Astrocytic stress response is induced by exposure to astrocyte-binding antibodies expressed by plasmablasts from pediatric patients with acute transverse myelitis.

BACKGROUND: Pediatric acute transverse myelitis (ATM) accounts for 20-30% of children presenting with a first acquired demyelinating syndrome (ADS) and may be the first clinical presentation of a relapsing ADS such as multiple sclerosis (MS). B cells have been strongly implicated in the pathogenesis of adult MS. However, little is known about B cells in pediatric MS, and even less so in pediatric ATM. Our lab previously showed that plasmablasts (PB), the earliest B cell subtype producing antibody, are expanded in adult ATM, and that these PBs produce self-reactive antibodies that target neurons. The goal of this study was to examine PB frequency and phenotype, immunoglobulin selection, and B cell receptor reactivity in pediatric patients presenting with ATM to gain insight to B cell involvement in disease. METHODS: We compared the PB frequency and phenotype of 5 pediatric ATM patients and 10 pediatric healthy controls (HC) and compared them to previously reported adult ATM patients using cytometric data. We purified bulk IgG from the plasma samples and cloned 20 recombinant human antibodies (rhAbs) from individual PBs isolated from the blood. Plasma-derived IgG and rhAb autoreactivity was measured by mean fluorescence intensity (MFI) in neurons and astrocytes of murine brain or spinal cord and primary human astrocytes. We determined the potential impact of these rhAbs on astrocyte health by measuring stress and apoptotic response. RESULTS: We found that pediatric ATM patients had a reduced frequency of peripheral blood PB. Serum IgG autoreactivity to neurons in EAE spinal cord was similar in the pediatric ATM patients and HC. However, serum IgG autoreactivity to astrocytes in EAE spinal cord was reduced in pediatric ATM patients compared to pediatric HC. Astrocyte-binding strength of rhAbs cloned from PBs was dependent on somatic hypermutation accumulation in the pediatric ATM cohort, but not HC. A similar observation in predilection for astrocyte binding over neuron binding of individual antibodies cloned from PBs was made in EAE brain tissue. Finally, exposure of human primary astrocytes to these astrocyte-binding antibodies increased astrocytic stress but did not lead to apoptosis. CONCLUSIONS: Discordance in humoral immune responses to astrocytes may distinguish pediatric ATM from HC.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.

The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.

Coordinating virus research: The Virus Infectious Disease Ontology.

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.