Projects

Islet Cartography

The Islet Cartography project will shed new light on the regulatory and functional architecture of the human pancreatic Islets of Langerhans to further understand how it contributes to health and disease, most notably to diabetes. The Islet Cartography project aims to collect, integrate, and jointly analyze highly resolved molecular datasets from multiple modalities with new data science methods to build a comprehensive map of the human islet. The map will be made publicly available for other researchers to pave the way for new insight that could lead to the prevention, diagnosis, and treatment of diabetes and other pancreas-related diseases.

Human Gene Regulatory Map

MadLab is a partner in the Human Gene Regulation Map (HGRM), a part of the new Novo Nordisk Foundation Center for Genomic Mechanisms of Disease (NNFC). HGRM aims to build comprehensive maps of gene regulation in the human genome as a resource for uncovering biological mechanisms on common genetic diseases, with an initial focus on cell types relevant to cardiometabolic diseases including type 2 diabetes. We aim to combine recent advances in genomics, molecular biology, single-cell analysis, and stem cell models with computational biology and machine learning approaches to identify fundamental rules of gene regulation.

ATLAS

MabLab is a partner in the Center for Functional Genomics and Tissue Plasticity (ATLAS). ATLAS aims to obtain a systems-level, mechanistic, and cell type-resolved understanding of adipose and hepatic tissue plasticity in response to diet-induced obesity and regression in mouse models; and to translate this for in-depth understanding of the functional changes in human adipose and hepatic tissues in response to severe obesity and reversal. The center applies a combination of functional genomics approaches, mouse models, proteomics, in vivo targeting, computational biology, and clinical studies.

ADIPOSIGN

MabLab is a partner in the Center for Adipocyte Signaling (ADIPOSIGN). ADIPOSIGN aims to obtain deep insight into how fat cells receive and respond to signals at the level of the (epi-)genome and the cell membrane. Specifically, we aim to understand how the signaling states of adipocytes depend on biological context, such as gender, depot, and genetic variation. To achieve those aims, the center applies a combination of functional genomics approaches and computational biology.