npj Systems Biology and Applications (Mar 2023)

Understanding repertoire sequencing data through a multiscale computational model of the germinal center

  • Rodrigo García-Valiente,
  • Elena Merino Tejero,
  • Maria Stratigopoulou,
  • Daria Balashova,
  • Aldo Jongejan,
  • Danial Lashgari,
  • Aurélien Pélissier,
  • Tom G. Caniels,
  • Mathieu A. F. Claireaux,
  • Anne Musters,
  • Marit J. van Gils,
  • María Rodríguez Martínez,
  • Niek de Vries,
  • Michael Meyer-Hermann,
  • Jeroen E. J. Guikema,
  • Huub Hoefsloot,
  • Antoine H. C. van Kampen

DOI
https://doi.org/10.1038/s41540-023-00271-y
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 14

Abstract

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Abstract Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.