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A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells.

ABSTRACT: The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic- and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

SUBMITTER: Ulfenborg B

PROVIDER: S-EPMC5487013 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells.

Ulfenborg Benjamin B Karlsson Alexander A Riveiro Maria M Améen Caroline C Åkesson Karolina K Andersson Christian X CX Sartipy Peter P Synnergren Jane J

PloS one 20170627 6

The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent ...[more]

PMID: 28654683

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Project description:Тhe most pressing issue in generating induced pluripotent stem cells (iPSCs) in clinical practice is the cell source. Compared to human dermal fibroblasts (HDFs), which have been widely used, human peripheral blood could be a more easily obtainable alternative. However, iPSCs generated from fresh peripheral blood require inconvenient specific methods including isolation. Recently, we succeeded in isolating and culturing human heart-derived circulating cells called circulating multipotent stem (CiMS) cells. Here, we investigated the generation efficiency of CiMS-derived iPSCs (CiMS-iPSCs) and tested their differentiation potential into mesodermal lineages and cardiovascular cells. We isolated and cultured CiMS cells from peripheral mononuclear cells with a high efficiency. Moreover, our method succeeded in reprogramming the CiMS cells and generating iPSCs with higher efficiency compared to when HDFs were used. Compared to HDF-iPSCs or human embryonic stem cells (hESCs), CiMS-iPSCs showed high differentiation potential into mesodermal lineage cells and subsequently into endothelial cells, vascular smooth muscle cells, and cardiomyocytes. Further, we checked the epigenetic status of each cell type. While methylation of the CpG site of the brachyury T promoter did not differ between cell types, the histone H3 lysine 4 trimethylation level in the brachyury T promoter region was enhanced in CiMS-iPSCs, compared to that in other cell types. In contrast, histone H3 lysine 9 acetylation was downregulated during the differentiation process of the CiMS-iPSCs. In the myocardial infarction model, the CiMS-iPSCs group showed more therapeutic potential in regenerating the myocardium than other cell types. Our study showed a new method to isolate human heart-derived stem cells from human peripheral blood and to generate iPSCs efficiently. Due to epigenetic memory, these CiMS-iPSCs easily differentiated into cardiovascular lineage cells, resulting in improved efficiency in vivo. These results suggest that our new method using CiMS cells has therapeutic potential in regenerative medicine using cell therapy.

Dataset Information

A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells.

Publications

A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells.

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