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
Recent Publications
Read more2024 Scientific Article in Developmental Cell
An integrated approach identifies the molecular underpinnings of murine anterior visceral endoderm migration.
2024 Scientific Article in Bioinformatics
Topological benchmarking of algorithms to infer Gene Regulatory Networks from Single-Cell RNA-seq Data.
2023 Scientific Article in Nucleic Acids Research
Single molecule MATAC-seq reveals key determinants of DNA replication origin efficiency.
2023 Scientific Article in Nature Methods
Multiplex-GAM: genome-wide identification of chromatin contacts yields insights overlooked by Hi-C.
2023 Scientific Article in Development / Company of Biologists
CIARA: A cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data.
2023 Scientific Article in Physical Review E
Emergent statistical laws in single-cell transcriptomic data.
2023 Scientific Article in Cell Reports
Single-copy locus proteomics of early- and late-firing DNA replication origins identifies a role of Ask1/DASH complex in replication timing control.
2022 Letter to the Editor in Development / Company of Biologists
In preprints: Improving and interrogating embryo models.
2022 Scientific Article in PLoS Computational Biology
The role of cell geometry and cell-cell communication in gradient sensing.
2022 Scientific Article in Cell Reports
A 3D transcriptomics atlas of the mouse nose sheds light on the anatomical logic of smell.
2022 Nature Structural & Molecular Biology
Author Correction: Retinoic acid signaling is critical during the totipotency window in early mammalian development.
2022 Scientific Article in Nature Genetics
DNA replication fork speed underlies cell fate changes and promotes reprogramming.
2021 Scientific Article in Nature
Single-cell transcriptomic characterization of a gastrulating human embryo.
2021 Scientific Article in Nature metabolism