Label-free biochemical imaging of neural organoids via deep learning–enhanced Raman microspectroscopy

Organoids have emerged as powerful models for studying human development, disease, and drug response in vitro. Yet, their analysis remains constrained by standard imaging and characterization techniques, which are invasive, require exogenous labeling, and offer limited multiplexing. Here, we present a label-free imaging platform that integrates Raman microspectroscopy with deep learning–based hyperspectral unmixing for unsupervised, spatially resolved biochemical analysis of neural organoids. Our approach enables mapping of cellular and subcellular structures in both cryosectioned and intact organoids, achieving improved imaging accuracy and robustness compared to conventional methods for hyperspectral analysis. This work establishes a versatile framework for high-content, label-free (bio)chemical phenotyping with broad applications in organoid research and beyond.