napari plugin for analyzing fluorescence-labeled proteins redistribution
Project description
domb-napari
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
Detection of fluorescence redistributions
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 (methodexp
) or bi-exponential (methodbi_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 fromcrop 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.
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.
Up masking
Generates labels for insertion sites (regions with increasing intensity) based on -red-green
images. Returns labels layer with _up-labels
suffix.
Parameters:
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.
Traffic monitoring with pH-sensitive tag
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.
Intensty profiles building and data frame saving
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 | ΔF/F0 |
---|---|
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.
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