ABSTRACT
Blood transcriptional profiling is a powerful tool to evaluate immune responses to infection; however, blood collection via traditional phlebotomy remains a barrier to precise characterization of the immune response in dynamic infections (e.g., respiratory viruses). Here we present an at-home self-collection methodology, homeRNA, to study the host transcriptional response during acute SARS-CoV-2 infections. This method uniquely enables high frequency measurement of the host immune kinetics in non-hospitalized adults during the acute and most dynamic stage of their infection. COVID-19+ and healthy participants self-collected blood every other day for two weeks with daily nasal swabs and symptom surveys to track viral load kinetics and symptom burden, respectively. While healthy uninfected participants showed remarkably stable immune kinetics with no significant dynamic genes, COVID-19+ participants, on the contrary, depicted a robust response with over 418 dynamic genes associated with interferon and innate viral defense pathways. When stratified by vaccination status, we detected distinct response signatures between unvaccinated and breakthrough (vaccinated) infection subgroups; unvaccinated individuals portrayed a response repertoire characterized by higher innate antiviral responses, interferon signaling, and cytotoxic lymphocyte responses while breakthrough infections portrayed lower levels of interferon signaling and enhanced early cell-mediated response. Leveraging cross-platform longitudinal sampling (nasal swabs and blood), we observed that IFI27, a key viral response gene, tracked closely with SARS-CoV-2 viral clearance in individual participants. Taken together, these results demonstrate that at-home sampling can capture key host antiviral responses and facilitate frequent longitudinal sampling to detect transient host immune kinetics during dynamic immune states.
ABSTRACT
Tuberculosis (TB) is one of the most potent infectious diseases in the world, causing more deaths than any other single infectious agent. TB infection is caused by inhalation of Mycobacterium tuberculosis (Mtb) and subsequent phagocytosis and migration into the lung tissue by innate immune cells (e.g., alveolar macrophages, neutrophils, and dendritic cells), resulting in the formation of a fused mass of immune cells known as the granuloma. Considered the pathological hallmark of TB, the granuloma is a complex microenvironment that is crucial for pathogen containment as well as pathogen survival. Disruption of the delicate granuloma microenvironment via numerous stimuli, such as variations in cytokine secretions, nutrient availability, and the makeup of immune cell population, can lead to an active infection. Herein, we present a novel in vitro model to examine the soluble factor signaling between a mycobacterial infection and its surrounding environment. Adapting a newly developed suspended microfluidic platform, known as Stacks, we established a modular microscale infection model containing human immune cells and a model mycobacterial strain that can easily integrate with different microenvironmental cues through simple spatial and temporal "stacking" of each module of the platform. We validate the establishment of suspended microscale (4 µL) infection cultures that secrete increased levels of proinflammatory factors IL-6, VEGF, and TNFα upon infection and form 3D aggregates (granuloma model) encapsulating the mycobacteria. As a proof of concept to demonstrate the capability of our platform to examine soluble factor signaling, we cocultured an in vitro angiogenesis model with the granuloma model and quantified morphology changes in endothelial structures as a result of culture conditions (P < 0.05 when comparing infected vs. uninfected coculture systems). We envision our modular in vitro granuloma model can be further expanded and adapted for studies focusing on the complex interplay between granulomatous structures and their surrounding microenvironment, as well as a complementary tool to augment in vivo signaling and mechanistic studies.
