Students Projects
If you are interested in joining our lab for an internship or a semester (BSc, MSc) research project, please contact Raphaëlle. Below is a list of possible projects but we have tons of data and are open to hear about your ideas.
Compartment-specific mRNA metabolism in MNs and ACs in ALS pathogenesis
Background
We recently uncovered cytoplasmic accumulation of aberrant intron retaining transcripts (IRTs) as the earliest detectable molecular phenotype in ALS 1–4. The mechanisms that control RNA binding protein mislocalization, the molecular hallmark of ALS, have yet to be elucidated and it remains unknown whether the early aCIRT relates to protein mislocalization, ER stress, mitochondrial depolarisation, oxidative stress, synaptic loss and cell death. Additionally the mechanism underlying the cytoplasmic IRTs mislocalization together with their putative role in ALS pathogenesis remain unknown. Non-coding RNA sequences such as 3’ UTR have recently emerged as potent regulators of protein localisation.
Goals of the project
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Study the temporal and spatial dynamics of intronic and 3’ UTR sequences in developing MNs and ACs derived from ALS-mutant and control iPSC cell lines using time-resolved RNA-sequencing data from nuclear and cytoplasmic fractions;
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Characterise the sequence features of cytoplasmic and nuclear cytoplasmic IRTs and 3’ UTR;
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Develop an mRNA subcellular localisation model using machine learning methods.
Context
This project is part of a larger one aiming to study how molecular biology shapes cellular morphology at early stage of ALS by integrating longitudinal cellular imaging with genomic data. It involves a close collaboration with the experimental laboratory of professor Rickie Patani (Francis Crick Institute/ UCL, London http://thepatanilab.com/).
Prerequisites
Candidates should have strong mathematical and computational skills. Candidates should be familiar with Python/R, and with the Linux environment. Experience in next-generation sequencing data, and machine learning is an asset. Candidates do not necessarily have to have a biological background but should have a strong desire to directly work with experimental biologists. The candidates should have a good knowledge of English.
Automated segmentation of high-content fluorescent microscopy data of developing MNs and ACs in culture
Background
Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive and incurable neurodegenerative disease. The early events underlying the disease remain poorly understood. As a dramatic consequence no effective treatment has been developed. We previously found that the molecular events leading to ALS start during early development. It remains however unknown how and when these affect individual cell behaviour.
Goals
Time-lapse fluorescence live-cell imaging are rich data that can be used to detect and track individual cell's changes (size, morphology, movement) in space and time. The goal of the project is to develop an image analysis pipeline to extract and analyse single-cell phenotypic measurements from large-scale time-lapse fluorescence imaging data from astrocytes (AC) and motor neurons (MNs) in culture. Specifically it will involve
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expansion on existing image analysis modules to obtain robust single-cell readouts from longitudinal images;
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development of statistical models to identify cellular trajectories associated with early stage of ALS development using the phenotypic features obtained in 1).
Context
This project is part of a larger one aiming to study how molecular biology shapes cellular morphology at early stage of ALS by integrating longitudinal cellular imaging with genomic data. It involves a close collaboration with the experimental laboratory of professor Rickie Patani (Francis Crick Institute/ UCL, London http://thepatanilab.com/).
Prerequisites
Candidates should have strong mathematical and computational skills. Candidates should be familiar with Python/R, and with the Linux environment. Experience in image processing and analysis, and machine learning is an asset. Candidates do not necessarily have to have a biological background but should have a strong desire to directly work with experimental biologists. The candidates should have a good knowledge of English.
Data-driven identification of prognostic tumor 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 mutation on mRNA metabolism in cancers and how this confers resistance to therapy.
Goals
In this project the student will develop and implement bioinformatics pipelines to study alternative splicing and polyadenylation from single-cell transcriptome of Braf inhibitors resistant melanoma. This will then serve to test whether combining measurements from gene and alternative 3’ UTR expression enable the identification of subtle subpopulations that confer drug resistances.
Context
This project is part of a larger one aiming to integrate single-cell sequencing data with imaging data in order to develop accurate machine learning methods to identify tumor subpopulations. It involves a close collaboration with the Department of oncology UNIL CHUV header by Prof. Olivier Michielin (https://www.unil.ch/dof/michielin) and the Novartis Institute for Biomedical Research.
Prerequisites
Candidates should have strong mathematical and computational skills. Candidates should be familiar with Python/R, and with the Linux environment. Experience in next-generation sequencing data, and machine learning is an asset. Candidates do not necessarily have to have a biological background but should have a strong desire to directly work with experimental biologists. The candidates should have a good knowledge of English.
Development of epigenetic biomarkers for chronic pain stratification
Background
Chronic pain is a major health care problem that affects millions of people worldwide. It has been demonstrated that complex interactions between biological, psychological, environmental, and social factors may influence pain chronicization. Therefore, it has been suggested that epigenetic factors could be the trigger to explain the transition from acute to chronic pain and chronic pain maintenance. However, so far little is known about the influence of these biopsychosocial factors on epigenetic modifications in a population of chronic musculoskeletal pain patients consecutively to an orthopedic trauma.
Goals
This project aimed to analyze the whole genome methylation levels in a population of chronic pain patients and healthy controls through the prism of specific biological (age, medication,) and psychological (anxiety/depression) factors. This biological project will require the bioinformatic analyses of methylation sites on the whole genome to identify specific genes that may be involved in the transition fromorm acute to chronic pain.
Context
This project will be set up in collaboration with the medical research group at the Clinique romande de readaptation (CRR, Betrand Leger) where the student is expected to spend 20% of his time.
Prerequisites
Candidates should have strong mathematical and computational skills. Candidates should be familiar with Python/R, and with the Linux environment. Experience in next-generation sequencing data, and machine learning is an asset. Candidates do not necessarily have to have a biological background but should have a strong desire to directly work with experimental biologists. The candidates should have a good knowledge of English.