DOI: 10.5061/dryad.vt4b8gtp6
title: Data from: Single cell RNA-seq analysis reveals that prenatal arsenic exposure results in long-term, adverse effects on immune gene expression in response to Influenza A infection

description: Arsenic exposure via drinking water is a serious environmental health
 concern. Epidemiological studies suggest a strong association between
 prenatal arsenic exposure and subsequent childhood respiratory infections,
 as well as morbidity from respiratory diseases in adulthood, long after
 systemic clearance of arsenic. We investigated the impact of exclusive
 prenatal arsenic exposure on the inflammatory immune response and
 respiratory health after an adult influenza A (IAV) lung infection.
 C57BL/6J mice were exposed to 100 ppb sodium arsenite in utero, and
 subsequently infected with IAV (H1N1) after maturation to adulthood.
 Assessment of lung tissue and bronchoalveolar lavage fluid (BALF) at
 various time points post IAV infection reveals greater lung damage and
 inflammation in arsenic exposed mice versus control mice. Single-cell RNA
 sequencing analysis of immune cells harvested from IAV infected lungs
 suggests that the enhanced inflammatory response is mediated by
 dysregulation of innate immune function of monocyte derived macrophages,
 neutrophils, NK cells, and alveolar macrophages. Our results suggest that
 prenatal arsenic exposure results in lasting effects on the adult host
 innate immune response to IAV infection, long after exposure to arsenic,
 leading to greater immunopathology. This study provides the first direct
 evidence that exclusive prenatal exposure to arsenic in drinking water
 causes predisposition to a hyperinflammatory response to IAV infection in
 adult mice, which is associated with significant lung damage.
description: <i>Whole lung homogenate preparation for single cell RNA
 sequencing (scRNA-seq).</i> Lungs were perfused
 with PBS via the right ventricle, harvested, and mechanically
 disassociated prior to straining through 70- and 30-µm filters to obtain a
 single-cell suspension. Dead cells were removed (annexin V EasySep kit,
 StemCell Technologies, Vancouver, Canada), and samples were enriched for
 cells of hematopoetic origin by magnetic separation using
 anti-CD45-conjugated microbeads (Miltenyi, Auburn, CA). Single-cell
 suspensions of 6 samples were loaded on a Chromium Single Cell system (10X
 Genomics) to generate barcoded single-cell gel beads in emulsion, and
 scRNA-seq libraries were prepared using Single Cell 3’ Version 2
 chemistry. Libraries were multiplexed and sequenced on 4 lanes of a
 Nextseq 500 sequencer (Illumina) with 3 sequencing runs. Demultiplexing
 and barcode processing of raw sequencing data was conducted using Cell
 Ranger v. 3.0.1 (10X Genomics; Dartmouth Genomics Shared Resource Core).
 Reads were aligned to mouse (GRCm38) and influenza A virus (A/PR8/34,
 genome build GCF_000865725.1) genomes to generate unique molecular index
 (UMI) count matrices. Gene expression data have been deposited in the NCBI
 GEO database and are available at accession # GSE142047.
 <i>Preprocessing of single cell RNA sequencing (scRNA-seq)
 data</i> Count matrices produced using Cell
 Ranger were analyzed in the R statistical working environment (version
 3.6.1). Preliminary visualization and quality analysis were conducted
 using scran (v 1.14.3, Lun et al., 2016) and Scater (v. 1.14.1, McCarthy
 et al., 2017) to identify thresholds for cell quality and feature
 filtering. Sample matrices were imported into Seurat (v. 3.1.1, Stuart.,
 et al., 2019) and the percentage of mitochondrial, hemoglobin, and
 influenza A viral transcripts calculated per cell. Cells with &lt;
 1000 or &gt; 20,000 unique molecular identifiers (UMIs: low quality
 and doublets), fewer than 300 features (low quality), greater than 10% of
 reads mapped to mitochondrial genes (dying) or greater than 1% of reads
 mapped to hemoglobin genes (red blood cells) were filtered from further
 analysis. Total cells per sample after filtering ranged from 1895-2482, no
 significant difference in the number of cells was observed in arsenic vs.
 control. Data were then normalized using SCTransform (Hafemeister et al.,
 2019) and variable features identified for each sample. Integration
 anchors between samples were identified using canonical correlation
 analysis (CCA) and mutual nearest neighbors (MNNs), as implemented in
 Seurat V3 (Stuart., et al., 2019) and used to integrate samples into a
 shared space for further comparison. This process enables identification
 of shared populations of cells between samples, even in the presence of
 technical or biological differences, while also allowing for
 non-overlapping populations that are unique to individual
 samples. <i>Clustering and reference-based cell
 identity labeling of single immune cells from IAV-infected lung with
 scRNA-seq</i> Principal components were
 identified from the integrated dataset and were used for Uniform Manifold
 Approximation and Projection (UMAP) visualization of the data in
 two-dimensional space. A shared-nearest-neighbor (SNN) graph was
 constructed using default parameters, and clusters identified using the
 SLM algorithm in Seurat at a range of resolutions (0.2-2). The first 30
 principal components were used to identify 22 cell clusters ranging in
 size from 25 to 2310 cells. Gene markers for clusters were identified with
 the findMarkers function in scran.<i> </i>To label individual
 cells with cell type identities, we used the singleR package (v. 3.1.1) to
 compare gene expression profiles of individual cells with expression data
 from curated, FACS-sorted leukocyte samples in the Immgen compendium (Aran
 D. et al., 2019; Heng et al., 2008). We manually updated the Immgen
 reference annotation with 263 sample group labels for fine-grain analysis
 and 25 CD45+ cell type identities based on markers used to sort Immgen
 samples (Guilliams et al., 2014). The reference annotation is provided in
 Table S2, cells that were not labeled confidently after label pruning were
 assigned “Unknown”. <i>Differential gene
 expression by immune cells</i> Differential gene
 expression within individual cell types was performed by pooling raw count
 data from cells of each cell type on a per-sample basis to create a
 pseudo-bulk count table for each cell type. Differential expression
 analysis was only performed on cell types that were sufficiently
 represented (&gt;10 cells) in each sample. In droplet-based scRNA-seq,
 ambient RNA from lysed cells is incorporated into droplets, and can result
 in spurious identification of these genes in cell types where they aren’t
 actually expressed. We therefore used a method developed by Young and
 Behjati (Young et al., 2018) to estimate the contribution of ambient RNA
 for each gene, and identified genes in each cell type that were estimated
 to be &gt; 25% ambient-derived. These genes were excluded from
 analysis in a cell-type specific manner. Genes expressed in less than 5
 percent of cells were also excluded from analysis. Differential expression
 analysis was then performed in Limma (limma-voom with quality weights)
 following a standard protocol for bulk RNA-seq (Law et al., 2014).
 Significant genes were identified using MA/QC criteria of P &lt; .05,
 log2FC &gt;1. <i>Analysis of arsenic effect
 on immune cell gene expression by scRNA-seq.</i>
 Sample-wide effects of arsenic on gene expression were identified
 by pooling raw count data from all cells per sample to create a count
 table for pseudo-bulk gene expression analysis. Genes with less than 20
 counts in any sample, or less than 60 total counts were excluded from
 analysis. Differential expression analysis was performed using limma-voom
 as described above.
description: Files include an annotation file for reference expression
 profiles of cells in the Immgen compendium used for single cell RNA
 sequencing analysis, and results of the analysis (differentially expressed
 genes identified in individual cell types and a pseudo-bulk analysis of
 all immune cells isolated from mouse lung). See README file for Table
 legends