Low-dose M.tb infection but not BCG or MTBVAC vaccination enhances heterologous antibody titres in non-human primates.

Introduction: Mycobacteria are known to exert a range of heterologous effects on the immune system. The mycobacteria-based Freund's Complete Adjuvant is a potent non-specific stimulator of the immune response used in immunization protocols promoting antibody production, and Mycobacterium bovis Bacille Calmette Guérin (BCG) vaccination has been linked with decreased morbidity and mortality beyond the specific protection it provides against tuberculosis (TB) in some populations and age groups. The role of heterologous antibodies in this phenomenon, if any, remains unclear and under-studied. Methods: We set out to evaluate antibody responses to a range of unrelated pathogens following infection with Mycobacterium tuberculosis (M.tb) and vaccination with BCG or a candidate TB vaccine, MTBVAC, in non-human primates. Results: We demonstrate a significant increase in the titer of antibodies against SARS-CoV-2, cytomegalovirus, Epstein-Barr virus, tetanus toxoid, and respiratory syncytial virus antigens following low-dose aerosol infection with M.tb. The magnitude of some of these responses correlated with TB disease severity. However, vaccination with BCG administered by the intradermal, intravenous or aerosol routes, or intradermal delivery of MTBVAC, did not increase antibody responses against unrelated pathogens. Discussion: Our findings suggest that it is unlikely that heterologous antibodies contribute to the non-specific effects of these vaccines. The apparent dysregulation of B cell responses associated with TB disease warrants further investigation, with potential implications for risk of B cell cancers and novel therapeutic strategies.

A novel deep learning-based method for automatic stereology of microglia cells from low magnification images.

Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.