3
|
Kramer KJ, Wilfong EM, Voss K, Barone SM, Shiakolas AR, Raju N, Roe CE, Suryadevara N, Walker LM, Wall SC, Paulo A, Schaefer S, Dahunsi D, Westlake CS, Crowe JE, Carnahan RH, Rathmell JC, Bonami RH, Georgiev IS, Irish JM. Single-cell profiling of the antigen-specific response to BNT162b2 SARS-CoV-2 RNA vaccine. Nat Commun 2022; 13:3466. [PMID: 35710908 PMCID: PMC9201272 DOI: 10.1038/s41467-022-31142-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 05/26/2022] [Indexed: 12/15/2022] Open
Abstract
RNA-based vaccines against SARS-CoV-2 have proven critical to limiting COVID-19 disease severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. Here we identify and characterize antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 using multiple single-cell technologies for in depth analysis of longitudinal samples from a cohort of healthy participants. Mass cytometry and unbiased machine learning pinpoint an expanding, population of antigen-specific memory CD4+ and CD8+ T cells with characteristics of follicular or peripheral helper cells. B cell receptor sequencing suggest progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlate with eventual SARS-CoV-2 IgG, and a participant lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms identify an antigen-specific cellular basis of RNA vaccine-based immunity.
Collapse
Affiliation(s)
- Kevin J Kramer
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA
| | - Erin M Wilfong
- Department of Medicine, Division of Rheumatology and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Nashville, TN, 37232, USA
| | - Kelsey Voss
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Sierra M Barone
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Andrea R Shiakolas
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA
| | - Nagarajan Raju
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA
| | - Caroline E Roe
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | | | - Lauren M Walker
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA
| | - Steven C Wall
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA
| | - Ariana Paulo
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA
| | - Samuel Schaefer
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA
| | - Debolanle Dahunsi
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA
| | - Camille S Westlake
- Department of Medicine, Division of Rheumatology and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - James E Crowe
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Nashville, TN, 37232, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Vanderbilt Program in Computational Microbiology and Immunology, Nashville, TN, 37232, USA
| | | | - Jeffrey C Rathmell
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA.
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Nashville, TN, 37232, USA.
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Vanderbilt Program in Computational Microbiology and Immunology, Nashville, TN, 37232, USA.
| | - Rachel H Bonami
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Department of Medicine, Division of Rheumatology and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA.
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Nashville, TN, 37232, USA.
- Vanderbilt Program in Computational Microbiology and Immunology, Nashville, TN, 37232, USA.
| | - Ivelin S Georgiev
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Vanderbilt Vaccine Center, Nashville, TN, 37232, USA.
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA.
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Nashville, TN, 37232, USA.
- Vanderbilt Program in Computational Microbiology and Immunology, Nashville, TN, 37232, USA.
| | - Jonathan M Irish
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Human Immunology Discovery Initiative of the Vanderbilt Center for Immunobiology, Nashville, TN, 37232, USA.
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Nashville, TN, 37232, USA.
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA.
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Vanderbilt Program in Computational Microbiology and Immunology, Nashville, TN, 37232, USA.
| |
Collapse
|
4
|
Zhou L, Romero N, Martínez-Miranda J, Conejero JA, García-Gómez JM, Sáez C. Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-sex unbiased meta-clustering technique. JMIR Public Health Surveill 2022; 8:e30032. [PMID: 35144239 PMCID: PMC9098229 DOI: 10.2196/30032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 01/29/2022] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes—the division of populations of patients into more meaningful subgroups driven by clinical features—and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. Objective We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. Methods We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. Results In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. Conclusions The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.
Collapse
Affiliation(s)
- Lexin Zhou
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València (UPV), Valencia, ES
| | - Nekane Romero
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València (UPV), Valencia, ES
| | - Juan Martínez-Miranda
- CONACyT - Centro de Investigación Científica y de Educación Superior de Ensenada - CICESE-UT3, Ensenada, MX
| | - J Alberto Conejero
- Instituto Universitario de Matemática Pura y Aplicada (IUMPA), Universitat Politècnica de València, Valencia, ES
| | - Juan M García-Gómez
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València (UPV), Valencia, ES
| | - Carlos Sáez
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia 46022, España, Valencia, ES
| |
Collapse
|