给新手学员的偏爱：一份他想要的文献复现bulk RNA-seq和单细胞联合分析实战
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TL;DR - A WeChat tutorial from 生信技能树 walking a training student through reproducing an Aging Cell 2024 study (GSE198666) that combines scRNA-seq and bulk RNA-seq to link macrophage subpopulation shifts and Trem2 downregulation to impaired fracture healing in aged mice. It matters as a hands-on, command-level template for joint bulk + single-cell reanalysis of public GEO data.
- Reproduces the paper's logic chain: aged mice heal fractures poorly → scRNA-seq of Day-3 callus CD45+ cells (42,070 cells, 7 immune types) shows state not count changes → macrophage re-clustering yields 6 subsets with a fibrosis-associated subset expanded in old mice → bulk RNA-seq pinpoints Trem2 downregulation → Trem2 knockout in young mice phenocopies aging.
- Dataset details: 10x Chromium 3′ scRNA-seq (old n=2, young n=2) with QC cutoffs of 300–3000 genes/cell, 20,000 UMI, <5% mitochondrial, <0.1% hemoglobin; bulk on sorted CD45+CD11b+F4/80+ macrophages (old n=10 vs young n=11), single-end 50bp HiSeq 4000, original pipeline STAR 2.4.2a + GRCm38.78 + DESeq2.
- The published upstream walkthrough covers only the bulk quantification: directory scaffolding, ENA/aspera (ascp with EBI key) or sratoolkit prefetch + fasterq-dump download of 21 SRX runs, FastQC/MultiQC, trim_galore filtering, HISAT2 alignment against Ensembl GRCm39.116, and featureCounts to build a clean count matrix.
- Practical deviations and caveats noted: the tutorial substitutes HISAT2 + GRCm39 for the paper's STAR + GRCm38.78, uses ParaFly for parallel job submission, flags high duplication in several samples at QC, and ends mid-workflow ("未完待续") before downstream differential expression and single-cell integration.
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给新手学员的偏爱：一份他想要的文献复现bulk RNA-seq和单细胞联合分析实战
TL;DR - A WeChat tutorial from 生信技能树 walking a training student through reproducing an Aging Cell 2024 study (GSE198666) that combines scRNA-seq and bulk RNA-seq to link macrophage subpopulation shifts and Trem2 downregulation to impaired fracture healing in aged mice. It matters as a hands-on, command-level template for joint bulk + single-cell reanalysis of public GEO data.
- Reproduces the paper's logic chain: aged mice heal fractures poorly → scRNA-seq of Day-3 callus CD45+ cells (42,070 cells, 7 immune types) shows state not count changes → macrophage re-clustering yields 6 subsets with a fibrosis-associated subset expanded in old mice → bulk RNA-seq pinpoints Trem2 downregulation → Trem2 knockout in young mice phenocopies aging.
- Dataset details: 10x Chromium 3′ scRNA-seq (old n=2, young n=2) with QC cutoffs of 300–3000 genes/cell, 20,000 UMI, <5% mitochondrial, <0.1% hemoglobin; bulk on sorted CD45+CD11b+F4/80+ macrophages (old n=10 vs young n=11), single-end 50bp HiSeq 4000, original pipeline STAR 2.4.2a + GRCm38.78 + DESeq2.
- The published upstream walkthrough covers only the bulk quantification: directory scaffolding, ENA/aspera (ascp with EBI key) or sratoolkit prefetch + fasterq-dump download of 21 SRX runs, FastQC/MultiQC, trim_galore filtering, HISAT2 alignment against Ensembl GRCm39.116, and featureCounts to build a clean count matrix.
- Practical deviations and caveats noted: the tutorial substitutes HISAT2 + GRCm39 for the paper's STAR + GRCm38.78, uses ParaFly for parallel job submission, flags high duplication in several samples at QC, and ends mid-workflow ("未完待续") before downstream differential expression and single-cell integration.