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  • Project No: NC-3
  • Intake: 2024 KIR Non Clinical


Squamous epithelial tissues, such as the skin epidermis, provide an impermeable protective barrier against external insults. To ensure its maintenance, specialised cells located in its basal layer, known as stem cells, divide and differentiate to replace cells lost through exhaustion and damage. However, the mechanisms that control stem cell renewal and the pathways that lead to their dysregulation in disease remain controversial.

Through advances in genetic lineage tracing, statistical modelling approaches have begun to reveal the functional identity and fate behaviour of stem cells in cycling epithelial tissues. These studies have highlighted the role of stochastic renewal programmes, in which stem cells are not individually long-lived, but are constantly lost and replaced by neighbouring cells [1]. Nevertheless, the mechanisms governing stochastic fate remain poorly understood. While some recent studies have emphasized the role of tissue mechanical properties in 'priming' epithelial stem cells for renewal or differentiation [2], other work on inflammatory diseases such as psoriasis or atopic dermatitis, where the balance between profliferation and differentiation is typically altered, has highlighted the role of 'niche' signals derived from immune [3] and neural cells [4] in modulating stem cell self-renewal potential and controlling tissue dynamics.

In this project, you will use the latest image-based spatial transcriptomics methods, including  seqFISH and Nanostring CosMx, to characterise the micro-architecture, gene expression patterns and cellular composition of skin at sub-cellular resolution, using intact tissue biopsies from wild type mouse and from mouse models for psoriasis or atopic dermatitis. Using a pioneering approach, spatial mechano-transcriptomics, recently developed by the Hallou (Kennedy Institute, University of Oxford) and Dumitrascu labs (Columbia University) [5], you will also simultaneously assess the mechanical state of the tissue at cellular resolution, allowing for the first time the joint characterisation of the mechanical, biochemical and cellular niches of epidermal stem cells and how their composition, mechanical properties and spatial organisation might be altered in inflammatory skin diseases.

Using image analysis, bioinformatics, machine learning and mathematical modelling this interdisciplinary project will focus on developing new spatial mechano-transcriptomics analysis modules to analyse and explore this unique dataset. Working closely with clinical and experimental colleagues, you will also have the opportunity to design and contribute to follow-up experiments and hypothesis testing using mouse models or epithelial organoids co-cultured with immune and/or neural cells, combined with advanced live imaging, atomic force microscopy (AFM), microfabrication, as well as spatial proteomics, genetic lineage tracing and functional genomics approaches such as CRISPR-based gene editing.

Ultimately, the results of this research will transform our understanding of the biological and physical mechanisms that regulate that regulate stem cell self-renewal and differentiation in the skin epidermis and provide a rational basis for the development of more effective therapeutics that target the causes rather than the symptoms of psoriasis and atopic dermatitis.


Mechanobiology, spatial transcriptomics, bioinformatics, stem cells, skin


The project is supported by a supervisory team with complementary computational and experimental expertise. The Hallou lab, based at the Kennedy Institute in Oxford, combines wet and dry laboratory approaches to study the role of mechano-chemical interactions in cell fate decisions and tissue dynamics, and has expertise in the use of biophysical, machine learning and single cell/spatial omics methods applied to a variety of biological systems.  Also at the Kennedy, the Sansom lab is using genomic approaches to study immune mediated diseases and are expert with the use of data science approaches for the analysis and integration of single-cell and spatial transcriptomics data. The Dumitrascu Lab at Columbia University has expertise in developing computational tools for analysing genomic data across scales and biological systems, using multimodal data analysis and interpretable machine learning.

In addtion of the supervisory team, this project will be supported by a strong network of  collaborations, with the group of Professor Graham Ogg which has world-leading expertise with the experimental and clinical investigation of inflammatory diseases in skin, and the group of Dr Callioppe Dendrou who has expertise in computational analysis of single cell and spatial genomics data sets in immune-mediated and infectious diseases.

As part of this project, you will develop strong data science skills by learning to code in Python and R to perform statistical data analysis and visualisation. You will become an expert in using machine learning approaches to analyse images and integrate single cell and spatial transcriptomics data. Working closely with experimental colleagues, you will gain an expert understanding of stem cells and tissue mechanobiology, and develop an in-depth understanding of experimental research, including the generation of single cell and spatial omics data.

You will be supervised on a day-to-day basis by Dr Hallou and there will be regular joint meetings with the Sansom and Dumitrascu labs. You will be expected to present your work regularly at the weekly Hallou group meetings and will have the opportunity to attend regular seminars within the Institute and relevant seminars at the wider University. You will also have the opportunity to present your research at local, national and international meetings and conferences.

A core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects including tissue biology, inflammation, epigenetics, translational immunology, data analysis and single cell genomics. You will also have access to various courses run by the Medical Sciences Division Skills Training Team and other departments, and, like all students of the program, you will be required to attend a 2-day Statistical and Experimental Design course at NDORMS. 


[1] A.M. Klein and B.D. Simons. Universal patterns of stem cell fate in cycling adult tissues. Development 138, 3103(2011).

[2] J. McGinn, A. Hallou, S. Han, et al., B.D. Simons and M.P. Alcolea. A biomechanical switch regulates the transition towards homeostasis in oesophageal epithelium. Nature Cell Biology 23(5), 511-525 (2021).

[3] S. Park et al., V. Greco. Skin-resident immune cells actively coordinate their distribution with epidermal cells during homeostasis. Nature Cell Biology 23, 476–484 (2021).

[4] S. Huang et al., P. Rompolas. Lgr6 marks epidermal stem cells with a nerve-dependent role in wound re-epithelialization. Cell stem cell, 28(9), 1582–1596.e6. (2021)

[5] A. Hallou, R. He,  B.D. Simons and  B. Dumitrascu. A computational pipeline for spatial mechano-transcriptomics. (


Stem cell and tissue biology, inflammation, spatial and single cell genomics, mechanobiology


Please contact Dr. Adrien Hallou (, Dr Steve Sansom ( or Dr. Bianca Dumitrascu ( for more information.