T cell perturbations persist for at least 6 months following hospitalization for COVID-19.

COVID-19 is being extensively studied, and much remains unknown regarding the long-term consequences of the disease on immune cells. The different arms of the immune system are interlinked, with humoral responses and the production of high-affinity antibodies being largely dependent on T cell immunity. Here, we longitudinally explored the effect COVID-19 has on T cell populations and the virus-specific T cells, as well as neutralizing antibody responses, for 6-7 months following hospitalization. The CD8+ TEMRA and exhausted CD57+ CD8+ T cells were markedly affected with elevated levels that lasted long into convalescence. Further, markers associated with T cell activation were upregulated at inclusion, and in the case of CD69+ CD4+ T cells this lasted all through the study duration. The levels of T cells expressing negative immune checkpoint molecules were increased in COVID-19 patients and sustained for a prolonged duration following recovery. Within 2-3 weeks after symptom onset, all COVID-19 patients developed anti-nucleocapsid IgG and spike-neutralizing IgG as well as SARS-CoV-2-specific T cell responses. In addition, we found alterations in follicular T helper (TFH) cell populations, such as enhanced TFH-TH2 following recovery from COVID-19. Our study revealed significant and long-term alterations in T cell populations and key events associated with COVID-19 pathogenesis.

Preexisting comorbidities shape the immune response associated with severe COVID-19.

BACKGROUND: Comorbidities are risk factors for development of severe coronavirus disease 2019 (COVID-19). However, the extent to which an underlying comorbidity influences the immune response to severe acute respiratory syndrome coronavirus 2 remains unknown. OBJECTIVE: Our aim was to investigate the complex interrelations of comorbidities, the immune response, and patient outcome in COVID-19. METHODS: We used high-throughput, high-dimensional, single-cell mapping of peripheral blood leukocytes and algorithm-guided analysis. RESULTS: We discovered characteristic immune signatures associated not only with severe COVID-19 but also with the underlying medical condition. Different factors of the metabolic syndrome (obesity, hypertension, and diabetes) affected distinct immune populations, thereby additively increasing the immunodysregulatory effect when present in a single patient. Patients with disorders affecting the lung or heart, together with factors of metabolic syndrome, were clustered together, whereas immune disorder and chronic kidney disease displayed a distinct immune profile in COVID-19. In particular, severe acute respiratory syndrome coronavirus 2-infected patients with preexisting chronic kidney disease were characterized by the highest number of altered immune signatures of both lymphoid and myeloid immune branches. This overall major immune dysregulation could be the underlying mechanism for the estimated odds ratio of 16.3 for development of severe COVID-19 in this burdened cohort. CONCLUSION: The combinatorial systematic analysis of the immune signatures, comorbidities, and outcomes of patients with COVID-19 has provided the mechanistic immunologic underpinnings of comorbidity-driven patient risk and uncovered comorbidity-driven immune signatures.

Distinct immunological signatures discriminate severe COVID-19 from non-SARS-CoV-2-driven critical pneumonia.

Immune profiling of COVID-19 patients has identified numerous alterations in both innate and adaptive immunity. However, whether those changes are specific to SARS-CoV-2 or driven by a general inflammatory response shared across severely ill pneumonia patients remains unknown. Here, we compared the immune profile of severe COVID-19 with non-SARS-CoV-2 pneumonia ICU patients using longitudinal, high-dimensional single-cell spectral cytometry and algorithm-guided analysis. COVID-19 and non-SARS-CoV-2 pneumonia both showed increased emergency myelopoiesis and displayed features of adaptive immune paralysis. However, pathological immune signatures suggestive of T cell exhaustion were exclusive to COVID-19. The integration of single-cell profiling with a predicted binding capacity of SARS-CoV-2 peptides to the patients' HLA profile further linked the COVID-19 immunopathology to impaired virus recognition. Toward clinical translation, circulating NKT cell frequency was identified as a predictive biomarker for patient outcome. Our comparative immune map serves to delineate treatment strategies to interfere with the immunopathologic cascade exclusive to severe COVID-19.

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.