Figure 1: scTRIP (for single-cell tri-channel processing) leverages strand-specific sequencing (Strand-seq) to computationally integrate read depth, DNA strand and haplotype-phase

Figure 1: scTRIP (for single-cell tri-channel processing) leverages strand-specific sequencing (Strand-seq) to computationally integrate read depth, DNA strand and haplotype-phase, in order to enable the scalable discovery of SVs in single cells, including copy-number variations, inversions, translocations and complex DNA rearrangements such as chromothripsis events (Sanders et al., Nat Biotechnol 2020).

Figure 2: Three-hit-process resulting in bi-allelic ELP1 and PTCH1 loss (Waszak et al., Nature 2020).

Figure 2: Three-hit-process resulting in bi-allelic ELP1 and PTCH1 loss (Waszak et al., Nature 2020).

The Korbel group combines computational and experimental approaches, including in single cells, to unravel determinants and consequences of germline and somatic genetic variation with a special focus on disease mechanisms.

Genetic variation studies have uncovered that genomic structural variants (SVs) such as deletions, insertions, and inversions account for most varying bases in human genomes. Recent studies indicate that somatic SVs occur post-zygotically throughout our lifespan, and show association with ageing and human diseases - calling into question the long-held belief that the genome is largely static within an individual, and preserved across all cells therein.

We employ a diversity of omics and imaging approaches - from single-cell multi-omics to spatial and bulk-cell omics as well as state-of-the-art microscopy - to investigate molecular mechanisms behind complex human phenotypes associated with genetic variants.

In addition to experimental methodologies applied to tissues and organoids, our laboratory is devising data science techniques including state-of-the-art machine learning methods for processing high-dimensional single-cell data sets, and for coupling genetic variation discovery with molecular and clinical phenotype data.

Previous and current research

Of particular interest is understanding patterns of genetic mosaicism at cellular resolution. Our scTRIP method (Sanders et al., Nat Biotechnol 2020, Fig. 1) enables the direct detection of SV mutational processes in single cells, and as such can be used to obtain insights into pathomechanisms acting in human tissues.

Another interest centres around uncovering commonalities and differences between molecular disease mechanisms in disparate cancer entities. In a rare-variant association study in medulloblastoma (MB) genomes/exomes, we recently described rare germline loss-of-function variants in the Elongator complex protein 1 (ELP1) gene in 15% of childhood MB genomes driven by Sonic hedgehog signalling (Waszak et al., Nature 2020). ELP1-associated MBs exhibit somatic loss of the wild-type ELP1 allele mediated by somatic large deletions that concomitantly cause loss of the PTCH1 gene residing adjacent to ELP1 on chromosome 9, involving an intriguing ‘three-hit’ molecular process (Fig. 2).

With respect to data science, our group has pioneered the utilisation of cloud computing to enable the global sharing and processing of large-scale biological data. We co-initiated and co-led the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project, an international study for sharing cancer genomes. Our group is also actively involved in building the German Human Genome-Phenome Archive (GHGA), a national research data infrastructure for disseminating and federating human genomics data from German studies nationally and internationally.

Future projects and goals

  • Single-cell multi-omics: coupling patterns of human genetic and epigenetic mosaicism at cellular resolution.
  • Dissecting SV formation processes driven by mitosis-associated genome instability. 
  • Completion of human genome variation maps using strand-specific and single-molecule DNA sequencing techniques.
  • Uncovering principles of genetic and functional heterogeneity at 3D resolution by coupling spatial omics, single-cell omics, and bioimaging.
ERC INVESTIGATOR