Workpackage 1.1: Time machine and statistics
During the first half of this funding period, we successfully adapted the DCM-TM system for neural stem cells and optimized the protocol for scNMT, enabling us to track both past and present transcriptomes at the single-cell level rather than in bulk.
Using in vitro cell culture systems, we fine-tuned the dose and duration of doxycycline administration to maximize DCM methylation signals. A key achievement was defining the optimal doxycycline concentration and timing to achieve the best signal-to-noise ratio, allowing us to distinguish real DCM methylation from background noise.
Our latest experiment involved treating subventricular zone explants with doxycycline for three days, followed by methylome and transcriptome analysis without a chase period. A promising outcome was the ability to differentiate neural cell types based solely on their DCM methylome.
Workpackage 2.1: Barcoded connectome
To track the interaction of neural stem cells with their neighbors, we leverage the exosomal trafficking between cells for barcode delivery. Two strategies are being tested for barcode transfer and donor-acceptor labeling. The first strategy involves protein cargo and the second RNA cargo. To benchmark both strategies, we needed to clone the different constructs and set up a double reporter mouse line to be used to distinguish between donor and acceptor cells. In the first place we are testing transfer of a Cre- and Dre-recombinases. In parallel to the cloning and production of the different construct we are testing different delivery systems for in vitro and in vivo transduction of adult v-SVZ neural stem cells.
Workpackage 1.2:
--> Mathematical framework for measure structured population models on metric spaces: (1) Development of a mathematical theory to describe models with structure on metric spaces (based on measure theory applied to partial differential equations; collaboration with the group of Piotr Gwiazda, IMPAN, Poland). Preparing publication (Düll et al., Math. Models Methods Appl. Sci., 34:109-143, 2024). (2) Development of a theoretical approach to a numerical method for structured population models on a metric space. Convergence of the Bayesian method for parameter estimation. Preparation of publication (to be submitted)
--> Transcriptomically-structured population models (collaboration between the AMC, AMV and SA groups): Construction of the first transcriptomically-structured population model and its well-posedness analysis.
Workpackage 2.2:
--> Mathematical models of NSC system dynamics in the mouse SVZ (collaboration between the AMC and AMV groups): (1) Construction of nonlinear models to identify systemic feedbacks. Analysis of the models in terms of their dynamics and stability. Model simulations and comparison with experimental data on wild-type and perturbed neurogenesis for model identification (identification of non-linear feedbacks). Implementation of various parameter estimation methods (in collaboration with the group of E. Kostin, Numerical Optimisation, IWR, University of Heidelberg). Preparation of publication (to be submitted) (2) Construction of stochastic models to explore potential information contained in observed data fluctuations
--> Mathematical models for NSC system dynamics in zebrafish (collaboration between the AMC and LBC groups): (3) Construction of preliminary models.
Workpackage 3.3:
--> Computational framework for parameter estimation in models coupling different structures (collaboration of AMC with numerical and statistical analysts Robert Scheichl, Heidelberg University and Heikki Haario, Laperantha University, Finnland): Development of a statistical approach for parameter estimation for PDE models with heterogeneity of outcome. Test implementation for a reaction-diffusion system of chemical reactions. Preparation of publication (to be submitted).
