Source Paper
Srikant Rangaraju, Eric B. Dammer, Syed Ali Raza, Priyadharshini Rathakrishnan, Hailian Xiao et al.
Molecular Neurodegeneration • 2018
We provide a predictive transcriptomic framework of microglial activation in neurodegeneration that can guide pre-clinical studies to characterize and therapeutically modulate neuroinflammation in AD.
Objective: Determine whether inhibition of pro-inflammatory gene expression and promotion of amyloid clearance is feasible in neuroinflammation and neurodegeneration mouse models, specifically using 5xFAD mice
This is a In-vivo Studies in 5xFAD Mouse Model protocol using mouse as the model organism. The procedure involves 7 procedural steps. Extracted from a 2018 paper published in Molecular Neurodegeneration.
Model and subjects
mouse • 5xFAD • unknown • Not specified • Not specified
Study window
Estimated timing pending
Core workflow
Weighted Co-expression Network Analysis (WGCNA) • Module Contrast Analysis • Flow Cytometric Validation
Primary readouts
Key equipment and reagents
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Apply WGCNA to existing microglial transcriptomic datasets from neuroinflammatory and neurodegenerative disease mouse models to identify modules of highly co-expressed genes
Note: Analysis performed on existing datasets
“Weighted co-expression network analysis (WGCNA) was applied to existing microglial transcriptomic datasets from neuroinflammatory and neurodegenerative disease mouse models to identify modules of highly co-expressed genes”
Contrast identified modules with known signatures of homeostatic microglia and DAM (disease-associated microglia) to reveal novel molecular heterogeneity within DAM
Note: Comparative analysis of gene expression patterns
“These modules were contrasted with known signatures of homeostatic microglia and DAM to reveal novel molecular heterogeneity within DAM”
Perform flow cytometric validation studies to confirm existence of distinct DAM sub-populations in AD mouse models predicted by WGCNA
Note: Validation performed in AD mouse models
“Flow cytometric validation studies were performed to confirm existence of distinct DAM sub-populations in AD mouse models predicted by WGCNA”
Perform gene ontology analyses coupled with bioinformatics approaches to reveal drug targets and transcriptional regulators of microglial modules predicted to favorably modulate neuroinflammation in AD
Note: Computational analysis to identify therapeutic targets
“Gene ontology analyses coupled with bioinformatics approaches revealed drug targets and transcriptional regulators of microglial modules predicted to favorably modulate neuroinflammation in AD”
Conduct in-vivo studies in 5xFAD mouse model of neuroinflammation and neurodegeneration to determine whether inhibition of pro-inflammatory gene expression and promotion of amyloid clearance is feasible
Note: Studies guided by results from gene ontology and bioinformatics analyses
“These guided in-vivo and in-vitro studies in mouse models of neuroinflammation and neurodegeneration (5xFAD) to determine whether inhibition of pro-inflammatory gene expression and promotion of amyloid clearance was feasible”
Conduct in-vitro studies in mouse models of neuroinflammation and neurodegeneration to complement in-vivo findings
Note: Parallel studies to in-vivo work
“These guided in-vivo and in-vitro studies in mouse models of neuroinflammation and neurodegeneration (5xFAD)”
Determine human relevance of findings by integrating results with AD genome-wide association studies and human AD and non-disease post-mortem brain proteomes
Note: Translational validation step
“We determined the human relevance of these findings by integrating our results with AD genome-wide association studies and human AD and non-disease post-mortem brain proteomes”
This section explains what the experiment is doing, which readouts matter, what the data artifacts usually look like, and how the analysis should flow from raw capture to reported result.
Determine whether inhibition of pro-inflammatory gene expression and promotion of amyloid clearance is feasible in neuroinflammation and neurodegeneration mouse models, specifically using 5xFAD mice
Objective
Determine whether inhibition of pro-inflammatory gene expression and promotion of amyloid clearance is feasible in neuroinflammation and neurodegeneration mouse models, specifically using 5xFAD mice
Subjects
From papermouse • 5xFAD • unknown • Not specified • Not specified
Cohort notes
From paperAD mouse models used for in-vivo and in-vitro studies
Weighted Co-expression Network Analysis (WGCNA) (Not specified)
Module Contrast Analysis (Not specified)
Flow Cytometric Validation (Not specified)
Gene Ontology and Bioinformatics Analysis (Not specified)
Identification of modules of highly co-expressed genes in microglial transcriptomic datasets
From paperWGCNA applied to transcriptomic datasets; gene ontology analyses; bioinformatics approaches; integration with AD genome-wide association studies and human post-mortem brain proteomes
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Confirmation of distinct DAM sub-populations in AD mouse models
From paperWGCNA applied to transcriptomic datasets; gene ontology analyses; bioinformatics approaches; integration with AD genome-wide association studies and human post-mortem brain proteomes
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Identification of drug targets and transcriptional regulators
From paperWGCNA applied to transcriptomic datasets; gene ontology analyses; bioinformatics approaches; integration with AD genome-wide association studies and human post-mortem brain proteomes
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Feasibility of inhibiting pro-inflammatory gene expression
From paperWGCNA applied to transcriptomic datasets; gene ontology analyses; bioinformatics approaches; integration with AD genome-wide association studies and human post-mortem brain proteomes
Artifact type
Endpoint measurements summarized by group or timepoint
Comparison focus
Compare endpoint magnitude between groups, timepoints, or both
Identification of modules of highly co-expressed genes in microglial transcriptomic datasets
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Confirmation of distinct DAM sub-populations in AD mouse models
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Identification of drug targets and transcriptional regulators
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Feasibility of inhibiting pro-inflammatory gene expression
From paperRaw artifact
Per-sample or per-animal endpoint measurements collected during the experiment
Processed artifact
Structured table with cleaned measurements ready for comparison
Final reported form
Summary statistics and between-group or across-timepoint comparisons
Acquisition
Collect raw experimental outputs with enough metadata to preserve sample identity, condition, and timing.
Preprocessing / cleaning
WGCNA applied to transcriptomic datasets; gene ontology analyses; bioinformatics approaches; integration with AD genome-wide association studies and human post-mortem brain proteomes
Scoring or quantification
Quantify the primary readouts for this experiment: Identification of modules of highly co-expressed genes in microglial transcriptomic datasets; Confirmation of distinct DAM sub-populations in AD mouse models; Identification of drug targets and transcriptional regulators; Feasibility of inhibiting pro-inflammatory gene expression.
Statistical comparison
Statistical method not yet structured for this page.
Reporting output
Report representative outputs alongside summary comparisons for Identification of modules of highly co-expressed genes in microglial transcriptomic datasets, Confirmation of distinct DAM sub-populations in AD mouse models, Identification of drug targets and transcriptional regulators, Feasibility of inhibiting pro-inflammatory gene expression.
Source links and direct wording from the methods section for validation and deeper review.
Citation
Srikant Rangaraju et al. (2018). Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease. Molecular Neurodegeneration
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7
Evidence Quotes
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