Anatomy and Neuroscience - Research Publications

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    Characterization of Phenotypic and Transcriptional Differences in Human Pluripotent Stem Cells under 2D and 3D Culture Conditions
    Kamei, K-I ; Koyama, Y ; Tokunaga, Y ; Mashimo, Y ; Yoshioka, M ; Fockenberg, C ; Mosbergen, R ; Korn, O ; Wells, C ; Chen, Y (WILEY, 2016-11-23)
    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.
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    An epigenomic roadmap to induced pluripotency reveals DNA methylation as a reprogramming modulator
    Lee, D-S ; Shin, J-Y ; Tonge, PD ; Puri, MC ; Lee, S ; Park, H ; Lee, W-C ; Hussein, SMI ; Bleazard, T ; Yun, J-Y ; Kim, J ; Li, M ; Cloonan, N ; Wood, D ; Clancy, JL ; Mosbergen, R ; Yi, J-H ; Yang, K-S ; Kim, H ; Rhee, H ; Wells, CA ; Preiss, T ; Grimmond, SM ; Rogers, IM ; Nagy, A ; Seo, J-S (NATURE PUBLISHING GROUP, 2014-12-01)
    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.
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    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.
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    A molecular classification of human mesenchymal stromal cells
    Rohart, F ; Mason, EA ; Matigian, N ; Mosbergen, R ; Korn, O ; Chen, T ; Butcher, S ; Patel, J ; Atkinson, K ; Khosrotehrani, K ; Fisk, NM ; Le Cao, K-A ; Wells, CA (PEERJ INC, 2016-03-24)
    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.