Abstract #4829

Modelling HR-MAS NMR spectra of human cerebral organoids using COLMAR1D Deep Neural Networks – comparison against peak integration

Alejandra Castilla Bolanos¹, Vorapin Chinchalongporn², Rajshree Ghosh Biswas³, Colleen Bailey², Ronald Soong³, Dawei Li⁴, Raphael Bruschweiler⁴, Andre Simpson³, Carol Schuurmans², and Jamie Near⁵

¹Medical Biophysics, University of Toronto, Toronto, ON, Canada, ²Sunnybrook Research Institute, Toronto, ON, Canada, ³University of Toronto, Toronto, ON, Canada, ⁴The Ohio State University, Columbus, OH, United States, ⁵Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada

Synopsis

Keywords: Software Tools, Spectroscopy, Organoids

Motivation: High-resolution magic-angle spinning (HR-MAS) NMR enables the study of metabolite concentrations in human cerebral organoids (COs). However, quantification remains challenging because HR-MAS spectra require precise NMR parameters for accurate modelling.

Goal(s): Our goal is to assess the performance of a deep neural network-based deconvolution method to model CO HR-MAS spectra for metabolite quantification.

Approach: CO HR-MAS spectra were modelled using the COLMAR1D Deep Neural Network Peak Picking tool. Quantification from COLMAR1D was compared against a peak integration.

Results: 21 metabolites were quantified in cerebral organoids. A significant correlation between COLMAR1D and peak integration was observed, although COLMAR1D estimates were significantly higher.

Impact: This study presents a convenient method for modelling and quantification of HR-MAS spectra in cerebral organoids using deep deconvolution. By reliably quantifying metabolite concentrations in human brain models, our approach will enable studies to better understand human brain metabolism.

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