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. 2020 Aug 25;1(5):100078.
doi: 10.1016/j.xcrm.2020.100078. Epub 2020 Aug 5.

Systems-Level Immunomonitoring from Acute to Recovery Phase of Severe COVID-19

Lucie Rodriguez  1 , Pirkka T Pekkarinen  2 , Tadepally Lakshmikanth  1 , Ziyang Tan  1 , Camila Rosat Consiglio  1 , Christian Pou  1 , Yang Chen  1 , Constantin Habimana Mugabo  1 , Ngoc Anh Nguyen  3 , Kirsten Nowlan  3 , Tomas Strandin  4 , Lev Levanov  4 , Jaromir Mikes  1 , Jun Wang  1 , Anu Kantele  5 , Jussi Hepojoki  4 , Olli Vapalahti  4 , Santtu Heinonen  6 , Eliisa Kekäläinen  3 , Petter Brodin  1   7
Affiliations

Systems-Level Immunomonitoring from Acute to Recovery Phase of Severe COVID-19

Lucie Rodriguez et al. Cell Rep Med. .

Abstract

Severe disease of SARS-CoV-2 is characterized by vigorous inflammatory responses in the lung, often with a sudden onset after 5-7 days of stable disease. Efforts to modulate this hyperinflammation and the associated acute respiratory distress syndrome rely on the unraveling of the immune cell interactions and cytokines that drive such responses. Given that every patient is captured at different stages of infection, longitudinal monitoring of the immune response is critical and systems-level analyses are required to capture cellular interactions. Here, we report on a systems-level blood immunomonitoring study of 37 adult patients diagnosed with COVID-19 and followed with up to 14 blood samples from acute to recovery phases of the disease. We describe an IFNγ-eosinophil axis activated before lung hyperinflammation and changes in cell-cell co-regulation during different stages of the disease. We also map an immune trajectory during recovery that is shared among patients with severe COVID-19.

Keywords: COVID-19; SARS-CoV-2; human immunology; mass cytometry; plasma proteins; systems immunology.

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Conflict of interest statement

P.B., T.L., and J.M. are the founders of Cytodelics AB, a company that commercializes reagents for blood sample preservation as used in this study.

Figures

None
Graphical abstract
Figure 1
Figure 1
Longitudinal Profiling of the Immune System in Moderate and Severe COVID-19 (A) A total of 180 unique plasma proteins were quantified using Olink assays (n = 76 plasma samples) and whole-blood immune cells analyzed by mass cytometry (n = 78 whole-blood samples). (B) Monitoring and longitudinal sampling of blood cells (x) and plasma (o) from 37 patients at the Helsinki University Hospital, with patient groups demarcated by colored sample IDs.
Figure 2
Figure 2
The Natural Course of Severe COVID-19 from Admission to Clinical Recovery (A) Body temperature measurements from representative patients over the course of 30 days, from admission to the hospital in ICU and non-ICU patients. (B and C) White blood cell counts (B) and lymphocyte counts (C) during acute and recovery phase in COVID-19 patients. (D–G) Plasma levels of the indicated proteins using Olink assays in longitudinal samples from 16 acute patients (left) and single measurements from 20 recovered patients (right). NPX, normalized protein expression.
Figure 3
Figure 3
Immune Cell Proportions in COVID-19 Proportion of 57 white blood cell populations determined by mass cytometry from acute to recovery phase of COVID-19 patients (n = 35 individuals). Loess smoothing in orange. See also Figures S1 and S2.
Figure 4
Figure 4
Eosinophil Changes from Admission to Recovery 2D representation generated by partition-based graph abstraction (PAGA) of eosinophils from patient COV-40 at 7 different time points from admission to recovery. (A and B) Louvain clusters are colored and annotated by key protein characteristics (A), and cell distributions at each individual time point indicate changes in immune cell states and composition over time (B). (C) Plasma IFN-γ levels as measured by Olink assay in plasma samples from patient COV-40. (D) IFN-γ-mediated upregulation of CD62L contributes to lung inflammation hyperinflammation.
Figure 5
Figure 5
Adaptive Immune Cell Changes from Admission to Recovery 2D representation generated by PAGA B cells (A), CD4 T cells (B), CD8 T cells (C), and γδT cells from patient COV-40 at 7 different time points from admission to recovery. The Louvain clusters are colored (top) and annotated by key protein characteristics, and cell distributions at each individual time point indicate changes in immune cell states and composition over time.
Figure 6
Figure 6
Cell-Cell Communications Network during Different Phases from Acute to Recovery of COVID-19 (A) Spearman correlation matrices from 35 patients, with samples collected at the indicated time intervals and ordered by top correlations. Co-regulated cell populations are highlighted by boxes. (B) Serum IgG antibodies against SARS-CoV-2 Spike protein receptor-binding domain (RBD) from 17 acute patients. RBD showed against days after admission. (C) Mixed-effects modeling (MEM) of plasma protein levels and immune cell population frequencies against anti-RBD IgG titers. The 5 most positively and negatively associated features in MEM are correlated with antibody responses when days from admission is taken into account as a fixed effect.
Figure 7
Figure 7
A Multiomics Immune Signature from Acute COVID-19 to Recovery Multiomics factor analysis (MOFA) is used to integrate 148 plasma protein levels and 63 immune cell frequencies across all 96 blood samples collected from 37 patients. (A) Fraction of total variance explained by type of measurement (view) and by latent factors (LFs) 1–10. (B) LF2 best represents the changes from acute to recovery over time and reveals a shared trajectory for most patients (non-ICU shown in purple and ICU shown in orange). (C) Lollipop plot shows plasma proteins explaining LF2. (D) Lollipop plot shows cell population frequencies explaining LF2.
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