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Spatial Transcriptomics through the lenses of statistical modeling and AI

Project description

Spatial transcriptomics through spatial structure and AI

Recently, spatial transcriptomics (a collection of techniques) has made it possible to quantify mRNA expression of large numbers of genes while preserving the spatial context of tissues and cells, thanks to several technological advances. This development is crucial for studying new processes, tissue spatial organisation, and various diseases that feature abnormal spatial organisation within tissues. The ERC-funded SPECOLA project will combine transcriptomics with morphology and spatial structure data to enhance analytical processes. It will also improve the interpretation of imaging-based data, better define cell types and states using AI and statistical models, as well as develop an inferential framework for modelling transcripts from cells and samples.

Objective

In recent years technological advances have made it possible to quantify the mRNA expression of large numbers of genes while preserving the spatial context of tissues and cells. These techniques, collectively known as spatial transcriptomics, are important because key biological processes depend on the physical proximity of cells and the spatial organization of tissues. Furthermore, several diseases are characterized by abnormal spatial organization within tissues.

Despite its early age, spatial transcriptomics is rapidly becoming a widely used tool, complementing single-cell RNA-seq as the tool of choice to study gene expression in complex tissues, e.g. in cancer research and neurobiology. In addition to the gene expression measurements and the spatial localization of transcripts, available data include images collected from the samples that can be used to learn cell-level and tissue-level morphological features. The main objective of this proposal is to combine transcriptomics, spatial structure, and morphology data to better inform key spatial transcriptomics analytical steps.

Specifically, we will:
- Enhance the comprehension of imaging-based spatial transcriptomics data by a characterization of the statistical properties of the data and a mechanistic modeling of the in situ transcriptional measurements.
- Combine imaging and tabular data to better define cell types and states through the use of statistical models and artificial intelligence.
- Develop an inferential framework to model the localization of transcripts within and across cells and of cells within and across samples.

Overall, this proposal will combine machine learning and artificial intelligence approaches with rigorous statistical modeling of transcriptomics data in a spatial, sub-cellular context. This will ultimately serve the biomedical community and provide a suite of tools that will help pave the way towards personalized medicine and computer-assisted pathology.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-COG

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Host institution

UNIVERSITA DEGLI STUDI DI PADOVA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 979 375,00
Address
VIA 8 FEBBRAIO 2
35122 PADOVA
Italy

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Region
Nord-Est Veneto Padova
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 979 375,00

Beneficiaries (1)

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