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Mass synaptometry: High-dimensional multi parametric assay for single synapses

Gajera, C.R., Fernandez, R., Postupna, N. et al.

BACKGROUND:
Synaptic alterations, especially presynaptic changes, are cardinal features of neurodegenerative diseases and strongly correlate with cognitive decline.

NEW METHOD:
We report "Mass Synaptometry" for the high-dimensional analysis of individual human synaptosomes, enriched nerve terminals from brain. This method was adapted from cytometry by time-of-flight mass spectrometry (CyTOF), which is commonly used for single-cell analysis of immune and blood cells.

RESULT:
Here we overcome challenges for single synapse analysis by optimizing synaptosome preparations, generating a 'SynTOF panel,' recalibrating acquisition settings, and applying computational analyses. Through the analysis of 390,000 individual synaptosomes, we also provide proof-of principle validation by characterizing changes in synaptic diversity in Lewy Body Disease (LBD), Alzheimer's disease and normal brain.

COMPARISON WITH EXISTING METHOD(S):
Current imaging methods to study synapses in humans are capable of analyzing a limited number of synapses, and conventional flow cytometric techniques are typically restricted to fewer than 6 parameters. Our method allows for the simultaneous detection of 34 parameters from tens of thousands of individual synapses.

CONCLUSION:
We applied Mass Synaptometry to analyze 34 parameters simultaneously on more than 390,000 synaptosomes from 13 human brain samples. This new approach revealed regional and disease-specific changes in synaptic phenotypes, including validation of this method with the expected changes in the molecular composition of striatal dopaminergic synapses in Lewy body disease and Alzheimer's disease. Mass synaptometry enables highly parallel molecular profiling of individual synaptic terminals.

Citation

Gajera, C.R., Fernandez, R., Postupna, N. et al. "Mass synaptometry: High-dimensional multi parametric assay for single synapses" Journal of Neuroscience Methods (2019): 73–83