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PhD Studentship/Masters by Research: Integration of Single Cell Tanscriptomics & Multiscale Hierarchical Tomography of Whole Human Organs

UCL

Qualification Type: PhD, Integrated Masters / Doctorate
Location: London
Funding for: UK Students
Funding amount: UK tuition fees £5,860 (2023/24)
Hours: Full Time
Placed On: 1st December 2022
Closes: 17th December 2022
Reference: TMRMEISING01

University College London working with ESRF, and part of the Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health

Supervision Team:
UCL: Prof. Peter D. Lee and Dr Claire Walsh
ESRF: Dr Paul Tafforeau
University of Cambridge: Prof Menna Clatworthy 

Project Background

The human body is complex and multi-scale; it contains ~37 trillion cells which interact and assemble into functional structures, tissues, and organs. Pathological change can occur at any scale in this system, often marked by both morphological and molecular changes. Linking molecular markers, from spatial transciptomics to multi-scale morphological imaging data, would revolutionise our understanding, detection, treatment, and prediction of many major diseases that burden today’s society.  

However, there is a gap in linking transciptome data to 3D multiscale imaging of human organs. This PhD will focus on the development of computational tools to link cutting edge transciptomics data to the latest development in multiscale human organ imaging - Hierarchical Phase-Contrast Tomography1 (HiP-CT), 

Hierarchical Phase-Contrast Tomography is a multiscale Synchrotron X-ray imaging technique which enables whole human organs to be scanned hierarchically from the whole organ at 20-8μm/voxel down to 1μm/voxel in local regions anywhere within the intact organ. It relys on the the exceptional coherence and high energy provided by the European Synchrotron Research Facility’s Extremely Bright Source (ESRF-EBS) (https://mecheng.ucl.ac.uk/HiP-CT).

You will be part of the HiP-CT team with the role to co-develop, the computational methods to intergrate single cell transciptome and spatial transciptomics data; with HiP-CT imaging data of human kidneys.

You will work closely with our collaborators at the University of Cambridge, where spatial transciptomic data is being collected. Making use of their databse of the single cell transcriptional signatures of all kidney cells, you will analyse newely collected correlated spatial transcriptomic data and HiP-CT data to identify morphological corrlates of transciptonal signatures.

The PhD project is jointly supervised by Prof. Peter Lee (Mech. Eng.) and Dr Claire Walsh (BioMed.Phy), with Prof. Menna Clatworthy (University of Cambridge and Wellcome Sanger Institute) and Dr Paul Tafforeau (ESRF, Grenoble France) where you will spend spend a portion of your time. The results will form part of the Human-Organ-Atlas.esrf.eu, a public database we’re compiling of our organs in health and disease, funded by the Chan Zuckerberg Initiative with a goal of eradicating disease. 

Person Specification

Applicants should ideally have a first-class undergraduate degree (or equivalent) in Physical Sciences (Computer Science, Engineering, Mathematics or Physics). Knowledge of image processing and strong computer programming skills are required. Applicants should have an interest in bio-medical imaging, and trasnciptomics. Excellent organisational, interpersonal and communication skills, along with a stated interest in interdisciplinary research, are essential. 

Eligibility

The position is open to students on Home Fees and applicants whose first language is not English are usually required to provide evidence of proficiency in English by UCL. Please do not enquire about this studentship if you are ineligible. Please refer to the following website for eligibility criteria: https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/medical-imaging-mres-mphil-phd 

Application Deadline: 17th December 2021 

Further details regarding this PhD studentship, including how to apply can be found here:

https://www.ucl.ac.uk/intelligent-imaging-healthcare/case-studies/2022/nov/intergration-single-cell-transciptomics-multiscale-heirarchical-tomography

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