Combining mutation and recombination statistics to infer clonal families in antibody repertoires

  1. Natanael Spisak
  2. Gabriel Athènes
  3. Thomas Dupic
  4. Thierry Mora  Is a corresponding author
  5. Aleksandra M Walczak  Is a corresponding author
  1. École Normale Supérieure - PSL, France
  2. Harvard University, United States

Abstract

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and 𝑑𝑁∕𝑑𝑆 ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. All data used is publicly available. The HILARy tool with Python implementations of the CDR3 and mutations-based methods introduced above can be found at github.com/statbiophys/HILARy. The standalone prefix tree implementation can be found at github.com/statbiophys/ATrieGC.

The following previously published data sets were used

Article and author information

Author details

  1. Natanael Spisak

    Laboratoire de physique de l'École normale supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    No competing interests declared.
  2. Gabriel Athènes

    Laboratoire de physique de l'École normale supérieure, École Normale Supérieure - PSL, Paris, France
    Competing interests
    No competing interests declared.
  3. Thomas Dupic

    Department of Organismic and Evolutionary Biologyy, Harvard University, Cambridge, United States
    Competing interests
    No competing interests declared.
  4. Thierry Mora

    Laboratoire de physique de l'École normale supérieure, École Normale Supérieure - PSL, Paris, France
    For correspondence
    thierry.mora@gmail.com
    Competing interests
    No competing interests declared.
  5. Aleksandra M Walczak

    Laboratoire de Physique de l'Ecole Normale Supérieure, École Normale Supérieure - PSL, Paris, France
    For correspondence
    aleksandra.walczak@phys.ens.fr
    Competing interests
    Aleksandra M Walczak, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2686-5702

Funding

European Research Council (COG 724208)

  • Natanael Spisak
  • Thomas Dupic
  • Thierry Mora
  • Aleksandra M Walczak

Agence Nationale de la Recherche (ANR-19-CE45-0018 `RESP- REP')

  • Natanael Spisak
  • Thomas Dupic
  • Thierry Mora
  • Aleksandra M Walczak

Deutsche Forschungsgemeinschaft (CRC 1310 `Predictability in Evolution'.)

  • Natanael Spisak
  • Gabriel Athènes
  • Thomas Dupic
  • Thierry Mora
  • Aleksandra M Walczak

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2024, Spisak et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Natanael Spisak
  2. Gabriel Athènes
  3. Thomas Dupic
  4. Thierry Mora
  5. Aleksandra M Walczak
(2024)
Combining mutation and recombination statistics to infer clonal families in antibody repertoires
eLife 13:e86181.
https://doi.org/10.7554/eLife.86181

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https://doi.org/10.7554/eLife.86181