Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications

Immune profiling of COVID-19 patients

Coronavirus disease 2019 (COVID-19) has affected millions of people globally, yet how the human immune system responds to and influences COVID-19 severity remains unclear. Mathew et al. present a comprehensive atlas of immune modulation associated with COVID-19. They performed high-dimensional flow cytometry of hospitalized COVID-19 patients and found three prominent and distinct immunotypes that are related to disease severity and clinical parameters. Arunachalam et al. report a systems biology approach to assess the immune system of COVID-19 patients with mild-to-severe disease. These studies provide a compendium of immune cell information and roadmaps for potential therapeutic interventions.

INTRODUCTION Many patients with coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, present with severe respiratory disease requiring hospitalization and mechanical ventilation. Although most patients recover, disease is complex and case fatality can be as high as 10%. How human immune responses control or exacerbate COVID-19 is currently poorly understood, and defining the nature of immune responses during acute COVID-19 could help identify therapeutics and effective vaccines.

RATIONALE Immune dysregulation during SARS-CoV-2 infection has been implicated in pathogenesis, but currently available data remain limited. We used high-dimensional cytometry to analyze COVID-19 patients and compare them with recovered and healthy individuals and performed integrated analysis of ~200 immune features. These data were combined with ~50 clinical features to understand how the immunology of SARS-CoV-2 infection may be related to clinical patterns, disease severity, and progression.

RESULTS Analysis of 125 hospitalized COVID-19 patients revealed that although CD4 and CD8 T cells were activated in some patients, T cell responses were limited in others. In many patients, CD4 and CD8 T cell proliferation (measured by KI67 increase) and activation (detected by CD38 and HLA-DR coexpression) were consistent with antiviral responses observed in other infections. Plasmablast (PB) responses were present in many patients, reaching >30% of total B cells, and most patients made SARS-CoV-2–specific antibodies. However, ~20% of patients had little T cell activation or PB response compared with controls. In some patients, responses declined over time, resembling typical kinetics of antiviral responses; in others, however, robust T cell and PB responses remained stable or increased over time. These temporal patterns were associated with specific clinical features. With an unbiased uniform manifold approximation and projection (UMAP) approach, we distilled ~200 immune parameters into two major immune response components and a third pattern lacking robust adaptive immune responses, thus revealing immunotypes of COVID-19: (i) Immunotype 1 was associated with disease severity and showed robust activated CD4 T cells, a paucity of circulating follicular helper cells, activated CD8 “EMRAs,” hyperactivated or exhausted CD8 T cells, and PBs. (ii) Immunotype 2 was characterized by less CD4 T cell activation, Tbet+ effector CD4 and CD8 T cells, and proliferating memory B cells and was not associated with disease severity. (iii) Immunotype 3, which negatively correlated with disease severity and lacked obvious activated T and B cell responses, was also identified. Mortality occurred for patients with all three immunotypes, illustrating a complex relationship between immune response and COVID-19.

CONCLUSION Three immunotypes revealing different patterns of lymphocyte responses were identified in hospitalized COVID-19 patients. These three major patterns may each represent a different suboptimal response associated with hospitalization and disease. Our findings may have implications for treatments focused on activating versus inhibiting the immune response.

Discussion The T and B cell response to SARS-CoV-2 infection remains poorly understood. Some studies suggest that an overaggressive immune response leads to immunopathology, whereas others suggest that the mechanism is T cell exhaustion or dysfunction. Autopsies revealed high virus levels in the respiratory tract and other tissues, suggesting ineffective immune responses. Nevertheless, nonhospitalized individuals who recovered from COVID-19 had evidence of virus-specific T cell memory. SARS-CoV-2–specific antibodies are also found in convalescent individuals, and patients are currently being treated with convalescent plasma therapy. However, COVID-19 patients in intensive care units (ICUs) have SARS-CoV-2–specific antibodies, raising the question of why patients with these antibody responses are not controlling disease. In general, these previous studies have reported on single patients or small cohorts and thus do not achieve comprehensive deep immune profiling of larger numbers of hospitalized COVID-19 patients. Such knowledge would address the critical question of whether there is a common profile of immune dysfunction in critically ill patients. Such data would also help guide testing of therapeutics to enhance, inhibit, or otherwise tailor the immune response in COVID-19 patients.

To elucidate the immune response patterns of hospitalized patients with COVID-19, we studied a cohort of ~125 patients. We used high-dimensional flow cytometry to perform deep immune profiling of individual B and T cell populations, with temporal analysis of immune changes during infection, and combined this profiling with extensive clinical data to understand the relationships between immune responses to SARS-CoV-2 and disease severity. This approach led us to several key findings. First, a defining feature of COVID-19 in hospitalized patients is heterogeneity of the immune response. Many COVID-19 patients displayed robust CD8 T cell and/or CD4 T cell activation and proliferation and PB responses, though a substantial subgroup of patients (~20%) had minimal detectable responses compared with controls. Furthermore, even within those patients who mounted detectable B and T cell responses during COVID-19, the immune characteristics of the responses were heterogeneous. With the use of deep immune profiling, we identified three immunotypes in hospitalized COVID-19 patients: (i) robust activation and proliferation of CD4 T cells, relative lack of cTFH cells, modest activation of EMRA-like cells, highly activated or exhausted CD8 T cells, and a signature of T-bet+ PBs (immunotype 1); (ii) Tbetbright effector-like CD8 T cell responses, less robust CD4 T cell responses, and Ki67+ PBs and memory B cells (immunotype 2); and (iii) an immunotype largely lacking detectable lymphocyte response to infection, which suggests a failure of immune activation (immunotype 3). UMAP embedding further resolved the T cell–activation immunotype, suggesting a link between CD4 T cell activation, immunotype 1, and increased severity score. Although differences in age and race existed between the cohorts and could affect some immune variables, the major UMAP relationships were preserved even after correcting for these variables. Thus, these immunotypes may reflect fundamental differences in the ways in which patients respond to SARS-CoV-2 infection. A second key observation from these studies was the robust PB response. Some patients had PB frequencies rivaling those found in acute Ebola or dengue infection. Furthermore, blood PB frequencies are typically correlated with blood-activated cTFH responses. However, in COVID-19 patients, this relationship between PBs and activated cTFH cells was weak. The lack of relationship between these two cell types in this disease could be due to T cell–independent B cell responses, lack of activated cTFH cells in peripheral blood at the time point analyzed, or lower CXCR5 expression observed across lymphocyte populations, making it more difficult to identify cTFH cells. Activated (CD38+HLA-DR+) CD4 T cells could play a role in providing B cell help, perhaps as part of an extrafollicular response, but such a connection was not robust in the current data. Most ICU patients made SARS-CoV-2–specific antibodies, suggesting that at least part of the PB response was antigen specific. Indeed, the cTFH response did correlate with antibodies, which indicates that at least some of the humoral response is targeted against the virus. Future studies will be needed to address the antigen specificity, ontogeny, and role in pathogenesis for these robust PB responses. A notable feature of some patients with strong T and B cell activation and proliferation was the durability of the PB response. This T and B cell activation was interesting considering the clinical lymphopenia in many patients. However, this lymphopenia was preferential for CD8 T cells. It may be notable that such focal lymphopenia preferentially affecting CD8 T cells is also a feature of acute Ebola infection of macaques and is associated with CD95 expression and severe disease. Indeed, CD95 was associated with activated T cell clusters in COVID-19. Nevertheless, the frequency of the KI67+ or CD38+HLA-DR+ CD8 and CD4 T cell responses in COVID-19 patients was similar in magnitude to those of other acute viral infections or live attenuated vaccines in humans. However, during many acute viral infections, the period for peak CD8 or CD4 T cell responses and the window for PB detection in peripheral blood are relatively short. The stability of CD8 and CD4 T cell activation and PB responses during COVID-19 suggests a prolonged period of peak immune responses at the time of hospitalization or perhaps a failure to appropriately down-regulate responses in some patients. These ideas would fit with an overaggressive immune response and/or “cytokine storm” in this subset of patients. Indeed, in some patients, we found elevated serum cytokines and that stimulation of T cells in vitro provoked cytokines and chemokines capable of activating and recruiting myeloid cells. A key question will be how to identify these patients for selected immune-regulatory treatment while avoiding treating patients with already weak T and B cell responses. An additional major finding was the ability to connect immune features to disease severity at the time of sampling as well as to the trajectory of disease severity change over time. Using correlative analyses, we observed relationships between features of the different immunotypes, patient comorbidities, and clinical features of COVID-19. By integrating ~200 immune features with extensive clinical data, disease severity scores, and temporal changes, we built an integrated computational model that connected patient immune response phenotype to disease severity. This UMAP embedding approach allowed us to connect these integrated immune signatures to specific clinically measurable features of disease. The integrated immune signatures captured by components 1 and 2 in this UMAP model provided support for the concept of immunotypes 1 and 2. These analyses suggested that immunotype 1—composed of robust CD4 T cell activation, paucity of cTFH cells with proliferating effector or exhausted CD8 T cells, and T-bet+ PB involvement—was connected to more-severe disease, whereas immunotype 2—characterized by more traditional effector CD8 T cells subsets, less CD4 T cell activation, and proliferating PBs and memory B cells—was better captured by UMAP component 2. Immunotype 3, in which minimal lymphocyte activation response was observed, may represent ~20% of COVID-19 patients and is a potentially important scenario to consider for patients who may have failed to mount a robust antiviral T and B cell response. This UMAP integrated modeling approach could be improved in the future with additional data on other immune cell types and/or comprehensive data for circulating inflammatory mediators for all patients. Nevertheless, these findings provoke the idea of tailoring clinical treatments or future immune-based clinical trials to patients whose immunotype suggests greater potential benefit. Respiratory viral infections can cause pathology as a result of an immune response that is too weak, resulting in virus-induced pathology, or too strong, leading to immunopathology. Our data suggest that the immune response of hospitalized COVID-19 patients may fall across this spectrum of immune response patterns, presenting as distinct immunotypes linked to clinical features, disease severity, and temporal changes in response and pathogenesis. This study provides a compendium of immune response data and an integrated framework to connect immune features to disease. By localizing patients on an immune topology map built on this dataset, we can begin to infer which types of therapeutic interventions may be most useful in specific patients.

Materials and methods Patients, participants, and clinical data collection Patients admitted to the Hospital of the University of Pennsylvania with a positive SARS-CoV-2 PCR test were screened and approached for informed consent within 3 days of hospitalization. Healthy donors (HDs) were adults with no prior diagnosis of or recent symptoms consistent with COVID-19. Normal reference ranges for HDs were the University of Pennsylvania clinical laboratory values shaded in green in Recovered donors (RDs) were adults with a prior positive COVID-19 PCR test by self-report who met the definition of recovery by the Centers for Disease Control and Prevention. HDs and RDs were recruited initially by word of mouth and subsequently through a centralized University of Pennsylvania resource website for COVID-19–related studies. Peripheral blood was collected from all participants. For inpatients, clinical data were abstracted from the electronic medical record into standardized case report forms. ARDS was categorized in accordance with the Berlin Definition, reflecting each individual’s worst oxygenation level and with physician adjudication of chest radiographs. APACHE III scoring was based on data collected in the first 24 hours of ICU admission or the first 24 hours of hospital admission for participants admitted to general inpatient units. Clinical laboratory data were abstracted from the date closest to that of research blood collection. HDs and RDs completed a survey about symptoms. After enrollment, the clinical team determined three patients to be COVID-negative and/or PCR false-positive. Two of these patients were classified as immunotype 3. In keeping with inclusion criteria, these individuals were maintained in the analysis. The statistical significance reported in Fig. 6K did not change when analysis was repeated without these three patients. All participants or their surrogates provided informed consent in accordance with protocols approved by the regional ethical research boards and the Declaration of Helsinki. Sample processing Peripheral blood was collected into sodium heparin tubes (BD, catalog no. 367874). Tubes were spun [15 min, 3000 rpm, room temperature (RT)], and plasma was removed and banked. Remaining whole blood was diluted 1:1 with 1% RPMI (table S7) and layered into a SEPMATE tube (STEMCELL Technologies, catalog no. 85450) preloaded with lymphoprep (STEMCELL Technologies, catalog no. 1114547). SEPMATE tubes were spun (10 min, 1200×g, RT), and the PBMC layer was collected, washed with 1% RPMI (10 min, 1600 rpm, RT), and treated with ACK lysis buffer (5 min, ThermoFisher, catalog no. A1049201). Samples were filtered with a 70-μm filter, counted, and aliquoted for staining. Antibody panels and staining Approximately 1 × 106 to 5 × 106 freshly isolated PBMCs were used per patient per stain. See table S7 for buffer information and table S8 for antibody panel information. PBMCs were stained with live/dead mix (100 μl, 10 min, RT), washed with fluorescence-activated cell sorting (FACS) buffer, and spun down (1500 rpm, 5 min, RT). PBMCs were incubated with 100 μl of Fc block (RT, 10 min) before a second wash (FACS buffer, 1500 rpm, 5 min, RT). Pellet was resuspended in 25 μl of chemokine receptor staining mix and incubated at 37°C for 20 min. After incubation, 25 μl of surface receptor staining mix was directly added, and the PBMCs were incubated at RT for a further 45 min. PBMCs were washed (FACS buffer, 1500 rpm, 5 min, RT) and stained with 50 μl of secondary antibody mix for 20 min at RT and then washed again (FACS buffer, 1500 rpm, 5 min, RT). Samples were fixed and permeabilized by incubating in 100 μl of Fix/Perm buffer (RT, 30 min) and washing in Perm Buffer (1800 rpm, 5 min, RT). PBMCs were stained with 50 μl of intracellular mix overnight at 4°C. The following morning, samples were washed (Perm Buffer, 1800 rpm, 5 min, RT) and further fixed in 50 μl of 4% paraformaldehyde (PFA). Before acquisition, samples were diluted to 1% PFA, and 10,000 counting beads were added per sample (BD, catalog no. 335925). Live/dead mix was prepared in phosphate-buffered saline (PBS). For the surface receptor and chemokine staining mix, antibodies were diluted in FACS buffer with 50% BD Brilliant Buffer (BD, catalog no. 566349). Intracellular mix was diluted in Perm Buffer. Flow cytometry Samples were acquired on a five-laser BD FACS Symphony A5. Standardized SPHERO rainbow beads (Spherotech, catalog no. RFP-30-5A) were used to track and adjust photomultiplier tubes over time. UltraComp eBeads (ThermoFisher, catalog no. 01-2222-42) were used for compensation. Up to 2 × 106 live PBMCs were acquired per sample. Luminex PBMCs from patients were thawed and rested overnight at 37°C in complete RPMI (table S7). Flat-bottom plates with 96 wells were coated with 1 μg/ml of anti-CD3 (UCHT1, no. BE0231, BioXell) in PBS at 4°C overnight. The next day, cells were collected and plated at 1 × 105 per well in 100 μl in duplicate. Anti-human CD28/CD49d (2 μg/ml) was added to the wells containing plate-bound anti-CD3 (Clone L293, 347690, BD). PBMCs were stimulated or left unstimulated for 16 hours and spun down (1200 rpm, 10 min), and supernatant (85 μl per well) was collected. Plasma from matched individuals was thawed on ice and spun (3000 rpm, 1 min) to remove debris, and 85 μl were collected in duplicate. Luminex assay was run according to manufacturer’s instructions, using a custom human cytokine 31-plex panel (EMD Millipore Corporation, SPRCUS707). The panel included EGF, FGF-2, eotaxin, sIL-2Ra, G-CSF, GM-CSF, IFN-α2, IFN-γ, IL-10, IL-12P40, IL-12P70, IL-13, IL-15, IL-17A, IL-1Ra, HGF, IL-1β, CXCL9/MIG, IL-2, IL-4, IL-5, IL-6, IL-7, CXCL8/IL-8, CXCL10/IP-10, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, RANTES, TNF-α, and VEGF. Assay plates were measured using a Luminex FlexMAP 3D instrument (ThermoFisher, catalog no. APX1342). Data acquisition and analysis were performed using xPONENT software. Data quality was examined on the basis of the following criteria: The standard curve for each analyte has a five-parameter R2 value > 0.95 with or without minor fitting using xPONENT software. To pass assay technical quality control, the results for two controls in the kit needed to be within the 95% confidence interval provided by the vendor for >25 of the tested analytes. No further tests were done on samples with results categorized as out-of-range low (<OOR). Samples with results that were out-of-range high (>OOR) or greater than the standard curve maximum value (SC max) were not tested at higher dilutions without further request. Intracellular stain after CD3/CD28 stimulation Flat-bottom plates (96 wells) were coated with 1 μg/ml of anti-CD3 (UCHT1, no. BE0231, BioXell) in PBS at 4°C overnight. The next day, cells were collected and plated at 1 × 105 per well in 100 μl with 1/1000 of GolgiPlug (BD, no. 555029). Anti-human CD28/CD49d (2 μg/ml) was added to the wells containing plate-bound anti-CD3 (Clone L293, 347690, BD). GolgiPlug-treated PBMCs were stimulated or left unstimulated for 16 hours, spun down (1200 rpm, 10 min), and stained for intracellular IFNγ. Longitudinal analysis D0 to D7 and patient grouping To identify participants in which the frequency of specific immune cell populations increased, decreased, or stayed stable over time (D0 to D7), we used a previously published dataset (where data were available) to establish a standard range of fold change over time in a healthy cohort (44). A fold change greater than the mean fold change ± 2 standard deviations was considered an increase, less than this range was considered a decrease, and within this range was considered stable. Where these data were not available, a fold change from D0 to D7 of between 0.5 and 1.5 was considered stable. A fold change <0.5 was considered a decrease, and >1.5 was considered an increase. To eliminate redundant tests and maximize statistical power, the pairwise statistical tests shown in Fig. 5G were performed using fold change as a continuous metric, irrespective of the discrete up, stable, or down classification described above. Similarly, as shown in fig. S9G, pairwise association tests between changes in UMAP component coordinates and clinical data were performed using each difference value as a continuous metric, irrespective of the up, stable, or down classification. Correlation plots and heatmap visualization Pairwise correlations between variables were calculated and visualized as a correlogram using R function corrplot. Spearman’s rank correlation coefficient (ρ) was indicated by square size and heat scale; significance was indicated by *P < 0.05, **P < 0.01, and ***P < 0.001; and a black box indicates a false-discovery rate (FDR) < 0.05. Heatmaps were created to visualize variable values using R function pheatmap or complexheatmap. Statistics Owing to the heterogeneity of clinical and flow cytometric data, nonparametric tests of association were preferentially used throughout this study unless otherwise specified. Correlation coefficients between ordered features (including discrete ordinal, continuous scale, or a mixture of the two) were quantified by the Spearman rank correlation coefficient, and significance was assessed by the corresponding nonparametric methods (null hypothesis: ρ = 0). Tests of association between mixed continuous versus nonordered categorical variables were performed by unpaired Wilcoxon test (for n = 2 categories) or Kruskal-Wallis test (for n > 2 categories). Association between categorical variables was assessed by Fisher’s exact test. For association testing illustrated in heatmaps, categorical variables with more than two categories (e.g., ABO blood type) were transformed into binary “dummy” variables for each category versus the rest. All tests were performed in a two-sided manner, using a nominal significance threshold of P < 0.05 unless otherwise specified. When appropriate to adjust for multiple hypothesis testing, FDR correction was performed using the Benjamini-Hochberg procedure at the FDR < 0.05 significance threshold. Joint statistical modeling to adjust for confounding of demographic factors (age, sex, and race) when testing for association of UMAP components 1 and 2 with the NIH Ordinal Severity Scale was performed using ordinal logistic regression provided by the polr function of the R package MASS. Statistical analysis of flow cytometry data was performed using the R package rstatix. Other details, if any, for each experiment are provided within the relevant figure legends. High-dimensional data analysis of flow cytometry data viSNE and FlowSOM analyses were performed on Cytobank. B cells, non-naïve CD4 T cells, and non-naïve CD8 T cells were analyzed separately. viSNE analysis was performed using equal sampling of 1000 cells from each FCS file, with 5000 iterations, a perplexity of 30, and a theta of 0.5. For B cells, the following markers were used to generate the viSNE maps: CD45RA, IgD, CXCR5, CD138, Eomes, TCF-1, CD38, CD95, CCR7, CD21, KI67, CD27, CX3CR1, CD39, T-bet, HLA-DR, CD16, CD19 and CD20. For non-naïve CD4 and CD8 T cells, the following markers were used: CD45RA, PD-1, CXCR5, TCF-1, CD38, CD95, Eomes, CCR7, KI67, CD16, CD27, CX3CR1, CD39, CD20, T-bet, and HLA-DR. Resulting viSNE maps were fed into the FlowSOM clustering algorithm. For each cell subset, a new self-organizing map (SOM) was generated using hierarchical consensus clustering on the tSNE axes. For each SOM, 225 clusters and 10 or 15 metaclusters were identified for B cells and T cells, respectively. To group individuals on the basis of B cell landscape, pairwise EMD values were calculated on the B cell tSNE axes for all COVID-19 D0 patients, HDs, and RDs using the emdist package in R, as previously described. Resulting scores were hierarchically clustered using the hclust package in R. Batch correction During the sample-acquisition period, the flow panel was changed to remove one antibody. Batch correction was performed for samples acquired before and after this change to remove potential bias from downstream analysis. Because the primary flow features were expressed as a fraction of the parent population (falling in the 0-to-1 interval), a variance stabilizing transform (logit) was first applied to each data value prior to recentering the second panel to have the same mean as the first. After mean-centering, data were transformed back to the original fraction of parent scale by inverse transform. This procedure was applied separately to all 553 flow features annotated in the main text and supplemental data. Notably, this procedure avoids any batch-corrected feature values artificially falling outside of the original 0-to-1 range. After batch correction, neither UMAP component 1 nor component 2 had a statistically significant difference between panels by unpaired Wilcoxon test. Visualizing variation of flow cytometric features across the UMAP embedding space A feature-weighted kernel density was computed across all COVID-19 patients and was displayed as a contour plot. Whereas traditional kernel density methods apply the same base kernel function to every point to visualize point density, in this case the base kernel function centered at each individual COVID-19 patient sample was instead weighted (multiplied) by the Z-transform (mean-centered and standard deviation–scaled) of the log-transformed input feature prior to computing the overall kernel density. This weighting procedure facilitated visualization of the overall feature gradients (from relatively low to high expression) across UMAP coordinates. independent of the different range of each input feature. A radially symmetric two-dimensional Gaussian was used as the base kernel function with a variance parameter of one-half, which was tuned to be sufficiently broad in order to smooth out local discontinuities and best visualize feature gradients. Definition of immunotype 3 To define COVID-19 patients with low or absent immune responses, classified as immunotype 3, the intersection of the bottom 50% of five different flow parameters was used: PB as percentage of B cells, KI67+ as percentage of non-naïve CD4 T cells, KI67+ as percentage of non-naïve CD8 T cells, HLA-DR+CD38+ as percentage of non-naïve CD4 T cells, and HLA-DR+CD38+ as percentage of non-naïve CD8 T cells

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