Wang, Y.-L. | bioRxiv Science

bioRxiv2020

Longitudinal single-cell immune profiling revealed distinct innate immune response in asymptomatic COVID-19 patients

Recent studies have characterized the single-cell immune landscape of host immune response of coronavirus disease 2019 (COVID-19), specifically focus on the severe condition. However, the immune response in mild or even asymptomatic patients remains unclear. Here, we performed longitudinal single-cell transcriptome sequencing and T cell/B cell receptor sequencing on 3 healthy donors and 10 COVID-19 patients with asymptomatic, moderate, and severe conditions. We found asymptomatic patients displayed distinct innate immune responses, including increased CD56briCD16- NK subset, which was nearly missing in severe condition and enrichment of a new Th2-like cell type/state expressing a ciliated cell marker. Unlike that in moderate condition, asymptomatic patients lacked clonal expansion of effector CD8+ T cells but had a robust effector CD4+ T cell clonal expansion, coincide with previously detected SARS-CoV-2-reactive CD4+ T cells in unexposed individuals. Moreover, NK and effector T cells in asymptomatic patients have upregulated cytokine related genes, such as IFNG and XCL2. Our data suggest early innate immune response and type I immunity may contribute to the asymptomatic phenotype in COVID-19 disease, which could in turn deepen our understanding of severe COVID-19 and guide early prediction and therapeutics.

immunology↗

bioRxiv2020

Traction Force Microscopy by Deep Learning

Cells interact mechanically with their surrounding by exerting forces and sensing forces or force-induced displacements. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiology. However, to solve the ill-posted mathematical problem, its implementation has involved regularization and the associated compromises in accuracy and resolution. Here we applied neural network-based deep learning as a novel approach for TFM. We modified a network for processing images to process vector fields of stress and strain. Furthermore, we adapted a mathematical model for cell migration to generate large sets of simulated stresses and strains for training the network. We found that deep learning-based TFM yielded results qualitatively similar to those from conventional methods but at a higher accuracy and resolution. The speed and performance of deep learning TFM make it an appealing alternative to conventional methods for characterizing mechanical interactions between cells and the environment. Statement of SignificanceTraction Force Microscopy has served as a fundamental driving force for mechanobiology. However, its nature as an ill-posed inverse problem has posed serious challenges for conventional mathematical approaches. The present study, facilitated by large sets of simulated stresses and strains, describes a novel approach using deep learning for the calculation of traction stress distribution. By adapting the UNet neural network for handling vector fields, we show that deep learning is able to minimize much of the limitations of conventional approaches to generate results with speed, accuracy, and resolution.

biophysics↗