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Related Experiment Video

Updated: Jan 17, 2026

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

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Published on: January 12, 2021

Decoding T and B cell dynamics in inborn errors of immunity: insights into immune dysfunction.

Canan Caka¹, Gamze Sonmez², Deniz Cagdas^1,3,4

¹Division of Pediatric Immunology, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey.

Expert Review of Clinical Immunology

|September 21, 2025

Summary

Detailed analysis of T and B lymphocyte subsets in combined immunodeficiencies (CID) aids diagnosis. Understanding these lymphocyte dynamics is crucial for effective management of inborn errors of immunity (IEI).

Keywords:

T and B cell subgroups combined immunodeficiency early diagnosis hematopoietic stem cell transplantation immune dysregulation inborn errors of immunity

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Published on: December 6, 2014

Area of Science:

Immunology
Genetics
Clinical Medicine

Background:

Inborn errors of immunity (IEI) present with altered T and B lymphocyte subsets, challenging early diagnosis.
Combined immunodeficiencies (CID) and related disorders are characterized by complex lymphocyte dynamics.

Purpose of the Study:

To review and synthesize current knowledge on T and B lymphocyte subgroup dynamics in IEI.
To highlight the role of flow cytometry in diagnosing and managing these disorders.

Main Methods:

Systematic literature review of studies published between 2000 and 2024.
Focus on flow cytometric profiling of T and B cell subpopulations in IEI.
Data extraction on immunophenotypic patterns, genetic defects, clinical phenotypes, and therapies.

Main Results:

Flow cytometry reveals quantitative and qualitative changes in naive, memory, and regulatory T and B cell subsets in IEI.
Correlations between immunophenotypic patterns and genetic defects/clinical phenotypes were analyzed.

Conclusions:

Detailed immunophenotyping can expedite IEI diagnosis and guide personalized treatment.
Barriers to implementation include assay standardization and access to technology.
Future research should integrate multi-omics and machine learning for improved diagnostic algorithms and patient outcomes.