. 2022 Jul 19;3(7):100680.

doi: 10.1016/j.xcrm.2022.100680. Epub 2022 Jun 28.

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

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: gnolan@stanford.edu.
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: gbrice@stanford.edu.

PMID: 35839768
PMCID: PMC9238057
DOI: 10.1016/j.xcrm.2022.100680

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

Dorien Feyaerts et al. Cell Rep Med. 2022.

. 2022 Jul 19;3(7):100680.

doi: 10.1016/j.xcrm.2022.100680. Epub 2022 Jun 28.

Authors

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: gnolan@stanford.edu.
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: gbrice@stanford.edu.

PMID: 35839768
PMCID: PMC9238057
DOI: 10.1016/j.xcrm.2022.100680

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.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests S.D.B. has consulted for Regeneron, Sanofi, Novartis, and Janssen on topics unrelated to this study and owns stocks in AbCellera Biologics. The other authors declare no competing interests.

Figures

**Figure 1**
Combined plasma and single-cell proteomic profiling of patients with mild, moderate, and severe COVID-19 (A) Patients with mild (n = 50), moderate (n = 21), and severe (n = 26) COVID-19 were examined together with healthy controls (n = 40). (B) Schematic representation of the experimental workflow. Plasma proteins were measured using the Olink Explore 1536 assay, while PBMCs were stimulated with either LPS + CL097, IFNα + IL-2 + IL-4 + IL-6, PMA + ionomycin (PI), or left unstimulated (Unstim) for endogenous signaling before barcoding, antibody staining, and analysis by single-cell mass cytometry. (C and D) Correlation networks of single-cell mass cytometry and proteome dataset. Each node represents a feature, with edges representing the correlation between features (cor > 0.9). Node size reflects -log10 of p value of the correlation with severity (Spearman), and node color represents the different data layers. (E) Bivariate scatterplot of patients with COVID-19 and healthy controls plotted along factors 3 and 10 identified by multi-omics factor analysis (MOFA; see also Figure S3).

**Figure 2**
Integrated modeling of plasma and single-cell proteomic events categorizes COVID-19 severity (A) LASSO linear regression models were trained for each individual data layer before integration of all six data layers (proteome, frequency, endogenous signaling, LPS/CL097 signaling response, IFNα/IL-2/IL-4/IL-6 signaling response, and PI signaling response) using a stacked generalization (SG) method. (B and C) Outcome of predicted versus true disease severity derived from SG model for the (B) training (r = 0.61, p = 4.2e-6, n = 74) and (C) validation cohort (r = 0.69, p = 7.7e-6, n = 73). (D and E) Multi-class area under the curve receiver operating characteristic (ROC) analysis of the training (D; AUC = 0.799, n = 74) and validation (E; AUC = 0.773, n = 63) severity model (see Table S2 for individual AUCs). 1 = control, 2 = mild, 3 = moderate, and 4 = severe. For boxplots, the center line represents the median value; upper and lower box limits indicate first (Q1) and third (Q3) quartile, respectively; whiskers, minimum (Q1−1.5∗IQR) and maximum (Q3+1.5∗IQR). IQR, interquartile range. AUC, area under the curve. See also Figure S6 and Tables S1–S4.

**Figure 3**
An iterative bootstrapping method identifies robust informative features for the differentiation of mild, moderate, and severe COVID-19 (A) Workflow of the iterative bootstrap method used to identify informative features in the six data layers of the severity model. LASSO regression model was run 1,000 times on random sub-samples with replacement for each data layer, X_i, then the number of times an individual feature was selected in one of the bootstrap iterations was counted, and the features were ranked according to the frequency of selection in the bootstrap models. (B and C) Correlation network depicting single-cell (B) or plasma (C) proteomic features. Edges represent the correlation between features (Spearman cor > 0.9). Blue/orange nodes highlight positive/negative correlation with disease severity. Node size reflects -log10 of p value (Spearman). Communities containing the bootstrap-selected informative single-cell (B) or plasma (C) proteomic features are highlighted and annotated. (D) Interomic correlations between features of the six data layers are visualized in a chord diagram. Interomic correlations of the top 10% features ranked by bootstrap with absolute Spearman correlation coefficients between 0.5 and 1.0 are shown.

**Figure 4**
Severity model features reveal biological signatures that demarcate patients with mild, moderate, and severe COVID-19 Boxplots, classified by disease severity, showing features informative to the model. r and p indicate Spearman coefficient and p value of Spearman correlation of the feature with disease severity. (A) Endogenous immune cell signaling (arcsinh transformed values; see STAR Methods). (B) Immune cell signaling response to PI stimulation is reported as the arcsinh transformed ratio over the endogenous signaling response (see STAR Methods). (C) Immune cell signaling responses to IFNα/IL-2/IL-4/IL-6 (IFN/IL) stimulation are reported as the arcsinh transformed ratio over the endogenous signaling response (see STAR Methods). (D) Plasma protein levels are reported as the normalized protein expression, an arbitrary unit provided by the Olink assay. Tmem, memory T cell; MERTK, tyrosine-protein kinase Mer. For boxplots, the center line represents the median value; upper and lower box limits indicate first (Q1) and third (Q3) quartile, respectively; whiskers, minimum (Q1−1.5∗IQR) and maximum (Q3+1.5∗IQR). IQR, interquartile range. AUC, area under the curve. See also Figure S7–S11.

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Update of

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.
Feyaerts D, Hédou J, Gillard J, Chen H, Tsai ES, Peterson LS, Ando K, Manohar M, Do E, Dhondalay GKR, Fitzpatrick J, Artandi M, Chang I, Snow TT, Chinthrajah RS, Warren CM, Wittman R, Meyerowitz JG, Ganio EA, Stelzer IA, Han X, Verdonk F, Gaudillière DK, Mukherjee N, Tsai AS, Rumer KK, Jiang S, Valdés Ferrer SI, Kelly JD, Furman D, Aghaeepour N, Angst MS, Boyd SD, Pinsky BA, Nolan GP, Nadeau KC, Gaudillière B, McIlwain DR. Feyaerts D, et al. bioRxiv [Preprint]. 2021 Feb 10:2021.02.09.430269. doi: 10.1101/2021.02.09.430269. bioRxiv. 2021. Update in: Cell Rep Med. 2022 Jul 19;3(7):100680. doi: 10.1016/j.xcrm.2022.100680. PMID: 33594362 Free PMC article. Updated. Preprint.

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

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

Authors

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Abstract

Conflict of interest statement

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Update of

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