napari plugin for analyzing fluorescence-labeled proteins redistribution

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napari Toolkit of Department of Molecular Biophysics
Bogomoletz Institute of Physiology of NAS of Ukraine, Kyiv, Ukraine

napari 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 application

A set of widgets designed for detecting fluorescence intensity redistribution through the analysis of differential image series (red-green detection).

Inspired by Dovgan et al., 2010 and Osypenko et al., 2019.

Image preprocessing

Provides 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 the gaussian blur option is selected, the image will be blurred with a Gaussian filter using sigma=gaussian sigma.
  • If the photobleaching correction option is selected, the image will undergo correction with exponential (method exp) or bi-exponential (method bi_exp) fitting.
  • If the crop ch option is selected, only a selected range of channel frames will be saved (corresponding to start and stop indexes from crop range).

Red-green series

Primary 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-green suffix.


  • 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.

Up masking

Generates labels for insertion sites (regions with increasing intensity) based on -red-green images. Returns labels layer with _up-labels suffix.


  • detection img index - index of the frame from -red-green image used for insertion sites detection.
  • insertion threshold - threshold value for insertion site detection, intensity on selected _red-green frame normalized in -1 - 0 range.
  • opening footprint - footprint size in pixels for mask filtering with morphology opening (disabled if 0).
  • save mask - if selected, a total up mask (containing all ROIs) will be created with the _up-mask suffix.

Intensity masking

Extension of Up Masking widget. Detects regions with increasing (masking mode - up) or decreasing (masking mode - down) intensity in -red-green images. Returns a labels layer with either _up-labels or _down-labels suffix, depending on the mode.

A collection of widgets designed for the analysis of image series containing the pH-sensitive fluorescence protein Superecliptic pHluorin (SEP).

Insipred by Fujii et al., 2017 and Sposini et al., 2020.

SEP image preprocessing

Processes image series obtained through repetitive pH exchange methods (such as U-tube or ppH approaches). pH 1st frame option indicates the 1st frame pH. By default 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).

If calc surface img is 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.

The calc projections option allows obtaining individual pH series projections (pixel-wise series MIP - pixel-wise series average) for the detection of individual exo/endocytosis events.

Individual labels profiles

Builds a plot with mean intensity profiles for each ROI in labels using absolute intensity (if absolute intensity is selected) or relative intensities (ΔF/F0).

The time scale sets 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). You could filter ROIs by minimum and maximum ΔF/F0 amplitudes with the ΔF aplitude lim option.

Note: amplitude filtering working with ΔF/F0 profiles only.

If the profiles crop option is selected, only a selected range of intensity profiles indexes will be plotted (corresponding to start and stop indexes from profiles range).

Additionally, you can save ROI intensity profiles as .csv using the save data frame option and specifying the saving path. The output data frames %img_name%_lab_prof.csv will contain the following columns:

  • id - unique image ID, the name of the input napari.Image object.
  • roi - ROI number, consecutively numbered starting from 1.
  • int - ROI mean intensity, raw or ΔF/F0 according to the raw intensity option.
  • index - frame index
  • time - frame time point according to the time scale.

Note: The data frame will contain information for all ROIs; amplitude filtering and crop options pertain to plotting only.

Absolute intensity

Labels stat profiles

Builds a plot with the averaged intensity of all ROIs in labels. Can take two images (img 0 and img 1) as input if two profiles are selected.

The time scale and ΔF win are the same as in the Individual Labels Profiles.

The stat method provides methods for estimation intensity and errors:

  • se - standard error of mean.
  • iqr - interquartile range.
  • ci - 95% confidence interval for t-distribution.


  • 0.1.1

Last updated:

  • 01 March 2024

First released:

  • 22 November 2023


Supported data:

  • Information not submitted

Plugin type:

GitHub activity:

  • Stars: 1
  • Forks: 0
  • Issues + PRs: 0

Python versions supported:

Operating system:


  • napari
  • domb

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