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

Hochan Cheon,Giulio Prevedello,Edwin D Hawkins,Susanne Heinzel,Ken R Duffy,Simon J Dovedi,Julia M Marchingo,Simone C Oostindie,Philip D Hodgkin,Andrey Kan

doi:10.3389/fbinf.2021.723337

Abstract

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.

Highlights

B and T lymphocytes are central contributors to the adaptive immune response
As has been observed experimentally (Hawkins et al, 2009; Marchingo et al, 2016; Horton et al, 2018; Mitchell et al, 2018), the resulting family trees of activated lymphocytes derived from a single founder cell, and clones, according to Cyton2 rules are largely regular (Figure 1B)
The vast majority of published mathematical models of lymphocyte population dynamics employed assume that a newly born cell’s fate is independent of its family’s history (Smith and Martin, 1973; Nordon et al, 1999; Revy et al, 2001; Ganusov et al, 2005; Yates et al, 2007; Lee et al, 2009; Banks et al, 2012; Hasenauer et al, 2012; Mazzocco et al, 2017), with a few notable exceptions (Hyrien et al, 2010; Wellard et al, 2010; Zilman et al, 2010; Shokhirev et al, 2015; Yates et al, 2017). These assumptions are adopted, not because they are consistent with experimental data from, for example, filming, fluorescenceactivated cell sorting (FACS) and lineage tracing, but for reasons of parsimony, model identifiability and computational ease of fitting (Dowling et al, 2005; Boer et al, 2006)

Summary

Introduction

When exposed to a foreign pathogen with epitopes that are complementary to their B or T cell receptors, they respond by proliferating to create a clone army capable of recognising the threat These cells differentiate into effector cells to fight the invasion, and into memory cells primed to fend off repeated insults. In the quest to better understand immune responses and therapeutic intervention, it remains an essential question to determine how signals are integrated to alter cell fate and how the cells process such information to yield diverse, yet appropriate outcomes. Answering this question requires an understanding of operational aspects of lymphocyte population dynamics, and the influence of signals on individual fates. Quantitative models and analytical techniques can be developed and used to monitor lymphocyte control under different conditions; they can recreate, and predict outcomes for complex situations (Duffy et al, 2012; Hodgkin, 2018)

Methods

Results

Conclusion