PLANCK: super-multiplex optical imaging without labeling

Abstract

Molecular information is vital for imaging technology. Optical imaging acquires molecular specificity almost exclusively via labeling strategy, which is fundamentally constrained by limited multiplexing capacity, high running costs, and experimental complexity. Conversely, label-free optical imaging offers substantial technical simplicity but is believed to have little true molecular specificity. Contrary to common belief, here we introduce super-multiplex optical imaging without labeling. By systematically studying paired vibrational spectroscopic imaging and mass spectrometry imaging, we discovered a surprisingly strong (more than 0.9) correlation between their latent space representations, supported by both experiments and theory. This insight prompts us to build supervised learning models to successfully predict spatial distribution of 100 molecular species directly from label-free vibrational images across diverse tissue systems. We developed this technology, named Prediction through Learning with AdvaNced Chemical Kaleidoscope (PLANCK), and demonstrated it with both infrared-based vibrational imaging of organ-scale tissues and Raman-based vibrational imaging of live tissues. Powered by AI, PLANCK decodes the exquisitely rich but otherwise hidden vibrational information into a surprisingly large number of ( [≥] 100) specific molecular species, providing a cost-effective and scalable solution for basic research and translation, including applications in live imaging.