Spectral discrimination of pediatric SF188 and adult glioblastoma stem cells by deep learning–enhanced Raman profiling

TL;DR

This study shows that Raman spectroscopy combined with deep learning can distinguish pediatric and adult glioblastoma cells based on their biochemical profiles.

Contribution

The novel use of deep learning-enhanced Raman spectroscopy for non-invasive, label-free discrimination of pediatric and adult glioblastoma stem cells.

Findings

The deep learning model achieved 83.6% accuracy and 0.855 ROC AUC in differentiating pediatric and adult GBM.

Distinct vibrational modes in lipid, protein, and nucleic acid content were identified between age groups.

The model showed high sensitivity for pediatric GBM with a 91.4% identification rate.

Abstract

Pediatric and adult glioblastomas (GBM) represent biologically distinct entities requiring age-tailored therapeutic strategies. However, rapid and non-invasive methods to distinguish these molecular subtypes remain an unmet clinical need. This study evaluates the potential of confocal Raman spectroscopy combined with deep learning as a label-free diagnostic tool to differentiate pediatric from adult GBM based on intrinsic biochemical fingerprints.

We acquired n=1,382 Raman spectra from a cohort of six patient-derived GBM neurosphere cell lines, comprising a pediatric model (SF188) and five adult-origin lines. A multilayer perceptron (MLP) neural network was trained to classify spectra by age group. To ensure rigorous validation and generalizability, performance was assessed on a strictly held-out external test set (20% of data), completely excluded from model optimization.

The deep learning model successfully differentiated pediatric from adult GBM signatures with an overall accuracy of 83.6% and an ROC AUC of 0.855 on the independent test set. Spectral analysis revealed distinct vibrational modes, highlighting significant variations in lipid, protein, and nucleic acid content between age groups. Notably, the model achieved a high sensitivity for the pediatric phenotype (91.4% identification rate) .

This proof-of-concept study demonstrates that Raman spectroscopy, augmented by deep learning, can identify age-related molecular variations in GBM without extrinsic labeling. By capturing the unique biochemical landscape of pediatric versus adult tumors, this approach lays the foundation for rapid, automated, and objective diagnostic workflows in precision neuro-oncology.

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