PMID- 36905526
OWN - NLM
STAT- MEDLINE
DCOM- 20230314
LR  - 20230424
IS  - 1940-6029 (Electronic)
IS  - 1064-3745 (Linking)
VI  - 2618
DP  - 2023
TI  - Harnessing Single-Cell RNA Sequencing to Identify Dendritic Cell Types, 
      Characterize Their Biological States, and Infer Their Activation Trajectory.
PG  - 319-373
LID - 10.1007/978-1-0716-2938-3_22 [doi]
AB  - Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating 
      the sensing of distinct danger signals into the induction of different effector 
      lymphocyte responses, to induce the defense mechanisms the best suited to face 
      the threat. Hence, DCs are very plastic, which results from two key 
      characteristics. First, DCs encompass distinct cell types specialized in 
      different functions. Second, each DC type can undergo different activation 
      states, fine-tuning its functions depending on its tissue microenvironment and 
      the pathophysiological context, by adapting the output signals it delivers to the 
      input signals it receives. Hence, to better understand DC biology and harness it 
      in the clinic, we must determine which combinations of DC types and activation 
      states mediate which functions and how.To decipher the nature, functions, and 
      regulation of DC types and their physiological activation states, one of the 
      methods that can be harnessed most successfully is ex vivo single-cell RNA 
      sequencing (scRNAseq). However, for new users of this approach, determining which 
      analytics strategy and computational tools to choose can be quite challenging, 
      considering the rapid evolution and broad burgeoning in the field. In addition, 
      awareness must be raised on the need for specific, robust, and tractable 
      strategies to annotate cells for cell type identity and activation states. It is 
      also important to emphasize the necessity of examining whether similar cell 
      activation trajectories are inferred by using different, complementary methods. 
      In this chapter, we take these issues into account for providing a pipeline for 
      scRNAseq analysis and illustrating it with a tutorial reanalyzing a public 
      dataset of mononuclear phagocytes isolated from the lungs of naive or 
      tumor-bearing mice. We describe this pipeline step-by-step, including data 
      quality controls, dimensionality reduction, cell clustering, cell cluster 
      annotation, inference of the cell activation trajectories, and investigation of 
      the underpinning molecular regulation. It is accompanied with a more complete 
      tutorial on GitHub. We hope that this method will be helpful for both wet lab and 
      bioinformatics researchers interested in harnessing scRNAseq data for deciphering 
      the biology of DCs or other cell types and that it will contribute to 
      establishing high standards in the field.
CI  - (c) 2023. The Author(s), under exclusive license to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Cheema, Ammar Sabir
AU  - Cheema AS
AD  - Aix Marseille Univ, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, 
      Turing Center for Living Systems, Marseille, France.
FAU - Duan, Kaibo
AU  - Duan K
AD  - Singapore Immunology Network, Agency for Science, Technology and Research, 8A 
      Biomedical Grove, Singapore 138648, Singapore.
FAU - Dalod, Marc
AU  - Dalod M
AD  - Aix Marseille Univ, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, 
      Turing Center for Living Systems, Marseille, France. dalod@ciml.univ-mrs.fr.
FAU - Vu Manh, Thien-Phong
AU  - Vu Manh TP
AD  - Aix Marseille Univ, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, 
      Turing Center for Living Systems, Marseille, France. vumanh@ciml.univ-mrs.fr.
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
SB  - IM
MH  - Animals
MH  - Mice
MH  - *Dendritic Cells
MH  - Computational Biology
MH  - *Neoplasms/metabolism
MH  - Sequence Analysis, RNA
MH  - Tumor Microenvironment
OTO - NOTNLM
OT  - Computational pipeline
OT  - Dendritic cell types
OT  - Monocle
OT  - Seurat
OT  - Single-cell RNA sequencing
OT  - Velocyto
OT  - cDC1
EDAT- 2023/03/12 06:00
MHDA- 2023/03/15 06:00
CRDT- 2023/03/11 11:17
PHST- 2023/03/11 11:17 [entrez]
PHST- 2023/03/12 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
AID - 10.1007/978-1-0716-2938-3_22 [doi]
PST - ppublish
SO  - Methods Mol Biol. 2023;2618:319-373. doi: 10.1007/978-1-0716-2938-3_22.