Decoding Spatial Mechanisms of Sex Differences in Alzheimer's Disease

Introduction: o Sex differences in Alzheimer's disease (AD) brains have been a subject of significant scientific inquiry. Studies have demonstrated variations in the accumulation and spatial distribution of hallmark AD pathologies, such as β-amyloid plaques and tau tangles, between male and female brains. Additionally, differences in neuroinflammatory responses and synaptic density suggesting distinct mechanistic pathways underlying disease progression in males and females. To better understand these differences, we developed a technique to detect differences in gene expression in brain tissues. In this research, we looked at a publicly available MERFISH dataset on human brain tissues from SEAAD.org and analyzed it. We used a layer notation system published by (Gabitto et al., 2023) to divide our tissue into 6 different layers (Layers 1 to 6), measured gene expression across layers and look for possible cell communication using a 5 nearest neighbor approach (KNN). For the gene expression analysis, we took differentially expressed genes and measured their relative expression across layers of all patients and compared it to disease progression. The analysis was conducted by moving a “dynamic box” along a straight line from the top of the tissue sample to the bottom, recording the gene expression of each of the cells. For the KNN analysis we recorded the 5 closest neighbors to a specific type of cell across all tissues and compared how neighborhoods changed as disease progressed. We determined that genes involved in neuronal function were downregulated as disease progressed; similarly neuronal interactions in cell neighborhoods decreased. Conversely, astro-glial interactions increased.

Materials and

Methods: o To determine what genes showed more variability we used the Morgan’s I Statistic formula in DESeq2, filtered for significant genes with a Log2 fold change gather than 1.5 and a q value of less than 0.01 as described in the paper by Stuart et al., 2019. We gathered those differentially expressed genes and measured their relative expression across layers from 1 to 6 in all patients with Thal scores from 0 to 2 and compare their gene expression to their Thal score. We developed a “dynamic box” of 100µm by approximately 2000 µm (depending on the tissue) that moves along a straight line from the top of the tissue sample to the bottom capturing the gene expression of each of the cells inside the dynamic box. We measured the means and standard deviations for each layer and arranged them accordingly. Statistical significance was calculated using the Mann Whitney U Test. Additionally, we quantify possible interactions among astrocytes, GABAergic neurons, and glutamatergic neurons because of their relative abundance in the brain (Siletti et al., 2023). For every cell belonging to the selected cell type, we examine its five nearest neighbors as shown by (Taunk et al., 2019) using the Euclidean metric distance between neighbors. The types of the neighboring cells were recorded and summed to determine the frequency of occurrence of each specific cell type among the five nearest neighbors. Cell neighborhoods were assessed across various patients with differing Thal scores and across all tissue layers.

Results, Conclusions, and Discussions: o Alzheimer's Disease research is evolving, every year researchers incorporate new ideas into this research to try to understand the nature of this disease and find ways to treat it or better yet cure it. In this research we measured the average gene expression of a total of 140 genes across 27 patients and 64 brain tissue slices. We were able to show differences at tissue and cellular level. Mainly, we found differences in three genes across the samples, namely: Solute Carrier Family 14 Member 1 (Kidd Blood Group) (SLC14A1): The protein encoded by this gene is a membrane transporter that mediates urea transport in erythrocytes, Solute Carrier Family 32 Member 1 (SLC32A1): The protein encoded by this gene is an integral membrane protein involved in gamma-aminobutyric acid (GABA) and glycine uptake into synaptic vesicles and Neuropeptide Y (NPY): involved in cortical excitability, stress response, food intake, circadian rhythms, and cardiovascular function. Our neighborhood analysis showed an overall shift in the cell interactions, showing less interactions between both GABAergic and Glutaminergic neurons. Conversely, astrocytes and microglia show increased interactions with other cells in the tissue highlighting the importance of these cells in disease progression. In this research, we showed that we can achieve single-cell resolution to understand the disease progression and the relationship between spatial distribution and gene expression across the temporal brain cortex.

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