Non-invasive, label-free biochemical imaging of intact cerebral organoids via deep learning-enhanced Raman microspectroscopy

Dimitar Georgiev, Ruoxiao Xie, Mauricio Barahona, Molly M. Stevens

Track: Tiny Paper Track

Keywords: machine learning, chemometrics, Raman spectroscopy, biochemical imaging, cerebral organoids

TL;DR: We present an approach for non-invasive, label-free organoid imaging based on Raman microspectroscopy combined with autoencoder representation learning

Abstract: Cerebral organoids have become a fundamental biological model system across developmental neuroscience, offering insights into early human brain development, neurological diseases and drug response in vitro. Despite its promise, cerebral organoid research remains constrained by the limitations of current analytical methods for organoid imaging and characterisation, which are destructive, require exogenous labelling and have limited multiplexing capabilities. Here, we present a non-invasive, label-free organoid imaging approach based on Raman microspectroscopy combined with physics-constrained autoencoder representation learning. Our approach enables in situ biochemical imaging of intact cerebral organoids, allowing the interrogation of structural, biochemical and morphological composition at cellular and near-subcellular resolution without compromising organoid integrity.

Attendance: Dimitar Georgiev

Submission Number: 17