Bioinformatics/computational biology pipeline

A wide range of modern technologies allow in-depth dissection of biological processes on multiple levels such as global high-throughput sequencing analysis. While data generated from such methods are extensive and provide a major resource in gaining new findings, their handling requires special dedication and focus. To achieve the most efficient extraction of meaningful data, in cooperation with our collaboration partners we established several analysis pipelines, including those for:

  • Exome/Methylome/ATAC/RNA sequencing data
  • Proteomic data
  • Single-cell RNA sequencing (scRNAseq) data
  • Integration of different scRNAseq data sets
  • Processing of large patient data sets including genomic and transcriptional data

 

 

Profilbild von M.Sc. Chantal Allgöwer

M.Sc. Chantal Allgöwer

PhD Student

Kontakt
Profilbild von jun.-Prof. Dr. rer. nat. Markus Breunig

jun.-Prof. Dr. rer. nat. Markus Breunig

Kontakt
Profilbild von Dr. med. Michael Melzer

Dr. med. Michael Melzer

Clinician Scientist

Kontakt
Schematic overview of methods including analysis of RNA, DNA, single cell RNA, and ATAC sequencing. In the middle of the figure, a Venn diagram is shown, depicting integration of the differently generated data sets

 

 

Collaboration partners:

Prof. Dr. Ivan G. Costa

Institute of Computational Genomics, University Hospital RWTH Aachen

Prof. Dr. Medhanie Mulaw

Central Unit Single Cell Sequencing, Medical Faculty, Ulm University, Ulm, Germany

Research topics

Only recently we published our work investigating the relevance of ONECUT1 in developing diabetes. Using pluripotent stem cell-based knockout models we could dig into the mechanism on how ONECUT1 shapes chromatin profiles and gene regulatory networks during embryonic development in pancreatic progenitor cells. (Heller, …, Kleger. 2021, Communications Biology; Philippi, …, Kleger. 2021, Nature Medicine)

A time line of pancreatic progenitor (PP) differentiation is shown, starting with pluripotent stem cells (PSC) on day 0, to definitive endoderm (DE) on day 3, to gut tube endoderm (GTE) on day 6, to pancreatic endoderm (PE) on day 9 and PP on day 13. For each stage, schematic symbols represent the performed analysis methods. ATACseq was performed for all time points. RNAseq was applied for PSCs, DE stage, PE stage and PP stage. Chip-seq was done in the later stages at GTE, PE and PP. On the right, a Venn diagram is shown, presenting transcription factors identified via Chip-seq within the different conditions
Stage-specific ATACseq, RNAseq and Chip-seq analysis of pancreatic differentiation

Many of our projects focus on embryonic disorders and critical developmental events. Recently, we performed a thorough single-cell RNA-based characterization of pluripotent stem cell-derived pancreatic progenitors to understand their trilineage potential. By receptor-ligand analysis in different subpopulations of PPs we could demonstrate the importance of cellular crosstalk for forward programming and lineage entry as well as similarity to different cell populations in the fetal pancreas. (Merz, …, Kleger. 2023, Theranostics, in press)

On the left-hand side, a UMAP with distinct clusters resulting from scRNAseq analysis of pancreatic progenitors is shown. The scRNAseq data was further processed by ligand-receptor analysis, which is schematically represented by a circle plot and a hierarchical plot, showing possible cell-cell interactions
Single cell analysis of pancreatic progenitors followed by ligand-receptor analysis to predict possible cell-cell interactions.