Mots-Clés

single-cell, multiome, T2D, perturbation, cell atlas

Description

Project:

Type 2 diabetes, or T2D, is a major public health problem worldwide. Over 61 million Europeans live with T2D, that induce more than a million premature death per year. It is characterized by a resistance of cells to insulin, coupled with an inability of the β-cells of the pancreatic islets to produce or release sufficient insulin, inducing a high level of glucose in the blood.

This dysfunction is due to both genetic and environmental factors. Genetic susceptibility factors, in particular, are largely enhancer variants that influence gene regulation in pancreatic islets. Furthermore, the major genes that are mutated in more severe monogenic forms of diabetes frequently encode transcription factors that are important for β-cells. Therefore, altered β-cell transcriptional programs play a crucial role in diabetes.

Currently, our reference maps to study the transcriptional behavior of β-cells are limited to a single average state. Knowledge of pancreatic islet cells facing all possible perturbations, both genetic and chemical, could give new insights into the transcriptional networks of β-cells, and how they are altered in diabetes mellitus. A perturbation atlas of human pancreatic islet transcriptional states could provide key knowledge to interpret transcriptional changes observed in single cells from T2D patients. It will allow the functional annotation of perturbed programs that respond genes to distinct signals, and provide a valuable resource to discover drug candidates that revert abnormal transcriptional states.

The HuPIPA project (Human Pancreatic Islets Perturbation Atlas) aims to establish the first perturbations atlas, exposing primary human pancreatic islets to multiple chemical and genetic perturbations and examine their transcriptional and chromatin landscapes at single cell resolution. This should provide a major reference for the field.

Position and assignment:

The overachieving goal of this internship is the participation in the analysis of the of multi-omics single-cell dataset produced in the HuPIPA project.

This internship can be structured in two ways, depending on the student's profile:
 

• A methodological approach, consisting on the exploration and/or development of bioinformatic tools used to study gene regulation at single-cell levels.
• An applied approach, where the intern will analyze the newly produced dataset, aiming to describe the transcriptional response to β-cells facing various perturbations.

This M2 internship is an interdisciplinary project, involving statistical, computational and biological knowledge.

Profile:

• Currently enrolled in a master’s program in Bioinformatics, Biostatistics, Machine Learning or a related field.
• Strong programming skills in R and/or Python.
• Comfortable using a UNIX environment including bash.
• Interest/experience in genomics application and next-generation sequencing technologies will be highly appreciated.
• Problem-solving skills and the ability to work independently as well as part of a team.
• Proficiency in English.