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Record W4296705669 · doi:10.1101/2022.09.19.508617

Synaptic scale dopamine disruption in Huntington’s Disease model mice imaged with near infrared catecholamine nanosensors

2022· preprint· en· W4296705669 on OpenAlexfundno aff
Sarah J. Yang, Jackson Travis Del Bonis-O’Donnell, Francesca Giordani, Jeffrey Wang, Alison Lui, David J. Piekarski, Ashvin Irrinki, David V. Schaffer, Markita P. Landry

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsCamille and Henry Dreyfus FoundationPhilomathia FoundationU.S. Department of EnergyGordon and Betty Moore FoundationAlfred P. Sloan FoundationNational Institute on Drug AbuseOffice of ScienceU.S. Department of AgricultureNational Institutes of HealthNational Science Foundation
KeywordsDopamineDopaminergicNeuroscienceStriatumAutoreceptorDopamine transporterRotigotineCatecholamineEndocrinologyInternal medicineChemistryPsychologyBiologyMedicineParkinson's diseaseSerotoninDisease

Abstract

fetched live from OpenAlex

ABSTRACT Dopamine neuromodulation is a critical process that facilitates learning, motivation, and motor control. Disruption of these processes has been implicated in several neurological and psychiatric disorders including Huntington’s Disease (HD). While several treatments for physical and psychiatric HD symptoms target dopaminergic neuromodulation, the mechanism by which dopaminergic dysfunction occurs during HD is unknown. This is partly due to limited capability to visualize dopamine dynamics at the spatiotemporal resolution of both neuromodulator release (ms) and dopaminergic boutons (µm). Here we employ near-infrared fluorescent catecholamine nanosensors (nIRCats) to image dopamine release within the brain striatum of R6/2 Huntington’s Disease Model (R6/2) mice. We find that stimulated dorsal striatal dopamine release decreases with progressive motor degeneration and that these decreases are primarily driven by a decrease in the number of dopamine hotspots combined with decreased release intensity and decreased release fidelity. Using nIRCat’s high spatial resolution, we show that dopamine hotspots in late HD show increased ability to add new dopamine hotspots at high extracellular calcium concentrations and track individual dopamine hotspots over repeated stimulations and pharmacological wash to measure dopamine hotspots release fidelity. Compellingly, we demonstrate that antagonism of D2-autoreceptors using Sulpiride and direct blocking of K v 1.2 channels using 4-Aminopyradine (4-AP) increases the fidelity of dopamine hotspot activity in WT striatum but not in late HD striatum, indicating that D2-autoreceptor regulation of dopamine release through K v 1.2 channels is compromised in late HD. These findings, enabled by nIRCats, provide a more detailed look into how dopamine release is disrupted and dysregulated during Huntington’s Disease to alter the coverage of dopamine modulation across the dorsal striatum. SIGNIFICANCE STATEMENT Huntington’s Disease (HD) is a neurodegenerative disorder with no cure. Dysfunction of dopamine signaling is known to deteriorate in HD but has not been studied at the spatial level of individual release sites. Here, we image dopamine release from individual hotspots in brain slices from R6/2 HD mice at early and late disease timepoints with dopamine nanosensors. We track single dopamine hotspots and find that dopamine hotspot number, release intensity, and release fidelity decrease in HD, and demonstrate that changes in D2-autoreceptor regulation manifest through changes in hotspot release fidelity thus compromising dopamine coverage across the dorsal lateral striatum. These findings highlight dopaminergic neurons in cortico-striatal signaling during HD as a promising new therapeutic target for HD treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.228
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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