For patients with COVID-19 we limited our analysis to samples in which flow cytometry identified distinct populations of CD206hi and CD206lo macrophages (Patient 1, 2, 3, 4, 5, 7, 8, 9, A and B). We included two additional patients, one with bacterial pneumonia secondary to infection with Pseudomonas aeruginosa and Acinetobacter baumannii (assigned as “other pneumonia”, Patient 6) and one intubated for airway protection to facilitate endoscopy for severe gastrointestinal bleeding without pneumonia (assigned as a “non-pneumonia control”, Patient C) to examine nonspecific effects of inflammation and mechanical ventilation, respectively, on transcriptomic signatures.

Samples were enriched via flow cytometry sorting for live cells, excluding granulocytes. Cells were sorted into 2% BSA in DPBS, pelleted by centrifugation at 400 rcf for 5 min at 4°C, resuspended in 0.1% BSA in DPBS to ~1000 cells/μl concentration. Concentration was confirmed using K2 Cellometer (Nexcelom) with AO/PI reagent and cells were loaded on 10x Genomics Chip A with Chromium Single Cell 5’ gel beads and reagents (10x Genomics) aiming to capture ~5,000–10,000 cells per library. Libraries were prepared according to the manufacturer’s protocol (10x Genomics, CG000086_RevM). After quality checks, single-cell RNA-seq libraries were pooled and sequenced on a NovaSeq 6000 instrument.

Data was processed using the Cell Ranger 3.1.0 pipeline (10x Genomics). To enable detection of viral RNA, reads were aligned to a custom hybrid genome containing GRCh38.93 and SARS-CoV-2 (NC_045512.2). An additional negative-strand transcript spanning the entirety of the SARS-CoV-2 genome was then added to the GTF and GFF files to enable detection of SARS-CoV-2 replication. Data were processed using Scanpy v1.5.162, doublets were detected with scrublet v0.2.163 and removed, ambient RNA was corrected with FastCAR (https://github.com/LungCellAtlas/FastCAR), and multisample integration was performed with BBKNN v1.3.1264. Only human transcripts were used during integration, selection of highly variable genes and clustering, SARS-CoV-2 transcripts did not influence clustering. Gene set enrichment analysis was performed with signatures retrieved from the gsea-msigdb.org website65 using following terms: HALLMARK_INTERFERON_GAMMA_RESPONSE M5913, HALLMARK_INTERFERON_ALPHA_RESPONSE M5911. Computations were automated with snakemake v5.5.466.

With exclusion of patients A and B, single-cell RNA-seq was performed without multiplexing, using cells from a single patient per single 10x Genomics chip channel. Cells from patients A and B were split into three 10x Genomics chip channels: sample 14 contained cells from patient A, sample 15 contained cells from patient B and sample 16 contained cells from patients A and B multiplexed together. To assign cells from this sample to patients, we used souporcell v2.067 (commit 34eade2ad3a361f045a31f53fee58c2e0c49423f) with the list of common variants for GRCh38 genome, provided on the souporcell page. We ran souporcell for samples 14, 15 and 16 with the number of clusters k=2. We computed Pearson correlation between integer-coded single-nucleotide polymorphisms in genotypic clusters in sample pairs 14–16 and 15–16 to determine which genotypic clusters come from the same patients. Genotypic doublets and unassigned cells were discarded. See code for details.

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