Periodic Reporting for period 1 - PEPS (PErPetuating Stemness: From single-cell analysis to mechanistic spatio-temporal models of neural stem cell dynamics)
Reporting period: 2023-06-01 to 2024-11-30
Neural stem cells (NSCs) in the brain help generate new brain cells throughout life, supporting the brain's ability to adapt, grow, and repair itself. These cells can turn into both neurons (the brain's messaging cells) and other types of brain cells, a process that is essential for brain health. However, scientists still don’t fully understand how these stem cells know when to stay as stem cells or transform into specialized brain cells.
We believe that NSCs interact with each other in a coordinated way to control the output of the entire group. To decode their organizing principles, we’re launching a research project that will examine their interactions over time and in different locations within the brain. By combining experimental approaches to generate multidimensional data with in silico statistic analysis and mathematical models, we hope to reveal how this stem cells ensemble functions over time and how their actions can be modulated.
Our research will focus on three main goals:
1. Tracking the life path of individual stem cells to understand their behavior.
2. Studying how cells work together and communicate within their environment.
3. Understanding how groups of stem cells act together across larger areas of the brain and over time.
We’ll use new tools and methods to look closely at how individual stem cells behave, and we’ll work with two different animal models — the mouse and zebrafish — which share a similar lifespan and yet exhibit diffential behaviour leading to different output over time.
In the end, our research will provide a better understanding of how stem cells stay "young" and maintain their ability to create new brain cells. It will also offer new techniques that can be used to study other cellular systems exhibiting cellular transitions such as stem cell ensemble in other tissues, or the immune system. This study will potentially reveal strategies to keep the function of stem cells in the old brain or regenerate it follwing brain injuries or neurodegenerative disorders.
We believe that NSCs interact with each other in a coordinated way to control the output of the entire group. To decode their organizing principles, we’re launching a research project that will examine their interactions over time and in different locations within the brain. By combining experimental approaches to generate multidimensional data with in silico statistic analysis and mathematical models, we hope to reveal how this stem cells ensemble functions over time and how their actions can be modulated.
Our research will focus on three main goals:
1. Tracking the life path of individual stem cells to understand their behavior.
2. Studying how cells work together and communicate within their environment.
3. Understanding how groups of stem cells act together across larger areas of the brain and over time.
We’ll use new tools and methods to look closely at how individual stem cells behave, and we’ll work with two different animal models — the mouse and zebrafish — which share a similar lifespan and yet exhibit diffential behaviour leading to different output over time.
In the end, our research will provide a better understanding of how stem cells stay "young" and maintain their ability to create new brain cells. It will also offer new techniques that can be used to study other cellular systems exhibiting cellular transitions such as stem cell ensemble in other tissues, or the immune system. This study will potentially reveal strategies to keep the function of stem cells in the old brain or regenerate it follwing brain injuries or neurodegenerative disorders.
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).
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).
Workpackage 1.1: Time machine and statistics
Most notably, combining DCM technology with the scNMT protocol provided a novel method to trace a cell’s history at the single-cell level—something not possible previously.
Additionally, we demonstrated that DCM methylation marks are highly specific and accurate, allowing us to identify different cell types and cluster cells based solely on their DCM methylation profiles.
Most notably, combining DCM technology with the scNMT protocol provided a novel method to trace a cell’s history at the single-cell level—something not possible previously.
Additionally, we demonstrated that DCM methylation marks are highly specific and accurate, allowing us to identify different cell types and cluster cells based solely on their DCM methylation profiles.