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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 of the 50%of genes with highest between-sample variance for the CD4+subtypes. Sections of the gene expression heatmap illustrating the local clusters around (A)Tbx21, (B)Gata3, (C)Rorc and (D)Foxp3. Genes and samples were clustered by Euclidean distance using rlog-transformed expression counts. Expression levels are presented as rlog-transformed counts (left) or as Z-scores (right). Colours to the left of the rows indicate the subtype to which SU genes were assigned.
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Fig5: Clustering of the 50%of genes with highest between-sample variance for the CD4+subtypes. Sections of the gene expression heatmap illustrating the local clusters around (A)Tbx21, (B)Gata3, (C)Rorc and (D)Foxp3. Genes and samples were clustered by Euclidean distance using rlog-transformed expression counts. Expression levels are presented as rlog-transformed counts (left) or as Z-scores (right). Colours to the left of the rows indicate the subtype to which SU genes were assigned.

Mentions: We used the 50% of genes with the highest variance to perform gene clustering and inspected the clusters associated with known canonical transcription factors to gain assurance that our data will be of use in exploring the regulatory networks involved in CD4+ T cell function (FigureĀ 5).Figure 5


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 of the 50%of genes with highest between-sample variance for the CD4+subtypes. Sections of the gene expression heatmap illustrating the local clusters around (A)Tbx21, (B)Gata3, (C)Rorc and (D)Foxp3. Genes and samples were clustered by Euclidean distance using rlog-transformed expression counts. Expression levels are presented as rlog-transformed counts (left) or as Z-scores (right). Colours to the left of the rows indicate the subtype to which SU genes were assigned.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig5: Clustering of the 50%of genes with highest between-sample variance for the CD4+subtypes. Sections of the gene expression heatmap illustrating the local clusters around (A)Tbx21, (B)Gata3, (C)Rorc and (D)Foxp3. Genes and samples were clustered by Euclidean distance using rlog-transformed expression counts. Expression levels are presented as rlog-transformed counts (left) or as Z-scores (right). Colours to the left of the rows indicate the subtype to which SU genes were assigned.
Mentions: We used the 50% of genes with the highest variance to perform gene clustering and inspected the clusters associated with known canonical transcription factors to gain assurance that our data will be of use in exploring the regulatory networks involved in CD4+ T cell function (FigureĀ 5).Figure 5

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