Efficient gene transfers into neocortical neurons connected by NMDA NR1-containing synapses.

Abstract:

BACKGROUND:Within a circuit, specific neurons and synapses are hypothesized to have essential roles in circuit physiology and learning, and dysfunction in these neurons and synapses causes specific disorders. These critical neurons and synapses are embedded in complex circuits containing many neuron and synapse types. NEW METHOD:We established technology that can deliver different genes into pre- and post-synaptic neurons connected by a specific synapse type. The first, presynaptic gene transfer employs standard gene transfer technology to express a synthetic peptide neurotransmitter which has three domains, a dense core vesicle sorting domain for processing the protein as a peptide neurotransmitter, a receptor-binding domain, here a small peptide that binds to NMDA NR1 subunits, and the His tag. Upon release, this peptide neurotransmitter binds to its cognate receptor on postsynaptic neurons. Gene transfer selectively into these postsynaptic neurons employs antibody-mediated, targeted gene transfer and anti-His tag antibodies, which recognize the His tag domain in the synthetic peptide neurotransmitter. RESULTS:For the model system, we studied the connection from projection neurons in postrhinal cortex to specific neurons in perirhinal cortex. In our initial report, gene transfer to connected neurons was 20+1% specific. Here, we optimized the technology; we improved the transfection for packaging by using a modern using a modern lipid, Lipofectamine 3000, and used a modern confocal microscope to collect data. We now report 80+2% specific gene transfer to connected neurons. COMPARISON WITH EXISTING METHODS:There is no existing method with this capability. CONCLUSIONS:This technology may enable studies on the roles of specific neurons and synapses in circuit physiology and learning, and support gene therapy treatments for specific disorders.

journal_name

J Neurosci Methods

authors

Nagayach A,Singh A,Geller AI

doi

10.1016/j.jneumeth.2019.108390

subject

Has Abstract

pub_date

2019-11-01 00:00:00

pages

108390

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(19)30247-X

journal_volume

327

pub_type

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