A postzygotic de novo NCDN mutation identified in a sporadic FTLD patient results in Neurochondrin haploinsufficiency and altered FUS granule dynamics.
This repository contains all code required to analyze confocal image z-stacks to produce figures 4f (FUS granules) and 6b (cytoplasmic NCDN). Sample images are included to test the method. The paper is available at https://doi.org/10.1186/s40478-022-01314-x The datasets can be downloaded at https://s3.valeria.science/ncdn-fus/index.html.
Norbin_CytoplasmIntensity_allslices_AllCytoplasm.py must be run for each experimental week. It takes as input the path to a folder containing .lsm files from the Zeiss confocal organized in subfolders with the condition names (PLKO, shFUS315, shFUS318) and the date of image acquisition (variable date). Example image stacks of each condition are provided.
It returns a foreground mask, a nucleus mask and the masked intensity of the Norbin channel for each image. It also produces a csv file containing the median intensity of each image and a .npy file containing the histogram data of all the images. These files are saved to a folder named masks_+date+_NCDN
The produced csv files can then be taken as input by NCDN_CummingsPlot_Alldates.py which calculates the bootstrapped differences between conditions and produces the Cummings Plot as shown below. The csv files from all experimental dates included in the paper are available here
The code for segmentation of the FUS granules is based on The Allen Cell & Structure Segmenter (https://github.com/AllenCell/aics-segmentation)
Since the segmentation parameters were slightly adjusted for each experimental week, a separate python code with the appropriate parameters was produced. For example, SliceSegmentation_LSM_2shFus_3DAICS_slicequantification_Fusclusters_2021data_statsCDF.py contains all the paths and parameters to analyze the data acquired on 12-04-2021. This code takes as input the path to a folder containing .lsm files from the Zeiss confocal organized in subfolders with the condition names (PLKO, shNorbin01, shNorbin02) and the date of image acquisition (variable date). Example image stacks of each condition from 12-04-2021 are provided.
It returns a nucleus mask and a mask of the FUS granules who respect the established thresholds for size and intensity for each image. It also returns a .npy array containing a dictionnary of the intensities of all the segmented clusters and the number of clusters for each image structured by knockdown condition. These files are saved to a folder named masks_+date+_FusGranules
The produced dictionaries in the .npy files can then be given as input to Fusgranule_CummingsPlot_Alldates.py which calculates the skewnorm fit on the histogram of Fus granule intensities for each image and calculates their bootstrapped differences between conditions and produces the Cummings Plot. The .npy files from all experimental dates included in the paper are available here.
