Prof. Dr. Achim Tresch

Research Area: Computational Biology, RNA Biology

Website: http://imsb.uni-koeln.de/bioinformatik.html

1. Research Background:

RNA metabolism, single cell technologies, statistical data analysis and machine learning, epigenetics

2. Research questions addressed by the group:

  1. 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 [3]. Our goal is to explain the mechanistic details of the transcription process, including initiation, elongation, stalling, drop-off, and termination.
  2. Image analysis
    In a collaboration with the radiology department of the university clinic, we design novel neural network architectures for the analysis of 3D breast images obtained by magnetic resonance imaging (MRI). We will develop a diagnostic decision support system that enhances the detection of malignant lesions in mammography screens.
  3. Spatio-temporal 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, it is virtually impossible to obtain both data from the same cell without compromising the information content of the respective measurements. Therefore, we aim at developing computational methods that allow us to match scRNA-Seq data with scATAC-Seq data from cells of the same kind in silico. We exploit additional information from almost single cell, spatial expression patterns obtained by the novel Visium technology.

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. Image analysis
    Based on multichannel fluorescent microscopic images, we want to derive clinically relevant scores that predict immune response and response to various therapies. Ideally, we want to develop a molecular stratification of patients. This project involves an industrial partner.
  3. Multilevel single cell analysis of olfactory receptor expression
    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. Intriguingly, 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.

4. Applied Methods and model organisms:

We develop models for genome annotation (e.g., hidden Markov models [4]), automated image analysis (e.g., neural networks, factor graphs [5]), time series analysis (e.g. recurrent neural networks, wavelets), gene network reconstruction (probabilistic graphical models), clustering of single cell data (e.g., tSNE and related methods), and the parameter estimation of RNA metabolism (e.g. MCMC algorithms).

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

6. References:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Niederberger T, Failmezger H, Uskat D, Poron D, Glauche I, Scherf N, Roeder  I, Schroeder T, Tresch, A. Factor graph analysis of live cell imaging data reveals mechanisms of cell fate decisions, Bioinformatics 2015.