Super-resolution single molecule localization microscopy (SMLM) data of caveolin-1 (Cav1) protein in prostate cancer cells (PC3). Identifying SMLM Cav1 domains using deep learning.

Super-resolution single molecule localization microscopy (SMLM) data of caveolin-1 (Cav1) protein. Our dataset consists of two populations of prostate cancer (PC3) cells. In every cell, we have super-resolution SMLM images for the localization of the Cav1 proteins. Specifically, PC3 and CAVIN1/PTRF transfected PC3 cells (also called PC3-PTRF). The PC3-PTRF population comes with the wide-field CAVIN1/PTRF mask images. The goal is to study the clusters of the Cav1 in the two populations and automatically identify the Cav1 molecular domains and complexes (Caveolae and scaffolds) using the machine and deep learning models using various data representations (3D point cloud, hand-crafted features, multi-view projections, etc.). The Cav1 clusters are pre-processed and extracted by processing the 3D point cloud data using Khater et. al., 2018 method "<i>3D SMLM Network Analysis</i>" pipeline (https://www.nature.com/articles/s41598-018-27216-4)