DOI: 10.6084/m9.figshare.21221291.v1
title: BRCs and immune cells from murine and human secondary lymphoid organs

description: SingleCellExperiment objects with BRCs or immune cells from murine secondary lymphoid organs or human lymph nodes and palatne tonsils. In detail: Cells isolated from murine lymph nodes, splenic white pulp or Peyer’s patches were sorted for TdTomato+EYFP+ reticular cells or hematopoeitic immune cell populations using a BD FACSMelody cell sorter (BD Biosciences). Isolated cells from human lymph nodes or tonsilar tissue were sorted for hematopoeitic as well as non-hematopoeitic, non-endothelial cells. Sorted single cell suspensions were emulsified for library generation using the droplet-based 10x Chromium (10x Genomics) system56. The cDNA libraries were generated according to the established commercial protocol for Chromium Single Cell 3’ Reagent Kit (v3 Chemistry) and sequenced by NovaSeq 6000 Illumina sequencing at the Functional Genomic Center Zurich. In order to get sufficient numbers of cells across organs and conditions, for murine BRCs a total of 19 samples (LN: 7 samples; SP: 7 samples; PP: 5 samples) were processed in 8 batches with batches spanning multiple organs. For murine immune cells 6 samples from immunized mice were processed (LN: 2 samples; SP: 2 samples; PP: 2 samples) in one batch. Gene expression estimation from sequencing files was done using CellRanger (v3.0.2) count with Ensembl GRCm38.9 release as reference to build the index for murine samples and GRCh38.9 used as reference for human samples. Next, quality control was performed in R v.4.0.0 using the R/Bioconductor package scater (v.1.16.0) and included removal of damaged and contaminating cells based on (1) very high or low UMI counts (&gt;2.5 median absolute deviation from the median across all cells), (2) very high or low total number of detected genes (&gt;2.5 median absolute deviation from the median across all cells) and (3) high mitochondrial gene content (&gt; 2.5 median absolute deviations above the median across all cells). In addition, in murine BRC samples only <em>Cxcl13</em>-expressing cells were kept for downstream analysis, while cells expressing one of the markers <em>Ptprc</em>, <em>Cd79a</em>, <em>Cd3e</em>, <em>Pecam1</em>, <em>Lyve1</em> or <em>Cldn5</em> were removed as contaminants. Similarly, in human BRC samples only cells expressing <em>CXCL13</em>, but not <em>CD3E</em>, <em>MKI67</em>, <em>PTPRC</em>, <em>CD79A</em>, <em>LYVE1</em>, <em>PECAM1</em> or <em>MYH11</em> were kept for downstream analysis. For downstream analysis, murine BRCs were first analysed for each organ individually, before they were merged and compared across organs. Downstream analysis was performed using the Seurat R package (v.4.0.1) and included normalization, scaling, dimensionality reduction with PCA and UMAP, graph-based clustering and calculation of unbiased cluster markers. Clusters were characterized based on the expression of calculated cluster markers and canonical marker genes. Following cluster characterization for each organ individually, BRC samples from all organs were merged and integrated across organs to compare subset identities independent of their organ identity and to confirm the presence of shared BRC subsets. Integration was performed using the IntegrateData function from the Seurat package. For immune cell samples only cell types known to interact with BRCs were kept for further analysis.