Automated Hypothesis Generation to Identify Signals Relevant in the Development of Mammalian Cell and Tissue Bioprocesses, With Validation in a Retinal Culture System
Frontiers in Bioengineering and Biotechnology, ISSN: 2296-4185, Vol: 8, Page: 534
2020
- 1Citations
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
We have developed an accessible software tool (receptoR) to predict potentially active signaling pathways in one or more cell type(s) of interest from publicly available transcriptome data. As proof-of-concept, we applied it to mouse photoreceptors, yielding the previously untested hypothesis that activin signaling pathways are active in these cells. Expression of the type 2 activin receptor (Acvr2a) was experimentally confirmed by both RT-qPCR and immunochemistry, and activation of this signaling pathway with recombinant activin A significantly enhanced the survival of magnetically sorted photoreceptors in culture. Taken together, we demonstrate that our approach can be easily used to mine publicly available transcriptome data and generate hypotheses around receptor expression that can be used to identify novel signaling pathways in specific cell types of interest. We anticipate that receptoR (available at https://www.ucalgary.ca/ungrinlab/receptoR) will enable more efficient use of limited research resources.
Bibliographic Details
10.3389/fbioe.2020.00534; 10.3389/fbioe.2020.00534.s004; 10.3389/fbioe.2020.00534.s003; 10.3389/fbioe.2020.00534.s005; 10.3389/fbioe.2020.00534.s002; 10.3389/fbioe.2020.00534.s001
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85086590663&origin=inward; http://dx.doi.org/10.3389/fbioe.2020.00534; http://www.ncbi.nlm.nih.gov/pubmed/32582664; https://www.frontiersin.org/articles/10.3389/fbioe.2020.00534/supplementary-material/10.3389/fbioe.2020.00534.s004; http://dx.doi.org/10.3389/fbioe.2020.00534.s004; https://www.frontiersin.org/article/10.3389/fbioe.2020.00534/full; https://www.frontiersin.org/articles/10.3389/fbioe.2020.00534/supplementary-material/10.3389/fbioe.2020.00534.s003; http://dx.doi.org/10.3389/fbioe.2020.00534.s003; https://www.frontiersin.org/articles/10.3389/fbioe.2020.00534/supplementary-material/10.3389/fbioe.2020.00534.s005; http://dx.doi.org/10.3389/fbioe.2020.00534.s005; https://www.frontiersin.org/articles/10.3389/fbioe.2020.00534/supplementary-material/10.3389/fbioe.2020.00534.s002; http://dx.doi.org/10.3389/fbioe.2020.00534.s002; https://www.frontiersin.org/articles/10.3389/fbioe.2020.00534/supplementary-material/10.3389/fbioe.2020.00534.s001; http://dx.doi.org/10.3389/fbioe.2020.00534.s001; https://dx.doi.org/10.3389/fbioe.2020.00534.s004; https://www.frontiersin.org/articles/10.3389/fbioe.2020.00534/full; https://dx.doi.org/10.3389/fbioe.2020.00534.s005; https://dx.doi.org/10.3389/fbioe.2020.00534.s002; https://dx.doi.org/10.3389/fbioe.2020.00534; https://dx.doi.org/10.3389/fbioe.2020.00534.s003; https://dx.doi.org/10.3389/fbioe.2020.00534.s001
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