Literature DB >> 29020642

cBiT: A transcriptomics database for innovative biomaterial engineering.

Dennie G A J Hebels1, Aurélie Carlier2, Maarten L J Coonen3, Daniël H Theunissen3, Jan de Boer2.   

Abstract

Creating biomaterials that are suited for clinical application is still hampered by a lack of understanding of the interaction between a cell and the biomaterial surface it grows on. This surface communication can strongly impact cellular behavior, which in turn affects the chances of a successful interaction between a material and the host tissue. Transcriptomics data have previously been linked to measurements of biomaterial properties in order to explain the biological mechanisms underlying these cell-biomaterial interactions. However, such multi-assay data are highly complex and therefore require careful and unambiguous characterization and storage. Failure to do so may result in loss of valuable data or erroneous data analysis. In order to start a new initiative that tackles these issues and offers a platform for innovative biomaterial development, we have created a publically accessible repository called The Compendium for Biomaterial Transcriptomics (cBiT, https://cbit.maastrichtuniversity.nl). cBiT is a data warehouse that gives users the opportunity to search through biomaterial-based transcriptomics data sets using a web interface. Data of interest can be selected and downloaded, together with associated measurements of material properties. Researchers are also invited to add their data to cBiT in order to further enhance its scientific value. We aim to make cBiT the hub for biomaterial-associated data, thereby enabling major contributions to a more efficient development of new materials with improved body integration. Here, we describe the structure of cBiT and provide a use case with clinically applied materials to demonstrate how cBiT can be used to correlate data across transcriptomics studies.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Keywords:  Biomaterials; Compendium; Data analysis; Database; Repository; Reverse engineering; Transcriptomics

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Year:  2017        PMID: 29020642     DOI: 10.1016/j.biomaterials.2017.10.008

Source DB:  PubMed          Journal:  Biomaterials        ISSN: 0142-9612            Impact factor:   12.479


  3 in total

Review 1.  Using Large Datasets to Understand Nanotechnology.

Authors:  Kalina Paunovska; David Loughrey; Cory D Sago; Robert Langer; James E Dahlman
Journal:  Adv Mater       Date:  2019-08-20       Impact factor: 30.849

2.  Biofabrication offers future hope for tackling various obstacles and challenges in tissue engineering and regenerative medicine: A Perspective.

Authors:  Tanveer Ahmad Mir; Shintaroh Iwanaga; Taketoshi Kurooka; Hideki Toda; Shinji Sakai; Makoto Nakamura
Journal:  Int J Bioprint       Date:  2018-12-31

3.  On the correlation between material-induced cell shape and phenotypical response of human mesenchymal stem cells.

Authors:  Aliaksei S Vasilevich; Steven Vermeulen; Marloes Kamphuis; Nadia Roumans; Said Eroumé; Dennie G A J Hebels; Jeroen van de Peppel; Rika Reihs; Nick R M Beijer; Aurélie Carlier; Anne E Carpenter; Shantanu Singh; Jan de Boer
Journal:  Sci Rep       Date:  2020-11-04       Impact factor: 4.379

  3 in total

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