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Cholesterol mobilization regulates dendritic cell maturation and the immunogenic response to cancer

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NIAID Data Ecosystem2026-05-02 收录
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282849
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Maturation of conventional dendritic cells (cDCs) is crucial for maintaining tolerogenic safeguards against auto-immunity and for promoting immunogenic responses to pathogens and cancer. The subcellular mechanism for cDC maturation remains poorly defined. We show that cDCs mature by leveraging an internal reservoir of cholesterol – generated by de novo synthesis and recycled from extracellular cell debris – to assemble lipid nanodomains on cell surfaces of maturing cDCs, enhance expression of maturation markers, and stabilize immune receptor signaling. This process is dependent on cholesterol transport through Niemann-Pick disease type C1 (NPC1) and mediates homeostatic and TLR-induced maturation. Importantly, we identified the receptor tyrosine kinase AXL as a regulator of the NPC1-dependent construction of lipid nanodomains. Deleting AXL from cDCs enhances their maturation, thus improving antitumor immunity. Altogether, our study presents novel insights into cholesterol mobilization as a fundamental basis for cDC maturation and highlights AXL as a therapeutic target for modulating cDCs. Sample preparation for single-cell RNA sequencing. Single-cell suspensions from lung tissues were obtained, as described above. For scRNAseq, these cells were suspended and stained in 100μL of multiplex hashing antibodies at 4°C for 20 minutes. Stained cells were washed three times in PBS+0.5% BSA to remove unbound antibodies. Washed cells were resuspended in 150μL of wash buffer and counted using a Nexcelom Cellometer Auto2000. Hashed samples were pooled in equal amounts of live cells. Volume was adjusted to achieve a target of 2x106 cells/mL. Hashed samples were loaded onto 10X Genomics NextGen 5’ v1.1 assay, as per the manufacturer’s instructions, for a target cell recovery of 20,000 cells/lane. Libraries were constructed, as per the manufacturer’s instructions. During cDNA amplification, hashtag oligonucleotides (HTO) were enriched during cDNA amplification with the addition of 3 pmol of HTO Additive primer (5’GTGACTGGAGTTCAGACGTGTGCTC). This PCR product was isolated from the mRNA-derived cDNA via SPRISelect size selection, and libraries were made as per the New York Genome Center Hashing protocol. All libraries were quantified via Agilent 2100 hsDNA Bioanalyzer and KAPA library quantification kit (Roche, Cat. No. 0796014001.) Gene expression libraries were sequenced at a targeted depth of 25,000 reads per cells, and HTO libraries were sequenced at a targeted read depth of 1,000 reads per cell. All libraries were sequenced on the Illumina NovaSeq S2 100 cycle kit with run parameters set to 28x8x0x60 (R1xi7xi5xR2). scRNAseq analysis. After library demultiplexing, gene-expression libraries were aligned to the mm10 reference transcriptome and count matrices were generated using the default Cell Ranger 2.1 workflow, using the ‘raw’ matrix output. Where applicable, doublets were removed based on co-staining of distinct sample-barcoding (‘Hashing’) antibodies (maximum staining antibody counts/second-most staining antibody counts = less than 5). Following alignment, cell barcodes corresponding to cells that contained more than 500 UMIs were extracted. From among these, cells whose transcripts constituted more than 25% mitochondrial genes were filtered from downstream analyses. The R package Seurat was used to scale the data, transform via a log normalization method, adjust for batch correction, cluster cells based on shared nearest neighbors, and perform dimensionality reduction based on the first 15 principal components. Gene module analyses were performed, based on the identification of groups of highly correlated genes, according to the Pearson correlation matrix of the most variable genes. This was done using the R package scDissector. Differentially expressed genes were identified using the FindMarkers function in Seurat. Mean UMI expression values were imputed to determine log fold change differences between cell types to further the analysis of markers of interest.
创建时间:
2025-02-04
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