To process the images of the fluorescently stained neurons acquired using a confocal fluorescence microscope use phenol link software for image segmentation and feature extraction. In the software load the plate of choice to access the data and select a desired image. To perform image segmentation on the illumination corrected raw images, adjust the fluorescent intensities to visualize the different channels.
Empirically determine the respective channel intensity thresholds per plate to ensure the desired segmented signal corresponds to the signal in the raw image and minimize the background signal. To separate living from dead cells, define the nucleus size and intensity thresholds. Measure the size of the nucleus by double clicking and visualize the displayed intensity.
When using 40 X images, maintain default parameters and run the processing. With the resulting tabular quantitative data construct phenotypic profiles to compare different cell lines or treatment conditions. Execute the Jupyter notebook cell by cell, using the Jupyter software for phenotypic profile generation and visualization.
Images were segmented and phenotypic features were extracted. A total of 126 quantitative features that could be aggregated into well-based phenotypic profiles were determined. Some features showed changes upon compound treatment.
For example, the alpha synuclein fluorescence intensity in tyrosine hydroxylase, or TH positive cells, decreased upon treatment with the LARK2 kinase inhibitor PFE-360. Other features, like the MAP2 neurite network length, or the ratio of TH positive neurons, presented differences only between the tested cell lines, but not upon compound treatment.
