Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies

Effective cancer treatment has been precluded by the presence of various forms of intratumoral complexity that drive treatment resistance and metastasis. Recent single-cell sequencing technologies are significantly facilitating the characterization of tumor internal architecture during disease progression. New applications and advances occurring at a fast pace predict an imminent broad application of these technologies in many research areas. As occurred with next-generation sequencing (NGS) technologies, once applied to clinical samples across tumor types, single-cell sequencing technologies could trigger an exponential increase in knowledge of the molecular pathways involved in cancer progression and contribute to the improvement of cancer treatment

Ageing compromises mouse thymus function and remodels epithelial cell differentiation.

Ageing is characterised by cellular senescence, leading to imbalanced tissue maintenance, cell death and compromised organ function. This is first observed in the thymus, the primary lymphoid organ that generates and selects T cells. However, the molecular and cellular mechanisms underpinning these ageing processes remain unclear. Here, we show that mouse ageing leads to less efficient T cell selection, decreased self-antigen representation and increased T cell receptor repertoire diversity. Using a combination of single-cell RNA-seq and lineage-tracing, we find that progenitor cells are the principal targets of ageing, whereas the function of individual mature thymic epithelial cells is compromised only modestly. Specifically, an early-life precursor cell population, retained in the mouse cortex postnatally, is virtually extinguished at puberty. Concomitantly, a medullary precursor cell quiesces, thereby impairing maintenance of the medullary epithelium. Thus, ageing disrupts thymic progenitor differentiation and impairs the core immunological functions of the thymus

Robject files for tissues processed by Seurat

Each tissue's gene expression profile was processed by experts to annotate clusters of cells with biological functions. These are the Robjects created using Seurat to normalize and cluster the single-cell RNA-seq expression data.<div><br></div><div>Update 2018-03-27: Updated to resubmitted Robj</div><div><br></div><div>Update 2018-09-20: Updated to accepted Robj</div

Gene expression on TSNE plots of single-cell data for 20 mouse tissues

TSNE plots of single-cell gene expression on 20 mouse tissues, with gene expression overlaid

Single-cell RNA-seq data from microfluidic emulsion

<div>Gene-count files and metadata files for single cells from different organs of mice processed on the 10X Genomics Platform. The counts are given using the .mtx file output by the CellRanger program, with one folder per run.</div><div><br></div><div>Includes data for 422,803 droplets, 55,656 of which passed a QC cutoff of 500 genes and 1000 UMI.</div><div><br></div><div><div>Cell annotations using the Cell Ontology [1] controlled vocabulary are in a separate csv.</div><div><br></div><div>[1] http://purl.obolibrary.org/obo/cl.owl</div></div><div><br></div

Single-cell RNA-seq data from Smart-seq2 sequencing of FACS sorted cells (v2)

<div>Gene-count tables for FACS sorted cells sequenced with Smart-Seq2 from 20 organs of 7 mice. Cells are grouped by tissue of origin.<br></div><div><br></div><div><div></div><div>Includes data for 53,760 cells, 44,879 of which passed a QC cutoff of at least 500 genes and 50,000 reads.</div></div><div><br></div><div><div>Cell annotations using the Cell Ontology [1] controlled vocabulary are in a separate csv.</div><div><br></div><div>This differs from v1 by renaming "Brain_Neurons" --> "Brain_Non-microglia" to be consistent with the manuscript.<br></div><div><br></div><div>Update 2018-09-20: Updated annotations to latest manuscript version</div><div><br></div><div>Update 2018-02-16: Separated Diaphragm cells from Muscle cells, and Aorta cells from Heart cells.</div><div><br></div><div>Update 2018-02-20: Aorta and Heart erroneously contained Diaphragm and Muscle data, and have now been corrected.</div><div><br></div><div>Update 2018-03-09: Renamed tissues for nomenclature standards: </div><div><ul><li> "Colon" --> "Large_Intestine"</li><li> "Muscle" --> "Limb_Muscle"</li><li> "Mammary" --> "Mammary_Gland"</li><li> "Brain_Microglia" --> "Brain_Myeloid"</li><li> "Brain_Non-microglia" --> "Brain_Non-Myeloid"</li></ul><div>Update 2018-03-22: Renamed subtissues:</div></div><div><br></div><div>- tissue: Heart, subtissue: ? --> tissue: Heart, subtissue: Unknown</div><div>- tissue: Skin, subtissue: NA --> tissue: Skin, subtissue: Telogen</div><div><br></div><div>Update 2018-03-23: Removed row numbers in first column of metadata_FACS.csv</div><div><br></div><div>Update 2018-03-27: Added tissue tSNEs and cluster ids</div><div><br></div><div><br></div><div>[1] http://purl.obolibrary.org/obo/cl.owl</div><div><br></div></div