Transcription in single cells
Most cellular processes involve the action of hundreds of different molecular species, and transcription is not an exception. Due to this complexity, many genes are transcribed in a discontinuous stochastic manner in single cells. Periods of active transcription are followed by gaps in which few transcripts are generated. This process is described by the two-state model of transcription. We study how these stochastic dynamics are coordinated at the genome-wide level in the context of nuclear compartmentalization and throughout differentiation.
Membraneless organelles during differentiation
The organization of the genome into higher-order chromatin structures as well as its spatial organization within the nucleus, for instance the dynamic positioning of genes relative to each other, influences transcriptional activity. Recent evidence suggests that the compartmentalization of the nucleus into functional condensates, so-called membraneless organelles (MLOs), contributes to transcriptional regulation.
Some MLOs form by liquid-liquid phase separation in the cytoplasm and nucleoplasm of eukaryotic cells and have been tightly linked to different aspects of RNA biogenesis and metabolism. In the nucleus, the most prominent MLOs are nucleoli, which are dedicated to the transcription and biogenesis of ribosomal RNA. Nuclear speckles (NSs) were originally linked to mRNA splicing since they were found to be enriched in splicing factors (e.g. SC35), pre-mRNA splicing metabolism and export factors. Recently it has been shown that NSs interact with active genes suggesting they might be hubs for pre-mRNA synthesis as well as metabolism. Transcriptional regulators are also enriched in PML (promyelocytic leukemia) nuclear bodies (PML-NBs). PML-NBs additionally contain proteins involved in DNA damage response and apoptosis, seem to control the balance between cell cycle progression and differentiation.
During differentiation, the morphology and number of several nuclear MLOs have been shown to change. For example, during differentiation of human embryonic stem cells the number of PML-NBs increases and their morphologies change. However, it is unknown if and how the interplay between PML-NBs, cell cycle dynamics, and transcriptional oscillations control lineage commitment in different systems.
Similar to PML-NBs, the morphology of NSs changes during differentiation, for instance in human embryonic stem cells or during differentiation of photoreceptors. Although morphological changes of NSs during differentiation have been described, it is not understood how their homeostasis affects the transcriptional programs that drive differentiation.
One key question to address is whether the described dynamics of MLO states are a consequence of the dynamics of transcription and chromatin organization, or if the formation of MLOs can actively regulate the temporal dynamics of transcription.
Single-cell genomics with temporal and spatial resolution.
We develop technologies to measure the temporal and spatial dynamics of transcription using single-cell sequencing platforms and single molecule imaging.
To investigate the function of different genetic elements in driving the structure of the genome and the dynamics of transcription we employ genetic perturbation methods such as CRISPR interference and activation and well as RNAi.
Deep-learning applied to single-cell biology.
We are keen in developing and applying deep learning methods to process imaging data sets as well as learning biological meaningful representations of single-cell sequencing data. We are working towards AI methods that extract interpretable parameters from large scale data-sets.