Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

RNA-Seq global gene expression of four Dendritic cells subpopulations from lung (CD8+ and CD8- pDCs, CD103-CD11b+ and CD103+CD11b- cDCs) of influenza infected mice at four time points following infection

ABSTRACT: Hundreds of immune cell types work in coordination to maintain tissue homeostasis. Upon infection, dramatic changes occur with the localization, migration and proliferation of the immune cells to first alert the body of the danger, confine it to limit spreading, and finally extinguish the threat and bring the tissue back to homeostasis. Since current technologies can follow the dynamics of only a limited number of cell types, we have yet to grasp the full complexity of global in vivo cell dynamics in normal developmental processes and disease. Here we devise a computational method, digital cell quantification (DCQ), which combines genomewide gene expression data with an immune cell compendium to infer in vivo dynamical changes in the quantities of 213 immune cell subpopulations. DCQ was applied to study global immune cell dynamics in mice lungs at ten time points during a 7-day time course of flu infection. We find dramatic changes in quantities of 70 immune cell types, including various innate, adaptive and progenitor immune cells. We focus on the previously unreported dynamics of four immune dendritic cell subtypes, and suggest a specific role for CD103+CD11b- cDCs in early stages of disease and CD8+ pDC in late stages of flu infection. To better understand the physiological role of these differential dynamic changes in the DCs, we measured the genome-wide RNA expression of all four DC subpopulations from lung of influenza infected mice at four time points following infections (two mice per time-point). For sorting dendritic cells from lungs, the lungs from infected and control uninfected C57BL/6J mice were immersed in cold PBS, cut into small pieces in 5 ml DMEM media containing 10% Bovine Fetal Serum, the cell suspensions were grinded using 1ml syringe cup on a 70 μm cell strainers (BD Falcon). The cells were washed with ice cold PBS. Remaining red blood cells were lysed using ammonium chloride solution (Sigma). Cells were harvested, immersed 1ml FACS buffer [PBS+2% FBS, 1mM EDTA], Fc receptors were blocked with anti-mouse CD16/CD32, washed with FACS buffer and divided into two tubes for sorting cDC and pDC cells.

ORGANISM(S): Mus musculus

SUBMITTER: Irit Gat-Viks

PROVIDER: E-GEOD-49932 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Digital cell quantification identifies global immune cell dynamics during influenza infection.

Altboum Zeev Z Steuerman Yael Y David Eyal E Barnett-Itzhaki Zohar Z Valadarsky Liran L Keren-Shaul Hadas H Meningher Tal T Mendelson Ella E Mandelboim Michal M Gat-Viks Irit I Amit Ido I

Molecular systems biology 20140228

Hundreds of immune cell types work in coordination to maintain tissue homeostasis. Upon infection, dramatic changes occur with the localization, migration, and proliferation of the immune cells to first alert the body of the danger, confine it to limit spreading, and finally extinguish the threat and bring the tissue back to homeostasis. Since current technologies can follow the dynamics of only a limited number of cell types, we have yet to grasp the full complexity of global in vivo cell dynam ...[more]

PMID: 24586061

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Project description:Purpose: In all vertebrates, the thymus is necessary and sufficient for production of classic adaptive T cells. The key components of the thymus are cortical and medullary thymic epithelial cells (cTECs and mTECs). Despite the capital role of TECs, our understanding of TEC biology is quite rudimentary. For instance, we ignore what might be the extent of divergence in the functional program of these two TECs populations. It also remains unclear why the number of TECs decreases rapidly with age, thereby leading to progressive thymic insufficiency. Methods: Systems level understanding of cell function begins with gene expression profiling, and the transcriptome is currently the only '-ome' that can be reliably tackled in its entirety in freshly harvested primary cells. In order to gain novel insights into TEC biology, we therefore decided to analyse the whole transcriptome of cTECs, mTECs and skin epithelial cells. We elected to analyse gene expression using RNA-seq rather microarrays because RNA-seq has higher sensitivity and dynamic range coupled to lower technical variations. Results: Our deep sequencing approach provides a unique perspective into the transcriptome of TECs. Consistent with their ability to express ectopic genes, we found that mTECs expressed more genes than other cell populations. Out of a total of 15,069 genes expressed in TECs, 25% were differentially expressed by at least 5-fold in cTECs vs. mTECs. Genes expressed at higher levels in cTECs than mTECs regulate numerous cell functions including cell differentiation, cell movement and microtubule dynamics. Almost all positive regulators of the cell cycle were overexpressed in skin ECs relative to TECs. Conclusions: Our RNA-seq data provide novel insights into the transcriptional landscape of TECs, highlight substantial divergences in the transcriptome of TEC subsets and suggest that cell cycle progression is differentially regulated in TECS and skinECs. We believe that our work will therefore represent a valuable resource and will be of great interest to readers working in biological sciences, particularly in the areas of immunology and systems biology. The mRNA profiles of cTEC, mTEC (from 14 thymi of 7-days old C57BL/6 mice) and skinEC (from the trunk and dorsum of seven newborn mice) were generated by RNA-sequencing using Illumina HiSeq2000.

Dataset Information

RNA-Seq global gene expression of four Dendritic cells subpopulations from lung (CD8+ and CD8- pDCs, CD103-CD11b+ and CD103+CD11b- cDCs) of influenza infected mice at four time points following infection

Publications

Digital cell quantification identifies global immune cell dynamics during influenza infection.

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