1. Research Background

Obesity is a major health risk and affects an increasing population world-wide. Over the past decades, the brain, and specifically the hypothalamus and hindbrain, have been established as key regulators of energy homeostasis and food intake, highlighting the brain’s importance in the development of obesity. Energy-state correlated hormones such as leptin, which is secreted by adipocytes, reach leptin-receptor expressing neurons in the brain, which then control satiety and hunger. Mutations in key genes expressed in neurons involved the leptin signaling pathway such as the melanocortin-circuit in the hypothalamus, comprising e.g., Pro-opiomelanocortin (POMC) and Agouti-related peptide (AgRP) neurons, have been shown to cause severe obesity in humans. In recent years we and others have demonstrated that the hypothalamus comprises a vast number of different neuronal cell types, many of which play a role in energy homeostasis, behavior or other bodily functions, emphasizing the importance to characterize neuronal circuits beyond the established models. Our newly founded research group employs computational biology and bioinformatics methods to study the brain's role in regulating metabolism and energy balance. Our work focuses on characterizing the spectrum of neuronal cell types in the hypothalamus and hindbrain at the transcriptomic, epigenomic, metabolomic, and spatial level. We generate perturbation data sets to explore how different conditions, such as feeding state or obesity affect neurons in these brain regions and we work closely with other groups at the MPI for Metabolism Research to translate in-silico findings back into mechanistic biology, testing the hypotheses we derive from these large biological data sets.

2. Research questions addressed by the group:

Cell type architecture of the hypothalamus and hindbrain  
We develop multi-modal reference atlases to describe cell type diversity in the hypothalamus and hindbrain 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 different neurons. We are also interested in adapting and generating molecular connectomics data to better map how these different molecularly defined cell types are connected with each other and to close the gap to functional definitions of neuronal cell types such as their electrophysiological properties and their synaptic connections. To increase accessibility of our results, we develop novel visualization tools and approaches to make integrated multi-modal data easier to interpret.

Effects of genetics and environment on metabolism-regulating cell types
We generate and analyze single-cell/nucleus transcriptomic and epigenomic data from mice subjected to various experimental conditions, studying how both neuronal and non-neuronal cells respond to environmental stimuli related to metabolism such as diet, feeding state or food perception. We aim to link human genetics data from rare variant studies and genome-wide association studies (GWAS) with our cross-species atlases to gain more mechanistic understanding of the effects of these variants in neurons and to prioritize both neuronal cell types, as well as, specific genes and their associated pathways and explore how these overlap with the cell types and pathways perturbed by experimental conditions. Utilizing cell type-specific epigenetic data we investigate how genetic variants, especially those in non-coding regions, affect gene regulation.

3. Possible project(s):

1. Cross-species integration of single-nucleus RNAseq and ATAC data
We have generated human single-nucleus transcriptomic data from hypothalamus and hindbrain and conducted initial cross-species comparison with our murine reference atlases. However, these comparisons can be vastly extended and improved on: evaluating and developing methods for both cell type integration and systematic comparison of gene expression and gene regulation across species is critical and will be a central part of this research project, along with putting the outcomes into the context of metabolism research, e.g., how hormonal sensing evolved and how it differs between species.

2. Studying molecular changes related to feeding state in the hypothalamus and hindbrain.
We have generated extensive snRNAseq data to identify activated neuronal cell types and explore transcriptomic changes upon different conditions (Fed - Fasted - Refed), which can further be integrated with publicly available data sets to systematically describe the molecular changes upon feeding state transitions in the hypothalamus. This involves detailed in-silico analysis of gene expression and molecular pathway changes, as well as perturbations of cell-cell signaling. Ultimately, we aim to link molecular changes to functionally relevant cell types and confirming these results in-vivo to derive new knowledge on the neuronal control of food intake and energy metabolism.

3. Connectivity mapping of murine neurocircuits using computational data integration and molecular connectomics.
With this project we (a) aim to integrate publicly available tracing-based transcriptomic and epigenomic data sets into our existing atlases of hypothalamus and hindbrain to explore how transcriptomically defined neuronal cell types are connected to other neurons. And (b) we aim to establish and develop new approaches for molecular connectomics and use this newly generated data to map complex neurocircuits in the control of metabolism.

4. Applied Methods and model organisms:

Methods:

• OMICs data analysis with a focus on the brain and neurons
• Single cell/nuclei & spatial transcriptomics
• Single-nuclei chromatin studies & analysis
• Human genetics
• Method development for single-cell and spatial data integration, clustering and visualization

5. Desirable skills and qualifications:

We seek a motivated PhD student interested in quantitative research and single-cell OMICS analysis, who wants to join a young, international team. A background or prior experience in bioinformatics, computational biology or systems biology will be advantageous.

6. References and key publications:

Human HYPOMAP: A comprehensive spatio-cellular map of the human hypothalamus.

John A. Tadross*, Lukas Steuernagel*, Georgina K.C. Dowsett*, Katherine A. Kentistou, Sofia Lundh, Marta Porniece-Kumar, Paul Klemm, Kara Rainbow, Henning Hvid, Katarzyna Kania, Joseph Polex-Wolf, Lotte Bjerre-Knudsen, Charles Pyke, John R. B. Perry, Brian Y.H. Lam, Jens C. Brüning & Giles S.H. Yeo

Nature, In press, (2025)

Reciprocal activity of AgRP and POMC neurons governs coordinated control of feeding and metabolism

Alain J. De Solis, Almudena Del Río-Martín, Jan Radermacher, Weiyi Chen, Lukas Steuernagel, Corinna A. Bauder, Fynn R. Eggersmann, Donald A. Morgan, Anna-Lena Cremer, Michael Sué, Maximilian Germer, Christian Kukat, Stefan Vollmar, Heiko Backes, Kamal Rahmouni, Peter Kloppenburg & Jens C. Brüning

Nature Metab 6, 473–493 (2024).

HypoMap—a unified single-cell gene expression atlas of the murine hypothalamus.

Lukas Steuernagel*, Brian Y. H. Lam*, Paul Klemm, Georgina K. C. Dowsett, Corinna A. Bauder, John A. Tadross, Tamara Sotelo Hitschfeld, Almudena del Rio Martin, Weiyi Chen, Alain J. de Solis, Henning Fenselau, Peter Davidsen, Irene Cimino, Sara N. Kohnke, Debra Rimmington, Anthony P. Coll, Andreas Beyer, Giles S. H. Yeo & Jens C. Brüning

Nat Metab 4, 1402–1419 (2022).