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Optimizing clustering of CDR3 sequences using natural language processing, Word2Vec, and KMeans.

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This study introduces a novel natural language processing pipeline to analyze T-cell receptor (TCR) CDR3 sequences. The method reveals distinct immune signatures in Acute Respiratory Distress Syndrome (ARDS) patients, aiding biomarker discovery.

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • T-cell receptor (TCR) sequencing is crucial for understanding adaptive immunity.
  • Analyzing the vast diversity of Complementarity-Determining Region 3 (CDR3) sequences presents a significant challenge.
  • Acute Respiratory Distress Syndrome (ARDS) involves complex immune responses in the lungs.

Purpose of the Study:

  • To develop and validate a novel natural language processing (NLP)-based pipeline for clustering TCR beta-chain CDR3 sequences.
  • To investigate differences in CDR3 repertoire structure between healthy controls, ARDS patients, and non-ARDS individuals.
  • To explore the potential of this NLP framework for immune monitoring and biomarker discovery in critical care.

Main Methods:

  • Utilized Word2Vec embeddings to represent CDR3 sequences.
  • Applied Principal Component Analysis (PCA) for dimensionality reduction.
  • Employed KMeans clustering to analyze TCR repertoire structure across different patient cohorts.

Main Results:

  • Dimensionality reduction revealed distinct structural topologies in CDR3 sequence space.
  • Healthy controls showed tight, low-diversity clusters, indicating a stable repertoire.
  • ARDS patients exhibited dispersed, numerous diffuse clusters, suggesting immune repertoire disruption.
  • Non-ARDS samples displayed intermediate repertoire organization.

Conclusions:

  • Immune activation states are reflected in the structural topology of the CDR3 sequence space.
  • The NLP pipeline effectively captures latent patterns in TCR repertoires.
  • This approach offers a scalable method for biomarker discovery and immune monitoring in critical care settings.
  • The study highlights the utility of NLP in immunological data analysis for personalized diagnostics.