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  • Transcriptomics and cBiT

    Transcriptomics and cBiT

  • Transcriptomics and cBiT

    Transcriptomics and cBiT

  • Transcriptomics and cBiT

    Transcriptomics and cBiT

Transcriptomics and cBiT

The Compendium for Biomaterial Transcriptomics (cBiT)

cBIT Logo wb

At cBITE we want to understand how cells respond when they come into contact with biomaterial surfaces. For this we identify hit surfaces that induce a desirable response in cells (e.g. an increase in cell migration, or enhanced ALP production) and follow this up with transcriptomics analysis. In order to collect the transcriptomics and biomaterial data we have produced so far in a standardized way and offer a repository for other researchers to store their data, we have recently set up the Compendium for Biomaterial Transcriptomics (cBiT):

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cBiT is a publically accessible repository that incorporates material science and transcriptomics-based cell biology, with a focus on clinically relevant materials. By generating and accumulating new information, including detailed biomaterial characterization data, we expect it is possible to one day predict cell response to biomaterials and develop new biomaterials that show an improved interaction with the host tissue.

cBiT flowchart

cBiT currently holds 8 data sets covering a total of 296 samples and will continue to grow in size. Please check the website for details on how you can contribute your data to cBiT.

Transcriptomics analysis of cell-biomaterial interaction

The accumulation of transcriptomics data is only step 1. Using well-developed bioinformatics approaches we then proceed to the next step where we try to better understand the cell-biomaterial interactions. For example, we can use pathway and network analysis (as shown below) to explore the relationships between genes and develop new hypotheses which can be further tested in the lab. Some of the tools we use for these analyses are R, CytoScape, ConsensusPathDB, and Enrichr. Ultimately we want to use prediction analysis strategies to identify gene expression profiles indicative of a favorable biomaterial-tissue integration. For this to work, the motto is: the more, the better. The more data we have, the more reliable prediction analyses can be. This concept is similar to the one employed in the Connectivity Map developed by the Broad Institute.


Some of our discoveries

Using the approach described above, we have made several discoveries that would be very difficult to achieve using "classical" cell biology techniques:

  • We have correlated the bone-forming capacity of human mesenchymal stromal cells (hMSCs) in an immune-deficient mouse model to the expression of genes during the expansion phase of hMSC culture. This led to the identification of the CADM1 gene as a marker capable of predicting bone formation with high accuracy (Mentink et al.).
  • In another study, we explored the transcriptional landscape of material-induced bone formation and were able to correlate the expression of individual genes, including hyaluronic acid synthase, to defined material parameters. The potential role of hyaluronic acid deposition by osteoprogenitors in the osteoinductive process is something we are now exploring in more detail (Groen et al.).
  • We also evaluate the role of cAMP/PKA signaling in osteogenic and adipogenic differentiation of hMSCs. We found that two cAMP analogs activating the same pathway (i.e. dibutyryl-cAMP and 8-bromo-cAMP) can have differential effects on long-term differentiation of hMSCs and gene expression levels, thus pointing to a role for the cAMP/PKA pathway in this balance (Doorn et al.).

Further reading

cBiT paper: Hebels et al., Biomaterials, 2017
Small molecule screen: Doorn et al., Biomaterials, 2012
Linking the transcriptome to biomaterial design parameters: Groen et al., Adv Mater, 2017
Mesenchymal stem cell bank: Mentink et al., Biomaterials, 2013