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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences neurobiology
- medical and health sciences basic medicine pathology
- medical and health sciences health sciences personalized medicine
- natural sciences mathematics applied mathematics statistics and probability
- natural sciences computer and information sciences artificial intelligence machine learning
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-COG
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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.
35122 Padova
Italy
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