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Precision Medicine DTP - Time-resolved single-cell analysis of neural cell differentiation in motor neuron disease (MND)


School of Biological Sciences

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Dr C Sibley , Dr R Grima No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

Background: Motor neuron disease (MND) is a relentlessly progressing and devastating condition caused by the selective degeneration of upper and lower motor neurons essential for all movement. It remains incompletely understood why motor neurons are so vulnerable, how early disease-relevant changes appear, and how other surrounding cells contribute to their demise.

In this mixed experimental and computational PhD project the student will learn to differentiate induced pluripotent stem cells from MND patients into clinically relevant models of motor neurons and astrocytes1. Notably, cells from two different well-defined genetic cohorts will be differentiated alongside the cells of patients with sporadic MND (i.e. no identified mutations) and age-matched controls. In conjunction with Dolomite Bio, the iCase partner, the student will then learn and apply single-cell RNA sequencing (scRNA-seq) and protein-based assays at frequent stages of these different differentiation trajectories2. Specifically, an extended period of ~4 months in year 1 will be spent at Dolomite Bio’s laboratories in Cambridgeshire to learn the state-of-the-art scRNA-seq technique, and to help develop a new mRNA capture-bead with increased capabilities of existing alternatives for this approach. Following return to the Sibley lab in Edinburgh, these methods will be applied to the aforementioned ALS samples. Sophisticated computational analyses will then be implemented by the student to identify dysregulated gene expression signatures, master regulators and developmental abnormalities associated with the different backgrounds in both a time-resolved and high-resolution manner2-4.

The student will use this strategy to elucidate and characterize the earliest stages of disease-relevant dysfunction for each genetic background, reveal key events that are targets for therapeutic intervention, determine when changes appear around known protein pathology, and provide penetrative insight into the differing vulnerabilities of distinct neural cell types to disease relevant genetics. Computational findings will be experimentally validated with standard molecular biology approaches (e.g. qPCR, western blotting, cell-based assays), whilst driving master regulators behind dysregulated gene networks will be followed up in more detail by the student with the powerful iCLIP and ChIP methods routinely used in the Sibley lab.

The thesis will be supervised by Dr Sibley (experimental / computational) and Professor Grima (computational) who have extensive expertise covering all wet and dry lab methodology. Meanwhile, Nadia Shakir (Operations manager at Dolomite Bio) will be the point of contact and support at the iCase partner, Dolomite Bio. Throughout the project, extra focus will be made to the provision of quantitative skills required for the computational skills required for single-cell RNA-seq analysis. The student will integrate into the University of Edinburgh’s globally recognised communities of leading RNA biologists and neuroscientists, and benefit from excellent graduate training opportunities that are on offer.


Aims:
1. Time-resolved transcriptome profiling of single cells differentiating into neural cells upon varied genetic backgrounds.
2. Elucidate and characterize the earliest stages of disease-relevant dysfunction associated with each MND-associated mutation being studied.

Training outcomes:
1. Experimental methods to culture human stem cells and differentiate them into disease relevant models.
2. Experimental methods to perform the increasingly important method of single-cell RNA sequencing
3. Standard molecular biology methods
4. Tuition in coding using different computational languages
5. Computational skills to analyze next generation sequencing datasets
6. Systems biology skills to computationally model high dimensional next generation sequencing datasets
7. Experience of R&D in non-academic partner


This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.

For more information about Precision Medicine visit:
http://www.ed.ac.uk/usher/precision-medicine

Funding Notes

Start: September 2021

Qualifications criteria: Applicants applying for an MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area. The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,285 (UKRI rate 2020/21).

Full eligibility details are available: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/

Enquiries regarding programme: [Email Address Removed]

References

1 Hall CE et al. Progressive Motor Neuron Pathology and the Role of Astrocytes in a Human Stem Cell Model of VCP-Related ALS. Cell Rep. 2017 May 30;19(9):1739-1749.
2 Macosko EZ et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015 May 21;161(5):1202-1214.
3 Aibar S et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017 Nov;14(11):1083-1086.
4 La Manno G et al. RNA velocity of single cells. Nature. 2018 Aug;560(7719):494-498.


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