Scialdone Lab
Physics and data-based modelling of cellular decision making
About our Research
We use a multidisciplinary approach to understand the fundamental biological processes behind cellular decision-making.
Cellular decision-making is a crucial process in the development of multicellular organisms, during which cells differentiate from a single type to the multitude of cell types that compose the adult organism. In this context, complex regulatory events occur both at the single-cell level and at the level of groups of interacting cells.
Recently developed experimental techniques have made it possible to generate a vast amount of large-scale biological data at the single-cell level. By using state-of-the-art and newly developed computational methods, we combine information obtained from the analysis of these data with the insights offered by physical models that can guide interpretation.
Our goal is to decode the molecular mechanisms underlying cellular decision-making at
1. the single-cell level, by looking at the interaction between gene expression and chromatin spatial organization during decision making
2. the inter-cellular level, by dissecting the role of cellular communication in collective cellular decision making
One of the model systems we work with is early mouse embryos, where a complex array of cell differentiation and migration takes place. In close collaboration with experimental groups, we combine methods from statistics, information theory and physics to fully exploit the data and construct general, quantitative models.
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 use machine learning and artificial intelligence to analyze experimental data and, based on this, develop physical models to get insights into the mechanisms underlying the formation of the anterior-posterior axis. This will help us understand how embryos get their shape right and how cellular signalling influences the ‘right’ cell fate.
The coexistence of the 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 approaches derived from physics and artificial intelligence through machine learning to understand the molecular basis 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 development and “maintenance” of our bodies. For example, it occurs in normal conditions during embryogenesis, organogenesis, and angiogenesis. However, it also takes place in pathogenic events, such as wound healing after tissue injury, immune responses, and cancer metastases. 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 the other cells and instruct them to adopt different fates, 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. Now, we aim to identify the mechanisms of cellular communication in different contexts and predict the properties of the molecules that mediate the communication.
Group Leader
Doctoral Researcher
Doctoral Researcher
Postdoc
Intern
Recent Publications
Scialdone, A. ; Rivron, N.
In preprints: Improving and interrogating embryo models.Beer, S. ; Wange, L.E. ; Zhang, X. ; Kuklik-Roos, C. ; Enard, W. ; Hammerschmidt, W. ; Scialdone, A. ; Kempkes, B.
EBNA2-EBF1 complexes promote MYC expression and metabolic processes driving S-phase progression of Epstein-Barr virus-infected B cells.Fiorentino, J. ; Scialdone, A.
The role of cell geometry and cell-cell communication in gradient sensing.Ruiz Tejada Segura, M.L. ; Abou Moussa, E. ; Garabello, E. ; Nakahara, T.S. ; Makhlouf, M. ; Mathew, L.S. ; Wang, L. ; Valle, F. ; Huang, S.S.Y. ; Mainland, J.D. ; Caselle, M. ; Osella, M. ; Lorenz, S. ; Reisert, J. ; Logan, D.W. ; Malnic, B. ; Scialdone, A. ; Saraiva, L.R.
A 3D transcriptomics atlas of the mouse nose sheds light on the anatomical logic of smell.Iturbide Martinez De Albeniz, A. ; Ruiz Tejada Segura, M.L. ; Noll, C. ; Schorpp, K.K. ; Rothenaigner, I. ; Lubatti, G. ; Agami, A. ; Hadian, K. ; Scialdone, A. ; Torres-Padilla, M.E.
Author Correction: Retinoic acid signaling is critical during the totipotency window in early mammalian development.Nakatani, T. ; Lin, J. ; Ji, F. ; Ettinger, A. ; Pontabry, J. ; Tokoro, M. ; Altamirano-Pacheco, L. ; Fiorentino, J. ; Mahammadov, E. ; Hatano, Y. ; Van Rechem, C. ; Chakraborty, D. ; Ruiz-Morales, E.R. ; Scialdone, A. ; Yamagata, K. ; Whetstine, J.R. ; Sadreyev, R.I. ; Torres-Padilla, M.E.
DNA replication fork speed underlies cell fate changes and promotes reprogramming.Tyser, R.C.V. ; Mahammadov, E. ; Nakanoh, S. ; Vallier, L. ; Scialdone, A. ; Srinivas, S.
Single-cell transcriptomic characterization of a gastrulating human embryo.Lima, A. ; Lubatti, G. ; Burgstaller, J. ; Hu, D. ; Green, A.P. ; di Gregorio, A. ; Zawadzki, T. ; Pernaute, B. ; Mahammadov, E. ; Perez-Montero, S. ; Dore, M. ; Sanchez, J.M. ; Bowling, S. ; Sancho, M. ; Kolbe, T. ; Karimi, M.M. ; Carling, D. ; Jones, N. ; Srinivas, S. ; Scialdone, A. ; Rodriguez, T.A.
Cell competition acts as a purifying selection to eliminate cells with mitochondrial defects during early mouse development.Iturbide Martinez De Albeniz, A. ; Ruiz Tejada Segura, M.L. ; Noll, C. ; Schorpp, K.K. ; Rothenaigner, I. ; Ruiz-Morales, E.R. ; Lubatti, G. ; Agami, A. ; Hadian, K. ; Scialdone, A. ; Torres-Padilla, M.E.
Retinoic acid signaling is critical during the totipotency window in early mammalian development.Tyser, R.C.V. ; Ibarra-Soria, X. ; McDole, K. ; A Jayaram, S. ; Godwin, J. ; van den Brand, T.A.H. ; Miranda, A.M.A. ; Scialdone, A. ; Keller, P.J. ; Marioni, J.C. ; Srinivas, S.
Characterization of a common progenitor pool of the epicardium and myocardium.Fiorentino, J. ; Torres-Padilla, M.E. ; Scialdone, A.
Measuring and modeling single-cell heterogeneity and fate decision in mouse embryos.Huang, S.S.Y. ; Makhlouf, M. ; AbouMoussa, E.H. ; Ruiz Tejada Segura, M.L. ; Mathew, L.S. ; Wang, K. ; Leung, M.C. ; Chaussabel, D. ; Logan, D.W. ; Scialdone, A. ; Garand, M. ; Saraiva, L.R.
Differential regulation of the immune system in a brain-liver-fats organ network during short-term fasting.Solovey, M. ; Scialdone, A.
COMUNET: A tool to explore and visualize intercellular communication.Angerer, P. ; Fischer, D.S. ; Theis, F.J. ; Scialdone, A. ; Marr, C.
Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data.Mrozek-Gorska, P. ; Buschle, A. ; Pich, D. ; Schwarzmayr, T. ; Fechtner, R. ; Scialdone, A. ; Hammerschmidt, W.
Epstein-Barr virus reprograms human B lymphocytes immediately in the prelatent phase of infection.Manoel, D. ; Makhlouf, M. ; Scialdone, A. ; Saraiva, L.R.
Interspecific variation of olfactory preferences in flies, mice, and humans.Griffiths, J.A. ; Scialdone, A. ; Marioni, J.C.
Using single-cell genomics to understand developmental processes and cell fate decisions.Sarrach, S. ; Huang, Y. ; Niedermeyer, S. ; Hachmeister, M. ; Fischer, L. ; Gille, S. ; Pan, M. ; Mack, B. ; Kranz, G. ; Libl, D. ; Merl-Pham, J. ; Hauck, S.M. ; Paoluzzi Tomada, E. ; Kieslinger, M. ; Jeremias, I. ; Scialdone, A. ; Gires, O.
Spatiotemporal patterning of EpCAM is important for murine embryonic endo-And mesodermal differentiation.Shahbazi, M.N. ; Scialdone, A. ; Skorupska, N. ; Weberling, A. ; Recher, G. ; Zhu, M. ; Jedrusik, A. ; Devito, L.G. ; Noli, L. ; Macaulay, I.C. ; Buecker, C. ; Khalaf, Y. ; Ilic, D. ; Voet, T. ; Marioni, J.C. ; Zernicka-Goetz, M.
Pluripotent state transitions coordinate morphogenesis in mouse and human embryos.Scialdone, A. ; Natarajan, K.N. ; Saraiva, L.R. ; Proserpio, V. ; Teichmann, S.A. ; Stegle, O. ; Marioni, J.C. ; Buettner, F.
Computational assignment of cell-cycle stage from single-cell transcriptome data.