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; Rich 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; Sizun Jiang; 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:10.1101/2021.02.09.430269

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

bioRxiv [Preprint]. 2021 Feb 10:2021.02.09.430269. doi: 10.1101/2021.02.09.430269.

Authors

Dorien Feyaerts¹, Julien Hédou¹, Joshua Gillard^{2

3}, Han Chen^{4

5}, Eileen S Tsai¹, Laura S Peterson⁶, Kazuo Ando¹, Monali Manohar⁷, Evan Do⁷, Gopal K R Dhondalay⁷, Jessica Fitzpatrick⁷, Maja Artandi⁸, Iris Chang⁷, Theo T Snow⁷, R Sharon Chinthrajah^{7

9

10}, Christopher M Warren⁷, Rich Wittman⁸, Justin G Meyerowitz¹, Edward A Ganio¹, Ina A Stelzer¹, Xiaoyuan Han^{1

11}, Franck Verdonk¹, Dyani K Gaudillière¹², Nilanjan Mukherjee^{4

5}, Amy S Tsai¹, Kristen K Rumer¹, Sizun Jiang^{4

5}, Sergio Iván Valdés Ferrer¹³, J Daniel Kelly^{14

15

16}, David Furman^{17

18

19}, Nima Aghaeepour^{1

20}, Martin S Angst¹, Scott D Boyd^{4

7}, Benjamin A Pinsky^{4

21}, Garry P Nolan^{4

5}, Kari C Nadeau^{7

9

10}, Brice Gaudillière¹, David R McIlwain^{4

5}

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.
³ Radboud Center for Infectious Diseases, Radboudumc, Nijmegen, the Netherlands.
⁴ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
⁵ Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Division of Neonatal and Developmental Medicine, Department of Pediatrics, 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.
⁸ Department of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
⁹ Division of Allergy, Immunology and Rheumatology, Department of Pediatrics, Stanford University, Stanford, CA, USA.
¹⁰ Department of Medicine, Division of Pulmonary, Allergy and Critical Care Medicine, Stanford University, Stanford, CA, USA.
¹¹ Department of Biomedical Sciences, University of the Pacific, Arthur A. Dugoni School of Dentistry, San Francisco, CA, USA.
¹² Division of Plastic & Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA.
¹³ Departamento de Neurología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
¹⁴ Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA.
¹⁵ Institute of Global Health Sciences, UCSF, San Francisco, CA, USA.
¹⁶ F.I. Proctor Foundation, UCSF, San Francisco, CA, USA.
¹⁷ Buck Artificial Intelligence Platform, the Buck Institute for Research on Aging, Novato, CA, USA.
¹⁸ Stanford 1000 Immunomes Project, Stanford University School of Medicine, Stanford, CA, USA.
¹⁹ Austral Institute for Applied Artificial Intelligence, Institute for Research in Translational Medicine (IIMT), Universidad Austral, CONICET, Pilar, Buenos Aires, Argentina.
²⁰ Department of Biomedical Informatics, Stanford University School of Medicine, Stanford, CA, USA.
²¹ Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Abstract

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

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Abstract

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