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Related Concept Videos

T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

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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.
Naive T cells that have not yet encountered an antigen express two primary CD...
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Related Experiment Video

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An Efficient and High Yield Method for Isolation of Mouse Dendritic Cell Subsets
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Characterization of Dendritic Cell Subsets Through Gene Expression Analysis.

Thien-Phong Vu Manh1,2,3, Marc Dalod4,5,6

  • 1Centre d'Immunologie de Marseille-Luminy, UNIV UM2, Aix Marseille Université, 163 Avenue de Luminy, 13288, Marseille, France. vumanh@ciml.univ-mrs.fr.

Methods in Molecular Biology (Clifton, N.J.)
|May 5, 2016
PubMed
Summary

This study simplifies bioinformatics analysis for dendritic cell (DC) transcriptomes, enabling researchers to identify DC subsets and assess sample quality. It democratizes functional genomics by providing accessible pipelines for interpreting gene expression data.

Keywords:
Cell identity characterizationDendritic cell subsetsGene set enrichment approachMicroarray analysis for beginnersTranscriptomic signaturesWorkflow analysis

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Area of Science:

  • Immunology
  • Genomics
  • Bioinformatics

Background:

  • Dendritic cells (DCs) are crucial immune sentinels that bridge innate and adaptive immunity, exhibiting plasticity in their functions and existing as specialized subsets.
  • Understanding DC plasticity and subset specialization is vital for controlling immune responses against various pathogens and for maintaining immune homeostasis.
  • High-throughput functional genomics data, such as microarrays, are increasingly generated but face significant bioinformatics analysis bottlenecks for experimental immunologists.

Purpose of the Study:

  • To democratize bioinformatics analysis of dendritic cell (DC) subset transcriptomes for experimental immunologists lacking computational expertise.
  • To provide accessible, pipeline-based bioinformatics procedures for characterizing DC subsets and interpreting gene expression data.
  • To develop practical tutorials for identifying DC subsets in novel contexts and for evaluating sample quality and potential contamination.

Main Methods:

  • Focuses on the analysis of dendritic cell (DC) subset transcriptomes using microarray data.
  • Employs a series of simple, sequential bioinformatics procedures within a pipeline framework.
  • Utilizes reanalysis of public gene expression data to create two practical tutorials.

Main Results:

  • Demonstrates that straightforward bioinformatics pipelines can effectively characterize dendritic cell (DC) subsets.
  • Provides a strategy for establishing the identity of DC subsets generated in vitro from human CD34(+) hematopoietic progenitors.
  • Offers methods for a posteriori bioinformatics analysis to assess contamination risks and improper DC subset identification in biological samples.

Conclusions:

  • Accessible bioinformatics tools are essential for enabling researchers to interpret functional genomics data related to dendritic cell (DC) plasticity and function.
  • The developed pipelines facilitate the characterization of DC subsets and aid in the quality control of experimental samples.
  • This approach accelerates the translation of gene expression data into biological insights, supporting the design of future immunological studies.