Physics and Data-Based Modeling of Cellular Identity Changes
Scialdone LabAbout our Research
We aim to understand the molecular bases of cell identity changes by combining single-cell and spatial omics data analysis with physical modeling.
In our work, we extract biologically relevant information from single-cell data with new machine learning methods and incorporate it into physical models, which guide data interpretation and experiment design.
Our goal is to decode the general principles behind cellular plasticity at
1. the single-cell level, by looking at the interaction between gene expression and chromatin spatial organization during cell identity changes
2. the inter-cellular level, by dissecting the role of cellular communication in collective cellular decision making.
We use several model systems, including mouse, X. laevis,and human, working in synergy with experimental labs.
Cells in the embryo have the remarkable ability to self-organize in complex spatial patterns. One of the first patterns to form determines where the anterior and the posterior region will be, and its establishment is controlled by a set of cells that migrate together and mark the anterior region.
We analyze single-cell multi-omic data and, based on these, develop physical models to get insights into the mechanisms underlying the formation of the anterior-posterior axis in mouse embryos. This will help us understand how embryos get their shape right and how cellular signaling induces the ‘right’ cell identity.
The coexistence of myriad cells in multi-cellular organisms is not always peaceful: cells with higher fitness eliminate cells with lower fitness through a process called cell competition, whose functioning is still largely unknown, especially in mammals.
We combine computational and modeling approaches to understand the molecular bases of cell competition: what sets winners apart from losers? How do cells communicate their fitness status with each other? Answering these questions can shed light on what happens when things go wrong, e.g., in embryos, leading to miscarriages, and in adults, leading to cancer.
Cell migration is crucial for the functioning of our bodies. For example, it occurs during embryogenesis, organogenesis, and angiogenesis. Migration can occur in response to a gradient of signaling molecules, and cells can improve their ability to measure gradients by clustering together and communicating: How do these cells coordinate and move together? How do they “talk” to each other and exchange information to adopt different identities based on their positions?
To answer these questions, we use our recently developed mathematical framework to analyze any specific cellular geometry and tissue size and find which type of cellular communication is more effective to measure signaling gradients. In this context, we aim to identify the mechanisms of cellular communication and predict the properties of the molecules that mediate communication.
Odors are detected by a specialized set of neurons called Olfactory Sensory Neurons (OSNs). In mice, there are ~1000 different OSNs sub-types, each expressing a single allele of an olfactory receptor (OR) gene out of thousands available in the genome. Interestingly, the OSN sub-types are positioned in stereotypic areas of the olfactory epithelium called “zones”, whose function is unknown.
Using spatial transcriptomics, we recently built the first 3D spatial transcriptomic atlas of the mouse olfactory mucosa and uncovered a functional logic behind the spatial distribution of OSN sub-types.
Cells in the embryo have the remarkable ability to self-organize in complex spatial patterns. One of the first patterns to form determines where the anterior and the posterior region will be, and its establishment is controlled by a set of cells that migrate together and mark the anterior region.
We analyze single-cell multi-omic data and, based on these, develop physical models to get insights into the mechanisms underlying the formation of the anterior-posterior axis in mouse embryos. This will help us understand how embryos get their shape right and how cellular signaling induces the ‘right’ cell identity.
The coexistence of myriad cells in multi-cellular organisms is not always peaceful: cells with higher fitness eliminate cells with lower fitness through a process called cell competition, whose functioning is still largely unknown, especially in mammals.
We combine computational and modeling approaches to understand the molecular bases of cell competition: what sets winners apart from losers? How do cells communicate their fitness status with each other? Answering these questions can shed light on what happens when things go wrong, e.g., in embryos, leading to miscarriages, and in adults, leading to cancer.
Cell migration is crucial for the functioning of our bodies. For example, it occurs during embryogenesis, organogenesis, and angiogenesis. Migration can occur in response to a gradient of signaling molecules, and cells can improve their ability to measure gradients by clustering together and communicating: How do these cells coordinate and move together? How do they “talk” to each other and exchange information to adopt different identities based on their positions?
To answer these questions, we use our recently developed mathematical framework to analyze any specific cellular geometry and tissue size and find which type of cellular communication is more effective to measure signaling gradients. In this context, we aim to identify the mechanisms of cellular communication and predict the properties of the molecules that mediate communication.
Odors are detected by a specialized set of neurons called Olfactory Sensory Neurons (OSNs). In mice, there are ~1000 different OSNs sub-types, each expressing a single allele of an olfactory receptor (OR) gene out of thousands available in the genome. Interestingly, the OSN sub-types are positioned in stereotypic areas of the olfactory epithelium called “zones”, whose function is unknown.
Using spatial transcriptomics, we recently built the first 3D spatial transcriptomic atlas of the mouse olfactory mucosa and uncovered a functional logic behind the spatial distribution of OSN sub-types.
The Scialdone Lab
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