2026 ECNP Congress

PS03-3245

Mass spectrometry imaging-based spatial lipid profiling of the human dorsolateral prefrontal cortex in health and schizophrenia

Cecilia Cabasino — P. Enrico, F. Padelli, D. De Santis, E. Leonetti, C. Cagnoli, R. Garbelli, C. Villa, L. Agnelli, S. Peri, G. Delvecchio, I. Bongarzone, Y. Torrente, P. Brambilla · Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy

2026 ECNP Congress

Introduction

Schizophrenia (SCZ) is a debilitating psychiatric disorder with substantial personal, societal, and economic burden, yet its pathophysiology remains poorly understood [1]. Among the molecular alterations implicated in SCZ, brain lipid dysregulation has been reported, although most evidence derives from bulk lipidomics, which lacks anatomical resolution [2]. The dorsolateral prefrontal cortex (DLPFC) is a major cortical region affected in SCZ, with structural and functional abnormalities associated with multiple symptoms [3]. In this context, spatial approaches, such as matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), are highly informative to characterize lipid distribution and disease-related alterations, enabling label-free localization of molecular species within tissue sections and supporting the identification of biomarkers associated to brain function and disease pathogenesis [4].

Aims

Considering the key role of lipids in myelination, neurotransmission, and immune-glial interactions [5], this study aimed to investigate the spatial distribution of lipid molecules in the human DLPFC and to identify SCZ-associated alterations. Leveraging a self-developed, rigorous and reproducible MALDI-MSI protocol for spatial lipid mapping across defined histological compartments, we compared healthy control (HC) and SCZ tissues to identify disease-related lipid alterations.

Methods

Post-mortem DLPFC tissues were obtained from the NIMH Human Brain Collection Core and included 5 extra-diagnosis cases for protocol setup, and 20 SCZ and 18 age- and sex-matched HC samples. MALDI-MSI was performed on 10 μm-thick tissue sections, and spectral data were processed using SCiLS Lab to map lipid distributions. We applied multivariate segmentation and dimensionality reduction to define regional molecular profiles, and ROC analysis to compare SCZ and control cases. Samples were also independently binary-segmented using the mean plus 3 median adjusted deviations as threshold, to reduce intensity-related variability and further validate group comparisons. Adjacent sections underwent immunohistochemical and immunofluorescent staining for overlay with MSI images. Lipid assignments were inferred by MALDI-LIFT MS/MS and validated through curated database and literature matching of fragment ions.

Results

The model obtained from extra-diagnosis cases enabled the identification and spatial localization of several lipid species and supported the establishment of a MALDI-MSI protocol for lipid imaging in human post-mortem DLPFC. K-means segmentation reproducibly separated white and grey matter areas and identified four consistent cortical layers. PCA showed that five components explained 87% of the variance, allowing correlation of lipid profiles with multilayer tissue segmentation and histological regions. For the first time, specific lipid species were also localized at the microstructural level in the human DLPFC. Preliminary SCZ-control analyses, performed on eight samples and stratified by white and grey matter areas, identified compartment-specific differences in lipid distribution. Altered phospholipid and sulfatide levels were observed, suggesting potential links with neuroglial and metabolic processes.

Conclusions

Our study proposes a robust MALDI-MSI-based pipeline for spatial lipid profiling in human brain tissue, enabling anatomical localization of lipid species in the DLPFC and revealing region- and layer-specific signatures. Preliminary case-control differences suggest that lipid dysregulation may contribute to molecular alterations in SCZ, highlighting the potential of spatial lipidomics for identifying disease-related metabolic alterations and candidate biomarkers for precision psychiatry.

References

  1. Wu, Y., Wang, L., Tao, M., Cao, H., Yuan, H., Ye, M., Chen, X., Wang, K., Zhu, C., 2023. Changing trends in the global burden of mental disorders from 1990 to 2019 and predicted levels in 25 years. Epidemiol. Psychiatr. Sci. 32, e63;
  2. Messinis, A., Panteli, E., Paraskevopoulou, A., Zymarikopoulou, A.K., Filiou, M.D., 2024. Altered lipidomics biosignatures in schizophrenia: a systematic review. Schizophr. Res. 271, 380–390;
  3. Smucny, J., Dienel, S.J., Lewis, D.A., Carter, C.S., 2022. Mechanisms underlying dorsolateral prefrontal cortex contributions to cognitive dysfunction in schizophrenia. Neuropsychopharmacology 47, 292–308;
  4. Zaima, N., Hayasaka, T., Goto-Inoue, N., Setou, M., 2010. Matrix-assisted laser desorption/ionization imaging mass spectrometry. Int. J. Mol. Sci. 11, 5040–5055;
  5. Yoon, J.H., Seo, Y., Jo, Y.S., Lee, S., Cho, E., Cazenave-Gassiot, A., Shin, Y.S., Moon, M.H., An, H.J., Wenk, M.R., Suh, P.G., 2022. Brain lipidomics: from functional landscape to clinical significance. Sci. Adv. 8, eadc9317.