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Post-traumatic osteoarthritis (PTOA) is a painful joint disease characterized by the degradation of bone, cartilage, and other connective tissues in the joint. PTOA is initiated by trauma to joint-stabilizing tissues, such as the anterior cruciate ligament, medial meniscus, or by intra-articular fractures. In humans, ~50% of joint injuries progress to PTOA, while the rest spontaneously resolve. To better understand molecular programs contributing to PTOA development or resolution, we examined injury-induced fluctuations in immune cell populations and transcriptional shifts by single-cell RNA sequencing of synovial joints in PTOA-susceptible C57BL/6J (B6) and PTOA-resistant MRL/MpJ (MRL) mice. We identified significant differences in monocyte and macrophage subpopulations between MRL and B6 joints. A potent myeloid-driven anti-inflammatory response was observed in MRL injured joints that significantly contrasted the pro-inflammatory signaling seen in B6 joints. Multiple CD206+ macrophage populations classically described as M2 were found enriched in MRL injured joints. These CD206+ macrophages also robustly expressed Trem2, a receptor involved in inflammation and myeloid cell activation. These data suggest that the PTOA resistant MRL mouse strain displays an enhanced capacity of clearing debris and apoptotic cells induced by inflammation after injury due to an increase in activated M2 macrophages within the synovial tissue and joint space., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2025 McCool et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Coccidioidomycosis, or Valley fever, is a lung disease caused by inhalation of Coccidioides fungi, prevalent in the Southwestern United States, Mexico, and parts of Central and South America. Annually, the United States reports 10,000-20,000 cases, although those numbers are expected to increase as climate change expands the fungal geographic range. While 60% of infections are asymptomatic, 40% symptomatic infections are often misdiagnosed due to similarities with bronchitis or pneumonia. A small subset of infection progress to severe illness, necessitating a better understanding of immune responses during lethal infection. Using single-cell RNA sequencing and spatial transcriptomics, we characterized lung responses during Coccidioides infection. We identified monocyte-derived Spp1 -expressing macrophages as potential mediators of tissue remodeling and fibrosis, marked by high expression of profibrotic and proinflammatory transcripts. These macrophages showed elevated TGF-β and IL-6 signaling, pathways involved in fibrosis pathogenesis. Additionally, we observed significant neutrophil infiltration and defective lymphocyte responses, indicating severe adaptive immunity dysregulation in lethal, acute infection. These findings enhance our understanding of Coccidioides infection and suggest new therapeutic targets.IMPORTANCECoccidioidomycosis, commonly known as Valley fever, is a lung disease caused by the inhalation of Coccidioides fungi, which is prevalent in the Southwestern United States, Mexico, and parts of Central and South America. With climate change potentially expanding the geographic range of this fungus, understanding the immune responses during severe infections is crucial. Our study used advanced techniques to analyze lung responses during Coccidioides infection, identifying specific immune cells that may contribute to tissue damage and fibrosis. These findings provide new insights into the disease mechanisms and suggest potential targets for therapeutic intervention, which could improve outcomes for patients suffering from severe Valley fever.

A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scRNAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA's performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses., Competing Interests: Competing interests E.J.F is on the Scientific Advisory Board for Resistance-Bio and Viosera Therapeutics, and a paid consultant for Mestag Therapeutics and Merck. Other authors declare no competing interests.

Motivation: Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies., Results: We train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models., Availability and Implementation: The codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Follicular CXCR5+ PD-1+ CD8 T cells (CD8 Tfc) arise in multiple models of systemic autoimmunity yet their functional contribution to disease remains in debate. Here we define the follicular localization and functional interactions of CD8 Tfc with B cells during autoimmune disease. The absence of functional T regulatory cells in autoimmunity allows for CD8 Tfc development that then expands with lymphoproliferation. CD8 Tfc are identifiable within the lymph nodes and spleen during systemic autoimmunity, but not during tissue-restricted autoimmune disease. Autoimmune CD8 Tfc cells are polyfunctional, producing helper cytokines IL-21, IL-4, and IFNγ while maintaining cytolytic proteins CD107a, granzyme B, and TNF. During autoimmune disease, IL-2-KO CD8 T cells infiltrate the B cell follicle and germinal center, including the dark zone, and in vitro induce activation-induced cytidine deaminase in naïve B cells via IL-4 secretion. CD8 Tfc represent a unique CD8 T cell population with a diverse effector cytokine repertoire that can contribute to pathogenic autoimmune B cell response., (Copyright © 2021. Published by Elsevier Ltd.)