Multimodal Data Analysis of the Brain Myeloid Leukocytes in CMV-related Neurodevelopmental Pathogenesis

Host laboratory and collaborators




Congenital cytomegalovirus (CMV) infections are one leading cause of neurodevelopmental disorders. Besides neural-based mechanisms, early altered brain immune responses might be pivotal in CMV-related neurodevelopmental pathogenesis. Using our model of CMV infection of the rat fetal brain in utero (1,2), we aim at better characterizing the early pathogenic immune alterations - with a strong focus on brain myeloid leukocytes and notably microglia (1-3). We will use a combination of transcriptomic (bulk & single-cell RNA sequencing as well as CITEseq of brain myeloid cells) and cytomic (multiparameter flow and mass cytometry of brain leukocytes) phenotyping. Our goal is to exploit these multimodal datasets by the means of computational tools (computational topology, trajectory inference, deep learning autoencoders…) adapted for high dimensional noisy data to precise cell identities and reveal maturation/activation pathways and developmental trajectories that are notoriously (4) drastically altered in the context of CMV infection.


CMV - neurodevelopment - neuroimmunity - microglia - brain macrophages - RNAseq - CITEseq - flow and mass cytometry - multimodal - trajectory


i/ better delineating myeloid cell states and identities in CMV-infected developing brains; ii/ subsequently identifying altered myeloid subsets of brain immune cells; iii/ better characterizing the altered subpopulation(s) and genes/molecular pathways iv/ interrogating whether such dysregulated signatures are reverted to non-infected conditions or show an alternate state in rescuing conditions; iv/ developing computational pipelines adapted for these specific biological questions.

Proposed approach (experimental / theoretical / computational)

We will use our rat model of CMV infection of the fetal brain in utero (1,2). In this model, altered fetal microglia (2) and the CMV-encoded RCK-3 chemokine (in progress) are pivotal to neuropathogenesis. Brains at P1 will be used to compare control vs CMV-infected vs rescuing (non-pathogenic CMV encoding dominant-negative RCK-3) conditions. Brain myeloid cells will be phenotyped by flow & mass cytometry using a rat-dedicated panel already developped by CELPHEDIA consortium. Brain myeloid cells will be sorted out for bulk & scRNA-Seq. Global map of immune cell perturbations will be established by CITEseq to facilitate data alignement and integration of the different datasets. We will use computational techniques (representation learning, computational topology, graph/network analysis etc) to develop high-dimensional multimodal data integration pipeline to exploit the topological and geometrical data structures and infer developmental trajectories or maturation/activation pathways involved in CMV pathogenesis (5).


The project brings together partners with complementary expertises and is inherently situated at the crossroads between neuroscience, immunology, biostatistics, bioinformatics, and computational genomics. It relies on the use of a comprehensive toolbox (e.g. recombinant rat CMVs, in utero cerebral infection) and a combination of most recent immunophenotyping and transcriptomic approaches coupled with biostatistics, bioinformatics and unsupervised data analyses. The two Centuri partners (INMED, CIPHE) have established long-term collaboration on the CMV topic since years. CIPHE has developed deep learning methodologies as well as topological data analysis in collaboration with Jan STUCHLY (CLIP, Prague, CZ) for high dimensional single cell data analysis of mass cytometry and scRNAseq datasets as demonstrated by joint supervision of one M2 student as well as a Central Marseille alternee. Great emphasis will be placed on the close cooperation with computational scientists to adapt/develop the tools for data analyses.

Expected profile

Adaptation, curiosity, critical thinking, independence but also team spirit, perseverance and great capacity for work.
Good background in either of neurosciences, neuroinflammation or neuroimmunity, and in computational sciences (computational genomics), with a strong interest into integrative approaches on animal models, combining the cellular, molecular and behavioral levels.

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 is a continuation of an existing project in which regular flow cytometry immunoprofiling was conducted (1,2). The limits of this preliminary study is congruent with the work of Baash et al. (4) and further strengthen the need to apploy extended single cell methodologies (mass cytometry and scRNAseq) in order to delineate by unprecedented resolution brain myeloid cells in normal versus pathological conditions

2 to 5 references related to the project

1. Cloarec et al. PLoS One 2016;11:e0160176.
2. Cloarec et al. Front Cell Neurosci. 2018;12:55.
3. Kvestak et al. J. Exp. Med. 2021. doi: 10.1084/jem.20201503
4. Baasch et al. Cell 2021 Jul 8;184(14):3774-3793.e25. doi: 10.1016/j.cell.2021.05.009
5. Saelens et al. Nat Biotechnol 2019; 37: 547–554

3 main publications from each PI over the last 5 years

PI Szepetowski (DR1 CNRS) leads a research team for more than 20 years. His research focuses on the relationships between neurologic disorders of genetic and nongenetic origins on the one hand, and brain development and maturation on the other hand. He has coordinated several collaborative projects including ANR grants and contributed as a PI to a European consortium (FP7) on developmental epilepsies. He has strong expertise in mechanisms of neurodevelopmental disorders in humans and rodents (PMID: 23933820, PMID: 23077017, PMID: 31158310, PMID: 23831613) with a recent focus on CMV-related neurodevelomental pathogenesis (PMID: 27472761, PMID: 29559892).

PI Luche (IRHC AMU) is an expert in immunology of murine Tc (PMID: 27193333, PMID: 752348) and myeloid cells (PMID: 27193333 PMID: 27637149), flow ( PMID: 31633216) and mass cytometry (PMID: 34912335). He has led the development of scRNAseq capabilities at CIPHE and has contributed to single cell data analysis pipeline generation. Group leader since 2018 and PI of various grants including one INCA grants and SFB from DFG.