Summary
Type 2 diabetes (or T2D) is a heterogeneous disease that affects nearly 1 in 10 adults worldwide, directly causing 1.5 million deaths each year. Human genetics and single cell genomic studies show that abnormal pancreatic β-cell transcriptional programs play a crucial role in type 2 diabetes. However, we lack an understanding of how this is translated into impaired β-cell function or mass.
Consequently, existing drug therapies for T2D aim to alleviate hyperglycaemia, but do not target causal mechanisms.
My overarching goal is to understand variation of β-cell transcriptional states.
I postulate that even if an infinite number of perturbations are conceivable, only a finite number of cell states exist. Identifying and characterizing major cellular states, by studying regulatory responses to perturbations could provide insights into T2D pathophysiology, and pave the way for disease-modifying strategies.
The concept of Human Cell Atlas currently depicts the cells landscape under one theoretical average condition, and does not represent the broad range of possible cell states that exist in different conditions. I'll be extending this concept towards the production of a Human Pancreatic Islet Perturbation Atlas (HuPIPA), which has the potential to be the first atlas characterizing cell states dynamics through multiple perturbations. I will harness this resource to unravel T2D mechanisms and to discover candidate therapeutic strategies.
To this end, I will design a large-scale single-cell multiomics experiment of human islets facing chemogenetic perturbations. I will
develop a novel statistical tool for the analysis of single-cell multiomics data under a broad range of conditions, and perform an
integrative analysis with T2D human genetic and single cell datasets.
This project, at the junction of statistics, genetics and computer science, has the potential to provide new knowledge for a better
understanding and treatment of T2D.
Consequently, existing drug therapies for T2D aim to alleviate hyperglycaemia, but do not target causal mechanisms.
My overarching goal is to understand variation of β-cell transcriptional states.
I postulate that even if an infinite number of perturbations are conceivable, only a finite number of cell states exist. Identifying and characterizing major cellular states, by studying regulatory responses to perturbations could provide insights into T2D pathophysiology, and pave the way for disease-modifying strategies.
The concept of Human Cell Atlas currently depicts the cells landscape under one theoretical average condition, and does not represent the broad range of possible cell states that exist in different conditions. I'll be extending this concept towards the production of a Human Pancreatic Islet Perturbation Atlas (HuPIPA), which has the potential to be the first atlas characterizing cell states dynamics through multiple perturbations. I will harness this resource to unravel T2D mechanisms and to discover candidate therapeutic strategies.
To this end, I will design a large-scale single-cell multiomics experiment of human islets facing chemogenetic perturbations. I will
develop a novel statistical tool for the analysis of single-cell multiomics data under a broad range of conditions, and perform an
integrative analysis with T2D human genetic and single cell datasets.
This project, at the junction of statistics, genetics and computer science, has the potential to provide new knowledge for a better
understanding and treatment of T2D.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101152820 |
| Start date: | 01-06-2024 |
| End date: | 31-05-2026 |
| Total budget - Public funding: | - 165 312,00 Euro |
Cordis data
Original description
Type 2 diabetes (or T2D) is a heterogeneous disease that affects nearly 1 in 10 adults worldwide, directly causing 1.5 million deaths each year. Human genetics and single cell genomic studies show that abnormal pancreatic β-cell transcriptional programs play a crucial role in type 2 diabetes. However, we lack an understanding of how this is translated into impaired β-cell function or mass.Consequently, existing drug therapies for T2D aim to alleviate hyperglycaemia, but do not target causal mechanisms.
My overarching goal is to understand variation of β-cell transcriptional states.
I postulate that even if an infinite number of perturbations are conceivable, only a finite number of cell states exist. Identifying and characterizing major cellular states, by studying regulatory responses to perturbations could provide insights into T2D pathophysiology, and pave the way for disease-modifying strategies.
The concept of Human Cell Atlas currently depicts the cells landscape under one theoretical average condition, and does not represent the broad range of possible cell states that exist in different conditions. I'll be extending this concept towards the production of a Human Pancreatic Islet Perturbation Atlas (HuPIPA), which has the potential to be the first atlas characterizing cell states dynamics through multiple perturbations. I will harness this resource to unravel T2D mechanisms and to discover candidate therapeutic strategies.
To this end, I will design a large-scale single-cell multiomics experiment of human islets facing chemogenetic perturbations. I will
develop a novel statistical tool for the analysis of single-cell multiomics data under a broad range of conditions, and perform an
integrative analysis with T2D human genetic and single cell datasets.
This project, at the junction of statistics, genetics and computer science, has the potential to provide new knowledge for a better
understanding and treatment of T2D.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
22-11-2024
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