M2 Internship: Single-cell transcriptomics data analysis and Metabolic modeling in Ataxia

SFBI; Marseille; On-site; EUR 8,900 - 12,000; Part time

Job summary

SFBI invites a motivated M2 student to join the Computational Biology team in Marseille for an internship focused on single-cell transcriptomics and metabolic modeling in Ataxia-telangiectasia. Work will involve processing Lai et al. data, constructing metacells, and applying metabolic activity inference methods.

Supervision by local researchers will support autonomous analysis and scientific contribution. The internship emphasizes Python programming, UNIX workflow, and data interpretation

Qualifications

• M2 in bioinformatics/computational biology or related field required.
• Strong Python coding skills and experience with UNIX systems.
• Experience with single-cell transcriptomics and metacell/metabolic analyses is preferred.

Responsibilities

• Analyze Lai et al. single-nucleus dataset from a metabolic perspective across cell types.
• Infer metabolic activity for metacells using approaches like COMPASS.
• Identify/differentially active metabolic reactions between A-T and controls across cell types.

Skills

Python

UNIX

Single-cell data analysis

Supercomputing cluster

English communication

Education

M2 in Bioinformatics or related field

Job description

M2 Internship: Single-cell transcriptomics data analysis and Metabolic modeling in Ataxia

Description

Comparative analysis of cellular metabolism in Ataxia-telangiectasia from single-nucleus transcriptomics data

Context

Ataxia-telangiectasia (A-T) is a rare autosomal recessive cerebellar ataxia characterized by progressive neurological impairment with cerebellar syndrome, oculocutaneous telangiectasia, defects in B and T cell-mediated immunity, and increased susceptibility to malignancies [ Orphanet ]

A-T is caused by biallelic loss-of-function pathogenic variants of the ATM gene, which encodes a protein kinase involved in double-strand-break DNA repair. While ATM is best known for its role in the DNA damage response, studies suggest that the ATM kinase is also involved in the regulation of redox signalling and the cellular response to oxidative stress [ Shiloh et al., 2013 ]. In addition, A-T has been associated with mitochondrial dysfunction and altered cellular metabolism, suggesting a role of ATM in mitochondrial homeostasis [ Valentin-Vega et al., 2012 ].

Neurodegeneration in A-T primarily affects cerebellar Purkinje and granule neurons [ Bottini et al., 2012 ]. Purkinje neurons have particularly high energetic demands and are known to be vulnerable to mitochondrial dysfunction, as observed in many inherited ataxias. Since cerebellar cell types rely on distinct metabolic strategies, the loss of ATM may have cell-type‑specific metabolic consequences.

A recent single-nucleus transcriptomic study of A-T cerebellum revealed cell-type-specific transcriptional changes, including microglial activation preceding the upregulation of apoptosis-related genes in granule and Purkinje neurons [ Lai et al., 2023 ]. However, the metabolic states of brain cell types in A-T were not investigated, and it remains unknown whether metabolic alterations contribute to microglial activation or to the selective vulnerability of cerebellar neurons.

During this internship, the student will analyze the rich single-nucleus dataset from Lai et al., 2023 from a metabolic perspective, comparing metabolic states between A-T patients and controls within each cell type. The aim is to identify metabolic alterations associated with A-T and to assess whether they relate to microglial activation and to the selective vulnerability of cerebellar neurons.

More specifically, the objectives of this internship are:

1. The analysis of the single-nucleus dataset from Lai et al. and the creation of metacells within each cell-type and donor;
2. The inference of metabolic activity for each metacell using dedicated approaches such as COMPASS [Wagner et al., 2021 ];
3. The identification of metabolic reactions and modules that are differentially active between A-T and controls, and their comparison across cell types.

The student will be hosted at the IBDM (Institut de Biologie du Développement de Marseille) and will join the Computational Biology team led by Bianca Habermann. The student will be supervised by Galadriel Brière, assistant professor at Aix‑Marseille University. The student will benefit from the team’s expertise in omics data analysis and metabolic modelling.

Student Profile

We are looking for an M2 student in bioinformatics, computational biology, applied mathematics/ statistics or a related field.

The ideal candidate:

• has experience coding in Python;
• has a strong interest in data analysis, especially transcriptomics data;
• is comfortable working with UNIX systems;
• is comfortable interacting in english;
• experience with a supercomputing cluster is plus;
• experience with single-cell data analysis is a plus.

We are looking for a motivated, autonomous and proactive student with genuine scientific curiosity.

References

VALENTIN-VEGA, Yasmine A., MACLEAN, Kirsteen H., TAIT-MULDER, Jacqueline, et al. Mitochondrial dysfunction in ataxia‑telangiectasia. Blood, The Journal of the American Society of Hematology, 2012, vol. 119, no 6, p. 1490-1500.

SHILOH, Yosef et ZIV, Yael. The ATM protein kinase: regulating the cellular response to genotoxic stress, and more. Nature reviews Molecular cell biology, 2013, vol. 14, no 4, p. 197-210.

WAGNER, Allon, WANG, Chao, FESSLER, Johannes, et al. Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell, 2021, vol. 184, no 16, p. 4168-4185. e21.