Genetic variation studies from us and others 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 aging and several human diseases – throwing into question the long-held belief that the genome is largely static within an individual, and preserved across all cells therein. Our lab is developing and utilising experimental, computational and data-driven methods to study the formation and selection of germline and somatic genetic variation in health and disease with an emphasis on SVs. We employ a diversity of omics and imaging approaches – from single cells, spatial and bulk omics to 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 and multi-omics for processing high-dimensional single cell data sets, coupling genetic variation disovery with chromatin accessibility and transcriptomic patterns. We are leveraging long-read sequencing, as well as Strand-seq, a technique allowing the scalable discovery of a wide variety of SV classes including complex rearrangements by single cell sequencing (Sanders et al. Nat Biotechnol 2020; Figure 1). Scientists in our group combine methods development, application, data generation and analysis with hypothesis generation and experimental testing to obtain insights into human biology.

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).

Previous and current research

One particular interest of our laboratory is to understand patterns of genetic mosaicism at cellular resolution. Our recently developed scTRIP method (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 of our group centers around uncovering commonalities and differences between molecular disease mechanisms in disparate cancer entities. Recently, in a rare variant association study across 1,100 medulloblastoma (MB) genomes/exomes, we discovered and replicated rare germline loss-of-function (LoF) variants in the Elongator Complex Protein 1 (ELP1) gene in 15% of childhood MB genomes driven by Sonic hedgehog (SHH) 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 process” (Figure 2).

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

Our laboratory has been a pioneer in the utilization of cloud computing to enable the global sharing and processing of large-scale biological data. Particularly, we co-initiated and co-led the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project, an international study for sharing cancer genomes to enable pan-cancer analyses (  Our group is also actively involved in building the German Human Genome-Phenome Archive (GHGA), a national research data infrastructure with the mandate to assist with disseminating human genomics data from German studies nationally and internationally.

Future projects and goals

  • Investigation of 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