Summary
The OrganoidAlign action will develop a solid computational framework for comparative modelling of organoids in the age of single-cell transcriptomics. It will provide direct payoffs to both, tissue engineering and single-cell genomics fields. The overarching goal is to build a set of statistically rigorous and consistent probabilistic models for aligning a single-cell transcriptomic profile of an organoid against its in vivo tissue, for quantitatively evaluating its recapitulatory power.
During the course of this action, a meticulous review will be conducted on the state-of-the-art single-cell data modelling and comparative analysis techniques prior to formulating the single-cell transcriptomic profile comparison problem in statistical learning theory. This will specifically focus on both cell clustering and cell trajectory inference methods. A new statistical framework will be implemented to accommodate a comprehensive comparison between a pair of in vitro and in vivo transcriptomic profiles. A rigorous scoring measure will be devised to quantify their alignment.
Overall, the inference components under the proposed framework will facilitate the prediction of missing or outlying cellular attributes, transcriptional factors and signaling pathways in organoids compared to their in vivo tissues, informing directions of organoid protocol improvement. Overall, its outcomes will have potential contributions towards engineering more reliable in vitro tissue models, as well as reference profiling of organoids under the Human Cell Atlas project.
During the course of this action, a meticulous review will be conducted on the state-of-the-art single-cell data modelling and comparative analysis techniques prior to formulating the single-cell transcriptomic profile comparison problem in statistical learning theory. This will specifically focus on both cell clustering and cell trajectory inference methods. A new statistical framework will be implemented to accommodate a comprehensive comparison between a pair of in vitro and in vivo transcriptomic profiles. A rigorous scoring measure will be devised to quantify their alignment.
Overall, the inference components under the proposed framework will facilitate the prediction of missing or outlying cellular attributes, transcriptional factors and signaling pathways in organoids compared to their in vivo tissues, informing directions of organoid protocol improvement. Overall, its outcomes will have potential contributions towards engineering more reliable in vitro tissue models, as well as reference profiling of organoids under the Human Cell Atlas project.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101026506 |
| Start date: | 15-06-2021 |
| End date: | 11-07-2023 |
| Total budget - Public funding: | 212 933,76 Euro - 212 933,00 Euro |
Cordis data
Original description
The OrganoidAlign action will develop a solid computational framework for comparative modelling of organoids in the age of single-cell transcriptomics. It will provide direct payoffs to both, tissue engineering and single-cell genomics fields. The overarching goal is to build a set of statistically rigorous and consistent probabilistic models for aligning a single-cell transcriptomic profile of an organoid against its in vivo tissue, for quantitatively evaluating its recapitulatory power.During the course of this action, a meticulous review will be conducted on the state-of-the-art single-cell data modelling and comparative analysis techniques prior to formulating the single-cell transcriptomic profile comparison problem in statistical learning theory. This will specifically focus on both cell clustering and cell trajectory inference methods. A new statistical framework will be implemented to accommodate a comprehensive comparison between a pair of in vitro and in vivo transcriptomic profiles. A rigorous scoring measure will be devised to quantify their alignment.
Overall, the inference components under the proposed framework will facilitate the prediction of missing or outlying cellular attributes, transcriptional factors and signaling pathways in organoids compared to their in vivo tissues, informing directions of organoid protocol improvement. Overall, its outcomes will have potential contributions towards engineering more reliable in vitro tissue models, as well as reference profiling of organoids under the Human Cell Atlas project.
Status
CLOSEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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