Anatomy and Neuroscience - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 5 of 5
  • Item
    Thumbnail Image
    Age-specific biological and molecular profiling distinguishes paediatric from adult acute myeloid leukaemias
    Chaudhury, S ; O'Connor, C ; Canete, A ; Bittencourt-Silvestre, J ; Sarrou, E ; Prendergast, A ; Choi, J ; Johnston, P ; Wells, CA ; Gibson, B ; Keeshan, K (NATURE PUBLISHING GROUP, 2018-12-11)
    Acute myeloid leukaemia (AML) affects children and adults of all ages. AML remains one of the major causes of death in children with cancer and for children with AML relapse is the most common cause of death. Here, by modelling AML in vivo we demonstrate that AML is discriminated by the age of the cell of origin. Young cells give rise to myeloid, lymphoid or mixed phenotype acute leukaemia, whereas adult cells give rise exclusively to AML, with a shorter latency. Unlike adult, young AML cells do not remodel the bone marrow stroma. Transcriptional analysis distinguishes young AML by the upregulation of immune pathways. Analysis of human paediatric AML samples recapitulates a paediatric immune cell interaction gene signature, highlighting two genes, RGS10 and FAM26F as prognostically significant. This work advances our understanding of paediatric AML biology, and provides murine models that offer the potential for developing paediatric specific therapeutic strategies.
  • Item
    Thumbnail Image
    Stemformatics: visualize and download curated stem cell data
    Choi, J ; Pacheco, CM ; Mosbergen, R ; Korn, O ; Chen, T ; Nagpal, I ; Englart, S ; Angel, PW ; Wells, CA (OXFORD UNIV PRESS, 2019-01-08)
    Stemformatics is an established gene expression data portal containing over 420 public gene expression datasets derived from microarray, RNA sequencing and single cell profiling technologies. Developed for the stem cell community, it has a major focus on pluripotency, tissue stem cells, and staged differentiation. Stemformatics includes curated 'collections' of data relevant to cell reprogramming, as well as hematopoiesis and leukaemia. Rather than simply rehosting datasets as they appear in public repositories, Stemformatics uses a stringent set of quality control metrics and its own pipelines to process handpicked datasets from raw files. This means that about 30% of datasets processed by Stemformatics fail the quality control metrics and never make it to the portal, ensuring that Stemformatics data are of high quality and have been processed in a consistent manner. Stemformatics provides easy-to-use and intuitive tools for biologists to visually explore the data, including interactive gene expression profiles, principal component analysis plots and hierarchical clusters, among others. The addition of tools that facilitate cross-dataset comparisons provides users with snapshots of gene expression in multiple cell and tissues, assisting the identification of cell-type restricted genes, or potential housekeeping genes. Stemformatics is freely available at stemformatics.org.
  • Item
    Thumbnail Image
    Transcriptional Profiling of Stem Cells: Moving from Descriptive to Predictive Paradigms
    Wells, CA ; Choi, J (CELL PRESS, 2019-08-13)
    Transcriptional profiling is a powerful tool commonly used to benchmark stem cells and their differentiated progeny. As the wealth of stem cell data builds in public repositories, we highlight common data traps, and review approaches to combine and mine this data for new cell classification and cell prediction tools. We touch on future trends for stem cell profiling, such as single-cell profiling, long-read sequencing, and improved methods for measuring molecular modifications on chromatin and RNA that bring new challenges and opportunities for stem cell analysis.
  • Item
    Thumbnail Image
    TCF-1 limits the formation of Tc17 cells via repression of the MAF-ROR gamma t axis
    Mielke, LA ; Liao, Y ; Clemens, EB ; Firth, MA ; Duckworth, B ; Huang, Q ; Almeida, FF ; Chopin, M ; Koay, H-F ; Bell, CA ; Hediyeh-Zadeh, S ; Park, SL ; Raghu, D ; Choi, J ; Putoczki, TL ; Hodgkin, PD ; Franks, AE ; Mackay, LK ; Godfrey, D ; Davis, MJ ; Xue, H-H ; Bryant, VL ; Kedzierska, K ; Shi, W ; Belz, GT (ROCKEFELLER UNIV PRESS, 2019-07-01)
    Interleukin (IL)-17-producing CD8+ T (Tc17) cells have emerged as key players in host-microbiota interactions, infection, and cancer. The factors that drive their development, in contrast to interferon (IFN)-γ-producing effector CD8+ T cells, are not clear. Here we demonstrate that the transcription factor TCF-1 (Tcf7) regulates CD8+ T cell fate decisions in double-positive (DP) thymocytes through the sequential suppression of MAF and RORγt, in parallel with TCF-1-driven modulation of chromatin state. Ablation of TCF-1 resulted in enhanced Tc17 cell development and exposed a gene set signature to drive tissue repair and lipid metabolism, which was distinct from other CD8+ T cell subsets. IL-17-producing CD8+ T cells isolated from healthy humans were also distinct from CD8+IL-17- T cells and enriched in pathways driven by MAF and RORγt Overall, our study reveals how TCF-1 exerts central control of T cell differentiation in the thymus by normally repressing Tc17 differentiation and promoting an effector fate outcome.
  • Item
    Thumbnail Image
    Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans
    Choi, J ; Baldwin, TM ; Wong, M ; Bolden, JE ; Fairfax, KA ; Lucas, EC ; Cole, R ; Biben, C ; Morgan, C ; Ramsay, KA ; Ng, AP ; Kauppi, M ; Corcoran, LM ; Shi, W ; Wilson, N ; Wilson, MJ ; Alexander, WS ; Hilton, DJ ; de Graaf, CA (OXFORD UNIV PRESS, 2019-01-08)
    During haematopoiesis, haematopoietic stem cells differentiate into restricted potential progenitors before maturing into the many lineages required for oxygen transport, wound healing and immune response. We have updated Haemopedia, a database of gene-expression profiles from a broad spectrum of haematopoietic cells, to include RNA-seq gene-expression data from both mice and humans. The Haemopedia RNA-seq data set covers a wide range of lineages and progenitors, with 57 mouse blood cell types (flow sorted populations from healthy mice) and 12 human blood cell types. This data set has been made accessible for exploration and analysis, to researchers and clinicians with limited bioinformatics experience, on our online portal Haemosphere: https://www.haemosphere.org. Haemosphere also includes nine other publicly available high-quality data sets relevant to haematopoiesis. We have added the ability to compare gene expression across data sets and species by curating data sets with shared lineage designations or to view expression gene vs gene, with all plots available for download by the user.