PHD2023-13
Integrating transcription and lineage to define spatiotemporal trajectories of cardiac progenitor cells
Host laboratory and collaborators
Robert G. Kelly (IBDM) / robert.kelly@univ-amu.fr
Paul Villoutreix (LIS) / paul.villoutreix@univ-amu.fr
Abstract
Integration of biological datasets at single cell resolution is a powerful approach to dissecting fundamental mechanisms underlying organogenesis. Cardiac morphogenesis is driven by addition of epithelial progenitor cells to the growing arterial and venous poles of the early heart. Perturbation of this process results in congenital heart defects, affecting 1% of births. T‐box transcription factor genes implicated in human heart defects regulate the emergence of a boundary between progenitor cells that contribute to alternate cardiac poles. Single cell morphometrics has revealed dynamic patterns of epithelial tension within the progenitor cell field. The mechanisms driving progenitor cell segregation and epithelial tension remain unknown. This project will generate and computationally integrate multiple single cell datasets (transcriptomic, lineage recording and morphometric) to build a comprehensive map of cardiac progenitor cell trajectories and decipher the mechanisms underlying these critical steps in normal and pathological heart development.
Keywords
Heart development, organogenesis, progenitor cells, data integration, bioinformatics
Objectives
The objective of the project is to combine embryological and computational approaches to identify the pathways and effector genes controlling progenitor cell deployment and boundary establishment during heart morphogenesis. By computationally integrating multiple datasets (transcriptomic, lineage and morphometric) at the single cell level a comprehensive map of cardiac progenitor cell trajectories will be generated, providing new insights into the mechanisms underlying congenital heart disease.
Proposed approach (experimental / theoretical / computational)
Experimental and computational approaches will be coordinated in each of the following steps:
1. Single cell and spatial transcriptomic datasets will be obtained for cardiac progenitor cells in the early mouse embryo. Using unsupervised machine learning approaches, these datasets will be merged with recently acquired single cell morphometric data of the progenitor cell field.
2. These data will be further merged with lineage recording information acquired using the Polylox system to uncover patterns of clonal growth within the progenitor field. These datasets will be computationally mined for trajectory information and identification of signaling pathways, regulators and effector genes.
3. Integrated datasets will be obtained for wildtype embryos as well as mouse models of congenital heart defects using Tbx1 and Tbx5 mutant alleles implicated in rare genetic syndromes associated with heart anomalies. Candidate genes and pathways emerging from the integrated analysis will be validated in vivo.
Interdisciplinarity
Investigating the complexity of heart morphogenesis requires integration of different disciplines. This project will synergistically combine cardiac embryology (Kelly) with computational approaches (Villoutreix). Spatiotemporal analysis of gene expression, lineage and single cell morphometrics in cardiac progenitor cells in an in vivo mouse model will be merged using computational data integration and analysis. This interdisciplinary approach will be essential at each step of the project to identify novel regulatory mechanisms that control cardiac progenitor cell trajectories during heart tube elongation. By addressing the computational challenge of integrating three datasets (transcriptional, lineage and morphometric) at the single cell level in time and space, the project will yield novel insights into early heart development relevant for understanding the origins of congenital heart defects.
Expected profile
The candidate will have training in computational biology, bioinformatics and programming and an interest in dissecting the mechanisms underlying organogenesis and congenital disease. Experience in mouse genetics and embryological approaches will be an advantage but is not essential.
Is this project the continuation of an existing project or an entirely new one? In the case of an existing project, please explain the links between the two projects
This project builds on an existing collaboration between the Kelly and Villoutreix labs to quantitatively characterise epithelial properties of cardiac progenitor cells in the early embryo. Ongoing work by Clara Guijarro-Calvo (3rd year Centuri PhD student) has generated a single cell morphometric map of cardiac progenitor cells during heart tube formation revealing dynamic patterns of epithelial stress associated with cell segregation to alternate cardiac poles. By computationally integrating transcriptomic and lineage data at the same level of resolution as the single cell morphometric data, the new project will provide mechanistic insight into the origins of epithelial stress and regulation of cell trajectories within the progenitor field.
2 to 5 references related to the project
De Bono C, Thellier C, Bertrand N, Sturny R, Jullian E, Cortes C, Stefanovic S, Zaffran S, Théveniau-Ruissy M, Kelly RG. T-box genes and retinoic acid signaling regulate the segregation of arterial and venous pole progenitor cells in the murine second heart field. Hum Mol Genet. 2018 Nov 1;27(21):3747-3760.
Nomaru H, Liu Y, De Bono C, Righelli D, Cirino A, Wang W, Song H, Racedo SE, Dantas AG, Zhang L, Cai CL, Angelini C, Christiaen L, Kelly RG, Baldini A, Zheng D, Morrow BE. Single cell multi-omic analysis identifies a Tbx1-dependent multilineage primed population in murine cardiopharyngeal mesoderm. Nat Commun. 2021 Nov 17;12(1):6645.
McKenna A, Gagnon JA. Recording development with single cell dynamic lineage tracing. Development. 2019 Jun 27;146(12):dev169730.
Villoutreix P. What machine learning can do for developmental biology. Development. 2021 Jan 10;148(1):dev188474.
Villoutreix P, Andén J, Lim B, Lu H, Kevrekidis IG, Singer A, Shvartsman SY. Synthesizing developmental trajectories. PLoS Comput Biol. 2017 Sep 18;13(9):e1005742.
3 main publications from each PI over the last 5 years
Robert Kelly
Rammah M, Théveniau-Ruissy M, Sturny R, Rochais F, Kelly RG. PPARγand NOTCH Regulate Regional Identity in the Murine Cardiac Outflow Tract. Circulation Research 2022 Oct 28;131(10):842-858.
Adachi N, Bilio M, Baldini A, Kelly RG. Cardiopharyngeal mesoderm origins of musculoskeletal and connective tissues in the mammalian pharynx. Development. 2020 Feb 3;147(3):dev185256.
De Bono C, Thellier C, Bertrand N, Sturny R, Jullian E, Cortes C, Stefanovic S, Zaffran S, Théveniau-Ruissy M, Kelly RG. T-box genes and retinoic acid signaling regulate the segregation of arterial and venous pole progenitor cells in the murine second heart field. Hum Mol Genet. 2018 Nov 1;27(21):3747-3760.
Paul Villoutreix
Rubin S, Agrawal A, Stegmaier J, Krief S, Felsenthal N, Svorai J, Addadi Y, Villoutreix P, Stern T, Zelzer E. Application of 3D MAPs pipeline identifies the morphological sequence chondrocytes undergo and the regulatory role of GDF5 in this process. Nat Commun. 2021 Sep 10;12(1):5363.
Villoutreix P. What machine learning can do for developmental biology. Development. 2021 Jan 10;148(1):dev188474.
Villoutreix P, Andén J, Lim B, Lu H, Kevrekidis IG, Singer A, Shvartsman SY. Synthesizing developmental trajectories. PLoS Comput Biol. 2017 Sep 18;13(9):e1005742.