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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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><tbody><tr><td>&lt;strong&gt;Database&lt;/strong&gt;</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 &quot;Alzheimer disease,&quot; 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.&lt;br/&gt; 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.&lt;br/&gt; 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 &quot;disease syndrome prescription&quot; 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.&lt;br/&gt; Weblink: http://www.tcmip.cn/TCMIP&amp;nbsp;</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.&lt;br/&gt; 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.&lt;br/&gt; 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&amp;amp;T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are&lt;br/&gt; some important differences, but much code written for S runs unaltered under R.&lt;br/&gt; Weblink: https://www.r-project.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 &amp;nbsp;H-bond&amp;nbsp;donor/acceptor &amp;nbsp;(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.&lt;br/&gt; 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 ...

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

Research

JoVE Journal

Neuroscience

760 Views.  Chengdu University of Traditional Chinese Medicine. Ons onderzoek is gericht op het identificeren van geslachtsspecifieke genen in gliacellen, het verkennen van geassocieerde routes en het evalueren van de mogelijke therapeutische effecten van TCM op de ziekte van Alzheimer. Het huidige protocol analyseert een groot aantal transcriptomen met één kern uit de GEO-database door middel van bioinformatica-analyse. Ons protocol biedt voordelen, waaronder resolutie van één cel, CTAP-identificatie van de ontdekking van biomarkers en vergelijking v...

Video: Auteur in de schijnwerpers: onderzoek naar seksespecifieke gliale handtekeningen en therapeutische aanwijzingen voor de ziekte van Alzheimer

SDS-PAGE/Immunoblot Detection of A&beta; Multimers in Human Cortical Tissue Homogenates using Antigen-Epitope Retrieval

The anomalous folding and polymerization of the &beta;-amyloid (A&beta;) peptide is thought to initiate the neurodegenerative cascade in Alzheimer's disease pathogenesis1. A&beta; is predominantly a 40- or 42-amino acid peptide that is prone to self-aggregation into &beta;-sheet-rich amyloid fibrils that are found in the cores of cerebral senile plaques in Alzheimer's disease. Increasing evidence suggests that low molecular weight, soluble A&beta; multimers are more toxic than fibrillar A&beta; amyloid2. The identification and quantification of low- and high-molecular weight multimeric A&beta; species in brain tissue is an essential objective in Alzheimer's disease research, and the methods employed also can be applied to the identification and characterization of toxic multimers in other proteopathies3. Naturally occurring A&beta; multimers can be detected by SDS-polyacrylamide gel electrophoresis followed by immunoblotting with A&beta;-specific antibodies. However, the separation and detection of multimeric A&beta; requires the use of highly concentrated cortical homogenates and antigen retrieval in small pore-size nitrocellulose membranes. Here we describe a technique for the preparation of clarified human cortical homogenates, separation of proteins by SDS-PAGE, and antigen-epitope retrieval/Western blotting with antibody 6E10 to the N-terminal region of the A&beta; peptide. Using this protocol, we consistently detect A&beta; monomers, dimers, trimers, tetramers, and higher molecular weight multimers in cortical tissue from humans with Alzheimer's pathology.

SDS-PAGE/Immunoblot Detection of A&#946; Multimers in Human Cortical Tissue Homogenates using Antigen-Epitope Retrieval

We describe a technique for the preparation of clarified human cortical homogenates, protein separation by SDS-PAGE, antigen retrieval and immunoblotting with an antibody to the A&beta; peptide. Using this protocol, we consistently detect monomeric and multimeric A&beta; in cortical tissue from humans with Alzheimer's pathology.

The anomalous folding and polymerization of the &beta;-amyloid (A&beta;) peptide is thought to initiate the neurodegenerative cascade in Alzheimer's ...

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.

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 ...

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, because the majority of the genome-wide significant (GWS) loci fall onto the non-coding genome, the functional impact of many remain unknown. Three-dimensional chromatin interactions identified by Hi-C or its derivatives can provide useful tools to annotate these loci by linking non-coding variants to their actionable genes. Here, we outline a protocol to map GWAS non-coding variants to their putative genes using Alzheimer&#39;s disease (AD) GWAS and Hi-C datasets from human adult brain tissue. Putative causal single-nucleotide polymorphisms (SNPs) are identified by application of fine-mapping algorithms. SNPs are then mapped to their putative target genes using enhancer-promoter interactions based on Hi-C. The resulting gene set represents AD risk genes, as they are potentially regulated by AD risk variants. To garner further biological insights into molecular mechanisms underlying AD, we characterize AD risk genes using developmental brain expression data and brain single-cell expression profiles. This protocol can be expanded to any GWAS and Hi-C datasets to identify putative target genes and molecular mechanisms underlying various human traits and diseases.

We present a protocol to identify functional implications of non-coding variants identified by genome-wide association studies (GWAS) using three-dimensional chromatin interactions.

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, ...

Genetics

Consensus Brain-derived Protein, Extraction Protocol for the Study of Human and Murine Brain Proteome Using Both 2D-DIGE and Mini 2DE Immunoblotting

Two-dimensional gel electrophoresis (2DE) is a powerful tool to uncover proteome modifications potentially related to different physiological or pathological conditions. Basically, this technique is based on the separation of proteins according to their isoelectric point in a first step, and secondly according to their molecular weights by SDS polyacrylamide gel electrophoresis (SDS-PAGE). In this report an optimized sample preparation protocol for little amount of human post-mortem and mouse brain tissue is described. This method enables to perform both two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) and mini 2DE immunoblotting. The combination of these approaches allows one to not only find new proteins and/or protein modifications in their expression thanks to its compatibility with mass spectrometry detection, but also a new insight into markers validation. Thus, mini-2DE coupled to western blotting permits to identify and validate post-translational modifications, proteins catabolism and provides a qualitative comparison among different conditions and/or treatments. Herein, we provide a method to study components of protein aggregates found in AD and Lewy body dementia such as the amyloid-beta peptide and the alpha-synuclein. Our method can thus be adapted for the analysis of the proteome and insoluble proteins extract from human brain tissue and mice models too. In parallel, it may provide useful information for the study of molecular and cellular pathways involved in neurodegenerative diseases as well as potential novel biomarkers and therapeutic targets.

A common protein extraction protocol using urea/thiourea/SDS buffer for human and mice brain tissue allows indentification of proteins by 2D-DIGE and their subsequent characterization by mini 2DE immunoblotting. This method enables one to obtain more reproducible and reliable results from human biopsies and experimental models.

Two-dimensional gel electrophoresis (2DE) is a powerful tool to uncover proteome modifications potentially related to different physiological or ...

Sex Differences in Mouse Hippocampal Astrocytes after In-Vitro Ischemia

Astrogliosis following hypoxia/ischemia (HI)-related brain injury plays a role in increased morbidity and mortality in neonates. Recent clinical studies indicate that the severity of brain injury appear to be sex dependent, and that the male neonates are more susceptible to the effects of HI-related brain injury, resulting in more severe neurological outcomes as compared to females with comparable brain injuries. The development of reliable methods to isolate and maintain highly enriched populations of sexed hippocampal astrocytes is essential to understand the cellular basis of sex differences in the pathological consequences of neonatal HI. In this study, we describe a method for creating sex specific hippocampal astrocyte cultures that are subjected to a model of in-vitro ischemia, oxygen-glucose deprivation, followed by reoxygenation. Subsequent reactive astrogliosis was examined by immunostaining for the Glial Fibrillary Acidic Protein (GFAP) and S100B. This method provides a useful tool to study the role of male and female hippocampal astrocytes following neonatal HI, separately.

Sex Differences in Mouse Hippocampal Astrocytes after In-Vitro Ischemia

Astrocytes are one of the most important key players in the central nervous system (CNS). Here, we are reporting a practical method of sexed hippocampal astrocyte culture protocol in order to study the mechanisms underlying the astrocyte function in male and female neonate pups after in-vitro ischemia.

Astrogliosis following hypoxia/ischemia (HI)-related brain injury plays a role in increased morbidity and mortality in neonates. Recent clinical studies ...

Using Enzyme-based Biosensors to Measure Tonic and Phasic Glutamate in Alzheimer's Mouse Models

Neurotransmitter disruption is often a key component of diseases of the central nervous system (CNS), playing a role in the pathology underlying Alzheimer's disease, Parkinson's disease, depression, and anxiety. Traditionally, microdialysis has been the most common (lauded) technique to examine neurotransmitter changes that occur in these disorders. But because microdialysis has the ability to measure slow 1-20 minute changes across large areas of tissue, it has the disadvantage of invasiveness, potentially destroying intrinsic connections within the brain and a slow sampling capability. A relatively newer technique, the microelectrode array (MEA), has numerous advantages for measuring specific neurotransmitter changes within discrete brain regions as they occur, making for a spatially and temporally precise approach. In addition, using MEAs is minimally invasive, allowing for measurement of neurotransmitter alterations in vivo. In our laboratory, we have been specifically interested in changes in the neurotransmitter, glutamate, related to Alzheimer's disease pathology. As such, the method described here has been used to assess potential hippocampal disruptions in glutamate in a transgenic mouse model of Alzheimer's disease. Briefly, the method used involves coating a multi-site microelectrode with an enzyme very selective for the neurotransmitter of interest and using self-referencing sites to subtract out background noise and interferents. After plating and calibration, the MEA can be constructed with a micropipette and lowered into the brain region of interest using a stereotaxic device. Here, the method described involves anesthetizing rTg(TauP301L)4510 mice and using a stereotaxic device to precisely target sub-regions (DG, CA1, and CA3) of the hippocampus.

Here, we describe the setup, software navigation, and data analysis for a spatially and temporally precise method of measuring tonic and phasic extracellular glutamate changes in vivo using enzyme-linked microelectrode arrays (MEA).

Neurotransmitter disruption is often a key component of diseases of the central nervous system (CNS), playing a role in the pathology underlying ...

A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status

Many neurodegenerative diseases are&#160;still lacking effective treatments. Reliable biomarkers for identifying and classifying these diseases will be important in the development of future novel therapies. Often&#160;potential new biomarkers do not make it&#160;into the clinic&#160;due to limitations&#160;in their development and&#160;high costs.&#160;However,&#160;targeted proteomics using Multiple Reaction Monitoring Liquid Chromatography-tandem/Mass Spectrometry (MRM LC-MS/MS), specifically using triple quadrupole mass spectrometers, is one method that can be used to rapidly evaluate and validate biomarkers&#160;for clinical translation into diagnostic laboratories. Traditionally, this platform has been used extensively for measurement of small molecules&#160;in clinical laboratories, but it is the potential to analyze proteins, that makes it an attractive alternative to ELISA (Enzyme-Linked Immunosorbent Assay)-based methods. We describe here how targeted proteomics can be used to measure multiplexed markers of dementia, including the detection and quantitation of the known risk factor apolipoprotein E isoform 4 (ApoE4).
In order to make the assay suitable for translation, it is designed to be rapid, simple, highly specific and cost effective. To achieve this, every step in the development of the assay must be optimized for the individual proteins and tissues they are analyzed in. This method describes a typical workflow including various tips and tricks to developing a targeted proteomics MRM LC-MS/MS for translation.
The method development is optimized using custom synthesized versions of tryptic quantotypic peptides, which calibrate&#160;the MS for detection and then spiked into CSF to determine correct identification of the endogenous peptide in the chromatographic separation prior to analysis in the MS. To achieve absolute quantitation, stable isotope-labeled internal standard versions of the peptides with short amino acid sequence tags and containing a trypsin cleavage site, are included in the assay.

We describe a high-throughput, multiplex, and targeted proteomic cerebrospinal fluid (CSF) assay developed with potential for clinical translation. The test can quantitate potential markers and risk factors for neurodegeneration, such as the apolipoprotein E variants (E2, E3 and E4), and measure their allelic expression.

Many neurodegenerative diseases are&#160;still lacking effective treatments. Reliable biomarkers for identifying and classifying these diseases will be ...

Medicine

Quantitative 3D In Silico Modeling (q3DISM) of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

Neuroinflammation is now recognized as a major etiological factor in neurodegenerative disease. Mononuclear phagocytes are innate immune cells responsible for phagocytosis and clearance of debris and detritus. These cells include CNS-resident macrophages known as microglia, and mononuclear phagocytes infiltrating from the periphery. Light microscopy has generally been used to visualize phagocytosis in rodent or human brain specimens. However, qualitative methods have not provided definitive evidence of in vivo phagocytosis. Here, we describe quantitative 3D in silico modeling (q3DISM), a robust method allowing for true 3D quantitation of amyloid-&#946; (A&#946;) phagocytosis by mononuclear phagocytes in rodent Alzheimer&#39;s Disease (AD) models. The method involves fluorescently visualizing A&#946; encapsulated within phagolysosomes in rodent brain sections. Large z-dimensional confocal datasets are then 3D reconstructed for quantitation of A&#946; spatially colocalized within the phagolysosome. We demonstrate the successful application of q3DISM to mouse and rat brains, but this methodology can be extended to virtually any phagocytic event in any tissue.

Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

We developed a methodology for quantitative 3D in silico modeling (q3DISM) of cerebral amyloid-&#946; (A&#946;) phagocytosis by mononuclear phagocytes in rodent models of Alzheimer&#39;s disease. This method can be generalized for the quantitation of virtually any phagocytic event in vivo.

Neuroinflammation is now recognized as a major etiological factor in neurodegenerative disease. Mononuclear phagocytes are innate immune cells responsible ...

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains

In a constantly aging population, the prevalence of neurodegenerative disorders is expected to rise. Understanding disease mechanisms is the key to find preventive and curative measures. The most effective way to achieve this is through direct examination of diseased and healthy brain tissue. The authors present a protocol to obtain, process, characterize and store good quality brain tissue donated by individuals registered in an antemortem brain donation program. The donation program includes a face-to-face empathic approach to people, a collection of complementary clinical, biological, social and lifestyle information and serial multi-dimensional assessments over time to track individual trajectories of normal aging and cognitive decline. Since many neurological diseases are asymmetrical, our brain bank offers a unique protocol for slicing fresh specimens. Brain sections of both hemispheres are alternately frozen (at -80 &#176;C) or fixed in formalin; a fixed slice on one hemisphere corresponds to a frozen one on the other hemisphere. With this approach, a complete histological characterization of all frozen material can be obtained, and omics studies can be performed on histologically well-defined tissues from both hemispheres thus offering a more complete assessment of neurodegenerative disease mechanisms. Correct and definite diagnosis of these diseases can only be achieved by combining the clinical syndrome with the neuropathological evaluation, which often adds important etiological clues necessary to interpret the pathogenesis. This method can be time consuming, expensive and limited as it only covers a limited geographical area. Regardless of its limitations, the high degree of characterization it provides can be rewarding. Our ultimate goal is to establish the first Italian Brain Bank, all the while emphasizing the importance of neuropathologically verified epidemiological studies.

This protocol describes a method to track individual brain-aging trajectories through a brain donation program and proper characterization of brains. Brain donors are involved in a long-term longitudinal study including serial multi-dimensional assessments. The protocol contains a detailed description of brain processing and an accurate diagnostic methodology.

In a constantly aging population, the prevalence of neurodegenerative disorders is expected to rise. Understanding disease mechanisms is the key to find ...

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive human diseases. Considerable advances have been made to uncover the molecular signatures of homeostatic microglia as well as alterations of their gene expression in response to environmental stimuli. With the advent and maturation of single-cell genomic methodologies, it is increasingly recognized that heterogenous microglia may underlie the diverse roles they play in different developmental and pathological conditions. Further dissection of such heterogeneity can be achieved through efficient isolation of microglia from a given region of interest, followed by sensitive profiling of individual cells. Here, we provide a detailed protocol for the rapid isolation of microglia from different brain regions in a single adult mouse brain hemisphere. We also demonstrate how to use these sorted microglia for plate-based deep single-cell RNA sequencing. We discuss the adaptability of this method to other scenarios and provide guidelines for improving the system to accommodate large-scale studies.

We provide a protocol for isolation of microglia from different dissected regions of an adult mouse brain hemisphere, followed by semi-automated library preparation for deep single-cell RNA sequencing of full-length transcriptomes. This method will help to elucidate functional heterogeneity of microglia in health and disease.

As resident macrophages in the central nervous system, microglia actively control brain development and homeostasis, and their dysfunctions may drive ...

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

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