Shape2Fate
A morphology-aware deep learning framework for tracking endocytic and exocytic carriers at nanoscale

Abstract

Plasma membrane homeostasis depends on balanced exocytosis and endocytosis, yet their spatiotemporal coordination has been difficult to resolve at the single-event level. We present Shape2Fate, a fully automated, shape-aware deep-learning pipeline that detects, tracks, and classifies individual exocytic and endocytic carriers in live-cell total internal reflection fluorescence structured illumination microscopy (TIRF-SIM) movies at ~100 nm resolution. Rather than relying on intensity, Shape2Fate exploits carrier morphology to classify cargo-delivery outcomes from shape evolution. Trained entirely on realistic synthetic data requiring no manual annotation, Shape2Fate reaches expert-level tracking accuracy on diverse experimental data. Applying Shape2Fate to chemically synchronized exocytosis and insulin-stimulated GLUT4 trafficking in adipocytes, we uncover an inverse coupling hierarchy: synchronized exocytic fusion nucleates de novo clathrin-coated pits (CCPs), whereas adipocyte exocytic carriers target pre-existing CCPs for rapid cargo capture. As an open-source framework, Shape2Fate yields quantitative, event-level maps of exo–endocytic coordination, enabling mechanistic dissection across cell types and pathways.