PMID- 27142020
OWN - NLM
STAT- MEDLINE
DCOM- 20171213
LR  - 20180110
IS  - 1940-6029 (Electronic)
IS  - 1064-3745 (Linking)
VI  - 1423
DP  - 2016
TI  - Characterization of Dendritic Cell Subsets Through Gene Expression Analysis.
PG  - 211-43
LID - 10.1007/978-1-4939-3606-9_16 [doi]
AB  - Dendritic cells (DCs) are immune sentinels of the body and play a key role in the 
      orchestration of the communication between the innate and the adaptive immune 
      systems. DCs can polarize innate and adaptive immunity toward a variety of 
      functions, sometimes with opposite roles in the overall control of immune 
      responses (e.g., tolerance or immunosuppression versus immunity) or in the 
      balance between various defense mechanisms promoting the control of different 
      types of pathogens (e.g., antiviral versus antibacterial versus anti-worm 
      immunity). These multiple DC functions result both from the plasticity of 
      individual DC to exert different activities and from the existence of various DC 
      subsets specialized in distinct functions. Functional genomics represents a 
      powerful, unbiased, approach to better characterize these two levels of DC 
      plasticity and to decipher its molecular regulation. Indeed, more and more 
      experimental immunologists are generating high-throughput data in order to better 
      characterize different states of DC based, for example, on their belonging to a 
      specific subpopulation and/or on their exposure to specific stimuli and/or on 
      their ability to exert a specific function. However, the interpretation of this 
      wealth of data is severely hampered by the bottleneck of their bioinformatics 
      analysis. Indeed, most experimental immunologists lack advanced computational or 
      bioinformatics expertise and do not know how to translate raw gene expression 
      data into potential biological meaning. Moreover, subcontracting such analyses is 
      generally disappointing or financially not sustainable, since companies generally 
      propose canonical analysis pipelines that are often unadapted for the structure 
      of the data to analyze or for the precise type of questions asked. Hence, there 
      is an important need of democratization of the bioinformatics analyses of gene 
      expression profiling studies, in order to accelerate interpretation of the 
      results by the researchers at the origin of the research project, of the data and 
      who know best the underlying biology. This chapter will focus on the analysis of 
      DC subset transcriptomes as measured by microarrays. We will show that simple 
      bioinformatics procedures, applied one after the other in the framework of a 
      pipeline, can lead to the characterization of DC subsets. We will develop two 
      tutorials based on the reanalysis of public gene expression data. The first 
      tutorial aims at illustrating a strategy for establishing the identity of DC 
      subsets studied in a novel context, here their in vitro generation in cultures of 
      human CD34(+) hematopoietic progenitors. The second tutorial aims at illustrating 
      how to perform a posteriori bioinformatics analyses in order to evaluate the risk 
      of contamination or of improper identification of DC subsets during preparation 
      of biological samples, such that this information is taken into account in the 
      final interpretation of the data and can eventually help to redesign the sampling 
      strategy.
FAU - Vu Manh, Thien-Phong
AU  - Vu Manh TP
AD  - Centre d'Immunologie de Marseille-Luminy, UNIV UM2, Aix Marseille Universite, 163 
      Avenue de Luminy, 13288, Marseille, France. vumanh@ciml.univ-mrs.fr.
AD  - U1104, INSERM, Marseille, France. vumanh@ciml.univ-mrs.fr.
AD  - UMR7280, CNRS, Marseille, France. vumanh@ciml.univ-mrs.fr.
FAU - Dalod, Marc
AU  - Dalod M
AD  - Centre d'Immunologie de Marseille-Luminy, UNIV UM2, Aix Marseille Universite, 163 
      Avenue de Luminy, 13288, Marseille, France.
AD  - U1104, INSERM, Marseille, France.
AD  - UMR7280, CNRS, Marseille, France.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Methods Mol Biol
JT  - Methods in molecular biology (Clifton, N.J.)
JID - 9214969
RN  - 0 (Antigens, CD34)
SB  - IM
MH  - Antigens, CD34/metabolism
MH  - Cell Differentiation
MH  - Computational Biology/methods
MH  - Dendritic Cells/*cytology/immunology
MH  - Gene Expression Profiling/*methods
MH  - Hematopoietic Stem Cells/classification/immunology
MH  - Humans
MH  - Oligonucleotide Array Sequence Analysis/*methods
MH  - Principal Component Analysis
OTO - NOTNLM
OT  - Cell identity characterization
OT  - Dendritic cell subsets
OT  - Gene set enrichment approach
OT  - Microarray analysis for beginners
OT  - Transcriptomic signatures
OT  - Workflow analysis
EDAT- 2016/05/05 06:00
MHDA- 2017/12/14 06:00
CRDT- 2016/05/05 06:00
PHST- 2016/05/05 06:00 [entrez]
PHST- 2016/05/05 06:00 [pubmed]
PHST- 2017/12/14 06:00 [medline]
AID - 10.1007/978-1-4939-3606-9_16 [doi]
PST - ppublish
SO  - Methods Mol Biol. 2016;1423:211-43. doi: 10.1007/978-1-4939-3606-9_16.