Neurodegenerative diseases Cell Models for Research

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), represent a growing global health challenge. According to the World Health Organization (WHO), the number of people living with dementia worldwide is estimated at over 55 million, with nearly 10 million new cases each year. AD is the most common cause of dementia, contributing to 60-70% of cases. PD affects over 8.5 million people globally, with prevalence increasing with age. ALS has an incidence of about 1-2 per 100,000 person-years, and HD affects approximately 5-10 per 100,000 individuals. These diseases are progressive and currently incurable, leading to significant morbidity, mortality, and socioeconomic burden. The economic cost of dementia alone was estimated at $1.3 trillion in 2019, and this is projected to double by 2030. Key risk factors include aging, genetic predisposition, and environmental factors. The 5-year survival rates vary widely: for ALS, median survival from onset is 2-5 years; for AD, average survival after diagnosis is 4-8 years; for PD, it is longer, often exceeding 10 years. These statistics underscore the urgent need for effective therapies and reliable research models.

Neurodegenerative diseases are ideal for mechanistic studies due to their well-defined genetic and pathological hallmarks. For AD, the accumulation of amyloid-beta plaques and tau neurofibrillary tangles provides clear endpoints. PD is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of Lewy bodies containing alpha-synuclein. ALS involves motor neuron degeneration with TDP-43 proteinopathy. HD is caused by an expanded CAG repeat in the huntingtin gene. Public datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Parkinson's Progression Markers Initiative (PPMI), and the International ALS Consortium provide rich clinical and genomic data. However, many open questions remain, including the precise molecular mechanisms of neurodegeneration, the role of neuroinflammation, and the contribution of genetic risk factors. Gene-edited cell models offer a powerful approach to dissect these pathways in a controlled environment, enabling functional validation of genetic variants and drug target identification.

Several key pathways are central to neurodegeneration:

• • Protein misfolding and aggregation: Misfolded proteins (e.g., amyloid-beta, tau, alpha-synuclein, TDP-43) aggregate and form toxic species, leading to cellular dysfunction.
• • Oxidative stress and mitochondrial dysfunction: Impaired mitochondrial function increases reactive oxygen species (ROS), causing damage to lipids, proteins, and DNA.
• • Neuroinflammation: Activated microglia and astrocytes release pro-inflammatory cytokines, contributing to neuronal death.
• • Autophagy-lysosomal dysfunction: Impaired clearance of protein aggregates and damaged organelles exacerbates toxicity.
• • Excitotoxicity: Excessive glutamate signaling leads to calcium overload and neuronal damage.
• • Axonal transport defects: Disruption of intracellular transport impairs neuronal function and survival.

Data from ClinVar, COSMIC, and NCBI Gene.

Key signaling networks implicated in neurodegeneration include:

• • MAPK/ERK pathway: Involved in cell survival and stress responses; dysregulated in AD and PD.
• • PI3K/AKT/mTOR pathway: Regulates autophagy and cell growth; impaired in many neurodegenerative conditions.
• • Wnt signaling: Plays a role in neurogenesis and synaptic function; altered in AD.
• • NF-kB pathway: Mediates neuroinflammation; activated in ALS and AD.
• • Notch signaling: Involved in neuronal differentiation; dysregulated in HD.
• • Autophagy pathway: Key components include ATG5, ATG7, LC3, and p62; mutations or dysregulation contribute to protein aggregation.

Organoids, such as cerebral organoids, recapitulate 3D brain-like structures and are valuable for studying neurodevelopment and disease. They can be derived from patient iPSCs and gene-edited to introduce or correct mutations.

Animal models are essential for studying neurodegeneration in vivo. Common models include:

• • Transgenic mice: Overexpress mutant human genes (e.g., APP/PS1 for AD, SNCA for PD, SOD1 for ALS, R6/2 for HD).
• • Knock-in mice: Introduce specific mutations into the endogenous mouse gene (e.g., APP knock-in, LRRK2 G2019S knock-in).
• • Viral vector-mediated models: Use AAV or lentivirus to deliver disease genes to specific brain regions.
• • Toxin-induced models: Use neurotoxins like MPTP or 6-OHDA for PD, and kainic acid for excitotoxicity.
• • Patient-derived xenograft (PDX) models: Less common for neurodegeneration but used for brain tumors; for neurodegenerative diseases, chimeric models with human cells are emerging.

CRISPR-based gene editing enables the creation of isogenic cell lines with precise genetic modifications, providing powerful tools for studying neurodegenerative diseases. Examples include:

• • APP knockout cell lines: Used to study amyloid-beta processing and the role of APP in neuronal function.
• • SNCA knockout cell lines: Help elucidate the normal function of alpha-synuclein and its role in PD.
• • LRRK2 G2019S knock-in cell lines: Model the most common PD mutation, allowing for drug screening and mechanistic studies.
• • MAPT P301L knock-in cell lines: Used to study tau aggregation and toxicity in AD and frontotemporal dementia.
• • C9orf72 repeat expansion cell lines: Model ALS/FTD, enabling studies of RNA toxicity and dipeptide repeat proteins.

These gene-edited models are commercially available and sequence-verified, ensuring reproducibility and accelerating research. They are essential for target validation, drug screening, and functional genomics.

Gene-edited cell lines are instrumental in functional genomics, allowing researchers to determine the impact of specific genetic variants on cellular phenotypes. For example:

• • Knockout of APOE in iPSC-derived neurons or SH-SY5Y cells can reveal its role in lipid metabolism and amyloid-beta clearance.
• • Knock-in of the APOE4 allele (a major risk factor for AD) can be compared to APOE3 to study isoform-specific effects on tau phosphorylation and neuroinflammation.
• • Knockout of TDP-43 in motor neuron-like cells (e.g., NSC-34) helps elucidate its role in RNA processing and stress granule dynamics.
• • Knock-in of the C9orf72 repeat expansion in iPSC-derived motor neurons allows for the study of repeat-associated non-ATG (RAN) translation and dipeptide repeat toxicity.

Isogenic pairs (wild-type vs. mutant) are ideal for drug screening, as they allow for the identification of compounds that specifically target the mutant allele. For example:

• • LRRK2 G2019S knock-in cells can be used to screen for kinase inhibitors that selectively block the mutant enzyme.
• • SOD1 A4V knock-in cells (a familial ALS mutation) can be used to test compounds that reduce oxidative stress or protein aggregation.
• • HTT knock-in cells with expanded CAG repeats can be used to screen for modulators of huntingtin aggregation.
• • Drug resistance can be modeled by exposing cells to increasing concentrations of a drug and selecting for resistant clones, then identifying the genetic changes using CRISPR screens.

CRISPR-based synthetic lethality screens can identify genes that, when knocked out, are lethal only in the presence of a specific disease mutation. This approach can uncover novel therapeutic targets and biomarkers. For example:

• • In C9orf72 repeat expansion cells, a genome-wide CRISPR screen could identify genes whose loss is synthetically lethal, revealing potential drug targets.
• • In LRRK2 G2019S cells, screens can identify genes involved in mitochondrial dysfunction that are selectively essential in mutant cells.
• • Secreted biomarkers can be identified by comparing the secretome of gene-edited cells to wild-type controls, using mass spectrometry or antibody arrays.