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Deciphering the Monocyte-Macrophage Lineage Differentiation With IPA

Deciphering the Monocyte-Macrophage Lineage Differentiation With IPA. Heikki Vuorikoski University of Turku Institute of Biomedicine Department of Anatomy. IPA and How We Use It. Analysis of Big Datasets DNA microarray data, solving the function of ”unknown” genes Literature Mining

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Deciphering the Monocyte-Macrophage Lineage Differentiation With IPA

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  1. Deciphering the Monocyte-Macrophage Lineage Differentiation With IPA Heikki Vuorikoski University of Turku Institute of Biomedicine Department of Anatomy

  2. IPA and How We Use It • Analysis of Big Datasets • DNA microarray data, solving the function of ”unknown” genes • Literature Mining • Gene and protein information • Data Comparison • DNA microarray data from our experiments vs.public expression data from databases, articles... • Data source • E.g. ”osteoclast” related information • Pathway Graphics • Co-operation projects • Information sharing

  3. Monocyte-macrophage System (MMS) Plasticity • CD14+ monocytes isolated from human peripheral blood can differentiate into bone resorbing osteoclasts (OCs), endothelial cells (ECs), dendritic cells (DCs) and macrophages (Ms) • Common key factors for different cell lineage differentiation includes M-CSF, c-fos, GM-CSF, and IL-4 • Capability of transdifferentiation: • immature DCs can transdifferentiate into OCs • DCs and Ms into each other • immature DCs into EC-like cells

  4. Systems Biology Approach to Cell Lineage Differentiation Methods: • Microarray gene expression profiling • Human OCs grown on plastic and bone • In silico promoter region analysis of OC specific genes • In silico transcription factor model prediction • Microarray data mining analyses • GO, Pathway analysis

  5. OC Differentiation Assay

  6. Time series analysis with Affymetrix HG-U133A

  7. Functional Analysis of the Genes The Functional Analysis of a network identified the biological functions and/or diseases that were most significant to the genes in the network. Genes in bold are up-regulated and in italic down-regulated.

  8. How to Use: Literature Mining

  9. How to use: Data Comparison • Data from external sources, e.g. articles • Import to IPA • Comparison analysis with your own data

  10. Genes categorised as “osteoclast related” in IPA are inspected in our microarray data How to Use: Data Source • Search and visualize in IPA • Color with your (or others) expression data

  11. How to Use: Pathway Graphics

  12. How to Use: Co-operation, data sharing

  13. Conclusions • Big datasets are easily handled with the software • Integration to other analysis programs is easy • Doesn’t require advanced computing skills (“biologistettavissa”) • Data analysis and data sharing between co-workers is easy • IPA is not an excuse to stop wet-lab work, but it is valuable tool for interpreting the data coming from the lab.

  14. Department of Anatomy, University of Turku Anne Seppänen Husheem Michael Teuvo Hentunen Tiina Laitala-Leinonen Kalervo Väänänen Department of Medical Microbiology, University of Turku Milja Möttönen Olli Lassila Department of Information Technology, University of Turku Eija Nordlund Jorma Boberg Tapio Salakoski Department of Physiology, University of Turku Markku Ahotupa National Public Health Institute, Department of Molecular Medicine, Helsinki Anna Kiialainen Thank You

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