Label-Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning.

ABSTRACT: High-aspect-ratio nanostructures enable nuclear delivery through transient nuclear envelope (NE) disruption, but sporadic nucleoporation limits efficiency. This study presents a label-free, machine-learning approach detecting nucleoporation from morphological changes. U2OS cells cultured on silicon nanopillars undergo NE disruption detected via Ku-80 mislocalization. A custom longest-line algorithm quantifies Ku-80 versus DAPI intensity profiles outside the nucleus, establishing ground truth for 714 cells (451 intact, 263 porated). An orientation-invariant variational autoencoder, pre-trained on publicly available mouse embryonic fibroblast images, generates 32-dimensional embeddings for cell and nuclear shapes from binary masks. These embeddings, combined with four morphological descripto

SUBMITTER: Rahmani K 

PROVIDER: S-EPMC12674106 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature