2025
J Neuropathol Exp Neurol. 2025 Jul 26:nlaf082. doi: 10.1093/jnen/nlaf082. Online ahead of print.
Lesion level and severity acutely influence metabolomic profiles in spinal cord injury
Department of Pharmacology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom. School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Brisbane, QLD, Australia. Acute Stroke Programme, RDM-Investigative Medicine, University of Oxford, Oxford, United Kingdom. Department of Chemistry, Mathematical, Physical and Life Sciences Division, University of Oxford, Oxford, United Kingdom.
Service type: Stock strains
Abstract
Changes in the peripheral metabolome, particularly in the blood, may provide biomarkers for assessing lesion severity and predicting outcomes after spinal cord injury (SCI). Using principal component analysis (PCA) and Orthogonal Partial Least Squares Discriminatory Analysis (OPLS-DA), we sought to discover how SCI severity and location acutely affect the nuclear magnetic resonance-acquired metabolome of the blood, spinal cord, and liver at 6 h post-SCI in mice. Unsupervised PCA of the spinal cord metabolome separated mild (30 kdyne) and severe (70 kdyne) contusion injury groups but did not distinguish between lesion level. However, OPLS-DA could discriminate thoracic level T2 from T9 lesions in both blood plasma (accuracy 86 ± 6%) and liver (accuracy 89 ± 5%) samples. These differences were dependent on alterations in energy metabolites (lactate and glucose), lipoproteins, and lipids. Lactate was the most discriminatory between mild and severe injury at T2, whereas overlapping valine/proline resonances were most discriminatory between injury severities at T9. Plasma lactate correlated with blood-spinal cord barrier breakdown and plasma glucose with microglial density. We propose that peripheral biofluid metabolites can serve as biomarkers of SCI severity and associated pathology at the lesion site; their predictive value is most accurate when the injury level is also considered.
Keywords: inflammation; metabolomics; spinal cord injury.
