Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2.

We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics.

Deep Visual Proteomics defines single-cell identity and heterogeneity.

Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.

α/ß-Peptides as Nanomolar Triggers of Lipid Raft-Mediated Endocytosis through GM1 Ganglioside Recognition.

Cell delivery of therapeutic macromolecules and nanoparticles is a critical drug development challenge. Translocation through lipid raft-mediated endocytic mechanisms is being sought, as it can avoid rapid lysosomal degradation. Here, we present a set of short α/ß-peptide tags with high affinity to the lipid raft-associated ganglioside GM1. These sequences induce effective internalization of the attached immunoglobulin cargo. The structural requirements of the GM1-peptide interaction are presented, and the importance of the membrane components are shown. The results contribute to the development of a receptor-based cell delivery platform.

Genome-wide RNAi screen identifies novel players in human 60S subunit biogenesis including key enzymes of polyamine metabolism.

Ribosome assembly is an essential process that is linked to human congenital diseases and tumorigenesis. While great progress has been made in deciphering mechanisms governing ribosome biogenesis in eukaryotes, an inventory of factors that support ribosome synthesis in human cells is still missing, in particular regarding the maturation of the large 60S subunit. Here, we performed a genome-wide RNAi screen using an imaging-based, single cell assay to unravel the cellular machinery promoting 60S subunit assembly in human cells. Our screen identified a group of 310 high confidence factors. These highlight the conservation of the process across eukaryotes and reveal the intricate connectivity of 60S subunit maturation with other key cellular processes, including splicing, translation, protein degradation, chromatin organization and transcription. Intriguingly, we also identified a cluster of hits comprising metabolic enzymes of the polyamine synthesis pathway. We demonstrate that polyamines, which have long been used as buffer additives to support ribosome assembly in vitro, are required for 60S maturation in living cells. Perturbation of polyamine metabolism results in early defects in 60S but not 40S subunit maturation. Collectively, our data reveal a novel function for polyamines in living cells and provide a rich source for future studies on ribosome synthesis.

Nucleus segmentation: towards automated solutions.

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.

Erratum to "Maternal Smoking Highly Affects the Function, Membrane Integrity, and Rheological Properties in Fetal Red Blood Cells".

[This corrects the article DOI: 10.1155/2019/1509798.].

OpSeF: Open Source Python Framework for Collaborative Instance Segmentation of Bioimages.

Various pre-trained deep learning models for the segmentation of bioimages have been made available as developer-to-end-user solutions. They are optimized for ease of use and usually require neither knowledge of machine learning nor coding skills. However, individually testing these tools is tedious and success is uncertain. Here, we present the Open Segmentation Framework (OpSeF), a Python framework for deep learning-based instance segmentation. OpSeF aims at facilitating the collaboration of biomedical users with experienced image analysts. It builds on the analysts' knowledge in Python, machine learning, and workflow design to solve complex analysis tasks at any scale in a reproducible, well-documented way. OpSeF defines standard inputs and outputs, thereby facilitating modular workflow design and interoperability with other software. Users play an important role in problem definition, quality control, and manual refinement of results. OpSeF semi-automates preprocessing, convolutional neural network (CNN)-based segmentation in 2D or 3D, and postprocessing. It facilitates benchmarking of multiple models in parallel. OpSeF streamlines the optimization of parameters for pre- and postprocessing such, that an available model may frequently be used without retraining. Even if sufficiently good results are not achievable with this approach, intermediate results can inform the analysts in the selection of the most promising CNN-architecture in which the biomedical user might invest the effort of manually labeling training data. We provide Jupyter notebooks that document sample workflows based on various image collections. Analysts may find these notebooks useful to illustrate common segmentation challenges, as they prepare the advanced user for gradually taking over some of their tasks and completing their projects independently. The notebooks may also be used to explore the analysis options available within OpSeF in an interactive way and to document and share final workflows. Currently, three mechanistically distinct CNN-based segmentation methods, the U-Net implementation used in Cellprofiler 3.0, StarDist, and Cellpose have been integrated within OpSeF. The addition of new networks requires little; the addition of new models requires no coding skills. Thus, OpSeF might soon become both an interactive model repository, in which pre-trained models might be shared, evaluated, and reused with ease.

Neuropilin-1 is a host factor for SARS-CoV-2 infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), uses the viral spike (S) protein for host cell attachment and entry. The host protease furin cleaves the full-length precursor S glycoprotein into two associated polypeptides: S1 and S2. Cleavage of S generates a polybasic Arg-Arg-Ala-Arg carboxyl-terminal sequence on S1, which conforms to a C-end rule (CendR) motif that binds to cell surface neuropilin-1 (NRP1) and NRP2 receptors. We used x-ray crystallography and biochemical approaches to show that the S1 CendR motif directly bound NRP1. Blocking this interaction by RNA interference or selective inhibitors reduced SARS-CoV-2 entry and infectivity in cell culture. NRP1 thus serves as a host factor for SARS-CoV-2 infection and may potentially provide a therapeutic target for COVID-19.

AnnotatorJ: an ImageJ plugin to ease hand annotation of cellular compartments.

AnnotatorJ combines single-cell identification with deep learning (DL) and manual annotation. Cellular analysis quality depends on accurate and reliable detection and segmentation of cells so that the subsequent steps of analyses, for example, expression measurements, may be carried out precisely and without bias. DL has recently become a popular way of segmenting cells, performing unimaginably better than conventional methods. However, such DL applications may be trained on a large amount of annotated data to be able to match the highest expectations. High-quality annotations are unfortunately expensive as they require field experts to create them, and often cannot be shared outside the lab due to medical regulations. We propose AnnotatorJ, an ImageJ plugin for the semiautomatic annotation of cells (or generally, objects of interest) on (not only) microscopy images in 2D that helps find the true contour of individual objects by applying U-Net-based presegmentation. The manual labor of hand annotating cells can be significantly accelerated by using our tool. Thus, it enables users to create such datasets that could potentially increase the accuracy of state-of-the-art solutions, DL or otherwise, when used as training data.

Test-time augmentation for deep learning-based cell segmentation on microscopy images.

Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require a massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques. A less common way relies on test-time augmentation (TTA) which yields transformed versions of the image for prediction and the results are merged. In this paper we describe how we have incorporated the test-time argumentation prediction method into two major segmentation approaches utilized in the single-cell analysis of microscopy images. These approaches are semantic segmentation based on the U-Net, and instance segmentation based on the Mask R-CNN models. Our findings show that even if only simple test-time augmentations (such as rotation or flipping and proper merging methods) are applied, TTA can significantly improve prediction accuracy. We have utilized images of tissue and cell cultures from the Data Science Bowl (DSB) 2018 nuclei segmentation competition and other sources. Additionally, boosting the highest-scoring method of the DSB with TTA, we could further improve prediction accuracy, and our method has reached an ever-best score at the DSB.

nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer.

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information.

Maternal Smoking Highly Affects the Function, Membrane Integrity, and Rheological Properties in Fetal Red Blood Cells.

An understanding of the basic pathophysiological mechanisms of neonatal diseases necessitates detailed knowledge about the wide range of complications in the circulating fetal RBCs. Recent publications on adult red blood cells (RBCs) provide evidence that RBCs carry an active nitric oxide synthase (NOS3) enzyme and contribute to vascular functioning and integrity via their active nitric oxide synthesis. The aim of this study was to determine the effect of maternal smoking on the phenotypical appearance and functionality of fetal RBCs, based on morphological and molecular studies. We looked for possible links between vascular dysfunction and NOS3 expression and activation and its regulation by arginase (ARG1). Significant morphological and functional differences were found between fetal RBCs isolated from the arterial cord blood of neonates born to nonsmoking (RBC-NS, n = 62) and heavy-smoking (RBC-S, n = 51) mothers. Morphological variations were quantified by Advanced Cell Classifier, microscopy-based intelligent analysis software. To investigate the relevance of the newly suggested "erythrocrine" function in fetal RBCs, we measured the levels of NOS3 and its phosphorylation in parallel with the level of ARG1, as one of the major influencers of NOS3 dimerization, by fluorescence-activated cell sorting. Fetal RBCs, even the "healthy-looking" biconcave-shaped type, exhibited impaired NOS3 activation in the RBC-S population, which was paralleled with elevated ARG1 level, thus suggesting an increased redox burden. Our molecular data indicate that maternal smoking can exert marked effects on the circulating fetal RBCs, which could have a consequence on the outcome of in utero development. We hypothesize that any endothelial dysfunction altering NO production/bioavailability can be sensed by circulating fetal RBCs. Hence, we are putting forward the idea that neonatal RBC could serve as a real-time sensor for not only monitoring RBC-linked anomalies but also predicting the overall status of the vascular microenvironment.

Failure in the compensatory mechanism in red blood cells due to sustained smoking during pregnancy.

Decrease in the bioavailability of vasoactive nitric oxide (NO), derived from the endothelial nitric oxide synthase (NOS3), underlines vascular endothelial damage. Our expanding knowledge on mature red blood cells (RBCs) makes it supposable that RBCs might contribute to vascular function and integrity via their active NO synthetizing system (RBC-NOS3). This "rescue" mechanism of RBCs could be especially important during pregnancy with smoking habit, when smoking acts as an additional stressor and causes active change in the redox status. In this study RBC populations of 82 non-smoking (RBC-NS) and 75 smoking (RBC-S) pregnant women were examined. Morphological variants were followed by confocal microscopy and quantified by a microscopy based intelligent analysis software. Fluorescence activated cell sorting was used to examine the translational and posttranslational regulation of RBC-NOS, Arginase-1 and the formation of the major product of lipid peroxidation, 4-hydroxy-2-nonenal. To survey the rheological parameters of RBCs like elasticity and plasticity atomic force microscopy-based measurement was applied. Significant morphological and functional differences of RBCs were found between the non-smoking and smoking groups. The phenotypic variations in RBC-S population, even the characteristic biconcave disc-shaped cells, could be connected to impaired NOS3 activation and are compromised in their physiological properties. Membrane lipid studies reveal an elevated lipid oxidation state well paralleled with the changed elastic and plastic activities. These features can form a basic tool in the prenatal health screening conditions; hence the compensatory mechanism of RBC-S population completely fails to sense and rescue the acute oxidative stress conditions.