Correlated Heterospectral Lipidomics for Biomolecular Profiling of Remyelination in Multiple Sclerosis

Mads S Bergholt, Andrea Serio, James S McKenzie, Amanda Boyd, Renata F Soares, Jocelyn Tillner, Ciro Chiappini, Vincen Wu, Andreas Dannhorn, Zoltan Takats, Anna Williams, Molly M Stevens

Research output: Contribution to journalArticlepeer-review

Abstract

Analyzing lipid composition and distribution within the brain is important to study white matter pathologies that present focal demyelination lesions, such as multiple sclerosis. Some lesions can endogenously re-form myelin sheaths. Therapies aim to enhance this repair process in order to reduce neurodegeneration and disability progression in patients. In this context, a lipidomic analysis providing both precise molecular classification and well-defined localization is crucial to detect changes in myelin lipid content. Here we develop a correlated heterospectral lipidomic (HSL) approach based on coregistered Raman spectroscopy, desorption electrospray ionization mass spectrometry (DESI-MS), and immunofluorescence imaging. We employ HSL to study the structural and compositional lipid profile of demyelination and remyelination in an induced focal demyelination mouse model and in multiple sclerosis lesions from patients ex vivo. Pixelwise coregistration of Raman spectroscopy and DESI-MS imaging generated a heterospectral map used to interrelate biomolecular structure and composition of myelin. Multivariate regression analysis enabled Raman-based assessment of highly specific lipid subtypes in complex tissue for the first time. This method revealed the temporal dynamics of remyelination and provided the first indication that newly formed myelin has a different lipid composition compared to normal myelin. HSL enables detailed molecular myelin characterization that can substantially improve upon the current understanding of remyelination in multiple sclerosis and provides a strategy to assess remyelination treatments in animal models.

Original languageEnglish
Pages (from-to)39-51
Number of pages13
JournalACS Central Science
Volume4
Issue number1
DOIs
Publication statusPublished - 27 Dec 2017

Keywords

  • Journal Article

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