji plugin.
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