Global Absence and Targeting of Protective Immune States in Severe COVID-19.

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense.

[1]  Sasikanth Manne,et al.  Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications , 2020, Science.

[2]  J. Derisi,et al.  ReScan, a Multiplex Diagnostic Pipeline, Pans Human Sera for SARS-CoV-2 Antigens , 2020, Cell Reports Medicine.

[3]  David van Dijk,et al.  Uncovering axes of variation among single-cell cancer specimens , 2020, Nature Methods.

[4]  Ronald R. Coifman,et al.  Visualizing structure and transitions in high-dimensional biological data , 2019, Nature Biotechnology.

[5]  C. Peano,et al.  Transcriptome Analysis of Reticulated Platelets Reveals a Prothrombotic Profile , 2019, Thrombosis and Haemostasis.

[6]  R. Satija,et al.  Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression , 2019, Genome Biology.

[7]  Fan Zhang,et al.  Fast, sensitive, and accurate integration of single cell data with Harmony , 2018, bioRxiv.

[8]  Christoph Hafemeister,et al.  Comprehensive integration of single cell data , 2018, bioRxiv.

[9]  Zev J. Gartner,et al.  DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors , 2018, bioRxiv.

[10]  Chun Jimmie Ye,et al.  Multiplexed droplet single-cell RNA-sequencing using natural genetic variation , 2017, Nature Biotechnology.

[11]  Hannah A. Pliner,et al.  Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.

[12]  K. Kain,et al.  Validation of two multiplex platforms to quantify circulating markers of inflammation and endothelial injury in severe infection , 2017, PloS one.

[13]  Andrew J. Hill,et al.  Single-cell mRNA quantification and differential analysis with Census , 2017, Nature Methods.

[14]  Deepak Kumar Jha,et al.  A high-resolution transcriptome map of cell cycle reveals novel connections between periodic genes and cancer , 2016, Cell Research.

[15]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[16]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

[17]  Heng Li,et al.  A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data , 2011, Bioinform..