A defining feature of resident gut macrophages is their high replenishment rate from blood monocytes, attributed to tonic commensal-stimulation of this site. In contrast, almost all other tissues contain locally maintained macrophage populations, which co-exist with monocyte-replenished cells at homeostasis. Here, we identified three transcriptionally distinct murine gut macrophage subsets that segregate based on expression of Tim-4 and CD4. Challenging current understanding, Tim-4+CD4+ gut macrophages were found to be locally maintained, while Tim-4–CD4+ macrophages had a slow turnover from blood monocytes; indeed, Tim-4–CD4– macrophages were the only subset with the high monocyte-replenishment rate currently attributed to gut macrophages. Moreover, all macrophage subpopulations required a live microbiota to sustain their numbers, not only those derived from blood monocytes. These findings oppose the prevailing paradigm that all macrophages in the adult murine gut rapidly turnover from monocytes in a microbiome-dependant manner; instead, supplanting it with a model of ontogenetic diversity, where locally maintained subsets co-exist with rapidly replaced, monocyte-derived populations.
Overall design: Tim-4–CD4–, Tim-4–CD4+ and Tim-4+CD4+ macrophages were isolated by fluorescence-activated cell sorting (FACS) from the small intestines of 3 pooled wild type adult male C57/BL6 mice, on four separate occasions. Similarly, Ly6Chi peripheral blood monocytes from 3-4 pooled wild type adult male C57/BL6 mice were isolated, on three separate occasions. Cells were sorted directly into RLT buffer (Qiagen) and stored on dry ice before storage at -80oC for subsequent RNA extraction. RNA was extracted from 120,000 – 200,000 cells using an RNeasy micro kit (Qiagen) following manufacturer’s instructions. RNA was quantified using a Qubit 2.0 Fluorometer (Thermo Fisher Scientifc) and quality was assessed using an RNA ScreenTape Assay and 2200 TapeStation (Agilent Technologies). Strand-specific RNA-seq libraries were prepared using the Illumina workflow with the TruSeq® Stranded mRNA Sample Preparation Kit. Paired-end reads (65bpx65bp) were generated from each sample. From 48 to 192 million reads were obtained from each sample. The fastq files generated by an Illumina HiSeq4000 platform were analysed with FastQC (Andrew S.; FastQC: A Quality Control tool for High Throughput Sequence Data, available online. Any low quality reads and contaminated barcodes were trimmed with Trimmomactic (Bolger et al., 2014). All libraries were aligned to GRCm38.p4 assembly of mouse genome using STAR-2.4.2 (Dobin et al., 2013) and only uniquely mapped reads were used in differential gene expression analysis. The mapped reads were counted by genes with HTSeq (Anders et al., 2015) agaist gencode.vM11.annotation.gtf. The differentially expressed (DE) genes were identified using DESeq2 (Love et al., 2014) by pairwise comparisons between the experimental groups. The DE genes with 0.05 were selected for further validation and analysis.
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