Mots-Clés
single-cell RNA sequencing, bioinformatic analysis, ligand–receptor interactions, metabolic coupling, AI-based analysis
Description
Internship description: Recent advances in neuroscience are reshaping the traditional view of neuronal communication, revealing the essential regulatory roles played by astrocytes in both normal brain function and diseases. Our project brings together teams and platforms in neuroscience, single-cell data production, and computational sciences to investigate glial cells as central actors of brain homeostasis. Building on jointly developed computational tools, we have established methods to predict molecular interactions between astrocytes and neurons, encompassing ligand-receptor signalling as well as metabolic exchanges such as cholesterol and lactate shuttling. In parallel, we are developing artificial intelligence approaches specifically tailored to our single-cell transcriptomic datasets.
The internship will aim to expand a proof of concept initially developed on a limited set of signalling and metabolic pathways. The student will extend this work to additional pathways and perform a systematic meta-analysis across multiple mouse and human single-cell datasets, including both control and pathological conditions. This effort will help strengthen, validate, and generalize our analytical workflow.
The student will download and curate publicly available datasets and characterize their cellular composition using R or Python packages (e.g., Seurat, Scanpy…). She/he will apply bioinformatic pipelines developed in the lab, including tools for ligand–receptor inference and metabolic interaction prediction. Additional tasks will include updating and improving analysis scripts, as well as integrating new knowledge of cell-cell interactions into the computational workflow.
By the end of the internship, the student will have gained solid experience in handling and analysing large-scale single-cell datasets, exploring major biological databases, and understanding both the potential and limitations of transcriptomic approaches for investigating neural tissue function and the mechanisms underlying its dysfunction in pathological conditions.
Some background work can be found in:
- Zhao, Wei, Kevin G. Johnston, Honglei Ren, Xiangmin Xu, et Qing Nie. « Inferring Neuron-Neuron Communications from Single-Cell Transcriptomics through NeuronChat ». Nature Communications 14, no 1 (2023): 1128. https://doi.org/10.1038/s41467-023-36800-w.
- Marcy, Guillaume, Louis Foucault, Elodie Babina, et al. « Single-Cell Analysis of the Postnatal Dorsal V-SVZ Reveals a Role for Bmpr1a Signaling in Silencing Pallial Germinal Activity ». Science Advances 9, no 18 (2023): eabq7553.
https://doi.org/10.1126/sciadv.abq7553.
- Alghamdi, Norah, Wennan Chang, Pengtao Dang, et al. « A Graph Neural Network Model to Estimate Cell-Wise Metabolic Flux Using Single-Cell RNA-Seq Data ». Genome Research 31, no 10 (2021): 1867-84. https://doi.org/10.1101/gr.271205.120.