Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a novel weakly-supervised machine learning method based on multiple instance learning (MIL), leveraging shape and network features of protein assemblies, to close this important gap in interpretable subcellular discovery. siM-ILe identifies condition-specific changes in protein structures, without requiring structure-level supervision, and improves structure classification by extending embedded instance selection (MILES) through adversarial erasing and a symmetric classifier. siMILe is validated on simulated SMLM data and by detecting caveolae from caveolin-1 (Cav1) labeled PC3 prostate cancer cells differentially expressing cavin-1. In PC3-CAVIN1 cells dually labeled for Cav1 and cavin-1, cavin-1 closely associates with siMILe-identified caveolae, to a lesser extent with higher-order non-caveolar Cav1 scaffolds, but not with base Cav1 oligomers that correspond to 8S complexes, supporting a role for progressive cavin-1 interaction in 8S complex oligomerization. These results highlight siMILes potential to identify differential molecular structures in distinct cell conditions. siMILe extends the SuperResNET SMLM software platform with the ability to detect interpretable structural differences across conditions.