The mechanisms behind Alzheimer’s disease (AD) are complex and myriad. Even when researchers narrow their focus to specific aspects, the variety of experimental models and disease stages—along with how they interact and differ—prevents a unified understanding from emerging. Without this big picture, further research and drug development are severely hindered. Take myeloid cells, for instance; they comprise a broad group of immune cells, including microglia, monocytes, and macrophages. For years, studies have demonstrated the role of several myeloid cells in AD progression, but their behavior varies across different models and diseasestages, making it challenging to interpret and compare findings from both animal models and human patient studies.
Mouse models of AD are a key example of this challenge. While they have proven valuable for studying how microglia and the immune system impact disease risk and progression, they have limitations. First, mouse microglia lack the complexity of their human counterparts. Second, traditional amyloid mouse models rely on familial AD mutations to drive disease. Since familial AD accounts for less than 5% of cases and most AD cases are late-onset and sporadic (LOAD), these models may not fully capture the mechanisms of AD. LOAD-relevant mouse models have been developed, but they are not as widely used as traditional amyloid mouse models.
A unified model that bridges human patient data with experimental models is critical for overcoming these challenges. Dr. Raj is building such a model by combining detailed single-cell RNA sequencing data from both human patients and various LOAD mouse models, along with measurements of cognitive function and diseaseprogression. This integrated approach aims to create a comprehensive picture ofhow microglia change throughout AD. Ultimately, this will help identify which specific changes in microglia are directly responsible for AD pathology.
In pursuit of this, Dr. Raj proposes three aims. In the first aim, he will collect, harmonize, and integrate sequencing data from over 3,500 unique human donors, and then link this data to disease progression data and cognitive measures. In doing so, he will lay the groundwork for the human portion of the proposed unified model, enabling reliable comparisons to the data generated from the LOAD animal models. The second aim will focus on examining the harmonized human data in greater detail by identifying risk mutations and genetic variations associated with different microglial states. Dr. Raj’s team will then connect the changes in microglial states to overall disease progression as measured by pathology markers and cognitive performance. With data from the first two aims in hand, Dr. Raj can then integrate the human data with the collected mouse data in the third aim. This will allow his team to compare microglial changes across human and mouse contexts and assess which mouse models have microglia that most resemble human AD microglia. They can also evaluate the changes over time in disease states and determine if different microglial states are causal mediators of the disease. This ambitious project offers a novel framework for cross-species comparisons that will enable a more relevant interpretation of data from animal models while also furthering our understanding of how different mutations impact microglial states in AD.
