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Related Concept Videos

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

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Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
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The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
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The adaptive immune system, a crucial component of the overall immune response, offers a highly specialized defense against pathogens. It involves specific cell types and features, enabling it to combat infections effectively and efficiently.
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T Cell Activation and Clonal Selection01:22

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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
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Related Experiment Video

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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
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Identification of B cell subsets based on antigen receptor sequences using deep learning.

Hyunho Lee1, Kyoungseob Shin1, Yongju Lee1

  • 1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.

Frontiers in Immunology
|April 5, 2024
PubMed
Summary

BCR-SORT, a deep learning model, predicts B cell subsets from B cell receptor (BCR) sequences. This method enhances B cell research by reducing costs and improving data accuracy for understanding immune responses.

Keywords:
B cell phylogenetic inferenceB cell receptorB cell subsetantibody repertoiredeep learningintegrated gradientsnext-generation sequencingsomatic hypermutation

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • B cell receptors (BCRs) define antigen specificity, and B cell subsets determine functionality.
  • Current methods for identifying BCRs and B cell subsets require costly individual cell isolation, leading to information loss and limiting high-throughput analysis.
  • Understanding B cell responses is crucial for vaccine development and disease treatment.

Purpose of the Study:

  • To develop a deep learning model, BCR-SORT, for predicting B cell subsets directly from BCR sequences.
  • To leverage B cell activation and maturation signatures within BCR sequences for accurate cell subset prediction.
  • To provide a cost-effective and high-throughput alternative to physical isolation methods for B cell analysis.

Main Methods:

  • Development of BCR-SORT, a deep learning model utilizing B cell activation and maturation signatures within BCR sequences.
  • Application of BCR-SORT to predict B cell subsets from BCR sequences.
  • Validation of BCR-SORT's performance by comparing its predictions with results from physical isolation-based methods and prior knowledge.
  • Reconstruction of BCR phylogenetic trees using BCR-SORT predictions.

Main Results:

  • BCR-SORT accurately predicts B cell subsets from BCR sequences, leveraging inherent biological signatures.
  • The model improves the reconstruction of BCR phylogenetic trees compared to traditional methods.
  • Results obtained using BCR-SORT are consistent with those from physical isolation and prior knowledge.
  • Analysis of COVID-19 vaccine recipient data revealed inter-individual heterogeneity in evolutionary trajectories toward Omicron-binding memory B cells.

Conclusions:

  • BCR-SORT offers a powerful, cost-effective, and high-throughput approach for B cell subset identification from BCR sequences.
  • The model enhances the understanding of B cell evolutionary trajectories and immune responses.
  • BCR-SORT has significant potential to advance immunological research, particularly in vaccine development and the study of infectious diseases.