Dabek, A. | Search bioRxiv

bioRxiv2026

B cells dominate the heart immune infiltrate during chronic infectious cardiomyopathy

Recent advances in single-cell and spatially resolved multi-omics have revolutionised our understanding of the cellular and molecular niches governing cardiac homeostasis and acute injury. However, the immune-stromal networks sustaining long-term, chronic heart pathology remain poorly defined, largely due to a paucity of physiologically relevant experimental models. Here, through a side-by-side multi-omic comparison of Trypanosoma cruzi-infected mouse models displaying contrasting degrees of chronic myocardial pathology and fibrosis, we define the long-term cellular landscape of the inflamed fibrotic heart. We reveal that class-switched, antibody-secreting plasma B cells represent the predominant immune subset infiltrating the fibrotic myocardium. CellChat intercellular communication modelling identifies plasma cell-fibroblast crosstalk as the strongest immune-stromal interaction in the fibrosis-prone heart, driven by predicted matrix anchoring (CD44-collagen/laminin) and molecular survival axes (App-Cd74, C3-Cr2). Supporting these computational predictions, immunofluorescence and spatial profiling confirm close proximity between plasma B cells and fibroblasts, alongside a striking accumulation of B cells embedded within extracellular matrix-rich cardiac scars. This localised B cell response maintains prominent pools of parasite-reactive and heart-autoreactive antibody-secreting cells despite a lack of detectable parasite signal in their immediate micro-vicinity. Together, these findings identify a matrix-associated plasma B cell-fibroblast niche as a central driver of chronic cardiac remodelling, revealing how persistent tissue scarring can become decoupled from direct local pathogen presence.

immunology↗

bioRxiv2026

Multi-Objective Bayesian Optimization for Data-Efficient Bioprocess Development

Process optimization for Chinese hamster ovary (CHO) cell culture remains a challenge in biopharmaceutical development because multiple interacting parameters jointly influence productivity and product quality attributes. Traditional design-of-experiments (DoE) methods, while systematic, become impractically expensive when extended across multiple parameters and clones. To address this challenge, we developed a multi-objective Bayesian Optimization (BO) framework that identifies optimal process conditions efficiently in grouped recommendations, which is well suited for experimental workflows in bioprocess development. The model integrates continuous variables such as pH, DO, temperature, and feed rate with categorical identifiers to enable knowledge transfer across clones and scales, optimizing titer, glycan profile, and charge variants. We validated the framework through in-silico benchmarks on analytic functions, retrospective cross-validation on historical CHO datasets, and forward experimental validation in small-scale bioreactors. Across these tests, our algorithm consistently outperformed Latin Hypercube Sampling (LHS) and Random Search baselines, achieving superior performance under a limited experimental budget. The framework improved titer by up to 37% under single-objective optimization. In the multi-objective setting, it increased titer by 25% while simultaneously reducing overall glycan-profile error by a factor of seven, demonstrating the ability to optimize multiple biologically coupled objectives simultaneously. Through comprehensive in-silico and experimental validation, this study establishes a framework that enables adaptive, AI-guided process development and improves decision-making across multiple objectives, clones, and scales while minimizing experimental runs in process development and optimization workflows.

bioengineering↗