Join us

If you are interested in joining the lab for an internship or a semester project (BSc, MSc), please contact Raphaëlle. Below is a list of possible projects, but we have plenty of data and are open to your ideas.

• Compartment-specific mRNA metabolism in motor neurons and astrocytes in ALS pathogenesis

  Background

  We recently uncovered cytoplasmic accumulation of aberrant intron retaining transcripts (IRTs) as the earliest detectable molecular phenotype in ALS. The mechanisms that control RNA binding protein mislocalization, the molecular hallmark of ALS, have yet to be elucidated. Non-coding RNA sequences such as 3′ UTRs have recently emerged as potent regulators of protein localisation.

  Goals

  • Study the temporal and spatial dynamics of intronic and 3′ UTR sequences in developing motor neurons and astrocytes derived from ALS-mutant and control iPSC lines, using time-resolved RNA-sequencing data from nuclear and cytoplasmic fractions.
  • Characterise the sequence features of cytoplasmic and nuclear IRTs and 3′ UTRs.
  • Develop an mRNA subcellular localisation model using machine learning methods.

  Context

  Part of a larger project studying how molecular biology shapes cellular morphology at early stages of ALS, in close collaboration with the laboratory of Prof. Rickie Patani (Francis Crick Institute / UCL, London).

  Prerequisites

  Strong mathematical and computational skills; familiarity with Python/R and the Linux environment. Experience in next-generation sequencing data and machine learning is an asset. A biological background is not required, but a strong desire to work directly with experimental biologists is. Good knowledge of English.

• Automated segmentation of high-content fluorescent microscopy data of developing motor neurons and astrocytes

  Background

  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive and incurable neurodegenerative disease. We previously found that the molecular events leading to ALS start during early development. It remains unknown how and when these affect individual cell behaviour.

  Goals

  • Expand existing image analysis modules to obtain robust single-cell readouts from longitudinal time-lapse fluorescence images of astrocytes and motor neurons.
  • Develop statistical models to identify cellular trajectories associated with early stages of ALS, using the phenotypic features obtained.

  Context

  Part of a larger project integrating longitudinal cellular imaging with genomic data, in close collaboration with the laboratory of Prof. Rickie Patani (Francis Crick Institute / UCL, London).

  Prerequisites

  Strong mathematical and computational skills; familiarity with Python/R and the Linux environment. Experience in image processing and analysis, and machine learning is an asset. A biological background is not required, but a strong desire to work directly with experimental biologists is. Good knowledge of English.

• Data-driven identification of prognostic tumour subpopulations from single-cell RNA sequencing data

  Background

  Accumulating evidence shows aberrant mRNA metabolism in cancer; however, relatively little is known about the impact of genetic mutations on mRNA metabolism in cancers and how this confers resistance to therapy.

  Goals

  • Develop and implement bioinformatics pipelines to study alternative splicing and polyadenylation from the single-cell transcriptome of BRAF-inhibitor-resistant melanoma.
  • Test whether combining gene and alternative 3′ UTR expression enables the identification of subtle subpopulations that confer drug resistance.

  Context

  Part of a larger project integrating single-cell sequencing data with imaging data, in collaboration with the Department of Oncology UNIL CHUV (Prof. Olivier Michielin) and the Novartis Institutes for BioMedical Research.

  Prerequisites

  Strong mathematical and computational skills; familiarity with Python/R and the Linux environment. Experience in next-generation sequencing data and machine learning is an asset. A biological background is not required, but a strong desire to work directly with experimental biologists is. Good knowledge of English.

• Development of epigenetic biomarkers for chronic pain stratification

  Background

  Chronic pain is a major health care problem. Complex interactions between biological, psychological, environmental and social factors may influence pain chronicisation, and epigenetic factors could explain the transition from acute to chronic pain.

  Goals

  • Analyse whole-genome methylation levels in chronic pain patients and healthy controls through the prism of biological (age, medication) and psychological (anxiety, depression) factors.
  • Identify specific genes that may be involved in the transition from acute to chronic pain.

  Context

  In collaboration with the medical research group of the Clinique romande de réadaptation (CRR, Bertrand Léger), where the student is expected to spend 20% of their time.

  Prerequisites

  Strong mathematical and computational skills; familiarity with Python/R and the Linux environment. Experience in next-generation sequencing data and machine learning is an asset. A biological background is not required, but a strong desire to work directly with experimental biologists is. Good knowledge of English.

Provisional Project list from luisierlab.com/student-projects, to be updated by the lab.