In this section
Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.
@article{Georgiev:2026,
author = {Georgiev, D and Xie, R and Reumann, D and Zhao, X and Fernandez-Galiana, A and Barahona, M and Stevens, M},
journal = {Science Advances},
title = {Label-free biochemical imaging and timepoint analysis of neural organoids via deep learning-enhanced Raman microspectroscopy},
year = {2026}
}
TY - JOUR
AB - 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, spatiallyresolved biochemical analysis of neural organoids. Our approach enables 2D and 3D 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 rosettewithin 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.
AU - Georgiev,D
AU - Xie,R
AU - Reumann,D
AU - Zhao,X
AU - Fernandez-Galiana,A
AU - Barahona,M
AU - Stevens,M
PY - 2026///
SN - 2375-2548
TI - Label-free biochemical imaging and timepoint analysis of neural organoids via deep learning-enhanced Raman microspectroscopy
T2 - Science Advances
ER -
If you are interested in meeting with members of the group please contact Marina Evangelou