Abstract:

SARS-CoV-2 infection and COVID-19 disease vary with respect to viral variant and host vaccination status. However, how vaccines, emergent variants, and their intersection shift host responses in the human nasal mucosa remains uncharacterized. We and others have shown during the first SARS-CoV-2 wave that a muted nasal epithelial interferon response at the site of infection underlies severe COVID-19. We sought to further understand how upper airway cell subsets and states associate with COVID-19 phenotypes across viral variants and vaccination. Here, we integrated new single-cell RNA-sequencing (scRNA-seq) data from nasopharyngeal swabs collected from 67 adult participants during the Delta and Omicron waves with data from 45 participants collected during the original (Ancestral) wave in our prior study. By characterizing detailed cellular states during infection, we identified changes in epithelial and immune cells that are both unique and shared across variants and vaccination status. By defining SARS-CoV-2 RNA+ cells for each variant, we found that Delta samples had a marked increase in the abundance of viral RNA+ cells. Despite this dramatic increase in viral RNA+ cells in Delta cases, the nasal cellular compositions of Delta and Omicron exhibit greater similarity, driven partly by myeloid subsets, than the Ancestral landscapes associated with specialized epithelial subsets. We found that vaccination prior to infection was surprisingly associated with nasal macrophage recruitment and activation rather than adaptive immune cell signatures. While patients with severe disease caused by Ancestral or Delta variants had muted interferon responses, Omicron-infected patients had equivalent interferon responses regardless of disease severity. Our study defines the evolution of cellular targets and signatures of disease severity in the upper respiratory tract across SARS-CoV-2 variants, and suggests that intramuscular vaccines shape myeloid responses in the nasal mucosa upon SARS-CoV-2 infection.

Brief summary of cohort and single cell transcriptomics methods: 

Nasopharyngeal swabs were collected from a total of 112 adult patients at the University of Mississippi Medical Center including 26 control participants who had no respiratory symptoms and tested negative for SARS-CoV-2 by PCR and 86 participants who had positive SARS-CoV-2 PCR tests. We combined data from our previously published study (Ziegler et al., Cell 2021) (13 controls and 32 "Ancestral" cases; April-November 2020) with samples collected during the Delta (13 controls and 33 cases; July-September 2021) and Omicron (21 cases; January-February 2022) waves of the COVID-19 pandemic. The Delta and Omicron wave cohorts included participants who had received at least 2 doses of an mRNA vaccine for COVID-19 prior to infection as well as participants who were unvaccinated. Within each variant cohort, as well as within vaccination groups, participants experienced a range of COVID-19 severity quantified by a previously described (WHO) score of required respiratory support (0-8). All swabs were collected while participants experienced acute respiratory symptoms. Samples from the nasopharyngeal epithelium were taken by a trained healthcare provider and rapidly cryopreserved to maintain cellular viability. Swabs were later processed to recover single-cell suspensions before generating single-cell transcriptomes via Seq-Well S^3 platform. Libraries were generated using Illumina Nextera XT Library Prep Kits and sequenced using NovaSeq S4 kits at the Broad Institute Sequencing Core: read 1: 21 (cell barcode, UMI), read 2: 50 (digital gene expression), index 1: 8 (N700 barcode). Libraries were aligned using STAR within the Drop-Seq Computational Protocol (https://github.com/broadinstitute/ Drop-seq) and implemented on Cumulus (https://cumulus.readthedocs.io/en/latest/drop_seq.html, snapshot 11, default parameters). A previously developed custom reference of the combined human GRCh38 (from CellRanger version 3.0.0, Ensembl 93) and SARS-CoV-2 RNA genomes was used for alignment. Aligned cell-by-gene matrices for each array were merged into one Seurat object across all participants. Following CellBender correction, cells were filtered to remove those with fewer than 200 UMI, fewer than 150 unique genes, and greater than 50% mitochondrial reads. This resulted in a dataset of 32994 genes and 55319 cells across 112 study participants (26 SARS-CoV-2-, 86 SARS-CoV-2+).