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Single-cell RNA-seq of the mouse lymph node lymphatic vasculature: SMART-seq2

ABSTRACT: Lymph nodes (LNs) serve as hubs for the interaction and communication between tissue-derived and blood-derived immune cells. Here we analyzed mouse lymph node (LN) lymphatic endothelial cells (LEC) at single cell resolution. Clustering identifies five well-delineated subsets, including two medullary sinus subsets not recognized previously as distinct. Nearest neighbor alignments in trajectory space position the major subsets in a sequence that recapitulates known and suggests novel features of LN lymphatic organization, providing a transcriptional map of the lymphatic endothelial niches and of the transitions between them. Differences in gene expression reveal specialized programs for (1) subcapsular ceiling endothelial interactions with the capsule connective tissue and cells, (2) subcapsular floor regulation of lymph borne cell entry into the LN parenchyma and antigen presentation, and (3) medullary subset specialization for pathogen interactions and LN remodeling. LEC of the subcapsular sinus floor and medulla, which represent major sites of cell entry and exit from the LN parenchyma respectively, respond robustly to oxazolone inflammation challenge with enriched signaling pathways that converge on both innate and adaptive immune responses.

ORGANISM(S): Mus musculus

PROVIDER: GSE143877 | GEO | 2020/01/18

REPOSITORIES: GEO

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Project description:Purpose: The goal of this study is to compare the transcriptome profiles of freshly isolated primary murine endothelial populations, namely the lymph node-derived lymphatic endothelial cell (LN-LEC) and blood endothelial cell (LN-BEC), and the diaphragm-derived LEC (D-LEC) in homeostasis. Methods: LN-LEC and LN-BEC total RNA samples were prepared by pooling inguinal, axillary, brachial, cervical, and mesenteric LNs from wild-type C57BL/6 (B6) mice (6-8 weeks of age, purchased from NCI), followed by mechanical and enzymatic digestion, CD45- cell lineage enrichment using MACS beads (deplete S protocol in AUTOMACS), and sorted by flow cytometry based on CD45, CD31, and podoplanin expressions using Influx (BD) into RNA Protect (Qiagen). D-LEC total RNA samples were prepared by mechanical and enzymatic digestion of tissues, followed directly by flow cytometry sorting as described above. Total RNA extraction was performed using RNAeasy mini kit (Qiagen) as per manufacturer’s instructions. The cDNA library preparation and sequencing were performed by the Genomic Services Laboratory at Hudson Alpha, USA. Briefly, purified total RNAs (RIN score of 7.0 or higher) were prepared for sequencing using the Ovation RNA-seq System V2 kit (Nugen) followed by RNA-sequencing of 100 paired-end reads using the Illumina HiSeq 2500 v4 platform. Raw RNA-sequencing read quality was assessed using FastQC and low quality regions were trimmed using Fastx-trimmer. Cleaned reads were aligned to the mouse reference genome (build mm9) using STAR and read counts on known mouse genes were calculated using featureCounts, part of the Subread package. Next, uniquely aligned reads were analyzed using the DEseq2 package in the R statistical computing environment to obtain normalized counts, estimate dispersion, and determine a negative binomial model for each gene. Differentially expressed genes (DEG) were determined using DESeq2 and the Benjamini-Hochberg False Discovery Rate procedure was used to re-estimate the adjusted p-values. Differentially expressed genes (DEGs) were subsequently identified as those with an FPKM of 1 or greater, p-adjusted < 0.05, and additionally 5X-DEG subsets were identified as those with fold-change of 5 or greater. Results: Post-sort analyses of LN-LEC, LN-BEC, and D-LEC replicates showed 92.6-98.6% purity. RNAseq yielded 48-98 million reads per replicate, with an average length of 180 nucleotides, and an average of 85.7% uniquely mapped reads. These reads mapped a total of 23284 genes, of which 15331 were considered expressed based on an average FPKM of 1 or greater in at least one cell type. Of this number, 14718, 14893, and 14384 genes were considered expressed in LN-LEC, LN-BEC, and D-LEC, respectively. Principal component analysis revealed that the transcriptional profiles of sample replicates clustered tightly, and that those of LN-LEC, LN-BEC, and D-LEC differed from each other. Conclusions: Our study provides insights into key genes involved in niche-associated functions of primary murine LEC and BEC in homeostasis. The RNA-seq data reported here may provide a conceptual framework for future comparative investigations of LN-LEC, LN-BEC, and D-LEC phenotypic expression profiles, heterogeneity, and niche-specific functions in homeostasis as well as disease.

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Single-cell RNA-seq of the mouse lymph node lymphatic vasculature: SMART-seq2

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