As part of my undergraduate research for my honors thesis, I wanted to quantify the volumes of nuclear condensates in fixed larval tissue samples tagged with fluorescent RNA-binding proteins to study how the transcription factor CLAMP affects their formation. Since no pre-built tools existed for this specific task, I was inspired by Baharom et al.'s method of using STED microscopy z-stacks to construct 3D cell volumes, and from there I developed my own 3D volume quantification tool in python.

In the script, I first pre-processed z-stack images to isolate the fluorescently tagged RNA-binding protein condensates, converting them into 3D binary arrays and applying a thresholding method refined through visual inspection. Using Sci-kit image’s connectivity labeling algorithm, I quantified the volumes of the nuclear condensates, storing the results in CSV files for further analysis, including distribution, mean, median, and variance calculations, which I visualized using custom scripts.

In addition to quantifying condensate volumes, I wanted to assess the colocalization between RNA-binding proteins and the transcription factor CLAMP, which were tagged in different channels. Using the same z-stack images converted into 3D numpy arrays, I wrote a script to perform colocalization analysis for calculating the Pearson Correlation Coefficient (PCC) and corresponding p-values between channels, with the results stored and statistically analyzed across different experimental conditions.

More information about the specific methods in my scripts can be found on my GitHub page: 3D Speckle Volume Quantifier, and this project was a part of my honors thesis: http://tiny.cc/SVHonorsThesis. Additionally, my work on this project is part of a paper currently available as a preprint: PubMed.

Visualization with Napari Viewer of the segmentation of the volumes