Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

Dorien Feyaerts; Julien Hédou; Joshua Gillard; Han Chen; Eileen S Tsai; Laura S Peterson; Kazuo Ando; Monali Manohar; Evan Do; Gopal K R Dhondalay; Jessica Fitzpatrick; Maja Artandi; Iris Chang; Theo T Snow; R Sharon Chinthrajah; Christopher M Warren; Richard Wittman; Justin G Meyerowitz; Edward A Ganio; Ina A Stelzer; Xiaoyuan Han; Franck Verdonk; Dyani K Gaudillière; Nilanjan Mukherjee; Amy S Tsai; Kristen K Rumer; Danielle R Jacobsen; Zachary B Bjornson-Hooper; Sizun Jiang; Sergio Fragoso Saavedra; Sergio Iván Valdés Ferrer; J Daniel Kelly; David Furman; Nima Aghaeepour; Martin S Angst; Scott D Boyd; Benjamin A Pinsky; Garry P Nolan; Kari C Nadeau; Brice Gaudillière; David R McIlwain

doi:10.1016/j.xcrm.2022.100680

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

Cell Rep Med. 2022 Jul 19;3(7):100680. doi: 10.1016/j.xcrm.2022.100680. Epub 2022 Jun 28.

Authors

Dorien Feyaerts¹, Julien Hédou¹, Joshua Gillard², Han Chen³, Eileen S Tsai¹, Laura S Peterson⁴, Kazuo Ando¹, Monali Manohar⁵, Evan Do⁵, Gopal K R Dhondalay⁵, Jessica Fitzpatrick⁵, Maja Artandi⁶, Iris Chang⁵, Theo T Snow⁵, R Sharon Chinthrajah⁷, Christopher M Warren⁵, Richard Wittman⁶, Justin G Meyerowitz¹, Edward A Ganio¹, Ina A Stelzer¹, Xiaoyuan Han⁸, Franck Verdonk¹, Dyani K Gaudillière⁹, Nilanjan Mukherjee³, Amy S Tsai¹, Kristen K Rumer¹, Danielle R Jacobsen¹, Zachary B Bjornson-Hooper³, Sizun Jiang³, Sergio Fragoso Saavedra¹⁰, Sergio Iván Valdés Ferrer¹¹, J Daniel Kelly¹², David Furman¹³, Nima Aghaeepour¹⁴, Martin S Angst¹, Scott D Boyd¹⁵, Benjamin A Pinsky¹⁶, Garry P Nolan¹⁷, Kari C Nadeau¹⁸, Brice Gaudillière¹⁹, David R McIlwain³

Affiliations

¹ Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
² Section Pediatric Infectious Diseases, Laboratory of Medical Immunology, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands; Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands; Center for Molecular and Biomolecular Informatics, Radboud University Medical Center, Nijmegen, the Netherlands.
³ Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.
⁴ Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
⁵ Sean N Parker Center for Allergy and Asthma Research, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA.
⁶ Department of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Sean N Parker Center for Allergy and Asthma Research, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA; Division of Allergy, Immunology and Rheumatology, Department of Pediatrics, Stanford University, Stanford, CA, USA; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁸ Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Sciences, University of the Pacific, Arthur A. Dugoni School of Dentistry, San Francisco, CA, USA.
⁹ Division of Plastic & Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
¹⁰ Departamento de Neurología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Plan de Estudios Combinados en Medicina (MD/PhD Program), Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico.
¹¹ Departamento de Neurología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
¹² Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA; Institute for Global Health Sciences, UCSF, San Francisco, CA, USA; F.I. Proctor Foundation, UCSF, San Francisco, CA, USA.
¹³ Buck Artificial Intelligence Platform, Buck Institute for Research on Aging, Novato, CA, USA; Stanford 1000 Immunomes Project, Stanford University School of Medicine, Stanford, CA, USA; Austral Institute for Applied Artificial Intelligence, Institute for Research in Translational Medicine (IIMT), Universidad Austral, CONICET, Pilar, Buenos Aires, Argentina.
¹⁴ Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.
¹⁵ Sean N Parker Center for Allergy and Asthma Research, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
¹⁶ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹⁷ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: [email protected].
¹⁸ Sean N Parker Center for Allergy and Asthma Research, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA; Division of Allergy, Immunology and Rheumatology, Department of Pediatrics, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Pulmonary, Allergy and Critical Care Medicine, Stanford University, Stanford, CA, USA.
¹⁹ Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Stanford University, Stanford, CA, USA. Electronic address: [email protected].

Abstract

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]_training = 0.799, p = 4.2e-6; multi-class AUC_validation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Keywords: COVID-19; CyTOF; Olink; PBMC; SARS-CoV-2; immunophenotyping; mass cytometry; phosphosignaling response; proteomics; stacked generalization.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.

MeSH terms

COVID-19*
Humans
NF-kappa B / metabolism
Proteomics
SARS-CoV-2
Signal Transduction

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

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