Thyroid autoimmune diseases, such as Hashimoto thyroiditis and Graves disease, are significantly more prevalent in women than in men, suggesting underlying biological differences in immune system function and regulation between sexes. Plasma B cells are crucial in autoimmunity due to their role in producing antibodies targeting self-antigens, but their presence in the thyroids of women without clinical autoimmune diseases remains largely unexplored. This study investigates the infiltration of plasma B cells in female thyroids specifically excluding those with any clinical signs of autoimmune diseases. Using bulk RNA-seq analysis, we identified significant sex differences in gene expression profiles, particularly in genes associated with plasma B cells. Single-cell RNA-seq and spatial transcriptomic analyses further revealed that the CXCL13-CXCR5 signaling axis plays a pivotal role in recruiting and organizing plasma B cells within the thyroid tissue. These findings suggest that the inherent presence of plasma B cells in the female thyroid, driven by CXCL13, may contribute to the higher risk of developing autoimmune thyroid diseases in women. Our study provides new insights into the immune landscape of the thyroid and underscores the importance of understanding sex-specific differences in immune cell distribution and function.

Artificial intelligence (AI) has become a transformative tool in cell biology, driving discoveries through the analysis of complex biological data. This review explores the diverse applications of AI, including its impact on microscopy, imaging, drug discovery, and synthetic biology. AI methods have significantly advanced our ability to analyze cellular images at single-cell resolution, uncover complex patterns in biological data, and predict cellular responses to various stimuli. Deep learning approaches have improved cell segmentation and tracking, facilitated precise single-cell transcriptomics analysis, and enhanced our understanding of protein structures and interactions. The application of AI to high-throughput technologies has also enabled detailed modeling of cell behavior. Key challenges are addressed, such as data quality requirements, model interpretability, and the need to democratize AI tools for broader accessibility in biology. Finally, the review considers future directions, highlighting AI’s potential to advance basic research and therapeutic applications.