以具深度學習之大面積多光子激發單分子影像與聯合療法於阿茲海默症的協同效應之研究(1/3)

Project Details

Description

Neurodegenerative diseases cause devastating progress in the damages of theneural structures and functions to induce the incurable pathology in the centralnervous system. Among neurodegenerative diseases, Alzheimer’s disease (AD)exhibits the progressive loss of brain functions leading to severe deteriorationand dysfunction of cognitive and the effective treatment for the AD patientsremains a daunting challenge. The accumulating evidence indicates thesynergistic effects between the amyloid-β (Aβ) and hyperphosphorylated tau,which induce the major pathological hallmarks of the AD, the senile plaques andneurofibrillary tangles. In the past hypothesis, the Aβ and tau pathologies areusually assumed to be independent without the specific interaction, except the Aβtrigger effect for the tau pathology. Consequently, exploring the synergisticeffects of the Aβ and tau is critical for elucidating the mechanism of ADprogression and the failure of previous treatment strategies. Therefore, animaging system with the capabilities of nanoscale-level resolution, large-areamapping, and deep tissue imaging is necessary to investigate the complexinteraction of AD pathology-related proteins in the brain surrounded by allphysiological environmental factors. In this project, we will explore the synergisticeffects of the Aβ and tau for the pathological mechanism using the novel deeplearning-based large-area multiphoton excitation single-molecule localizationmicroscopy (SMLM), which integrates the SMLM, line-scanning temporalfocusingmultiphoton excitation (LsTFMPE), deep learning image reconstruction,and modified expansion specimen approach. The AD pathology-related proteinswith multiple fluorophore labeling are visualized by the wavelength-tunableTFMPE and spectrally-resolved imaging system to have the optimal two-photonexcitation efficiency of each fluorophore and high-spectral resolution multicolorfluorescence images. The LsTFMPE and deep learning-based SMLM can notonly maintain the substantial merits of the optical section, fast frame rate, andminimum photobleaching, but also significantly reduce the requirement of theexcitation power density for the two-photon excitation and single-moleculeblinking fluorescence to further improve the imaging area and temporalresolution, and deliver the single-molecule information of the AD pathologyrelatedproteins for the quantitative data analysis. The modified expansionspecimen approach provides the improvements in the scattering and aberrationissues of brain tissues and the localization precision and density of SMLMimaging for implementing the deeper penetration in imaging and reveal thesubcellular fine structures of neurons. Furthermore, based on the synergisticeffects of the Aβ and tau, the combination treatment via the disease-specificprotein-targeting carbon nanomaterials, which have the capabilities of blood-brainbarrier penetration and nonsignificant toxicity, to deliver the therapeutic drugs willbe adopted to explore the effectiveness for the reduction and clearance of theneurotoxic proteins and aggregates and the responses in the synergistic effectsbetween the Aβ and tau. Therefore, we believe that the observations andanalysis results of this work will prove beneficial for elucidating the pathogenesisof AD in this complex biological system and inspiring the design of nextgenerationclinical trials.
StatusFinished
Effective start/end date1/08/2231/01/24

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 12 - Responsible Consumption and Production
  • SDG 17 - Partnerships for the Goals

Keywords

  • Alzheimer’s disease
  • amyloid-β
  • tau
  • deep learning
  • line-scanning temporalfocusing multiphoton excitation
  • single-molecule localization microscopy
  • expansion specimen
  • quantitative data analysis

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