1. Research Background:
RNA metabolism, single cell technologies, statistical data analysis and machine learning, epigenetics
2. Research questions addressed by the group:
- Models of RNA metabolism
RNA and protein homeostasis are essential components of cellular integrity that deteriorate with age. Using RNA labelling techniques, we were able to measure RNA synthesis- and degradation rates at a genome-wide scale [1, 2]. We were further able to measure Polymerase II elongation and initiation speed, which are critical steps in RNA synthesis . We apply our techniques to quantify the parameters of a two-compartment model of the cell (nucleus, cytosol) that includes RNA synthesis, RNA export, and nuclear and cytosolic RNA degradation, gaining intriguing insights into the life cycle of an RNA.
- Neural networks for single cell analyses
Single cell RNA-Seq was the first technology that allow us to monitor gene regulatory processes on the level of the individual cell . Since then, single cell ATAC-Seq was developed to complement this data by information on the chromatin accessibility. However, the simultaneous extraction of both data from the same cell is confronted with significant problems. Based on our experience in neural networks & image analysis , we have developed variational autoencoder networks for the purpose of meaningfully co-embedding single cell RNA-Seq and ATAC-Seq data into the same latent space (unpublished).
3. Possible projects:
1. RNA metabolism
Using our recently established protocol to measure nuclear RNA export, we will target the following questions:
- What is the contribution of nuclear export to RNA quality control? We want to quantify how much RNA does not pass the filter of nuclear export, in relation to RNAs filtered out by nuclear / cytosolic decay.
- Which RNA populations are held back in the nucleus, and why? What are the sequence patterns / folding patterns / protein binding patterns that are recognized by the export machinery?
- How does quality filtering change with age and under environmental changes?
2. Neural networks for single cell analyses
- Maturation of olfactory cells.
Olfactory receptor (OR) genes are a large group of evolutionary conserved genes (ca. 350 in human) that act as chemo sensors, once they are expressed and translocated into the cell membrane. Apart from their primary role in the olfactory epithelium, ORs are also expressed in various other cells and tissues, such as immune cells. Strikingly, most mature OR-expressing cells display exactly one OR receptor, and even among the two copies of one OR gene, only one copy is active. We collaborate with the cardiology department of the university clinic and the genetics department to understand how a cell’s microenvironment influences OR selection, and how specific ORs sense local injuries in the heart. In this project, we will perform spatial transcriptomics in conjunction with single cell RNA-seq of healthy and inflamed heart valves.
- Multi-level single cell analysis
Neural networks are incredibly good at finding patterns in seemingly heterogenous data. We want to exploit their power for the integration of scRNA-Seq, scATAC-Seq and spatial transcriptomics data.
4. Applied Methods and model organisms:
Statistical and Machine learning methods: Neural networks, generalized linear models, support vector machines, hidden Markov models , clustering techniques Dimension reduction techniques: PCA, tSNE, UMAP, canonical correlation analysis Optimization and sampling methods: (Stochastic) gradient descent methods, Expectation-Maximization, variational inference, Markov Chain Monte Carlo
5. Desirable skills and qualifications:
- A background in Statistics / Mathematics / Physics / Computer Science / Computational Biology
- Experience with R, Python, Matlab etc. A high awareness of quality in statistical data analysis (reproducibility, reliability, interpretability)
- Interest in interdisciplinary research, interest in modeling biological processes
- Sun M, Schwalb B, Pirkl N, Maier K, Failmezger H, Tresch A, Cramer P. Global analysis of mRNA degradation reveals Xrn1-dependent buffering of transcript levels. Mol. Cell 2013.
- Miller C, Schwalb B, Maier K, Schulz D, Dümcke S, Zacher B, Mayer A, Sydow J, Marcinowski L, Dölken L, Martin D, Tresch A (co-correspondence), Cramer P. Dynamic transcriptome analysis reveals dynamics of mRNA synthesis and decay in yeast. Molecular Systems Biology, 2011.
- Schwalb, B, Margaux, M, Zacher B, Fühauf K, Demel K, Tresch A, Gagneur J, Cramer P. TT-seq maps the human transient transcriptome, Science 2016.
- Anna Katharina Schlusche, Sabine Ulrike Vay, Niklas Kleinenkuhnen, Steffi Sandke, Rafael Campos-Martin, Marta Florio, Wieland Huttner, Achim Tresch, Jochen Roeper, Maria Adele Rueger, Igor Jakovcevski, Malte Stockebrand. Developmental HCN channelopathy results in decreased neural progenitor proliferation and microcephaly in mice. To appear in PNAS. Preprint on Bioarxiv: doi: doi.org/10.1101/2021.04.24.441237
- Failmezger H, Zwing N, Tresch A, Korski K, Schmich F. Computational Tumor Infiltration Phenotypes Enable the Spatial and Genomic Analysis of Immune Infiltration in Colorectal Cancer. Front Oncol. 2021 Mar 15;11:552331. doi: 10.3389/fonc.2021.552331. PMID: 33791196; PMCID: PMC8006941.
- Zacher B, Lidschreiber M, Cramer P, Gagneur J, Tresch A. Annotation of directed genomic states unveils variations in the Pol II transcription cycle, Molecular Systems Biology 2014.