Nicolas Battich Lab
Quantitative Cell BiologyNicolas´lab is interested in the strategies that cells use to control gene expression levels and variability during differentiation.
Nicolas´lab is interested in the strategies that cells use to control gene expression levels and variability during differentiation.
About our research
We are interested in the strategies that cells use to control gene expression levels and variability during differentiation. The links between the dynamic nuclear structures and transcriptional changes are a focus of our lab. The compartmentalization of the nucleus into several functional condensates, so-called membrane-less organelles, contributes to transcriptional regulation. We study how different compartments in the nucleus spatially organize the genome and how this impacts gene expression throughout development.
We develop cutting-edge quantitative methods for single-cell genomics and combine them with cellular phenotyping using high-content confocal microscopy. We design data-driven approaches, we build quantitative predictive models and use state-of-the-art machine-learning to gain insights into the cellular processes we study.
- 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.
Helmholtz Munich | Nicolas BattichSome 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.
Helmholtz Munich | ©Nicolas BattichDuring differentiation nuclear MLOs are dynamic. One key question to address is the functional link between these dynamics and the changes in the gene regulatory networks and transcriptional output observed as cell differentiate. Are the dynamics of MLO states a consequence of transcription and chromatin organization, or do MLOs actively regulate the temporal dynamics of transcription?
Our Technologies
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.
Genetic perturbations: 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.
