Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19.

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating interleukin (IL)-15. CD8+ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17-23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19.

Developing synthetic tools to decipher the tumor-immune interactome.

The ability of immune cells to directly interact with transformed cells is an essential component of immune surveillance and critical for optimal tissue function. The tumor-immune interactome (the collective cellular interactions between oncogenic cells and immune cells) is distinct and varied based on the tissue location and immunogenicity of tumor subtypes. However, comprehensive landscape and the consequences of tumor-interacting immune cells in the tumor microenvironment are not well understood. Current tools are limited in their ability to identify and record interactors in vivo or be utilized for downstream analysis. Here, we describe the development and validation of a technology leveraging synthetic Notch receptors reporting physical tumor cell-immune cell contact in vivo in order to decipher the tumor-immune interactome. We call this approach, Tumor-Immune Interactome Non-biased Discovery Retroviral Reporter or TIINDRR. Using TIINDRR, we identify the tumor-immune interactomes that define immunological refractory and sensitive tumors and how different immunotherapies alter these interactions. Thus, TIINDRR provides a flexible and versatile tool for studying in-vivo tumor-immune cell interactions, aiding in the identification of biologically relevant information needed for the rational design of immune-based therapies.

A unified metric of human immune health.

Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine.

Multiomics integration of 22 immune-mediated monogenic diseases reveals an emergent axis of human immune health.

Monogenic diseases are often studied in isolation due to their rarity. Here we utilize multiomics to assess 22 monogenic immune-mediated conditions with age- and sex-matched healthy controls. Despite clearly detectable disease-specific and "pan-disease" signatures, individuals possess stable personal immune states over time. Temporally stable differences among subjects tend to dominate over differences attributable to disease conditions or medication use. Unsupervised principal variation analysis of personal immune states and machine learning classification distinguishing between healthy controls and patients converge to a metric of immune health (IHM). The IHM discriminates healthy from multiple polygenic autoimmune and inflammatory disease states in independent cohorts, marks healthy aging, and is a pre-vaccination predictor of antibody responses to influenza vaccination in the elderly. We identified easy-to-measure circulating protein biomarker surrogates of the IHM that capture immune health variations beyond age. Our work provides a conceptual framework and biomarkers for defining and measuring human immune health.

Prevention of host-to-host transmission by SARS-CoV-2 vaccines.

As the number of individuals vaccinated against SARS-CoV-2 rises worldwide, population-level data regarding the vaccines' ability to reduce infection are being generated. Randomised trials have shown that these vaccines dramatically reduce symptomatic COVID-19; however, less is known about their effects on transmission between individuals. The natural course of infection with SARS-CoV-2 involves infection of the respiratory epithelia and replication within the mucosa to sufficient viral titres for transmission via aerosol particles and droplets. Here we discuss the available data on the existing, approved SARS-CoV-2 vaccines' capacity to reduce transmissibility by reducing primary infection, viral replication, capacity for transmission, and symptomaticity. The potential for mucosal-targeted SARS-CoV-2 vaccine strategies to more effectively limit transmission than intramuscular vaccines is considered with regard to known immunological mechanisms. Finally, we enumerate the population-level effects of approved vaccines on transmission through observational studies following clinical trials and vaccine distribution in real-world settings.