Package: domb-napari
domb-naparinapari Toolkit of Department of Molecular BiophysicsBogomoletz Institute of Physiology of NAS of Ukraine, Kyiv,  Ukrainenapari plugin for analyzing fluorescence-labeled proteins redistribution. Offers widgets designed for analyzing the redistribution of fluorescence-labeled proteins in widefield epifluorescence time-lapse acquisitions. Particularly useful for studying various phenomena such as calcium-dependent translocation of neuronal calcium sensors, synaptic receptor traffic during long-term plasticity induction, and membrane protein tracking.Hippocalcin (neuronal calcium sensor) redistributes in dendritic branches upon NMDA applicationDetection of fluorescence redistributionsA set of widgets designed for detecting fluorescence intensity redistribution through the analysis of differential image series (red-green detection).Inspired byDovgan et al., 2010andOsypenko et al., 2019.Image preprocessingProvides functions for preprocessing multi-channel fluorescence acquisitions:If the input image has 4 dimensions (time, channel, x-axis, y-axis), channels will be split into individual 3 dimensions images (time, x-axis, y-axis) with the_ch%index%suffix.If thegaussian bluroption is selected, the image will be blurred with a Gaussian filter using sigma=gaussian sigma.If thephotobleaching correctionoption is selected, the image will undergo correction with exponential (methodexp) or bi-exponential (methodbi_exp) fitting.Red-green seriesPrimary method for detecting fluorescent-labeled targets redistribution in time. Returns a series of differential images representing the intensity difference between the current frame and the previous one as new image with the_red-greensuffix.Parameters:left frames- number of previous frames for pixel-wise averaging.space frames- number of frames between the last left and first right frames.right frames- number of subsequent frames for pixel-wise averaging.save mask series- if selected, a series of labels will be created for each frame of the differential image with the thresholdinsertion threshold.Up maskingGenerates labels for insertion sites (regions with increasing intensity) based on-red-greenimages. Returns labels layer with_up-labelssuffix.Parameters:detection img index- index of the frame from-red-greenimage used for insertion sites detection.insertion threshold- threshold value for insertion site detection, intensity on selected_red-greenframe normalized in -1 - 0 range.save mask- if selected, a total up mask (containing all ROIs) will be created with the_up-masksuffix.Intensity maskingExtension ofUp Maskingwidget. Detects regions with increasing (masking mode-up) or decreasing (masking mode-down) intensity in-red-greenimages. Returns a labels layer with either_up-labelsor_down-labelssuffix, depending on the mode.Individual labels profilesBuilds a plot with mean intensity profiles for each ROI inlabelsusing absolute intensity (ifraw intensityis selected) or relative intensities (ΔF/F0).Thetime scalesets the number of seconds between frames for x-axis scaling.The baseline intensity for ΔF/F0 profiles is estimated as the mean intensity of the initial profile points (ΔF win).Filters ROIs by minimum (min amplitude) and maximum (max amplitude) intensity amplitudes.Note: Intensity filtering is most relevant for ΔF/F0 profiles.Additionally, you can save ROI intensity profiles as .csv using thesave data frameoption and specifying thesaving path. The output data frames%img_name%_lab_prof.csvwill contain the following columns:id- unique image ID, the name of the inputnapari.Imageobject.roi- ROI number, consecutively numbered starting from 1.int- ROI mean intensity, raw or ΔF/F0 according to theraw intensityoption.index- frame indextime- frame time point according to thetime scale.Note: The data frame will contain information for all ROIs; filtering options pertain to plotting only.Labels profileBuilds a plot with the averaged intensity of all ROIs inlabels. Can take two images (img 0andimg 1) as input iftwo profilesare selected.Thetime scaleandΔF winare the same as in theIndividual Labels Profiles.Thestat methodprovides methods for calculating intensity errors:se- standard error of mean.iqr- interquartile range.ci- 95% confidence interval for t-distribution.Traffic monitoring with pH-sensitive tagA collection of widgets designed for the analysis of image series containing the pH-sensitive fluorescence protein Superecliptic pHluorin (SEP).Insipred byFujii et al., 2017,Gao et al., 2018andSposini et al., 2020.SEP image preprocessingProcesses image series obtained through repetitive pH exchange methods (such as U-tube or ppH approaches). Frames with odd indexes, including index 0, are interpreted as images acquired at pH 7.0, representing total fluorescence intensity (saved with the suffix_total). Even frames are interpreted as images obtained at acidic pH (5.5-6.0), representing intracellular fluorescence only (saved with the suffix_intra).Ifcalc surface imgis selected, an additional total fluorescence image with subtracted intracellular intensity will be saved as the cell surface fluorescence fraction (suffix_surface). The input image should be a 3-dimensional single-channel time-lapse.