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Summary

This study introduces a Bayesian method to accurately analyze immune receptor repertoires by accounting for technical noise. This approach helps identify specific immune cells responding to challenges like vaccination.

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • High-throughput sequencing enables immune repertoire tracking but is affected by read count variability.
  • Stochastic effects from sampling and library preparation complicate quantitative repertoire comparisons.

Purpose of the Study:

  • To develop a general Bayesian approach to differentiate true immune repertoire variations from technical noise.
  • To establish a baseline model of natural variability for accurate clonal expansion inference.

Main Methods:

  • Utilized replicate experiments to learn read count variability and infer clone size distributions.
  • Developed an explicit noise model relating true clone frequencies to observed read counts.
  • Applied a null model to infer clonal expansion from pre- and post-challenge repertoire time points.

Main Results:

  • Successfully disentangled repertoire variations from stochastic effects using a Bayesian framework.
  • Quantified natural variability in immune receptor clonotype read counts.
  • Identified candidate clones involved in the immune response to yellow fever vaccination.

Conclusions:

  • The proposed Bayesian method provides a robust framework for analyzing immune repertoire data.
  • Accurate quantification of immune cell responses is crucial for understanding adaptive immunity.
  • This approach can be applied to various immunological studies, including disease and vaccination responses.