Quantitative Network Classification with CLaSSiNet Reveals Nanoscale Organizational Principles of the Membrane Skeleton.

ABSTRACT: Quantitative analysis of nanoscale biological network architectures remains challenging in super-resolution fluorescence imaging, where cytoskeletal and network-like structures often appear as sparse localization clusters rather than continuous filaments. We introduce CLaSSiNet, a first-of-its-kind modular computational pipeline that integrates connectivity, 1D periodicity, and 2D regularity classifiers to automatically segment, classify, and map nanoscale network architectures with unprecedented classification resolution (~256 nm) in single-molecule localization microscopy datasets. CLaSSiNet uniquely distinguishes four organizational states (1D periodic networks, 2D polygonal ordered networks, disordered networks, and non-network states) and is robust to experimental noise and compatible

SUBMITTER: Tao Y 

PROVIDER: S-EPMC12776128 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature