Germinal centers play a key role in the adaptive immune system since they are able to produce memory B cells and plasma cells that produce high affinity antibodies for an effective immune protection. The mechanisms underlying cell-fate decisions are not well understood but asymmetric division of antigen, B-cell receptor affinity, interactions between B-cells and T follicular helper cells (triggering CD40 signaling), and regulatory interactions of transcription factors have all been proposed to play a role. In addition, a temporal switch from memory B-cell to plasma cell differentiation during the germinal center reaction has been shown. To investigate if antigen affinity-based Tfh cell help recapitulates the temporal switch we implemented a multiscale model that integrates cellular interactions with a core gene regulatory network comprising BCL6, IRF4, and BLIMP1. Using this model we show that affinity-based CD40 signaling in combination with asymmetric division of B-cells result in switch from memory B-cell to plasma cell generation during the course of the germinal center reaction. We also show that cell fate division is unlikely to be (solely) based on asymmetric division of Ag but that BLIMP1 is a more important factor. Altogether, our model enables to test the influence of molecular modulations of the CD40 signaling pathway on the production of germinal center output cells.

Memory B cells and antibody-secreting plasma cells are generated within germinal centers during affinity maturation in which B-cell proliferation, selection, differentiation, and self-renewal play important roles. The mechanisms behind memory B cell and plasma cell differentiation in germinal centers are not well understood. However, it has been suggested that cell fate is (partially) determined by asymmetric cell division, which involves the unequal distribution of cellular components to both daughter cells. To investigate what level and/or probability of asymmetric segregation of several fate determinant molecules, such as the antigen and transcription factors (BCL6, IRF4, and BLIMP1) recapitulates the temporal switch and DZ-to-LZ ratio in the germinal center, we implemented a multiscale model that combines a core gene regulatory network for plasma cell differentiation with a model describing the cellular interactions and dynamics in the germinal center. Our simulations show that BLIMP1 driven plasma cell differentiation together with coupled asymmetric division of antigen and BLIMP1 with a large segregation between the daughter cells results in a germinal center DZ-to-LZ ratio and a temporal switch from memory B cells to plasma cells that have been observed in experiments.

Chronic lymphocytic leukemia (CLL) cells are highly dependent on microenvironmental cells and signals. The lymph node (LN) is the critical site of in vivo CLL proliferation and development of resistance to both chemotherapy and targeted agents. We present a new model that incorporates key aspects of the CLL LN which enables investigation of CLL cells in the context of a protective niche. We describe a 3D in vitro culture system utilizing ultra-low attachment (ULA) plates to create spheroids of CLL cells derived from peripheral blood (PB). Starting from CLL:T cell ratios as observed in LN samples, CLL activation was induced by either direct stimulation and/or indirectly via T cells. Compared to 2D cultures, 3D cultures promoted CLL proliferation in a T cell-dependent manner, and enabled expansion for up to 7 weeks, including the formation of follicle-like structures after several weeks of culture. Addition of LN-derived stromal cells further enhanced the proliferative capacity. This model enables high-throughput drug screening, of which we describe response to Btk inhibition, venetoclax resistance, and T cell-mediated cytotoxicity as examples. In summary, we present the first LN-mimicking in vitro 3D culture for primary CLL, which enables readouts such as real-time drug screens, kinetic growth assays and spatial localization. This is the first in vitro CLL system that allows testing of response and resistance to venetoclax and Btk inhibitors in the context of the tumor microenvironment, thereby opening up new possibilities for clinically useful applications.

Diffuse large B-cell lymphoma is the most common subtype of non-Hodgkin’s lymphoma. It is a germinal center (GC)–derived, aggressive, and heterogeneous disease. Several transcription factors and signaling pathways that play a central role in the progression of the GC reaction and B-cell differentiation have been shown to play an oncogenic role in diffuse large B-cell lymphoma. B-cell lymphoma 6 (BCL6) is a transcriptional repressor that induces the GC B-cell phenotype and blocks plasma cell (PC) differentiation, while interferon regulatory factor 4 (IRF4) and B lymphocyte-induced maturation protein 1 (BLIMP1), a transcriptional promoter, both mediate PC differentiation and exit from the GC (1). Computational models are useful alternatives to trial-and-error experimental investigation. Ordinary differential equation (ODE) models have been used to study different known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations (2). Furthermore, multi-scale models (MSMs) have been used to study the role of cellular and molecular mechanisms involved in tumor growth (3–6). In this study, we used an existing MSM of PC differentiation in the GC to simulate eight different models with several candidate genetic alterations of the BCL6-IRF4-BLIMP1 regulatory network that lead to transcription factor deregulation and could explain the onset of diffuse large B-cell lymphoma and recapitulate the GC dynamics observed in such conditions. We observed that models with loss of BLIMP1 function (BLIMPloss and BLIMPlossIRFinc) result in an accumulation of B cells in the GC and a block of PC differentiation and thus correctly recapitulate the observed GC and transcription factor dynamics. Models with constitutive activation of the nuclear factor kappa-light-chain-enhancer of activated B-cell (NF-kB) pathway alone and in codominance or co-expression with the enforced BCL6 expression (IRFinc and BCLincIRFinc) result in a decrease of GC B cells and unaltered PC production at early stages of the GC reaction, as observed experimentally. Interestingly, we also found that in IRFinc and BCLincIRFinc models, an increase in PC production could happen at later stages of the GC reaction. Nevertheless, models with enforced BCL6 expression (BCLauto and BCLinc) result in an expansion of GC B cell population and a block in the PC production that was not observed experimentally. Finally, models with loss of IRF4- and BLIMP1-mediated silencing of BCL6 (IRFsil and BLIMPsil) did not affect GC and transcription factor dynamics.