Identification of structural stability and fragility of mouse liver glycogen via label-free Raman spectroscopy coupled with convolutional neural network algorithm

Author Identifier

Liang Wang: https://orcid.org/0000-0001-5339-7484

Document Type

Journal Article

Publication Title

International Journal of Biological Macromolecules

Volume

286

PubMed ID

39638186

Publisher

Elsevier

School

Centre for Precision Health / School of Medical and Health Sciences

RAS ID

77379

Funders

National Natural Science Foundation of China (32171281) / Research Foundation for Advanced Talents of Guandong Provincial People's Hospital (KY012023293)

Comments

Wang, L., Ma, Z. W., Tang, J. W., Mou, J. Y., Liu, Q. H., Wang, Z. Y., ... & Tang, D. Q. (2025). Identification of structural stability and fragility of mouse liver glycogen via label-free Raman spectroscopy coupled with convolutional neural network algorithm. International Journal of Biological Macromolecules, 286. https://doi.org/10.1016/j.ijbiomac.2024.138340

Abstract

Glycogen structure is closely associated with its physiological functions. Previous studies confirmed that liver glycogen structure had two dominant states: mainly stable during the day and largely fragile at night. However, the diurnal change of glycogen structure is impaired, with dominant fragility in diseased conditions such as diabetes mellitus and liver fibrosis. Therefore, the persistent structural fragility of glycogen particles could be a potential molecular-level pathological biomarker for early screening of certain liver diseases. However, the current method for identifying glycogen structural stability and fragility suffers from sophisticated procedures and reliance on expensive instruments, which demands developing novel methods for rapidly discriminating the two types of glycogen particles. This study applied surface-enhanced Raman spectroscopy (SERS) to generate SERS spectra of glycogen samples, revealing distinct structural differences between fragile and stable glycogen particles. Machine learning models were then constructed to predict the structural states of unknown glycogen samples via SERS spectra, according to which the convolutional neural network (CNN) model achieved the best discrimination capacity. Taken together, the SERS technique coupled with the CNN model can identify stable and fragile liver glycogen samples, facilitating the application of glycogen structural fragility as a biomarker in diagnosing liver diseases.

DOI

10.1016/j.ijbiomac.2024.138340

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