SELECTIVE MAPPING OF LIPIDS IN THE BRAIN USING DESORPTION ELECTROSPRAY IONIZATION COUPLED WITH MULTIPLE REACTION MONITORING

Posters | 2024 | Waters | ASMSInstrumentation
LC/MS, LC/MS/MS, LC/QQQ
Industries
Clinical Research, Lipidomics
Manufacturer
Waters

Importance of the Topic


Alzheimer’s disease (AD) is the leading cause of dementia worldwide and is characterized by progressive cognitive decline. Beyond hallmark protein aggregates, alterations in brain lipid metabolism have emerged as critical indicators of disease progression. Spatial mapping of lipid species provides insight into regional dysregulation and may reveal novel biomarkers and therapeutic targets.

Objectives and Study Overview


This study aimed to develop and apply a targeted imaging mass spectrometry approach for selective mapping of key lipid classes in human brain tissue. By comparing specimens from donors with low and high AD burden, the authors sought to characterize changes in phosphatidylcholines, sphingomyelins, ceramides, and cerebrosides within the primary visual cortex.

Used Instrumentation


  • Waters DESI XS Source mounted on a Waters Xevo TQ Absolute triple quadrupole mass spectrometer
  • Waters High Definition Imaging (HDI) software version 1.8

Methodology and Instrumentation


Twenty-micron-thick flash-frozen human visual cortex sections from low- and high-AD cases were mounted on glass slides. Desorption electrospray ionization (DESI) was performed at 50×50 μm lateral resolution using a 95:5 methanol/water solvent with 0.01% formic acid and leucine enkephalin as a lock-mass. Spray voltage was set to +0.8 kV with nitrogen nebulizing gas at 1 atm and a flow rate of 2 μL/min. The Xevo TQ Absolute operated in multiple reaction monitoring (MRM) mode at unit mass resolution with optimized collision energies and cone voltages for each lipid standard. Data were acquired at 5 pixels per second and visualized with HDI software.

Main Results and Discussion


Reconstructed ion images revealed distinct spatial patterns of key lipids. Phosphatidylcholine PC 34:2 showed altered intensity between low- and high-AD tissues, indicating membrane remodeling. Sphingomyelins (dC18:1 C16:0, C18:0, C20:0, C22:0, C24:1, C24:0) exhibited differential distributions, with accumulation in certain cortical regions in high-AD specimens. Ceramides and cerebrosides also displayed region-specific enrichment, particularly within white matter tracts. Quantitative differences in targeted MRMs confirmed significant upregulation of several ceramide species in AD brains. These findings align with previous reports of sphingolipid dysregulation in neurodegeneration.

Benefits and Practical Applications


  • Minimal sample preparation preserves native lipid distribution.
  • High spatial resolution enables region-specific profiling.
  • MRM targeting ensures sensitivity and selectivity for low-abundance lipids.
  • Approach is adaptable to biomarker studies and pharmacodynamic assessments.

Future Trends and Potential Uses


Expansion of this targeted imaging workflow to encompass additional lipid classes such as plasmalogens, lysophosphatidylcholines, sulfated cerebrosides, and gangliosides is anticipated. Integrating lipid maps with protein-based imaging of amyloid and tau pathology could yield deeper insights into disease mechanisms. The method may also be applied to other neurodegenerative disorders and preclinical models to accelerate biomarker discovery and therapeutic evaluation.

Conclusion


This work demonstrates a rapid, sensitive DESI-MRM imaging approach for selective mapping of brain lipids in AD. The capacity to resolve and quantify sphingolipids and phospholipids with spatial fidelity provides a powerful tool for investigating lipid-mediated processes in neurodegeneration.

Reference


  1. Evans DA, et al. JAMA 1989, 262, 2551–2556.
  2. Ballard C, et al. Lancet 2011, 377(9770), 1019–1031.
  3. Foley P. Biochim Biophys Acta Mol Cell Biol Lipids 2010, 1801, 750–753.
  4. Varma VR, et al. PLOS Med. 2018, 15, e1002482.
  5. Barupal DK, et al. Alzheimer’s Dementia 2019, 11, 619–627.
  6. Han X. J Neurochem. 2007, 103, 171–179.
  7. Huang HX, et al. J Neurochem. 2024, 00, 1–22.
  8. Takats Z, et al. J Mass Spectrom. 2005, 40, 1261–1275.
  9. Haughey NJ, et al. Biochim Biophys Acta 2010, 1801, 878–886.

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