Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Single-cell RNA-sequencing of mouse thymic epithelial cells across the first year of life

ABSTRACT: The thymus shapes the T cell receptor repertoire, and is one of the first organs to rapidly age, with gross changes in cellularity and architecture. To resolve the nature of these changes we used SMART-Seq2 on FACS purified thymic epithelial cells (TEC) across the first year of mouse life. We sorted 4 TEC populations in each of 5 mice at each age (1 week, 4 weeks, 16 weeks, 32 weeks and 52 weeks old).

INSTRUMENT(S): Illumina NovaSeq 6000

ORGANISM(S): Mus musculus

SUBMITTER: Jeanette Baran-Gale

PROVIDER: E-MTAB-8560 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

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

Single-cell RNA-sequencing of mouse thymic epithelial cells across the first year of life

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure