Lineage tracing reveals B cell antibody class switching is stochastic, cell-autonomous, and tuneable.

To optimize immunity to pathogens, B lymphocytes generate plasma cells with functionally diverse antibody isotypes. By lineage tracing single cells within differentiating B cell clones, we identified the heritability of discrete fate controlling mechanisms to inform a general mathematical model of B cell fate regulation. Founder cells highly influenced clonal plasma-cell fate, whereas class switch recombination (CSR) was variegated within clones. In turn, these CSR patterns resulted from independent all-or-none expression of both activation-induced cytidine deaminase (AID) and IgH germline transcription (GLT), with the latter being randomly re-expressed after each cell division. A stochastic model premised on these molecular transition rules accurately predicted antibody switching outcomes under varied conditions in vitro and during an immune response in vivo. Thus, the generation of functionally diverse antibody types follows rules of autonomous cellular programming that can be adapted and modeled for the rational control of antibody classes for potential therapeutic benefit.

Survival and division fate programs are preserved but retuned during the naïve to memory CD8+ T-cell transition.

Memory T cells are generated from naïve precursors undergoing proliferation during the initial immune response. Both naïve and memory T cells are maintained in a resting, quiescent state and respond to activation with a controlled proliferative burst and differentiation into effector cells. This similarity in the maintenance and response dynamics points to the preservation of key cellular fate programs; however, whether memory T cells have acquired intrinsic changes in these programs that may contribute to the enhanced immune protection in a recall response is not fully understood. Here we used a quantitative model-based analysis of proliferation and survival kinetics of in vitro-stimulated murine naïve and memory CD8+ T cells in response to homeostatic and activating signals to establish intrinsic similarities or differences within these cell types. We show that resting memory T cells display heightened sensitivity to homeostatic cytokines, responding to interleukin (IL)-2 in addition to IL-7 and IL-15. The proliferative response to αCD3 was equal in size and kinetics, demonstrating that memory T cells undergo the same controlled division burst and automated return to quiescence as naïve T cells. However, perhaps surprisingly, we observed reduced expansion of αCD3-stimulated memory T cells in response to activating signals αCD28 and IL-2 compared with naïve T cells. Overall, we demonstrate that although sensitivities to cytokine and costimulatory signals have shifted, fate programs regulating the scale of the division burst are conserved in memory T cells.

Quantifying Human Naïve B Cell Proliferation Kinetics and Differentiation in Controlled In Vitro Cell Culture.

Division tracking dyes like Cell Trace Violet (CTV) enable the quantification of cell proliferation, division, and survival kinetics of human naïve B cell responses in vitro. Human naïve B cells exhibit distinct responses to different stimuli, with CpG and anti-Ig inducing a T cell-independent (TI) response, while CD40L and IL-21 promote a T cell-dependent (TD) response that induces isotype switching and differentiation into antibody-secreting cells (ASCs). Both stimulation methods yield valuable insights into the intrinsic programming of B cell health within individuals, making them useful for clinical investigations. For instance, quantitative analysis from these B cell populations could reveal biologically meaningful measurements such as the average number of division rounds and the time to cells' fate. Here, we describe a novel in vitro culture setup for CTV-labelled human naïve B cells and a method for obtaining precise time-based data on proliferation, division-linked isotype switching, and differentiation.

Cyton2: A Model of Immune Cell Population Dynamics That Includes Familial Instructional Inheritance.

Lymphocytes are the central actors in adaptive immune responses. When challenged with antigen, a small number of B and T cells have a cognate receptor capable of recognising and responding to the insult. These cells proliferate, building an exponentially growing, differentiating clone army to fight off the threat, before ceasing to divide and dying over a period of weeks, leaving in their wake memory cells that are primed to rapidly respond to any repeated infection. Due to the non-linearity of lymphocyte population dynamics, mathematical models are needed to interrogate data from experimental studies. Due to lack of evidence to the contrary and appealing to arguments based on Occam's Razor, in these models newly born progeny are typically assumed to behave independently of their predecessors. Recent experimental studies, however, challenge that assumption, making clear that there is substantial inheritance of timed fate changes from each cell by its offspring, calling for a revision to the existing mathematical modelling paradigms used for information extraction. By assessing long-term live-cell imaging of stimulated murine B and T cells in vitro, we distilled the key phenomena of these within-family inheritances and used them to develop a new mathematical model, Cyton2, that encapsulates them. We establish the model's consistency with these newly observed fine-grained features. Two natural concerns for any model that includes familial correlations would be that it is overparameterised or computationally inefficient in data fitting, but neither is the case for Cyton2. We demonstrate Cyton2's utility by challenging it with high-throughput flow cytometry data, which confirms the robustness of its parameter estimation as well as its ability to extract biological meaning from complex mixed stimulation experiments. Cyton2, therefore, offers an alternate mathematical model, one that is, more aligned to experimental observation, for drawing inferences on lymphocyte population dynamics.