Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles

Our research aims to identify gender-specific genes in glial cells, explore associated pathways, and evaluate the potential therapeutic effects of TCM on Alzheimer's disease. The present protocol analyzes a large number of single-nuclei transcriptomes from the GEO database through bioinformatics analysis. Our protocol offers advantages, including single-cell resolution, CTAP identification of biomarker discovery, and comparison of growth in different genders.

Our laboratory will focus on the field of neuroscience, with a parameter focused on utilizing acupuncture to enhance the treatment of neurological disorders. To begin, download the Alzheimer's data files for sample merging, and configure the data paths as well as sample names. Import the downloaded samples and assign gender-specific names to them under the function names.

Using the functions list in Read10X, generate Seurat objects for all samples in a batch processing manner, and specify the parameters of minimum cells to three and minimum features to 200. With the RenameCells function, add sample IDs as prefixes to the cell barcodes to preserve them during the merging process. For quality control, employing the PercentageFeatureSet function, calculate the mitochondrial erythrocyte and ribosome gene ratios for each cell.

Store these computed ratios in the metadata using the double square brackets operator to attach this information directly to each cell's metadata. Utilizing the subset function, conduct cell filtration with the appropriate parameters for RNA, mitochondria, ribosomes, and erythrocytes. Normalize the data using the NormalizeData function.

With the FindVariableFeatures, identify the top 2000 variable features in the dataset. Use RunPCA to conduct principle component analysis on the data, retaining 50 principle components. Employing the ElbowPlot function, generate an elbow plot to determine the optimal number of dimensions for subsequent analysis.

Considering the first 50 dimensions, select scale data to bring all features on a comparable scale. With the FindNeighbors function, identify nearest neighbors based on 30 dimensions and RunUMAP algorithm to reduce the dimensionality of the data to 30 dimensions. Select DimPlot function to visualize the processed data with the reduction parameter set to UMAP, and the group.

by parameter set to original identity. Using SCTransform function, normalize and standardize the data and apply the harmony algorithm to integrate the remaining single nuclei data. Select the SCT assay for integration and set the maximum number of harmony iterations to 20.

Then run the FindClusters function and set the resolution parameter to 0.07 to identify distinct clusters within the data, while employing the RunUMAP function to reduce the dimensionality of the data further and visualize the clusters in a lower dimensional space. For cell type annotation, identify the cellular cluster heterogeneity and classify the type of each cluster cell using the explicitly expressed marker genes. Present various cellular types with UMAP visualization using the ggplot 2 package where various cell types are highlighted with different color codes.

Finally, calculate the proportions of each cell type stratified by gender. Using this method, the proportions for each cell type split by gender were identified in 17 male and 17 female Alzheimer's disease patients'data. Average expressions of the known cell type markers for each glial cell type were projected on the UMAP plots to identify the cell populations.