GSE171052
Case Report: Deep Immune Profiling Links Neutrophil and Plasmablast Dysregulation to Microvascular Damage in post-COVID-19 Multisystem Inflammatory Syndrome of Adults (MIS-A).
Despite surviving a SARS-CoV-2 infection, some individuals experience an intense post-infectious Multisystem Inflammatory Syndrome (MIS) of uncertain etiology. Children with this syndrome (MIS-C) can experience a Kawasaki-like disease, but mechanisms in adults (MIS-A) are not clearly defined. Here we utilize a deep phenotyping approach to examine immunologic responses in an individual with MIS-A. Results are contextualized to healthy, convalescent, and acute COVID-19 patients. The findings reveal systemic inflammatory changes involving novel neutrophil and B-cell subsets, autoantibodies, complement, and hypercoagulability that are linked to systemic vascular dysfunction. This deep patient profiling generates new mechanistic insight into this rare clinical entity and provides potential insight into other post-infectious syndromes. Overall design: Single-cell RNA-Seq library construction, alignment, and quality control: A total of 15,000 single cells (containing an equal proportion of leukocytes and lymphocytes) were loaded for partitioning using 10X Genomics NextGEM Gel Bead emulsions. All samples were processed as per manufacturer's protocol (with both PCR amplification steps run 12X). Quality control and pre-loading cDNA quantification was performed using TapeStation D1000 ScreenTape assay. Sequencing was performed using Illumina NovaSeq S2 and SP 100 cycle dual lane flow cells over multiple rounds to ensure each sample received approximately 32,000 reads per cell. Sequencing reads were aligned using CellRanger 3.1.0 pipeline to the standard pre-built GRCh38 reference genome. Samples that passed alignment QC were aggregated into single datasets using CellRanger aggr with between-sample normalization to ensure each sample received an equal number of mapped reads per cell. Aggregated healthy (n = 3) and recovered COVID-19 (n = 3) samples recovered 30,514 cells that were sequenced to 17,157 post-normalization reads per cell. Single-cell RNA-Seq computational analyses and workflows: Filtered feature-barcode HDF5 matrices from aggregated datasets were imported into the R package Seurat v.3.9 for normalization, scaling, integration, multi-modal reference mapping, louvain clustering, dimensionality reduction, differential expression analysis, and visualization. Briefly, cells with abnormal transcriptional complexity (fewer than 500 UMIs, greater than 25,000 UMIs, or greater than 15% of mitochondrial reads) were considered artifacts and were removed from subsequent analysis. Cell identity was classified by mapping single cell profiles to the recently published PBMC single-cell joint RNA/CITE-Seq multi-omic AZIMUTH reference. Since no published reference automates granulocyte annotations, neutrophil clusters were manually annotated by querying known markers (i.e. CSF3R, S100A8, S100A9, MMP8, MMP9, ELANE, MPO). A cell state-specific 'perturbation score' was calculated to reflect the magnitude of response elicited by factoring in number and cumulative FC of consensus DEGs. Perturbation scores were visualized using Nebulosa-generated density plots. Cells with high mitochondrial read were subsetted to enable further characterization of these cells, as there were high mitochondrial reads specifically in the first MIS-A sample.