Automated Multimodal Correlative Registration for Organelle-Specific Molecular Imaging
Lu, C.; ZHAO, K.; Cui, D.; Chen, G.; Yang, Q.; Yang, H.; Zhao, M.; Song, K.; Nikan, M.; Li, Z.; Zhao, S.; Cen, J.; Qiu, X.; Young, S.; Bennett, C. F.; Seth, P.; Chen, K.; Qi, X.; Jiang, H.
Show abstract
Mapping subcellular drug distribution is essential for understanding trafficking and off-target effects. NanoSIMS enables chemical imaging of labeled therapeutics, but signal interpretation requires ultrastructural correlation with electron microscopy, a manual and laborious process. We present an automated AI-driven pipeline for correlating chemical and ultrastructural images, enabling multiscale, organelle-precise imaging of molecules in cells and tissues. The method integrates bidirectional optical flow, confidence-guided affine transformation, and automated template matching for cross-scale EM alignment. Morphology-rich ion channels (e.g., 32S) estimate transformations that propagate to sparse therapeutic signals (e.g., 79Br, 15N), overcoming low signal-to-noise challenges. We validate this framework across diverse cell and tissue types, tracking oligonucleotide and antibody therapeutics in vitro and in vivo to reveal cell-type- and organelle-specific distribution patterns. This work establishes a generalizable platform for automated multimodal registration and organelle-resolved subcellular pharmacology.
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