A novel outer membrane vesicle adjuvant improves vaccine protection against Bordetella pertussis.

Pertussis is a vaccine-preventable respiratory disease caused by the Gram negative coccobacillus Bordetella pertussis. The licensed acellular pertussis (aP) vaccines protect against disease but do not prevent bacterial colonization and transmission. Here, we developed and tested an intranasal vaccine composed of aP antigens combined with T-vant, a novel adjuvant derived from bacterial outer membrane vesicles, that elicits both mucosal and systemic immune responses. We hypothesized that immunization of mice with aP-T-vant would enhance mucosal immunity and eliminate B. pertussis in the respiratory tract. In contrast to mice immunized intramuscularly with the licensed aP vaccine, intranasal immunization with aP-T-vant eliminated bacteria in both the lung and nasopharynx. Protection was associated with IFN-gamma and IL-17-producing, non-circulating CD4 + T cells in the lung and nasopharynx, and sterilizing immunity in the nasopharynx was dependent on IL-17. Novel mucosal adjuvants, such as T-vant, warrant further investigation to enhance the efficacy of next generation pertussis vaccines.

Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings.

Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.