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Related Experiment Video

Updated: Apr 29, 2026

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Modeling Late-Onset Sporadic Alzheimer's Disease Using Patient-Derived Cells: A Review.

Alisar Katbe1, Ismaïla Diagne1, Gilbert Bernier1,2

  • 1Stem Cell and Developmental Biology Laboratory, Maisonneuve-Rosemont Hospital, 5690 Boulevard Rosemont, Montreal, QC H1T 2H2, Canada.

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|January 27, 2026
PubMed
Summary

Late-onset sporadic Alzheimer's disease (LOAD) research is advancing with new patient-derived neuronal models. These models, created using induced pluripotent stem cell and induced neuron technologies, aim to better understand LOAD pathophysiology.

Keywords:
Alzheimer’s diseaseagingepigenomeinduced neuron (iN)induced pluripotent stem cell (iPSC)neuronreprogramming

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Area of Science:

  • Neuroscience
  • Genetics
  • Cell Biology

Background:

  • Late-onset sporadic Alzheimer's disease (LOAD) is the most prevalent form of dementia, characterized by progressive neurodegeneration.
  • Advanced aging is the primary risk factor for LOAD, but its mechanisms are not fully understood.
  • Existing animal models often recapitulate early-onset familial Alzheimer's disease (EOAD), limiting insights into LOAD.

Purpose of the Study:

  • To review and compare reprogramming technologies for generating patient-specific neuronal models of LOAD.
  • To evaluate the fidelity of these models in mimicking LOAD pathologies.
  • To advance the understanding of LOAD's origin and pathophysiology.

Main Methods:

  • Review of current literature on induced pluripotent stem cell (iPSC) and induced neuron (iN) technologies.
  • Comparison of reprogramming methods for generating LOAD patient-derived neurons.
  • Evaluation of cellular and molecular pathologies in generated LOAD neuronal models.

Main Results:

  • Both iPSC and iN technologies can generate LOAD patient-derived neurons.
  • The study evaluates the capacity of these models to replicate key LOAD cellular and molecular features.
  • Specific findings on model fidelity and mimicry of LOAD pathologies are discussed.

Conclusions:

  • Patient-derived neuronal models are crucial for studying LOAD due to limitations of traditional models.
  • Reprogramming technologies offer promising avenues for creating more accurate LOAD disease models.
  • Further research is needed to fully validate these models for LOAD drug discovery and therapeutic development.