PHD2020-02
Computational modelling of Innate Lymphoid Cell differentiation during embryogenesis
Host laboratory and collaborators
Serge van de Pavert / CIML / vandepavert@ciml.univ-mrs.fr
Denis Puthier / TAGC / denis.puthier@univ-amu.fr
Abstract
Innate Lymphoid Cells (ILCs) are a novel described immune family. In adult they are derived from bone-marrow residing stem cells, but since there is no bone-marrow in the embryo, embryonic lineages must be derived from other progenitors. ILC differentiation into the 3 family members is notoriously dynamic and possibly depends on micro-environmental cues which during embryogenesis are most likely provided within the fetal liver. The main objective of this project is to analyze the differentiation stages involved in embryonic ILC differentiation. We previously observed that the common intermediate precursor stage cell is highly dynamic and mapping stages and lineages in clusters using single cell sequencing and classical bio-informatic tools was not satisfactory. Here, we will adapt a machine learning based method, DBF-MCL (Density-based filtering and Markov clustering), to unbiasedly identify the ILC (precursor) populations within different embryonic stages and aim to identify the pathways involved in the differentiation of each ILC family member.
Keywords
Pathways, ontogeny, ILC, fetal liver, single cell sequencing, machine learning, clustering, DBF-MCL, Density-based filtering and Markov clustering
Objectives
(1) Establish the appearance of the ILC lineages and their progenitors within the fetal liver and embryonic periphery. (2) Establish a novel machine-based learning algorithm to qualify the ILC lineages and their progenitors. (3) Establish the dynamicity of the ILC progenitor by novel bio-informatical tools and establish all ILC lineage pathways in single-cell sequencing analysis.
Proposed approach (experimental / theoretical / computational)
Cells will be isolated from fetal liver, the major hematopoietic organ during embryogenesis, and periphery, where the definitive stages reside, at different gestational stages. These will be individually sequenced using the in-house dropseq sequencing protocol, as was performed previously for a one ILC lineage. To reveal cellular heterogeneity, dimensionality reduction and clustering compare cellular events with regard to their gene expression profiles. We previously developed a machine-learning approach (DBF-MCL) that uses a density analysis combined with Markov Clustering. We have shown that DBF-MCL was able to efficiently filter microarray datasets, isolate natural clusters with results being robust to parameter variations. We expect that the DBF-MCL algorithm will be successfully adapted to scRNA-seq experiment and used to reveal ILC heterogeneity and dynamicity.
Interdisciplinarity
The described processes take place during embryogenesis and are essential for immune system development. Therefore, both developmental biology as well as immunology are involved. As we have previously shown, ILC lineage ontogeny involves highly dynamic precursors which cannot be precisely identified using the classical bio-informatical tools. An important role for the bioinformatician is thus to develop a novel approach that will rely on the previously published DBF-MCL algorithm and adapt it to the much larger dimensional single cell seq data sets obtained from 2 embryonic tissues at different gestational stages. This novel method involves machine-learning to improve separation of the clusters observed in the analysis of all data sets. Revealing unbiasedly these highly dynamic populations will undoubtedly contribute to improve our understanding of the pathways involved in ILC differentiation.
Expected profile
A bio-informatician with developmental biology or immunological experience. Highly motivated to gain insights into the biological system. Since cell-isolation and sequencing protocols have been setup at CIML and are rather straight-forward. We thus rely on the computational (e.g. Python, Rust, R, C/C++) and the creative skills of the candidate that will benefit from the bioinformatics and mathematical expertise of TAGC researchers and engineers.