ArticleMolecular neurobiology2024
Advanced Immunolabeling Method for Optical Volumetric Imaging Reveals Dystrophic Neurites of Dopaminergic Neurons in Alzheimer's Disease Mouse Brain.
Article in Molecular neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- In vivo mapping of cortical amyloid beta-myelin coupling in preclinical Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Current progress of labeling strategies in tissue clearing for large-scale biological visualization.Frontiers in cell and developmental biology · 2026Review
- Sleep and stress as modifiable drivers of Alzheimer's disease.NPJ dementia · 2026Review
- Navigating the Autophagy Maze: ATG and Their Impact on Neurodegenerative Diseases.Molecular neurobiology · 2025Review
- Harnessing human iPSC-microglia for CNS-wide delivery of disease-modifying proteins.Cell stem cell · 2025Article
- Impact of noradrenergic inhibition on neuroinflammation and pathophysiology in mouse models of Alzheimer's disease.Journal of neuroinflammation · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Optical brain clearing combined with immunolabeling is valuable for analyzing molecular tissue structures, including complex synaptic connectivity. However, the presence of aberrant lipid deposition due to aging and brain disorders poses a challenge for achieving antibody penetration throughout the entire brain volume. Herein, we present an efficient brain-wide immunolabeling method, the immuno-active clearing technique (iACT). The treatment of brain tissues with a zwitterionic detergent, specifically SB3-12, significantly enhanced tissue permeability by effectively mitigating lipid barriers. Notably, Quadrol treatment further refines the methodology by effectively eliminating residual detergents from cleared brain tissues, subsequently amplifying volumetric fluorescence signals. Employing iACT, we uncover disrupted axonal projections within the mesolimbic dopaminergic (DA) circuits in 5xFAD mice. Subsequent characterization of DA neural circuits in 5xFAD mice revealed proximal axonal swelling and misrouting of distal axonal compartments in proximity to amyloid-beta plaques. Importantly, these structural anomalies in DA axons correlate with a marked reduction in DA release within the nucleus accumbens. Collectively, our findings highlight the efficacy of optical volumetric imaging with iACT in resolving intricate structural alterations in deep brain neural circuits. Furthermore, we unveil the compromised integrity of DA pathways, contributing to the underlying neuropathology of Alzheimer's disease. The iACT technique thus holds significant promise as a valuable asset for advancing our understanding of complex neurodegenerative disorders and may pave the way for targeted therapeutic interventions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.