Protein-based cell population discovery and annotation for CITE-seq data identifies cellular phenotypes associated with critical COVID-19 severity (preprint)

Technologies such as Cellular Indexing of Transcriptomes and Epitopes sequencing (CITE-seq) and RNA Expression and Protein sequencing (REAP-seq) augment unimodal single-cell RNA sequencing (scRNA-seq) by simultaneously measuring expression of cell-surface proteins using antibody derived oligonucleotide tags (ADT). These protocols have been increasingly used to resolve cellular populations that are difficult to infer from gene expression alone, and to interrogate the relationship between gene and protein expression at a single-cell level. However, the ADT-based protein expression component of these assays remains widely underutilized as a primary tool to discover and annotate cell populations, in contrast to flow cytometry which has used surface protein expression in this fashion for decades. Therefore, we hypothesized that computational tools used for flow cytometry data analysis could be harnessed and scaled to analyze ADT data. Here we apply Ozette Discovery, a recently-developed method for flow cytometry analysis, to re-analyze a large (>400,000 cells) published COVID-19 CITE-seq dataset. Using the protein expression data alone, Ozette Discovery is able to identify granular, robust, and interpretable cellular phenotypes in a high-throughput manner. In particular, we identify a population of CLEC12A+CD11b+CD14- myeloid cells that are specifically expanded in patients with critical COVID-19, and can only be resolved by their protein expression profiles. Using the longitudinal gene expression data from this dataset, we find that early expression of interferon response genes precedes the expansion of this subset, and that early expression of PRF1 and GZMB within specific Ozette Discovery phenotypes provides a RNA biomarker of critical COVID-19. In summary, Ozette Discovery demonstrates that taking a protein-centric approach to cell phenotype annotation in CITE-seq data can achieve the potential that dual RNA/protein assays provide in mixed samples: instantaneous in silico flow sorting, and unbiased RNA-seq profiling.

A differential regulatory T cell signature distinguishes the immune landscape of COVID-19 hospitalized patients from those hospitalized with other respiratory viral infections (preprint)

SARS-CoV-2 infection has caused a lasting global pandemic costing millions of lives and untold additional costs. Understanding the immune response to SARS-CoV-2 has been one of the main challenges in the past year in order to decipher mechanisms of host responses and interpret disease pathogenesis. Comparatively little is known in regard to how the immune response against SARS-CoV-2 differs from other respiratory infections. In our study, we compare the peripheral blood immune signature from SARS-CoV-2 infected patients to patients hospitalized pre-pandemic with Influenza Virus or Respiratory Syncytial Virus (RSV). Our in-depth profiling indicates that the immune landscape in patients infected by SARS-CoV-2 is largely similar to patients hospitalized with Flu or RSV. Similarly, serum cytokine and chemokine expression patterns were largely overlapping. Unique to patients infected with SARS-CoV-2 who had the most critical clinical disease state were changes in the regulatory T cell (Treg) compartment. A Treg signature including increased frequency, activation status, and migration markers was correlated with the severity of COVID-19 disease. These findings are particularly relevant as Tregs are being discussed as a therapy to combat the severe inflammation seen in COVID-19 patients. Likewise, having defined the overlapping immune landscapes in SARS-CoV-2, existing knowledge of Flu and RSV infections could be leveraged to identify common treatment strategies.