Rowland Mosbergen

An epigenomic roadmap to induced pluripotency reveals DNA methylation as a reprogramming modulator

Reprogramming of somatic cells to induced pluripotent stem cells involves a dynamic rearrangement of the epigenetic landscape. To characterize this epigenomic roadmap, we have performed MethylC-seq, ChIP-seq (H3K4/K27/K36me3) and RNA-Seq on samples taken at several time points during murine secondary reprogramming as part of Project Grandiose. We find that DNA methylation gain during reprogramming occurs gradually, while loss is achieved only at the ESC-like state. Binding sites of activated factors exhibit focal demethylation during reprogramming, while ESC-like pluripotent cells are distinguished by extension of demethylation to the wider neighbourhood. We observed that genes with CpG-rich promoters demonstrate stable low methylation and strong engagement of histone marks, whereas genes with CpG-poor promoters are safeguarded by methylation. Such DNA methylation-driven control is the key to the regulation of ESC-pluripotency genes, including Dppa4, Dppa5a and Esrrb. These results reveal the crucial role that DNA methylation plays as an epigenetic switch driving somatic cells to pluripotency.

Stemformatics: visualisation and sharing of stem cell gene expression.

Genome-scale technologies are increasingly adopted by the stem cell research community, because of the potential to uncover the molecular events most informative about a stem cell state. These technologies also present enormous challenges around the sharing and visualisation of data derived from different laboratories or under different experimental conditions. Stemformatics is an easy to use, publicly accessible portal that hosts a large collection of exemplar stem cell data. It provides fast visualisation of gene expression across a range of mouse and human datasets, with transparent links back to the original studies. One difficulty in the analysis of stem cell signatures is the paucity of public pathways/gene lists relevant to stem cell or developmental biology. Stemformatics provides a simple mechanism to create, share and analyse gene sets, providing a repository of community-annotated stem cell gene lists that are informative about pathways, lineage commitment, and common technical artefacts. Stemformatics can be accessed at stemformatics.org.

Four simple recommendations to encourage best practices in research software

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

A molecular classification of human mesenchymal stromal cells

Mesenchymal stromal cells (MSC) are widely used for the study of mesenchymal tissue repair, and increasingly adopted for cell therapy, despite the lack of consensus on the identity of these cells. In part this is due to the lack of specificity of MSC markers. Distinguishing MSC from other stromal cells such as fibroblasts is particularly difficult using standard analysis of surface proteins, and there is an urgent need for improved classification approaches. Transcriptome profiling is commonly used to describe and compare different cell types; however, efforts to identify specific markers of rare cellular subsets may be confounded by the small sample sizes of most studies. Consequently, it is difficult to derive reproducible, and therefore useful markers. We addressed the question of MSC classification with a large integrative analysis of many public MSC datasets. We derived a sparse classifier (The Rohart MSC test) that accurately distinguished MSC from non-MSC samples with >97% accuracy on an internal training set of 635 samples from 41 studies derived on 10 different microarray platforms. The classifier was validated on an external test set of 1,291 samples from 65 studies derived on 15 different platforms, with >95% accuracy. The genes that contribute to the MSC classifier formed a protein-interaction network that included known MSC markers. Further evidence of the relevance of this new MSC panel came from the high number of Mendelian disorders associated with mutations in more than 65% of the network. These result in mesenchymal defects, particularly impacting on skeletal growth and function. The Rohart MSC test is a simple in silico test that accurately discriminates MSC from fibroblasts, other adult stem/progenitor cell types or differentiated stromal cells. It has been implemented in the www.stemformatics.org resource, to assist researchers wishing to benchmark their own MSC datasets or data from the public domain. The code is available from the CRAN repository and all data used to generate the MSC test is available to download via the Gene Expression Omnibus or the Stemformatics resource.

Characterization of Phenotypic and Transcriptional Differences in Human Pluripotent Stem Cells under 2D and 3D Culture Conditions

Human pluripotent stem cells hold great promise for applications in drug discovery and regenerative medicine. Microfluidic technology is a promising approach for creating artificial microenvironments; however, although a proper 3D microenvironment is required to achieve robust control of cellular phenotypes, most current microfluidic devices provide only 2D cell culture and do not allow tuning of physical and chemical environmental cues simultaneously. Here, the authors report a 3D cellular microenvironment plate (3D-CEP), which consists of a microfluidic device filled with thermoresponsive poly(N-isopropylacrylamide)-β-poly(ethylene glycol) hydrogel (HG), which enables systematic tuning of both chemical and physical environmental cues as well as in situ cell monitoring. The authors show that H9 human embryonic stem cells (hESCs) and 253G1 human induced pluripotent stem cells in the HG/3D-CEP system maintain their pluripotent marker expression under HG/3D-CEP self-renewing conditions. Additionally, global gene expression analyses are used to elucidate small variations among different test environments. Interestingly, the authors find that treatment of H9 hESCs under HG/3D-CEP self-renewing conditions results in initiation of entry into the neural differentiation process by induction of PAX3 and OTX1 expression. The authors believe that this HG/3D-CEP system will serve as a versatile platform for developing targeted functional cell lines and facilitate advances in drug screening and regenerative medicine.

Identifying genes and pathways deregulated in chronic myeloid leukaemia stem cells through meta-analysis of transcriptomic data

Chronic myeloid leukaemia (CML) is characterised by the presence of the fusion protein BCR-ABL1. The addiction of CML cells to the tyrosine kinase (TK) activity of the oncoprotein has been successfully exploited by the introduction of tyrosine kinase inhibitors (TKI), such as imatinib, which have shown a great success at managing the disease. However, these compounds fail to eradicate a primitive cell population, the leukaemic stem cells (LSCs), which persist in the patients. This translates in the need of life-long therapy for most of the patients, meaning a higher risk of treatment side effects and the prolonged psychological burden of living a leukaemia patient. Life-long therapy is also translating in a continuous increase in CML prevalence in developed countries and sustaining a big patient population on TKI treatment is becoming a challenge for national health systems. Recent reports in chronic myeloid leukaemia biology have confirmed that CML LSCs are not addicted to the TK activity of BCR-ABL1 and they retain repopulation and leukaemic properties even during BCR-ABL1 TK inhibition. Thus, the discovery of new therapeutic targets capable of eliminating this cell population is required for curing the disease. Previous reports have already shown great success at reducing the number of CML LSCs by targeting JAK2, STAT5, EZH2, MYC and p53 pathways as well as autophagocytosis. However, none of them have shown complete eradication of the clone and they failed to define a global gene expression signature that may explain the persistence of CML LSCs during TKI treatment. Taken together, the work presented in this thesis confirms the existence of a BCR-ABL1 transcriptional signature in CML LSCs. Also, it shows that targeting CD33, a member of the TKIi signature, reduces the number of CML CD34+ cells and induces a transcriptional and phenotypic change towards a cycling and repopulating cell population. Additionally, the use of the TKIi signature has shown potential as a molecular biomarker for predicting TKI response in CML patients.

Erratum: Genome-wide characterization of the routes to pluripotency (Nature (2014) 516 (198-206) DOI: 10.1038/nature14046)

This corrects the article DOI: 10.1038/nature14046

Erratum: Corrigendum: Genome-wide characterization of the routes to pluripotency

This corrects the article DOI: 10.1038/nature14046

Corrigendum: Genome-wide characterization of the routes to pluripotency

This corrects the article DOI: 10.1038/nature14046

Stemformatics: a user-friendly database of well-curated biological data

We were motivated by the plethora of exciting cell models in the public and private domains, and the realisation that for many biologists these were mostly inaccessible. We wanted a fast, user-friendly way to find and visualise interesting genes in these exemplar stem cell datasets. With Stemformatics, anyone can find data from leading stem cell laboratories in a format easy to search, visualise and export. No prior bioinformatics knowledge is required. Stemformatics is aimed at bioinformaticians and biologists wishing to collaborate. It is designed and maintained with the intention to remove many of the friction points common to such collaborations. 4. Visualisation tools