Forschung

Forschungsgruppe Steuernagel

Die Neurogenomics-Gruppe untersucht die neuronale Heterogenität im Gehirn im Kontext des Stoffwechsels, wobei sie computergestützte Ansätze verwendet und neue Werkzeuge und Ressourcen entwickelt.

Our research addresses three main areas:

1. Mapping the cell type architecture of the hypothalamus and hindbrain

We develop computational methods to analyze OMICS data, particularly single-cell/nucleus and spatial transcriptomics data from multiple species, to define the cellular architecture of key brain regions involved in metabolism, especially the hypothalamus and hindbrain. This includes building multi-modal reference atlases to describe cell type diversity at high molecular and spatial resolution. Using machine learning methods, we integrate, cluster, and annotate OMICS data across different modalities and species to understand the conservation of cell types between humans and model organisms and to explore how gene expression is conserved and regulated in neuronal populations.

2. Effects of genetics and environment on metabolism-regulating cell types

We generate and analyze single-nucleus transcriptomic and epigenomic data from mice subjected to various experimental conditions, studying how both neuronal and non-neuronal cells respond to environmental stimuli. By leveraging human genetic data from rare variant studies and genome-wide association studies (GWAS), we link metabolic traits to specific genes and cell types within key brain regions. This allows us to investigate how genetic variants, especially those in non-coding regions, affect gene regulation in these cell types. We aim to integrate effects of cell type perturbations with genetics to better characterize how specific cell types in the brain are involved in the development of obesity and other metabolic disorders. We then functionally validate these hypotheses in collaborations with our colleagues at the institute.

3. Interpreting and visualizing large-scale OMICS Data

The increasing volume of biological data presents a major challenge for analysis and interpretation. By integrating large-scale datasets across multiple modalities, we aim to unlock the insights contained in individual datasets and the synergies that arise from their combination. For instance, we merge single-cell data with spatial transcriptomics and connectivity mapping, or spatial metabolomics, to gain a deeper understanding of brain function. Data visualization is critical for interpreting complex datasets and generating new hypotheses. To support this, we develop novel tools and approaches to make integrated data more accessible and interpretable, advancing our understanding of the neuronal circuits that control metabolism.