Hamprecht, F. A. | bioRxiv Science

bioRxiv2020

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera

Emergence of the novel pathogenic coronavirus SARS-CoV-2 and its rapid pandemic spread presents numerous questions and challenges that demand immediate attention. Among these is the urgent need for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. For this, sensitive, specific and quantitative serological assays are required. Here we describe the development of a semi-quantitative high-content microscopy-based assay for detection of three major classes (IgG, IgA and IgM) of SARS-CoV-2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi-automated image analysis workflow resulted in specific, sensitive and unbiased assay which complements the portfolio of SARS-CoV-2 serological assays. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high-throughput microscopy to rapidly develop serological assays for emerging virus infections.

microbiology↗

bioRxiv2020

Accurate And Versatile 3D Segmentation Of Plant Tissues At Cellular Resolution

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, and acquisition settings. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.

plant biology↗