The prospects of single-cell analysis in autoimmunity

Autoantibodies in COVID-19 and Other Viral Diseases: Molecular, Cellular, and Clinical Perspectives

Autoantibodies are immune system-produced antibodies that wrongly target the body's cells and tissues for attack. The COVID-19 pandemic has made it possible to link autoantibodies to both the severity of pathogenic infection and the emergence of several autoimmune diseases after recovery from the infection. An overview of autoimmune disorders and the function of autoantibodies in COVID-19 and other infectious diseases are discussed in this review article. We also investigated the different categories of autoantibodies found in COVID-19 and other infectious diseases including the potential pathways by which they contribute to the severity of the illness. Additionally, it also highlights the probable connection between vaccine-induced autoantibodies and their adverse outcomes. The review also discusses the therapeutic perspectives of autoantibodies. This paper advances our knowledge about the intricate interaction between autoantibodies and COVID-19 by thoroughly assessing the most recent findings.

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.