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An atlas of mouse CD4(+) T cell transcriptomes.

Stubbington MJ, Mahata B, Svensson V, Deonarine A, Nissen JK, Betz AG, Teichmann SA - Biol. Direct (2015)

Bottom Line: During an immune response Th cells mature from a naive state into one of several effector subtypes that exhibit distinct functions.To facilitate its use by others, we have made the data available in an easily accessible online resource at www.th-express.org .This article was reviewed by Wayne Hancock, Christine Wells and Erik van Nimwegen.

View Article: PubMed Central - PubMed

Affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. mstubb@ebi.ac.uk.

ABSTRACT

Background: CD4(+) T cells are key regulators of the adaptive immune system and can be divided into T helper (Th) cells and regulatory T (Treg) cells. During an immune response Th cells mature from a naive state into one of several effector subtypes that exhibit distinct functions. The transcriptional mechanisms that underlie the specific functional identity of CD4(+) T cells are not fully understood.

Results: To assist investigations into the transcriptional identity and regulatory processes of these cells we performed mRNA-sequencing on three murine T helper subtypes (Th1, Th2 and Th17) as well as on splenic Treg cells and induced Treg (iTreg) cells. Our integrated analysis of this dataset revealed the gene expression changes associated with these related but distinct cellular identities. Each cell subtype differentially expresses a wealth of 'subtype upregulated' genes, some of which are well known whilst others promise new insights into signalling processes and transcriptional regulation. We show that hundreds of genes are regulated purely by alternative splicing to extend our knowledge of the role of post-transcriptional regulation in cell differentiation.

Conclusions: This CD4(+) transcriptome atlas provides a valuable resource for the study of CD4(+) T cell populations. To facilitate its use by others, we have made the data available in an easily accessible online resource at www.th-express.org .

Reviewers: This article was reviewed by Wayne Hancock, Christine Wells and Erik van Nimwegen.

No MeSH data available.


Related in: MedlinePlus

Clustering CD4+subtypes based upon gene expression. (A) Plot of first two principal components for each sample calculated from regularised-log (rlog) transformed gene expression counts for all genes. (B) Heatmap showing hierarchical clustering performed using Euclidean distances for each subtype replicate. Distances were calculated using rlog-transformed gene expression counts for all genes.
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Fig4: Clustering CD4+subtypes based upon gene expression. (A) Plot of first two principal components for each sample calculated from regularised-log (rlog) transformed gene expression counts for all genes. (B) Heatmap showing hierarchical clustering performed using Euclidean distances for each subtype replicate. Distances were calculated using rlog-transformed gene expression counts for all genes.

Mentions: To study overall characteristics of the CD4+ transcriptomes, we calculated the regularised-log (rlog) transformed expression counts [25] and used these to perform principal component analysis and hierarchical clustering (FigureĀ 4) of the cell types. The individual replicates from each cell type are most similar to each other and the differentiated subtypes cluster separately from naive cells. Furthermore, we found that Th1 cluster with Th2 and Th17 cluster with Treg.Figure 4


An atlas of mouse CD4(+) T cell transcriptomes.

Stubbington MJ, Mahata B, Svensson V, Deonarine A, Nissen JK, Betz AG, Teichmann SA - Biol. Direct (2015)

Clustering CD4+subtypes based upon gene expression. (A) Plot of first two principal components for each sample calculated from regularised-log (rlog) transformed gene expression counts for all genes. (B) Heatmap showing hierarchical clustering performed using Euclidean distances for each subtype replicate. Distances were calculated using rlog-transformed gene expression counts for all genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4384382&req=5

Fig4: Clustering CD4+subtypes based upon gene expression. (A) Plot of first two principal components for each sample calculated from regularised-log (rlog) transformed gene expression counts for all genes. (B) Heatmap showing hierarchical clustering performed using Euclidean distances for each subtype replicate. Distances were calculated using rlog-transformed gene expression counts for all genes.
Mentions: To study overall characteristics of the CD4+ transcriptomes, we calculated the regularised-log (rlog) transformed expression counts [25] and used these to perform principal component analysis and hierarchical clustering (FigureĀ 4) of the cell types. The individual replicates from each cell type are most similar to each other and the differentiated subtypes cluster separately from naive cells. Furthermore, we found that Th1 cluster with Th2 and Th17 cluster with Treg.Figure 4

Bottom Line: During an immune response Th cells mature from a naive state into one of several effector subtypes that exhibit distinct functions.To facilitate its use by others, we have made the data available in an easily accessible online resource at www.th-express.org .This article was reviewed by Wayne Hancock, Christine Wells and Erik van Nimwegen.

View Article: PubMed Central - PubMed

Affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. mstubb@ebi.ac.uk.

ABSTRACT

Background: CD4(+) T cells are key regulators of the adaptive immune system and can be divided into T helper (Th) cells and regulatory T (Treg) cells. During an immune response Th cells mature from a naive state into one of several effector subtypes that exhibit distinct functions. The transcriptional mechanisms that underlie the specific functional identity of CD4(+) T cells are not fully understood.

Results: To assist investigations into the transcriptional identity and regulatory processes of these cells we performed mRNA-sequencing on three murine T helper subtypes (Th1, Th2 and Th17) as well as on splenic Treg cells and induced Treg (iTreg) cells. Our integrated analysis of this dataset revealed the gene expression changes associated with these related but distinct cellular identities. Each cell subtype differentially expresses a wealth of 'subtype upregulated' genes, some of which are well known whilst others promise new insights into signalling processes and transcriptional regulation. We show that hundreds of genes are regulated purely by alternative splicing to extend our knowledge of the role of post-transcriptional regulation in cell differentiation.

Conclusions: This CD4(+) transcriptome atlas provides a valuable resource for the study of CD4(+) T cell populations. To facilitate its use by others, we have made the data available in an easily accessible online resource at www.th-express.org .

Reviewers: This article was reviewed by Wayne Hancock, Christine Wells and Erik van Nimwegen.

No MeSH data available.


Related in: MedlinePlus