We currently focus on:
- Breast cancer endocrine therapy resistance development (EU H2020 EpiPredict consortium coordinated by Verschure)
- UV-induced damage and repair (Zon-MW TOP)
- Age determination for forensic applications (collaboration NFI and CLHC).
Conceptual principles are tested to determine the contribution of epigenetic plasticity in the development of particular cell subpopulations. Such cell populations with potential selective advantage or unwanted behavior, enable phenotypic diversification.
We use a combined experimental-computational approach together with epigenetic editing (CRISPR/dCas, Zinc finger-based) and development of engineered mammalian cells, to obtain cause-consequence relationships. Technologies of use are quantitative single cell biochemistry and (super)resolution microscopy, i.e. single cell RNA-seq, single molecule RNA FISH, ChIP-seq, DNAme bisulfite MPS as well as stochastic simulation and machine learning.
Future activities concern quantitative interpretation of single cell genome-wide, real-time and single molecule measurements to explain non-trivial noise, transcription bursting and epigenetic state switching. We will consolidate our approach to map-out various mechanisms through which epigenetic transcription regulation derived heterogeneity explains cell decision-making. We will further build on our strong (inter)national collaborative network and renew funded collaborative initiatives. Verschure (EpiPredict) organized the international conference 'Systems epigenetics: Towards precision cancer medicine' 2018 (Royal Tropical Institute, Amsterdam) that created ample opportunities for new collaborative funding initiatives.
Within NWA route Origins of Life/Origins Center a consortium initiative is started by Verschure and Tans (AMOLF) to interpret the role of cellular diversity in cell survival upon altered environmental conditions comparing organisms with different complexity (bacteria, plants, human).
► We illustrated with a conceptual approach using engineered mammalian cells and computer modeling the role of transcription variability in a defined genomic context [Kempe et al. 2015]. We showed that the precise relationship between transcript number and cell volume sets transcriptional stochasticity and that mRNA statistics is gene location dependent. Stochastic simulations pointed out that chromatin connectivity introduces fast epigenetic state switching along a single gene [Anink-Groenen et al. 2014]. Together with collaborators of Hubrecht and UMCU we used epigenetic targeting and biochemical chromatin capture determining genomic interactions, to demonstrate that physical gene repositioning per se is insufficient to alter the epigenetic chromatin state and gene activity in embryonic stem cells [Wijchers et al.2016]
► We determined the role of transcription cell-to-cell variability driving development of cell subpopulations with a defined phenotypic outcome using quantitative smRNA FISH measurements [Beckman et al. 2018]. We showed that transcript levels of the CYP19A1 gene in ERa+ breast cancer cells are upregulated in a potentially epigenetically altered cell population acting as a selective mechanism of resistance development in ERa metastatic breast cancer [Magnani et al. 2017]
► We demonstrated the causal relationship between changes in genome-wide transcription and aggregate formation in early-state Huntington's disease development [van Hagen et al. 2017]
► We highlighted the consequences of artificial reproduction technologies on the epigenome and its impact on imprinting disorders such as Angelman and Beckwith-Wiedemann syndrome [Hoeijmakers et al. 2016].
► In collaboration with the Dutch Forensic Institute we developed an epigenetic-based tool to determine the chronological age of human individuals. We showed that this epigenetic forensic assay based on machine learning in combination with precision DNA methylation next generation sequencing is able to predict the age of an individual in blood with a variance of approximately 3-4 years [Naue et al. 2017]. We determined that the age prediction is also applicable to analyze other forensic relevant tissues (such as brain, muscle, buccal swaps and bone) [Naue et al. 2018]. In a conceptual simulation study we pointed out that biological variability in DNA methylation plays an important role in predicting chronological age when analyzing samples of minimal DNA amount (i.e. single cells) [Naue et al. 2018].