The authors are grateful to Jessica S Sadick, Michael R O&#39;Dea, Philip Hasel, etc., for providing the GSE167490 dataset. The authors appreciate that Faten A Sayed, Lay Kodama, Li Fan, etc., offer the GSE183068 dataset. The authors thank Shuqing Liu for the help with data analysis and Wen Yang for providing the data analysis platform. This study was supported by National Natural Science Foundation of China (82174511), Chengdu University of Traditional Chinese Medicine Apricot Grove Scholars, Discipline Talent Research Enhancement Program (QJJJ2022001), LiaoNing Revitalization Talents Program (XLYC 1807083), Sichuan Administration Bureau Fund of Chinese Medicine and Herbs (2023MS578), National Undergraduate Innovation and Entrepreneurship Training Project (202310633003X), and Innovative topics of scientific research practice for college students in Chengdu University of Traditional Chinese Medicine (ky-2023100). Hanjie Liu and Hui Yang contributed to the design of the study, collection, interpretation of data, and drafting, and revising the manuscript. Shuqing Liu and Siyu Li participated in the design of the study, collection of data and drafting the manuscript. Wen Yang and Anwar Ayesha were responsible for the collection and interpretation of data. Xin Tan prepared figures and/or tables. Cen Jiang, Yi Liu, and Lushuang Xie conceived the study and reviewed/edited the manuscript. All authors contributed to the article and approved the submitted version.

Steps 2 to 9 of the analysis were implemented using R software (see Supplementary Figure 1 and Supplementary File 1), while the remaining steps were executed on the online platforms. The details of the databases used in this protocol (along with the weblinks) are provided in the Table of Materials.
1. Data acquisition
<ol>
	<li>Access the publicly available Gene Expression Omnibus (GEO) database at the National Center of Biotechnology Information.</li>
	<li>Search the GEO data named Alzheimer&#39;s disease in the search box.</li>
	<li>Select the Top Organisms as Homo sapiens on the right side. 
	NOTE: The search results were data on Alzheimer&#39;s disease in Homo sapiens.</li>
	<li>After filtering the searched information, download the GSE167490 and GSE183068 data files, which encompass features.tsv, barcode.tsv, and matrix.mtx for each individual single nuclei sample. The datasets comprised 34 samples of AD originating from the frontal cortex, with an equal distribution of 17 male samples and 17 female samples (Supplementary Table 1).</li>
</ol>
2. Sample merging
<ol>
	<li>Configure the data paths and sample names accordingly on the computer. Import the 34 downloaded samples and assign gender-specific names to the samples using the function names.</li>
	<li>Generate Seurat objects for all samples in a batch-processing manner using the functions list and Read10X, specifying the parameters as min.cells = 3 and min.features = 200.</li>
	<li>Use the RenameCells function to add sample IDs as prefixes to the cell barcodes to preserve cell barcodes during the merging process. This ensured that each cell retained its unique identity and could be traced back to its original sample source after merging.</li>
</ol>
3. Quality control (QC)
<ol>
	<li>Employ the PercentageFeatureSet function to calculate the mitochondrial gene ratios, erythrocyte gene ratios, and ribosome gene ratios for each cell.</li>
	<li>Store these computed ratios in the metadata using the [[ ]] operator to attach this information directly to each cell&#39;s metadata.</li>
	<li>Utilize the subset function to conduct cell filtration, specifying the parameters as nFeature_RNA &#62; 200, nFeature_RNA &#60; 10000, nCount_RNA &#60; 60000, percent.mt &#60; 10, percent.rb &#60; 5, and percent.HB &#60; 75.</li>
	<li>Exclude GSM5106107 from the analysis.</li>
</ol>
4. Batch effect checking
<ol>
	<li>Perform data processing.
	<ol>
		<li>Normalize the data using the NormalizeData function.</li>
		<li>Identify the top 2000 variable features in the dataset using the FindVariableFeatures function.</li>
		<li>Conduct principal component analysis (PCA)17 on the data using RunPCA, retaining 50 principal components.</li>
		<li>Generate an elbow plot using the ElbowPlot function to determine the optimal number of dimensions for subsequent analysis. Consider the first 50 dimensions.</li>
		<li>Scale the data using ScaleData to ensure that all features are on a comparable scale.</li>
		<li>Identify nearest neighbors using FindNeighbors based on 30 dimensions.</li>
		<li>Apply the UMAP algorithm using RunUMAP to reduce the dimensionality of the data to 30 dimensions.</li>
	</ol>
	</li>
	<li>Visualize the processed data using the DimPlot function with the reduction parameter set to umap and the group.by parameter set to orig.ident. 
	NOTE: This step could generate a plot visualizing the data in the reduced UMAP space, grouped by the original cell identities. Upon examining the UMAP plots, it became apparent that there was a presence of batch effect. The distinct clustering or separation of cells based on their batch or experimental origin suggested that the experimental batches had influenced the gene expression profiles.</li>
</ol>
5. Data integration
<ol>
	<li>Normalize and standardize the data using the SCTransform function.</li>
	<li>Apply the harmony algorithm18 to integrate the remaining 33 single-nuclei data. Use the SCT assay for integration and set the maximum number of harmony iterations to 20.</li>
	<li>Use FindClusters function with a resolution parameter set to 0.07 to identify distinct clusters within the data.</li>
	<li>Employ the RunUMAP function with a specified number of dimensions (dims = 30) to further reduce the dimensionality of the data and visualize the clusters in a lower-dimensional space.</li>
</ol>
6. Cell type annotation
<ol>
	<li>Collect the marker genes (Supplementary Table 2) of cells through an extensive review of the existing literature.</li>
	<li>Following the identification of cellular cluster heterogeneity, classify the type of each cluster cell by the marker genes expressed specifically.</li>
	<li>Present various cellular types with UMAP visualization using ggplot2 package, where oligodendrocyte was highlighted with the color code #DB7093, excitatory neuron with #FF69B4, astrocyte with #1874CD, microglia with #63B8FF, oligodendrocyte precursor cell with #DB7093, inhibitory neuron with #FFC0CB, and endothelial cell with #FF69B4.</li>
	<li>Calculate the proportions of each cell type stratified by gender.</li>
</ol>
7. Glial cell data extraction
<ol>
	<li>Extract astrocyte data from the integrated bulk data using subset function.</li>
	<li>Extract microglia data from the integrated bulk data using subset function.</li>
	<li>Extract oligodendrocyte data from the integrated bulk data using subset function.</li>
</ol>
8. Glial sex-specific differentially expressed genes (DEGs) capturing
<ol>
	<li>Identify sex-specific DEGs of astrocytes using the FindMarkers function (ident.1 = male, ident.2 = female, group.by = group.sum, assay = RNA) with threshold values: p-value &#60; 0.05 and |avg_log2FC| &#62; 30. Label the up-regulated DEGs as Up, down-regulated DEGs as Down, and the rest as Stable.
	<ol>
		<li>Visualize the DEGs using the ggplot function, with the x-axis representing the difference in percentage between two conditions (pct.1 - pct.2), and the y-axis depicting the avg_log2FC. The up-regulated genes were highlighted by using the color PaleVioletRed, the down-regulated genes with Pink, and the stable genes with DodgerBlue3.</li>
	</ol>
	</li>
	<li>Identify sex-specific DEGs of microglia using the FindMarkers function (ident.1 = male, ident.2 = female, group.by = group.sum, assay = RNA) with threshold values: p-value &#60; 0.05 and |avg_log2FC| &#62; 1. Label the up-regulated DEGs as Up, down-regulated DEGs as Down, and the rest as Stable.
	<ol>
		<li>Visualize the DEGs using the ggplot function, with the x-axis representing the difference in percentage between two conditions (pct.1 - pct.2), and the y-axis depicting the avg_log2FC. The up-regulated genes were highlighted by using the color OrangeRed, the down-regulated genes with LightSalmon, and the stable genes with SteelBlue1.</li>
	</ol>
	</li>
	<li>Identify sex-specific DEGs of oligodendrocyte using the FindMarkers function (ident.1 = male, ident.2 = female, group.by = group.sum, assay = RNA) with threshold values: p-value &#60; 0.05 and |avg_log2FC| &#62; 10. Label the up-regulated DEGs as Up, down-regulated DEGs as Down, and the rest as Stable.
	<ol>
		<li>Visualize the DEGs using the ggplot function, with the x-axis representing the difference in percentage between two conditions (pct.1 - pct.2), and the y-axis depicting the avg_log2FC. The up-regulated genes were highlighted by using the color DeepPink, the down-regulated genes with HotPink, and the stable genes with DeepSkyBlue3.</li>
	</ol>
	</li>
</ol>
9. Functional enrichment analyses of sex-specific DEGs
<ol>
	<li>Perform Gene ontology (GO) enrichment analysis on sex-specific DEGs for each glial cell type using the enrichGO function. Set the following parameters: OrgDb = org.Hs.eg.db, keyType = SYMBOL, ont = ALL, pAdjustMethod = BH, pvalueCutoff = 0.01, and qvalueCutoff = 0.05.</li>
	<li>Convert the gene symbols into corresponding gene IDs using function bitr. Conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on sex-specific DEGs for each glial cell type by using enrichKEGG function. Adjust the settings as follows: organism = hsa, keyType = kegg, pAdjustMethod = BH, pvalueCutoff = 0.01, and qvalueCutoff = 0.05.</li>
</ol>
10. Frequency statistics of glial DEGs in go and kegg pathways, venn diagrams of each glial sex-specific DEGs, and PPI network construction
<ol>
	<li>Calculate the frequency of glial sex-specific DEGs in GO and KEGG pathways using a frequency histogram.</li>
	<li>Access the STRING database to construct the PPI networks.</li>
	<li>Choose the Multiple proteins. Search for the List Of Names in the search box. Set &#34;Organisms&#34; as Homo sapiens.</li>
	<li>Review the list of proteins obtained from the search. Click on Continue to proceed.</li>
	<li>Export the PPI networks by selecting the download option, preferably in PNG format with higher resolution.</li>
	<li>Visualize the distribution of co-expression for the top sex-specific genes by using the Venn diagrams.</li>
	<li>Identify the shared gene(s) as key gene(s) in the study based on the Venn diagram analysis.</li>
</ol>
11. Multifactor regulatory network construction
<ol>
	<li>Access the NetworkAnalyst.</li>
	<li>Click on Gene List Input and specify organism as H. sapiens (human). Set ID type as Official Gene Symbol. Enter the gene name in the search field, and then click on Upload and Proceed.</li>
	<li>Select Gene-miRNA Interactions and choose miRTarBase v8.0. Confirm the selection by clicking on OK.</li>
	<li>Proceed to TF-gene Interactions and select the ENCODE database. Click on OK to confirm the selection.</li>
	<li>Next, navigate to TF-miRNA Coregulatory Network and click on OK to proceed.</li>
	<li>Finally, choose Proceed to generate the multifactor regulatory network incorporating gene-miRNA interactions and TF-gene interactions.</li>
</ol>
12. Gene and target TCM analysis
<ol>
	<li>Access the Coremine Medical online database.</li>
	<li>Enter the specific gene name and select the corresponding gene with the suffix gene/protein, human into the search box under the Explore section.</li>
	<li>Navigate the Drugs section and identify the TCMs associated with the searched drugs. 
	NOTE: Statistically significant drugs were marked in blue.</li>
	<li>Determine the top five TCMs based on their &#34;significance&#34; value as therapeutic TCMs.</li>
</ol>
13. Research summary of TCM ingredients in targeting key gene
<ol>
	<li>Access the Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine (TCMIP) and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Enter the names of the herbs into the search bar to retrieve their corresponding ingredients.</li>
	<li>Retrieve the ingredients in the PubMed database with a time limit up until April 10th, 2023. The search terms used included the Molecule Name in TCMSP and Chemical Components in TCMIP as search terms, and were limited to articles published in English.</li>
	<li>Summarize and analyze the herbs and their corresponding ingredients acting on AD.</li>
</ol>
14. Confirming of targeting TCMs&#39; treatment function in the sex specificity of AD
<ol>
	<li>Import herbs into the TCMIP and navigate to the corresponding description page.</li>
	<li>Utilize the Export data function and select the CSV format to download the enrichment terms of GO - Biological Process, GO - Cellular Component, GO - Molecular Function, and Reactome Pathway.</li>
	<li>Visualize the downloaded enrichment terms for each herb using bar charts.</li>
</ol>

Sex-Specific Glial Biomarkers in Alzheimer&#39;s Disease: A Transcriptome Analysis

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	<li>Alzheimers Dement. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alzheimer's+disease+facts+and+figures.">Alzheimer's disease facts and figures.</a> Alzheimers Dement. 19 (4), 1598-1695 (2023).</li><li>Xie, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electroacupuncture+improves+M2+microglia+polarization+and+glia+anti-inflammation+of+hippocampus+in+Alzheimer's+disease.">Electroacupuncture improves M2 microglia polarization and glia anti-inflammation of hippocampus in Alzheimer's disease.</a> Front Neurosci. 15, 689629 (2021).</li><li>Xie, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inflammatory+factors+and+amyloid+beta-induced+microglial+polarization+promote+inflammatory+crosstalk+with+astrocytes.">Inflammatory factors and amyloid beta-induced microglial polarization promote inflammatory crosstalk with astrocytes.</a> Aging (Albany NY). 12 (22), 22538-22549 (2020).</li><li>Hampel, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+amyloid-beta+pathway+in+Alzheimer's+disease.">The amyloid-beta pathway in Alzheimer's disease.</a> Mol Psychiatry. 26 (10), 5481-5503 (2021).</li><li>Baik, S. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+breakdown+in+metabolic+reprogramming+causes+microglia+dysfunction+in+Alzheimer's+disease.">A breakdown in metabolic reprogramming causes microglia dysfunction in Alzheimer's disease.</a> Cell Metab. 30 (3), 493-507 (2019).</li><li>Fisher, D. W., Bennett, D. A., Dong, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sexual+dimorphism+in+predisposition+to+Alzheimer's+disease.">Sexual dimorphism in predisposition to Alzheimer's disease.</a> Neurobiol Aging. 70, 308-324 (2018).</li><li>Pan, R. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Positive+feedback+regulation+of+microglial+glucose+metabolism+by+histone+h4+lysine+12+lactylation+in+Alzheimer's+disease.">Positive feedback regulation of microglial glucose metabolism by histone h4 lysine 12 lactylation in Alzheimer's disease.</a> Cell Metab. 34 (4), 634-648 (2022).</li><li>Hansen, D. V., Hanson, J. E., Sheng, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microglia+in+Alzheimer's+disease.">Microglia in Alzheimer's disease.</a> J Cell Biol. 217 (2), 459-472 (2018).</li><li>Brandebura, A. N., Paumier, A., Onur, T. S., Allen, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Astrocyte+contribution+to+dysfunction,+risk+and+progression+in+neurodegenerative+disorders.">Astrocyte contribution to dysfunction, risk and progression in neurodegenerative disorders.</a> Nat Rev Neurosci. 24 (1), 23-39 (2023).</li><li>Peng, L., Bestard-Lorigados, I., Song, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+synapse+as+a+treatment+avenue+for+Alzheimer's+disease.">The synapse as a treatment avenue for Alzheimer's disease.</a> Mol Psychiatry. 27 (7), 2940-2949 (2022).</li><li>Tubi, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=White+matter+hyperintensities+and+their+relationship+to+cognition:+Effects+of+segmentation+algorithm.">White matter hyperintensities and their relationship to cognition: Effects of segmentation algorithm.</a> Neuroimage. 206, 116327 (2020).</li><li>Wu, H., Kirita, Y., Donnelly, E. L., Humphreys, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advantages+of+single-nucleus+over+single-cell+RNA+sequencing+of+adult+kidney:+Rare+cell+types+and+novel+cell+states+revealed+in+fibrosis.">Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: Rare cell types and novel cell states revealed in fibrosis.</a> J Am Soc Nephrol. 30 (1), 23-32 (2019).</li><li>Soreq, L., Bird, H., Mohamed, W., Hardy, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+analysis+of+human+Alzheimer's+disease+brain+samples+reveals+neuronal+and+glial+specific+cells+differential+expression.">Single-cell RNA sequencing analysis of human Alzheimer's disease brain samples reveals neuronal and glial specific cells differential expression.</a> PLoS One. 18 (2), e0277630 (2023).</li><li>Sadick, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Astrocytes+and+oligodendrocytes+undergo+subtype-specific+transcriptional+changes+in+Alzheimer's+disease.">Astrocytes and oligodendrocytes undergo subtype-specific transcriptional changes in Alzheimer's disease.</a> Neuron. 110 (11), 1788-1805 (2022).</li><li>Chen, Y., Colonna, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microglia+in+Alzheimer's+disease+at+single-cell+level.+Are+there+common+patterns+in+humans+and+mice.">Microglia in Alzheimer's disease at single-cell level. Are there common patterns in humans and mice.</a> J Exp Med. 218 (9), e20202717 (2021).</li><li>Brase, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-nucleus+RNA-sequencing+of+autosomal+dominant+Alzheimer+disease+and+risk+variant+carriers.">Single-nucleus RNA-sequencing of autosomal dominant Alzheimer disease and risk variant carriers.</a> Nat Commun. 14 (1), 2314 (2023).</li><li>Ringner, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+is+principal+component+analysis.">What is principal component analysis.</a> Nat Biotechnol. 26 (3), 303-304 (2008).</li><li>Korsunsky, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=sensitive+and+accurate+integration+of+single-cell+data+with+harmony.">sensitive and accurate integration of single-cell data with harmony.</a> Nat Methods. 16 (12), 1289-1296 (2019).</li><li>Vegeto, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+sex+and+sex+hormones+in+neurodegenerative+diseases.">The role of sex and sex hormones in neurodegenerative diseases.</a> Endocr Rev. 41 (2), 273-319 (2020).</li><li>Hafemeister, C., Satija, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Normalization+and+variance+stabilization+of+single-cell+RNA-seq+data+using+regularized+negative+binomial+regression.">Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.</a> Genome Biol. 20 (1), 296 (2019).</li></ol>

There is no conflict of interest in this manuscript, and all authors have approved the submission for publication.

Gender-specificity has been identified in epidemiology, pathology, and clinical manifestation of AD19. Here, we confirmed the potential pathological mechanism of the "hormone-synapse-neuron axis" from gender-specific glial genes and related pathways in AD patients. NLGN4Y was the only shared gene in the three glia and was chosen as the biomarker for the gender specificity of AD. TF and miRNAs regulating NLGN4Y were strongly linked to gender differences and the development of the nervous system. Fu...

Alzheimer&#39;s disease (AD) is a global disease with high incidence, and it accounts for 60%-80% of dementia1. Despite its high incidence, the mechanistic pathogenesis of AD is not clearly delineated, and there have been no effective therapeutics until now2. The main pathologies in AD were identified as neuronal atrophy and the accumulation of pathological debris, mainly microtubule-associated protein Tau, and &#946;-amyloid (A&#946;)3,4. The pathogenesis of AD is associated with abnormal autophagy, oxidative stress, mitochondrial dysfunction, inflammation, and energy metabolism disorder5. Prevalence surveys proved that two-thirds of AD patients were women6. Sex-specific differences in AD exist in the etiology, clinical manifestations, prevention, and treatment. Thus, revealing the biological mechanism that causes sex-specific differences in AD and targeting traditional Chinese medicine (TCM) can potentially provide a more comprehensive theoretical framework to understand the pathogenesis of AD, and to further guide accurate treatment strategy.
Neuroglial cells, especially microglia, astrocytes, and oligodendrocytes, potentially contribute to the pathogenesis of AD. In AD, microglia are activated and genetically altered, which contribute to inflammatory response, phagocytosis, and A&#946; clearance7,8; astrocyte is genetically altered, which affects synaptic activity, ion homeostasis, and energy and lipid metabolism9; oligodendrocyte is genetically altered with sex specificity, which contributes to neuronal loss, neurofibrillary tangles, and white matter lesions10,11.
In this study, we employed single-nuclei RNA sequencing (snRNA-seq) as a superior technique. Compared to single-cell RNA sequencing (scRNA-seq), snRNA-seq offers advantages in terms of sample richness, cell type integrity, and data reliability12,13. SnRNA-seq has been extensively utilized in studies focusing on AD and exploring the role of glial cells14,15,16. Its wide adoption in these research areas highlights its effectiveness in providing valuable insights into the transcriptional characteristics of glial cells in AD. By leveraging the advantages of snRNA-seq, researchers have been able to uncover crucial information regarding the involvement of glial cells in AD pathology and identify potential therapeutic targets. In order to explore sex-specific neuroglial transcriptional characteristics in AD and potential TCMs for sex specificity of AD, this study analyzed snRNA-seq data from the frontal cortex of AD patients from the NCBI GEO public database. Sex-specific differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-protein interaction (PPI) network, and gene-TF-miRNA network are further analyzed to reveal key biomarkers and potential pathogenesis. Finally, potential TCMs were suggested, and their active ingredients were displayed with tables by searching the Coremine Medical, TCMIP, and TCMSP databases.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Database</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Coremine Medical database</td>
			<td>Jointly developed by Norway, the Chinese Academy of Sciences, the Chinese Academy of Medical Sciences, the National Medical Library of the United States and other institutions</td>
			<td></td>
			<td>When you explore concepts in CoreMine Medical you access a database that is structured to relate important concepts, ranked by statistical relevance, to your topic. For example, if you type in &#34;Alzheimer disease,&#34; in addition to retrieving documents and resources that discuss the disease, you will be able to view networks and lists that show how your query concept is related to other bio-medical concepts. This provides an overview of concepts that relate to your search as well as being an interface for navigating information on these concepts. 
			Weblink: https://coremine.com/medical/</td>
		</tr>
		<tr>
			<td>Gene Expression Omnibus (GEO)</td>
			<td>National Center for Biotechnology Information in the United States (NCBI)</td>
			<td></td>
			<td>GEO is a public functional genomics data repository supporting MIAME-compliant data submissions. Array- and sequence-based data are accepted. Tools are provided to help users query and download experiments and curated gene expression profiles. 
			Weblink: https://www.ncbi.nlm.nih.gov/geo/</td>
		</tr>
		<tr>
			<td>Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine (TCMIP, version: 2.0)</td>
			<td>None</td>
			<td></td>
			<td>Introduction to the Integrated Pharmacology Based Network Computational Research Platform for Traditional Chinese Medicine [TCMIP v2.0], http://www.tcmip.cn/ ) It is an intelligent data mining platform based on the online database of the Encyclopedia of Traditional Chinese Medicine (ETCM), which integrates medical big data management and pharmacological computing services. It aims to reveal the scientific connotation of traditional Chinese medicine theory and the scientific value of original thinking in traditional Chinese medicine, summarize and pass on the experience of famous doctors, control the quality of traditional Chinese medicine, explain the principles of traditional Chinese medicine action, research and development of new Chinese medicine, especially the discovery and optimization of modern drug combinations, Provide a strong data foundation and analytical tools. Based on TCMIP v1.0, a comprehensive upgrade is implemented, including five major databases and seven functional modules. Through system integration and module integration, a comprehensive analysis of the multi-level correlation of the &#34;disease syndrome prescription&#34; interaction network can be quickly achieved. As an intelligent data mining platform, TCMIP v2.0 will provide a strong data foundation and analysis platform for revealing the scientific connotation of traditional Chinese medicine theory and the scientific value of original thinking in traditional Chinese medicine, summarizing and inheriting the experience of famous doctors, quality control of traditional Chinese medicine, elucidating the principles of traditional Chinese medicine action, research and development of new traditional Chinese medicine drugs, especially modern drug combination discovery and optimization. 
			Weblink: http://www.tcmip.cn/TCMIP&#160;</td>
		</tr>
		<tr>
			<td>NetworkAnalyst</td>
			<td>None</td>
			<td></td>
			<td>Networkanalyze is an online visualization analysis platform for gene expression analysis and meta-analysis. It can perform comparative, quantitative, differential and enrichment analysis of gene expression, protein-protein interaction analysis, integration analysis of multiple datasets, and can also draw high-value images such as PCA, protein-protein interaction network diagram, heatmap, volcano diagram, Wayne diagram, etc. 
			Weblink: https://www.networkanalyst.ca/NetworkAnalyst/</td>
		</tr>
		<tr>
			<td>PubMed database</td>
			<td>National Center for Biotechnology Information in the United States (NCBI)</td>
			<td></td>
			<td>The Pubmed database is a biomedical literature database maintained by the National Library of Medicine (NLM) in the United States, aimed at providing the latest medical research results to scientists, doctors, researchers, and students worldwide. This database collects biomedical literature from around the world, including journal articles, papers, books, etc. As of now, the Pubmed database has collected over 30 million articles and is continuously updated every week. 
			Weblink: https://pubmed.ncbi.nlm.nih.gov/</td>
		</tr>
		<tr>
			<td>R software</td>
			<td>Ross Ihaka and Robert Gentleman</td>
			<td></td>
			<td>R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&#38;T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are 
			some important differences, but much code written for S runs unaltered under R. 
			Weblink: https://www.r-project.org/</td>
		</tr>
		<tr>
			<td>STRING database (STRING, version 11.0)&#160;</td>
			<td>Swiss Institute of Bioinformatics</td>
			<td></td>
			<td>STRING is a database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations 
			Weblink: https://string-db.org/</td>
		</tr>
		<tr>
			<td>Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)</td>
			<td>Zhejiang Jiuwei Health Co., Ltd</td>
			<td></td>
			<td>TCMSP is not only a data repository, but also an analysis platform for users to comprehensively study Traditional Chinese Medicines (TCM): including identification of active components, screening of drug targets and generation of compounds-targets-diseases networks, as well as the detailed drug pharmacokinetic information involving drug-likeness (DL), oral bioavailability (OB), blood-brain barrier (BBB),intestinal epithelial permeability (Caco-2), ALogP,fractional negative surface area (FASA-) and number of &#160;H-bond&#160;donor/acceptor &#160;(Hdon/Hacc). So far, TCMSP has attracted broad attentions and several groups have published more than 10 papers by using our TCMSP database within about one year. 
			Weblink: https://tcmsp-e.com</td>
		</tr>
	</tbody>
</table>

biomarker identification for gender specificity of alzheimer's disease based on the glial transcriptome profiles

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study analyzed 220,095 single-nuclei transcriptomes from the frontal cortex of thirty-three AD individuals in the GEO database. Sex-specific Differentially Expressed Genes (DEGs) were identified in glial cells, including 243 in astrocytes, 1,154 in microglia, and 572 in oligodendrocytes. Gene Ontology (GO) functional annotation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed functional concentration in synaptic, neural, and hormone-related pathways. Protein-protein interaction network (PPI) identified MT3, CALM2, DLG2, KCND2, PAKACB, CAMK2D, and NLGN4Y in astrocytes, TREM2, FOS, APOE, APP, and NLGN4Y in microglia, and GRIN2A, ITPR2, GNAS, and NLGN4Y in oligodendrocytes as key genes. NLGN4Y was the only gene shared by the three glia and was identified as the biomarker for the gender specificity of AD. Gene-transcription factor (TF)-miRNA coregulatory network identified key regulators for NLGN4Y and its target TCMs. Ecklonia kurome Okam (Kunbu) and Herba Ephedrae (Mahuang) were identified, and the effects of the active ingredients on AD were displayed. Finally, enrichment analysis of Kunbu and Mahuang suggested that they might act as therapeutic candidates for gender specificity of AD.

SnRNA-seq analysis of frontal glial transcriptome profiles and the annotation of cell types 
In total, 220,095 nuclei and 32,077 genes in the frontal cortex of 17 male AD and 17 female AD were obtained&#160;(Figure 1A). UMAP plot visualized the total single-nuclei frontal transcriptomes displaying distinct types of nuclei after dimension reduction analysis (Figure 1B). Total numbers of annotated nuclei captured by gender were shown, which m...

Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer&#39;s Disease

This study analyzed single-nuclei transcriptomes of thirty-three individuals with Alzheimer's disease (AD), revealing sex-specific DEGs in glial cells. Functional enrichment analysis highlighted synaptic, neural, and hormone-related pathways. Key genes, namely NLGN4Y and its regulators, were identified, and potential therapeutic candidates for gender-specific AD were proposed.

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

Many sex-specific biomarkers have been recently revealed in Alzheimer&#39;s disease (AD); however, cerebral glial cells were rarely reported. This study ...

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 focus on utilizing acupuncture to enhance the treatment of neurological disorders.

biomarker-identification-for-gender-specificity-alzheimer-s-disease

Research

JoVE Journal

Neuroscience

705 Views.  Chengdu University of Traditional Chinese Medicine. 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 o...

Video: Author Spotlight: Exploring Sex-Specific Glial Signatures and Therapeutic Leads for Alzheimer's Disease

Preparation of Oligomeric &beta;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). &beta;-amyloid (A&beta;), a peptide produced in high amounts in AD, is known to reduce Long-Term Potentiation (LTP), a cellular correlate of learning and memory. Indeed, LTP impairment caused by A&beta; is a useful experimental paradigm for studying synaptic dysfunctions in AD models and for screening drugs capable of mitigating or reverting such synaptic impairments. Studies have shown that A&beta; produces the LTP disruption preferentially via its oligomeric form. Here we provide a detailed protocol for impairing LTP by perfusion of oligomerized synthetic A&beta;1-42 peptide onto acute hippocampal slices. In this video, we outline a step-by-step procedure for the preparation of oligomeric A&beta;1-42. Then, we follow an individual experiment in which LTP is reduced in hippocampal slices exposed to oligomerized A&beta;1-42 compared to slices in a control experiment where no A&beta;1-42 exposure had occurred.

Preparation of Oligomeric &amp;#946;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

One feature of Alzheimer's Disease is the elevation of A&beta;1-42 peptide. Here we provide a protocol for preparing synthetic A&beta;1-42 oligomers, which impairs hippocampal Long-Term Potentiation, a cellular correlate of memory. This procedure is useful for investigating mechanisms of A&beta;-induced pathology and drug screening.

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). ...

Selection of Aptamers for Amyloid &beta;-Protein, the Causative Agent of Alzheimer&#39;s Disease

Alzheimer's disease (AD) is a progressive, age-dependent, neurodegenerative disorder with an insidious course that renders its presymptomatic diagnosis difficult1. Definite AD diagnosis is achieved only postmortem, thus establishing presymptomatic, early diagnosis of AD is crucial for developing and administering effective therapies2,3.
Amyloid &beta;-protein (A&beta;) is central to AD pathogenesis. Soluble, oligomeric A&beta; assemblies are believed to affect neurotoxicity underlying synaptic dysfunction and neuron loss in AD4,5. Various forms of soluble A&beta; assemblies have been described, however, their interrelationships and relevance to AD etiology and pathogenesis are complex and not well understood6. Specific molecular recognition tools may unravel the relationships amongst A&beta; assemblies and facilitate detection and characterization of these assemblies early in the disease course before symptoms emerge. Molecular recognition commonly relies on antibodies. However, an alternative class of molecular recognition tools, aptamers, offers important advantages relative to antibodies7,8. Aptamers are oligonucleotides generated by in-vitro selection: systematic evolution of ligands by exponential enrichment (SELEX)9,10. SELEX is an iterative process that, similar to Darwinian evolution, allows selection, amplification, enrichment, and perpetuation of a property, e.g., avid, specific, ligand binding (aptamers) or catalytic activity (ribozymes and DNAzymes).
Despite emergence of aptamers as tools in modern biotechnology and medicine11, they have been underutilized in the amyloid field. Few RNA or ssDNA aptamers have been selected against various forms of prion proteins (PrP)12-16. An RNA aptamer generated against recombinant bovine PrP was shown to recognize bovine PrP-&beta;17, a soluble, oligomeric, &beta;-sheet-rich conformational variant of full-length PrP that forms amyloid fibrils18. Aptamers generated using monomeric and several forms of fibrillar &beta;2-microglobulin (&beta;2m) were found to bind fibrils of certain other amyloidogenic proteins besides &beta;2m fibrils19. Ylera et al. described RNA aptamers selected against immobilized monomeric A&beta;4020. Unexpectedly, these aptamers bound fibrillar A&beta;40. Altogether, these data raise several important questions. Why did aptamers selected against monomeric proteins recognize their polymeric forms? Could aptamers against monomeric and/or oligomeric forms of amyloidogenic proteins be obtained? To address these questions, we attempted to select aptamers for covalently-stabilized oligomeric A&beta;4021 generated using photo-induced cross-linking of unmodified proteins (PICUP)22,23. Similar to previous findings17,19,20, these aptamers reacted with fibrils of A&beta; and several other amyloidogenic proteins likely recognizing a potentially common amyloid structural aptatope21. Here, we present the SELEX methodology used in production of these aptamers21.

Selection of Aptamers for Amyloid &amp;#946;-Protein, the Causative Agent of Alzheimer&#039;s Disease

Aptamers are short ribo-/deoxyribo-oligonucleotides selected by in-vitro evolution methods based on affinity for a specific target. Aptamers are molecular recognition tools with versatile therapeutic, diagnostic, and research applications. We demonstrate methods for selection of aptamers for amyloid &beta;-protein, the causative agent of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive, age-dependent, neurodegenerative disorder with an insidious course that renders its presymptomatic diagnosis ...

Transcriptome Analysis of Single Cells

Many gene expression analysis techniques rely on material isolated from heterogeneous populations of cells from tissue homogenates or cells in culture.1,2,3 In the case of the brain, regions such as the hippocampus contain a complex arrangement of different cell types, each with distinct mRNA profiles. The ability to harvest single cells allows for a more in depth investigation into the molecular differences between and within cell populations. We describe a simple and rapid method for harvesting cells for further processing. Pipettes often used in electrophysiology are utilized to isolate (using aspiration) a cell of interest and conveniently deposit it into an Eppendorf tube for further processing with any number of molecular biology techniques. Our protocol can be modified for the harvest of dendrites from cell culture or even individual cells from acute slices.
We also describe the aRNA amplification method as a major downstream application of single cell isolations. This method was developed previously by our lab as an alternative to other gene expression analysis techniques such as reverse-transcription or real-time polymerase chain reaction (PCR).4,5,6,7,8 This technique provides for linear amplification of the polyadenylated RNA beginning with only femtograms of material and resulting in microgram amounts of antisense RNA. The linearly amplified material provides a more accurate estimation than PCR exponential amplification of the relative abundance of components of the transcriptome of the isolated cell. The basic procedure consists of two rounds of amplification. Briefly, a T7 RNA polymerase promoter site is incorporated into double stranded cDNA created from the mRNA transcripts. An overnight in vitro transcription (IVT) reaction is then performed in which T7 RNA polymerase produces many antisense transcripts from the double stranded cDNA. The second round repeats this process but with some technical differences since the starting material is antisense RNA. It is standard to repeat the second round, resulting in three rounds of amplification. Often, the third round in vitro transcription reaction is performed using biotinylated nucleoside triphosphates so that the antisense RNA produced can be hybridized and detected on a microarray.7,8

In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.

Many gene expression analysis techniques rely on material isolated from heterogeneous populations of cells from tissue homogenates or cells in ...

Detection of Neuritic Plaques in Alzheimer's Disease Mouse Model

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks of Alzheimer's disease. The central component of neuritic plaques is a small filamentous protein called amyloid &beta; protein (A&beta;)1, which is derived from sequential proteolytic cleavage of the beta-amyloid precursor protein (APP) by &beta;-secretase and &gamma;-secretase. The amyloid hypothesis entails that A&gamma;-containing plaques as the underlying toxic mechanism in AD pathology2. The postmortem analysis of the presence of neuritic plaque confirms the diagnosis of AD. To further our understanding of A&gamma; neurobiology in AD pathogenesis, various mouse strains expressing AD-related mutations in the human APP genes were generated. Depending on the severity of the disease, these mice will develop neuritic plaques at different ages. These mice serve as invaluable tools for studying the pathogenesis and drug development that could affect the APP processing pathway and neuritic plaque formation. In this protocol, we employ an immunohistochemical method for specific detection of neuritic plaques in AD model mice. We will specifically discuss the preparation from extracting the half brain, paraformaldehyde fixation, cryosectioning, and two methods to detect neurotic plaques in AD transgenic mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thiofalvin S staining method.

Detection of Neuritic Plaques in Alzheimer&#039;s Disease Mouse Model

One of the pathological characteristics of AD is the formation of Amyloid &#x03B2; protein positive neuritic plaques. In this protocol we describe two methods to detect neuritic plaques in transgenic AD model mice: immunohistochemical detection using the ABC and DAB method and fluorescent detection using thioflavin S staining method.

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaque formation is one of the pathological hallmarks ...

Morris Water Maze Test for Learning and Memory Deficits in Alzheimer&#39;s Disease Model Mice

The Morris Water Maze (MWM) was first established by neuroscientist Richard G. Morris in 1981 in order to test hippocampal-dependent learning, including acquisition of spatial memoryand long-term spatial memory 1. The MWM is a relatively simple procedure typically consisting of six day trials, the main advantage being the differentiation between the spatial (hidden-platform) and non-spatial (visible platform) conditions 2-4. In addition, the MWM testing environment reduces odor trail interference 5. This has led the task to be used extensively in the study of the neurobiology and neuropharmacology of spatial learning and memory. The MWM plays an important role in the validation of rodent models for neurocognitive disorders such as Alzheimer&#x2019;s Disease 6, 7. In this protocol we discussed the typical procedure of MWM for testing learning and memory and data analysis commonly used in Alzheimer&#x2019;s disease transgenic model mice.

Morris Water Maze Test for Learning and Memory Deficits in Alzheimer&#039;s Disease Model Mice

The Morris Water Maze is a behavioral task to test hippocampal-dependent learning and memory. It has been widely used in the study of neurobiology, neuropharmacology and neurocognitive disorders in rodent models.

The Morris Water Maze (MWM) was first established by neuroscientist Richard G. Morris in 1981 in order to test hippocampal-dependent learning, including ...

Stereotaxic Infusion of Oligomeric Amyloid-beta into the Mouse Hippocampus

Alzheimer&#8217;s disease is a neurodegenerative disease affecting the aging population. A key neuropathological feature of the disease is the over-production of amyloid-beta and the deposition of amyloid-beta plaques in brain regions of the afflicted individuals. Throughout the years scientists have generated numerous Alzheimer&#8217;s disease mouse models that attempt to replicate the amyloid-beta pathology. Unfortunately, the mouse models only selectively mimic the disease features. Neuronal death, a prominent effect in the brains of Alzheimer&#8217;s disease patients, is noticeably lacking in these mice. Hence, we and others have employed a method of directly infusing soluble oligomeric species of amyloid-beta - forms of amyloid-beta that have been proven to be most toxic to neurons - stereotaxically into the brain. In this report we utilize male C57BL/6J mice to document this surgical technique of increasing amyloid-beta levels in a select brain region. The infusion target is the dentate gyrus of the hippocampus because this brain structure, along with the basal forebrain that is connected by the cholinergic circuit, represents one of the areas of degeneration in the disease. The results of elevating amyloid-beta in the dentate gyrus via stereotaxic infusion reveal increases in neuron loss in the dentate gyrus within 1 week, while there is a concomitant increase in cell death and cholinergic neuron loss in the vertical limb of the diagonal band of Broca of the basal forebrain. These effects are observed up to 2 weeks. Our data suggests that the current amyloid-beta infusion model provides an alternative mouse model to address region specific neuron death in a short-term basis. The advantage of this model is that amyloid-beta can be elevated in a spatial and temporal manner.

Here, we present a protocol for direct stereotaxic brain infusion of amyloid-beta. This methodology provides an alternative in vivo mouse model to address the short-term effects of amyloid-beta on brain neurons.

Alzheimer&#8217;s disease is a neurodegenerative disease affecting the aging population. A key neuropathological feature of the disease is the ...

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of correlation of the BOLD signal in spatially independent regions of the brain defines the functional connectivity of those regions. During a cognitive fMRI task, a psychophysiological interaction (PPI) analysis can be used to examine changes in the functional connectivity during specific contexts defined by the cognitive task. An example of such a task is one that engages the memory system, asking participants to learn pairs of unrelated words (encoding) and recall the second word in a pair when presented with the first word (retrieval). In the present study, we used this type of associative memory task and a generalized PPI (gPPI) analysis to compare changes in hippocampal connectivity in older adults who are carriers of the Alzheimer's disease (AD) genetic risk factor apolipoprotein-E epsilon-4 (APOE&#949;4). Specifically, we show that the functional connectivity of subregions of the hippocampus changes during encoding and retrieval, the two active phases of the associative memory task. Context-dependent changes in functional connectivity of the hippocampus were significantly different in carriers of APOE&#949;4 compared to non-carriers. PPI analyses make it possible to examine changes in functional connectivity, distinct from univariate main effects, and to compare these changes across groups. Thus, a PPI analysis may reveal complex task effects in specific cohorts that traditional univariate methods do not capture. PPI analyses cannot, however, determine directionality or causality between functionally connected regions. Nevertheless, PPI analyses provide powerful means for generating specific hypotheses regarding functional relationships, which can be tested using causal models. As the brain is increasingly described in terms of connectivity and networks, PPI is an important method for analyzing fMRI task data that is in line with the current conception of the human brain.

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer&#039;s Disease

This manuscript describes how to implement a psychophysiological interaction analysis to reveal task-dependent changes in functional connectivity between a selected seed region and voxels in other regions of the brain. Psychophysiological interaction analysis is a popular method to examine task effects on brain connectivity, distinct from traditional univariate activation effects.

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of ...

Cannula Implantation into the Cisterna Magna of Rodents

Cisterna magna cannulation (CMc) is a straightforward procedure that enables direct access to the cerebrospinal fluid (CSF) without operative damage to the skull or the brain parenchyma. In anesthetized rodents, the exposure of the dura mater by blunt dissection of the neck muscles allows the insertion of a cannula into the cisterna magna (CM). The cannula, composed either by a fine beveled needle or borosilicate capillary, is attached via a polyethylene (PE) tube to a syringe. Using a syringe pump, molecules can then be injected at controlled rates directly into the CM, which is continuous with the subarachnoid space. From the subarachnoid space, we can trace CSF fluxes by convective flow into the perivascular space around penetrating arterioles, where solute exchange with the interstitial fluid (ISF) occurs. CMc can be performed for acute injections immediately following the surgery, or for chronic implantation, with later injection in anesthetized or awake, freely moving rodents. Quantitation of tracer distribution in the brain parenchyma can be performed by epifluorescence, 2-photon microscopy, and magnetic resonance imaging (MRI), depending on the physico-chemical properties of the injected molecules. Thus, CMc in conjunction with various imaging techniques offers a powerful tool for assessment of the glymphatic system and CSF dynamics and function. Furthermore, CMc can be utilized as a conduit for fast, brain-wide delivery of signaling molecules and metabolic substrates that could not otherwise cross the blood brain barrier (BBB).

Here we describe a protocol to perform cisterna magna cannulation (CMc), a minimally invasive way to deliver tracers, substrates and signaling molecules into the cerebrospinal fluid (CSF). Combined with different imaging modalities, CMc enables glymphatic system and CSF dynamics assessment, as well as brain-wide delivery of various compounds.

Cisterna magna cannulation (CMc) is a straightforward procedure that enables direct access to the cerebrospinal fluid (CSF) without operative damage to ...

Cheek Injection Model for Simultaneous Measurement of Pain and Itch-related Behaviors

Itch was defined as &#34;an unpleasant cutaneous sensation that provokes a desire to scratch&#34;&#160;by Rothman in 1941. In mouse models, scratch bouts are typically counted to evaluate itch induced by pruritogens. However, previous reports have shown that algesic substances also induce scratching behaviors in a mouse neck injection model, which is the most common test used for scratching behaviors. This finding makes it difficult to study itch in mice. &#160;In contrast, capsaicin, a common algogen, reduced scratching behaviors in some neck injection experiments. Therefore, the effect of pain on scratching behaviors remains unclear. It is thus necessary to develop a method to concurrently investigate itch and pain sensation using behavioral tests. Here, a cheek injection model is introduced which can be used to simultaneously measure pain- and itch-related behaviors. In this model, pruritogens induce scratching behaviors while algesic substances induce wiping behaviors. Using this model, lysophosphatidic acid (LPA), an itch mediator found in cholestatic patients with itch, is shown to exclusively induce itch but not pain. However, in mouse models, LPA has been reported to be both a pruritogen and an algogen. Investigation into the effects of LPA in a mouse cheek injection model showed that LPA only induced scratching, but not wiping behaviors. This indicates that LPA acts as a pruritogen similarly in mice and humans, and demonstrates the utility of a cheek injection model for itch research.

Typically, the mouse neck injection model is used for evaluate pruritogen-induced scratch behaviors. However, the model provides information only on itch, not pain. Here, a cheek injection model is introduced in mice which can be used to simultaneously measure pain and itch-related behaviors.

Itch was defined as &#34;an unpleasant cutaneous sensation that provokes a desire to scratch&#34;&#160;by Rothman in 1941. In mouse models, scratch bouts ...

Generation of 3-D Collagen-based Hydrogels to Analyze Axonal Growth and Behavior During Nervous System Development

This protocol uses natural type I collagen to generate three-dimensional (3-D) hydrogel for monitoring and analyzing the axonal growth. The protocol is centered on culturing small pieces of embryonic or early postnatal rodent brains inside a 3-D hydrogel formed by the rat tail tendon-derived type I collagen with specific porosity. Tissue pieces are cultured inside the hydrogel and confronted to specific brain fragments or genetically-modified cell aggregates to produce and secrete molecules suitable for creating a gradient inside the porous matrix. The steps of this protocol are simple and reproducible but include critical steps to be considered carefully during its development. Moreover, the behavior of growing axons can be monitored and analyzed directly using a phase-contrast microscope or mono/multiphoton fluorescence microscope after fixation by immunocytochemical methods.

Here, we provide a method for analyzing the behavior of growing axons in 3D matrices, mimicking their natural development.

This protocol uses natural type I collagen to generate three-dimensional (3-D) hydrogel for monitoring and analyzing the axonal growth. The protocol is ...