Label-free biochemical imaging and timepoint analysis of neural organoids via deep learning-enhanced Raman microspectroscopy

Abstract

Three-dimensional 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 characterisation techniques, which are invasive, require exogenous labelling and offer limited multiplexing. Here, we present a non-invasive, 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 high-resolution 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. Using our platform, we demonstrate volumetric imaging of a neural rosette within a neural organoid, and interrogate changes in biochemical composition during early developmental stages in intact neural organoids, revealing spatiotemporal variations in lipids, proteins and nucleic acids. This work establishes a versatile framework for high-content, label-free (bio)chemical phenotyping with broad applications in organoid research and beyond.