ABSTRACT
MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) provides more granular biological information than bulk RNA-sequencing; bulk RNA sequencing remains popular due to lower costs which allows processing more biological replicates and design more powerful studies. As scRNA-seq costs have decreased, collecting data from more than one biological replicate has become more feasible, but careful modeling of different layers of biological variation remains challenging for many users. Here, we propose a statistical model for scRNA-seq gene counts, describe a simple method for estimating model parameters and show that failing to account for additional biological variation in scRNA-seq studies can inflate false discovery rates (FDRs) of statistical tests. RESULTS: First, in a simulation study, we show that when the gene expression distribution of a population of cells varies between subjects, a naïve approach to differential expression analysis will inflate the FDR. We then compare multiple differential expression testing methods on scRNA-seq datasets from human samples and from animal models. These analyses suggest that a naïve approach to differential expression testing could lead to many false discoveries; in contrast, an approach based on pseudobulk counts has better FDR control. AVAILABILITY AND IMPLEMENTATION: A software package, aggregateBioVar, is freely available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/aggregateBioVar.html) to accommodate compatibility with upstream and downstream methods in scRNA-seq data analysis pipelines. SUPPLEMENTARY INFORMATION: Raw gene-by-cell count matrices for pig scRNA-seq data are available as GEO accession GSE150211. Supplementary data are available at Bioinformatics online.
ABSTRACT
RATIONALE: Renal inflammation contributes to the pathophysiology of hypertension. CD161a+ immune cells are dominant in the (SHR) spontaneously hypertensive rat and expand in response to nicotinic cholinergic activation. OBJECTIVE: We aimed to phenotype CD161a+ immune cells in prehypertensive SHR after cholinergic activation with nicotine and determine if these cells are involved in renal inflammation and the development of hypertension. METHODS AND RESULTS: Studies used young SHR and WKY (Wistar-Kyoto) rats. Splenocytes and bone marrow cells were exposed to nicotine ex vivo, and nicotine was infused in vivo. Blood pressures, kidney, serum, and urine were obtained. Flow cytometry, Luminex/ELISA, immunohistochemistry, confocal microscopy, and Western blot were used. Nicotinic cholinergic activation induced proliferation of CD161a+/CD68+ macrophages in SHR-derived splenocytes, their renal infiltration, and premature hypertension in SHR. These changes were associated with increased renal expression of MCP-1 (monocyte chemoattractant protein-1) and VLA-4 (very-late antigen-4). LLT1 (lectin-like transcript 1), the ligand for CD161a, was overexpressed in SHR kidney, whereas vascular cellular and intracellular adhesion molecules were similar to those in WKY. Inflammatory cytokines were elevated in SHR kidney and urine after nicotine infusion. Nicotine-mediated renal macrophage infiltration/inflammation was enhanced in denervated kidneys, not explained by angiotensin II levels or expression of angiotensin type-1/2 receptors. Moreover, expression of the anti-inflammatory α7-nAChR (α7-nicotinic acetylcholine receptor) was similar in young SHR and WKY rats. CONCLUSIONS: A novel, inherited nicotinic cholinergic inflammatory effect exists in young SHR, measured by expansion of CD161a+/CD68+ macrophages. This leads to renal inflammation and premature hypertension, which may be partially explained by increased renal expression of LLT-1, MCP-1, and VLA-4.
