Animal studies
C57BL/6J mice were used as WT controls and bred in the Northwestern Center for Comparative Medicine facility. MerKD (referred to herein as MerTK−/−) have been previously described
Ex vivo efferocytosis assay, microscopy, and flow cytometry
Resident peritoneal cells were harvested after lavage with cold saline. Peritoneal cells from 3 mice were pooled together for each experimental group. Initial cell selection was achieved through adherence to non-treated, low-adherence cell culture plates for 1 h and rinsed to remove non-adherent cells. Apoptotic cells (ACs) were generated using GFP-Jurkat Human T cells (GenTarget) exposed to UV radiation for 7 min followed by a 2-h incubation at 37 °C. Apoptosis was confirmed by annexin V positive, propidium iodide negative identification affirming greater than 80% apoptosis. Adherent resident peritoneal cells were co-cultivated with ACs at a ratio of 5 ACs to 1 peritoneal cell for 2 h or 6 h as indicated. Control cells were given a media change corresponding to the 6-h timepoint. Non-engulfed ACs were removed from the co-culture through rigorous rinsing with warm saline. Adherent macrophages were removed from the plate with Accutase (StemCell Technologies) and resuspended into a single cell suspension. Apoptotic cell engulfment was confirmed with confocal fluorescence microscopy.
For in vivo efferocytosis microscopy, fluorescently labelled (green calcein-AM) apoptotic Jurkat cells were injected into the peritoneum of Mertk+/+ versus Mertk−/− mice and subsequently peritoneal lavages were collected and stained for F4/80 + resident macrophages. Percent double positive F4/80 + calcein-AM + cells as function of total F4/80 + cells were enumerated. *p
Single cell library preparation and RNA sequencing
The single cell RNA-Seq libraries were prepared using the 10X Genomics Single Cell 5′ Gel Bead and Library Kit pipeline following manufacturer’s protocols. Cell suspensions were diluted to target a recovery of 4,000 cells per sample. A total of 7,674 resident peritoneal cells were sequenced to a read depth ~ 100,000 reads per cell. The Illumina libraries were run on an Agilent Bioanalyzer High Sensitivity Chip and Kapa Library Quantification Kits for Illumina platform (KAPA Biosystems) for quality control before sequencing. In collaboration with the Northwestern University Sequencing Core (NuSeq), the libraries were sequenced on the Illumina HiSeq 4,000 with the following parameters: Read 1—26 cycles; i7 Index—8 cycles; Read 2—98 cycles.
Cell ranger, read alignment, and quantifying cells with ‘unaligned reads’
The sequenced data were processed with the Cell Ranger Single Cell software suite 1.3.1 by 10 × Genomics (GEO accession number pending). Briefly, raw base-call files from a HiSeq4000 sequencer were demultiplexed into FASTQ files. FASTQ files from each of the samples were mapped and the genes were counted using cellranger count. Given that the sequencing parameters included paired-end reads and a full length (150 bp) second read, the “SC5P-PE” option was used for the chemistry parameter. The sequencing reads were aligned to both hg38 (Homo sapiens) and mm10 (Mus musculus) reference genomes using STAR aligner. Reads mapped to mm10 were used for downstream analysis with Seurat.
The unaligned reads were extracted from the ‘possorted_bam.bam’ file created by CellRanger using Samtools with the ‘-f 4’ flag. Awk was then used to remove any duplicate reads with the ‘!seen[$0] +’ distinction. The unmapped read ids were then used as a pattern to grep the FASTQ reads from the raw FASTQ file with ‘seqkit’ for both read one and two. These reads were then mapped to a custom genome containing only the APOL1 genes using the STAR aligner. After alignment, the mapped reads were extracted using Samtools view with the ‘-F 4’ flag and deduplicated using ‘awk’ with the ‘!seen[$0]++’ distinction. For the purpose of determining the number of filtered cells containing the human DNA; the mapped read ids were grepped by ‘seqkit’ to extract the reads from the raw fastq files that had been mapped to APOL1 followed by the 10 × cell barcodes being identified using ‘sed’ with the ‘2 ~ 4p’ distinction for the ‘-n’ parameter. Lastly, the 10 × barcodes from the Seurat object were extracted and compared using ‘comm’ to the read extracted 10X barcodes to count the number of cells containing the human APOL1 DNA.
Single-cell RNA sequencing analysis and visualization
The barcode-gene matrices from the Cell Ranger pipeline were further analyzed using the Seurat R package (v.3.1)
Normalization and variance stabilization of molecular count data was carried out using the R package sctransform (v0.2.1)
Clusters were identified using graph-based clustering approach implemented by the FindCluster function in Seurat at resolutions of 0.2, 0.4, 0.6, 0.8, and 1.0 to determine transcriptionally distinct populations of resident peritoneal cells. Clusters with high cell cycle scores were excluded from further downstream analyses. To unbiasedly identify resident peritoneal cells present in the dataset, SingleR (v1.0.5) was employed. Briefly, SingleR infers the origin of each individual cell by referencing transcriptomic datasets of pure cell types. We utilized the ImmGen database, which contains normalized expression values for immune cells from 830 murine microarrays to ID our peritoneal cell types. These classifications were confirmed with canonical immune cell markers.
For all differential expression tests, we utilized the model-based analysis of single-cell transcriptomics (MAST) test in the Seurat package. To identify unique expression profiles for each cluster, differential expression was tested between each macrophage cluster and all other macrophage clusters combined. The top 50 differentially expressed genes (based on average log fold change) unique to each cluster were visualized in a heatmap. Differential expression testing was also performed between two individual clusters, between genotypes, and between timepoints.
Pathway enrichment analysis
To identify enriched molecular pathways based on differentially expressed genes, gProfiler
Single-cell trajectory inference
Slingshot (v1.4.0)
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