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Published April 13, 2022 | public
Book Section - Chapter

Studying Axonal Transport in the Brain by Manganese-Enhanced Magnetic Resonance Imaging (MEMRI)

Abstract

From the earliest notions of dynamic movements within the cell by Leeuwenhoek, intracellular transport in eukaryotes has been primarily explored by optical imaging. The giant axon of the squid became a prime experimental model for imaging transport due to its size, optical transparency, and physiological robustness. Even the biochemical basis of transport was identified using optical assays based on video microscopy of fractionated squid axoplasm. Discoveries about the dynamics and molecular components of the intracellular transport system continued in many model organisms that afforded experimental systems for optical imaging. Yet whether these experimental systems reflected a valid picture of axonal transport in the opaque mammalian brain was unknown. Magnetic resonance imaging (MRI) provides a non-destructive approach to peer into opaque tissues like the brain. The paramagnetic ion, manganese (MnII), gives a hyperintense signal in T1 weighted MRI that can serve as a marker for axonal transport. Mn(II) enters active neurons via voltage-gated calcium channels and is transported via microtubule motors down their axons by fast axonal transport. Clearance of Mn(II) is slow. Scanning live animals at successive time points reveals the dynamics of Mn(II) transport by detecting Mn(II)-induced intensity increases or accumulations along a known fiber tract, such as the optic nerve or hippocampal-forebrain projections. Mn(II)-based tract tracing also reveals projections even when not in fiber bundles, such as projections in the olfactory system or from medial prefrontal cortex into midbrain and brain stem. The rate of Mn(II) accumulation, detected as increased signal intensity by MR, serves as a proxy for transport rates. Here we describe the method for measuring transport rates and projections by mangeses-enhanced magnetic resonance imaging, MEMRI.

Additional Information

© The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022. First Online: 13 April 2022. We are grateful to many people in our labs who contributed to this protocol over more than 15 years. We thank Taylor Uselman and Chris Medina for assistance with our computational image analysis pipeline, and for critical reading of this MS. We are grateful to Sharon Wu Lin for animal husbandry. Development of this protocol was supported by the Beckman Institute at Caltech (R.E.J. and X.Z.), the Moore Foundation (E.L.B.), the Harvey Family Foundation (E.L.B.), and a series of NIH grants: NIDA RO1-DA018184 (R.E.J.); NINDS RO1 NS062184 (E.L.B.); and NIMH RO1 MH096093 (E.L.B.).

Additional details

Created:
August 20, 2023
Modified:
January 15, 2024