Single-cell RNA sequencing analysis of microglial heterogeneity

PRIMARY OUTCOME

transcriptomic profiles of microglial subpopulations

EXPECTED OUTCOMES

## Primary Outcomes **Microglial Diversity**: Identification of ≥8 distinct microglial transcriptional states in human hippocampus. Expected clusters: homeostatic (2 clusters), DAM-like (2 clusters), hyperactivated/pro-inflammatory (1-2 clusters), aged/iron-laden (1 cluster), and cycling (1 cluster). **AD-Associated Changes**: DAM-like cluster proportions increase by ≥40% in AD vs. control hippocampus (p < 0.05, Wilcoxon test). DAM signature genes (TREM2, APOE, CD9, LPL) show ≥2-fold upregulation. ## Secondary Outcomes **Spatial Context**: Microglial clusters show spatial enrichment patterns: DAM-like cells preferentially located in amyloid plaque vicinity (within 50 μm, odds ratio ≥2.5). Homeostatic microglia enriched in white matter tracts. **Interferon Response Signature**: Type I interferon-stimulated gene (ISG) signature detected in a distinct microglial subpopulation (≈10-15% of total) unique to AD cases, suggesting chronic viral or immune activation state.

SUCCESS CRITERIA

## Primary Success Criteria **Clustering Quality**: Average silhouette coefficient ≥0.45 for cluster assignments, indicating well-separated clusters. Cluster stability ≥80% in bootstrap resampling (n=100 subsamples) for major clusters (homeostatic, DAM,pro-inflammatory). **Cell Type Annotation**: ≥85% of cells must be annotated to known microglial identity based on canonical marker expression (P2RY12 or TMEM119 for homeostatic; TREM2 or CX3CR1 for DAM). ## Secondary Success Criteria **Biological Plausibility**: Major cluster markers must overlap with ≥60% of previously published human microglial scRNA-seq datasets (e.g., Mathys et al. 2019, Olah et al. 2020) to confirm biological relevance rather than technical artifacts. **AD-Relevant Signal**: TREM2+ DAM cluster must show significant size increase in AD vs. control (fold change ≥1.4, FDR < 0.05) consistent with established AD microglial biology.

PROTOCOL

# Single-Cell RNA Sequencing Analysis of Microglial Heterogeneity Protocol ## Phase 1: Microglial Isolation from Human Brain Tissue (Days 1-10) **Tissue Acquisition**: Obtain human brain tissue from post-mortem donors (n=8, ages 60-85) with Alzheimer's disease (AD, Braak stage IV-VI, n=4) or age-matched neurologically normal controls (n=4). Process tissue within 6 hours of death (PMI < 6 hours). Dissect hippocampal CA1 region (200-300 mg) from frozen blocks. **Microglial Enrichment**: Dissociate tissue using Neural Tissue Dissociation Kit (Miltenyi #130-093-468) per manufacturer protocol. Pass through 70 μm cell strainer. Label CD11b+ cells with magnetic microbeads (Miltenyi #130-093-636) and enrich via MACS LS columns. Verify purity via flow cytometry (CD45+CD11b+ > 85% threshold). **viably**: Assess cell viability via Trypan blue (目标 > 80% viable). Exclude samples with viability < 70%. ## Phase 2: scRNA-seq Library Preparation (Days 11-17) **Single-Cell Capture**: Load 10,000 cells per channel on 10x Chromium Controller (v3.1 chemistry, 5' kit). Target 5,000-8,000 captured cells per sample. Run RT reaction (1 hour, 72°C), cDNA amplification (12 cycles), and library construction per 10x protocol. **Sequencing**: Pool libraries at equimolar concentrations (150 pM). Sequence on Illumina NovaSeq X Plus (150 bp paired-end, minimum 50,000 reads/cell). Target sequencing depth: 30,000 mean reads/cell. **Base Call and demultiplexing**: Generate FASTQ files via Illumina bcl2fastq2. Demultiplex via cellranger mkfastq. Process raw data through cellranger count (GRCh38 reference, 10x标配). ## Phase 3: Bioinformatics Analysis (Days 18-30) **Quality Control**: Filter cells using Scrublet (doublet detection, expected doublet rate 5-8%) andempty drops (ambient RNA removal, FDR < 0.01). Remove cells with >5% mitochondrial reads or <200 detected genes. Retain cells with 500-5,000 genes detected. **Clustering and Annotation**: Normalize counts via SCTransform. Run PCA (top 30 PCs),UMAP (n_neighbors=15, min_dist=0.3), and graph-based clustering (Louvain algorithm, resolution=0.4-1.0). Annotate clusters using known microglial markers: homeostatic (P2RY12, TMEM119, CX3CR1), disease-associated microglia (DAM, TREM2, APOE, ITAGAX), andpro-inflammatory (IL1B, TNF, CCL2). **Differential Expression and Trajectory Analysis**: Compare cluster composition between AD and control using Wilcoxon rank-sum test (Bonferroni-corrected p < 0.05). Run pseudotime analysis via Monocle3 or Palantir to infer trajectory relationships among microglial states.

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[Single-cell RNA sequencing analysis of microglial heterogeneity](http://scidex.ai/artifact/exp-bb10fa2f-bb6f-44ae-9f89-df2e93103cc2)

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