Influenza vaccine format mediates distinct cellular and antibody responses in human immune organoids

Highly effective vaccines elicit specific, robust, and durable adaptive immune responses. To advance informed vaccine design, it is critical that we understand the cellular dynamics underlying responses to different antigen formats. Here, we sought to understand how antigen-specific B and T cells were activated and participated in adaptive immune responses within the mucosal site. Using a human tonsil organoid model, we tracked the differentiation and kinetics of the adaptive immune response to influenza vaccine and virus modalities. Each antigen format elicited distinct B and T cell responses, including differences in their magnitude, diversity, phenotype, function, and breadth. These differences culminated in substantial changes in the corresponding antibody response. A major source of antigen format-related variability was the ability to recruit naive vs. memory B and T cells to the response. These findings have important implications for vaccine design and the generation of protective immune responses in the upper respiratory tract

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber crosssectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the “patchy” distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigatorspecific needs and provides novel analytical approaches for quantifying muscle morphology

Characterization of the Inflammatory Response in Dystrophic Muscle Using Flow Cytometry

Although mutations of the dystrophin gene are the causative defect in Duchenne muscular dystrophy (DMD) patients, secondary disease processes such as inflammation contribute greatly to the pathogenesis of DMD. Genetic and histological studies have shown that distinct facets of the immune system promote muscle degeneration or regeneration during muscular dystrophy through mechanisms that are only beginning to be defined. Although histological methods have allowed the enumeration and localization of immune cells within dystrophic muscle, they are limited in their ability to assess the full spectrum of phenotypic states of an immune cell population and its functional characteristics. This chapter highlights flow cytometry methods for the isolation and functional study of immune cell populations from muscle of the mdx mouse model of DMD. We include a detailed description of preparing single-cell suspensions of dystrophic muscle that maintain the integrity of cell-surface markers used to identify macrophages, eosinophils, group 2 innate lymphoid cells, and regulatory T cells. This method complements the battery of histological assays that are currently used to study the role of inflammation in muscular dystrophy, and provides a platform capable of being integrated with multiple downstream methodologies for the mechanistic study of immunity in muscle degenerative diseases