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  Stochasticity in cell fate specification and reversal


   School of Biological Sciences

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Dr N Nakayama Prof Ramon Grima  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Plant cells have amazing capacity to regenerate. When isolated, any living mature cell within a plant body is thought to be able to regain totipotency and regenerate into a complete organism. Recently a protocol was developed to enhance totipotency and regeneration in protoplasts isolated from leaf (Chupeau et al., 2013). Transcriptome analysis of the isolated cells revealed that the cells undergo dedifferentiation within 24 hours following the release from the tissue context and acquire stem cell fate within few days, eventually beginning to divide and re-differentiate in a week. While these data are collected from a population of cells, we are interested in cell-to-cell variability, since stochasticity plays important roles in how cells loose and regain identity.

To capture the stochasticity in cell dedifferentiation, stem cell activation, and cell re-differentiation, we will employ the microfluidics lab-on-a-chip technology that allows in situ quantitative observation of single cell behaviours over time. Inspired by cell traps made for other organisms (e.g. Crane et al., 2014), we have developed a bespoke cell trap to hold plant single cells. In each experiment, more than 2,000 cells can be trapped and monitored over several days, using an automated live-imaging platform. The temporal dynamics of fluorescent marker activity in each cell will be characterised and collated to provide new insights into the cell fate reversal and regaining of pluri- or toti-potency. In subsequent experiments, stochasticity will be modulated by induction of stem cell activity or specific cell identify, to test the roles of noise in cell dedifferentiation and re-differentiation. Stochasticity seems to be increased or reduced depending on the architecture of regulatory mechanism, as well as the cell-cell interaction level (Smith and Grima, 2018). Such theories will be incorporated into comparative analysis of differences in stochasticity levels between single-cell and tissue contexts.

This project will experimentally test the roles of stochasticity in cell fate specification and reversal. It will provide a student with first-hand, state-of-the-art trainings in both experimental and computational approaches of systems and integrated biology.


The student will be supervised by the plant cell and developmental biologist Dr Naomi Nakayama, with the secondary supervision by the theoretical physicist Dr Ramon Grima. S/he will be based in the Nakayama group, but will interact with the Grima group on the regular basis. For more information about the both groups, please visit the group websites: www.bfflab.org (the Nakayama Group, also called the Biological Form and Function Lab) and http://grimagroup.bio.ed.ac.uk (the Grima Group).





Funding Notes

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If you would like us to consider you for one of our scholarships you must apply by 12 noon on 13 December 2018 at the latest.

References

Chupeau et al. (2013) Characterization of early events leading to totipotentcy in an Arabidospis protoplast liquid culture by temporal transcript profiling. Plant Cell. 25: 2444.

Crane et al. (2014) A microfluidic system for studying ageing and dynamic single-cell responses in budding yeast. PLoS One. DOI: 10.1371/journal.pone.0100042.

Smith and Grima. (2018) Single-cell variability in multicellular organisms. Nature Communications. 9:345.

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Project supervisors

Career overview

Naomi Nakayama received a PhD in Molecular, Cellular, and Developmental Biology from Yale University, USA, in 2006. Following this, they held postdoctoral researcher positions at the University of Bern, Switzerland, from 2006 to 2012, and at Ecole Normale Superieure de Lyon, France, from 2012 to 2013. They were a Chancellor's Fellow from 2013 to 2018 and became a Royal Society University Research Fellow in 2015. Their research focuses on the biological and engineering mechanisms underpinning the adaptation of living architectures, studying how plants modify their construction and architecture in response to environmental challenges. This work involves interdisciplinary collaboration, employing methods from cell and developmental biology as well as mechanical engineering.


Research interests

Naomi Nakayama's research focuses on the biological and engineering mechanisms underpinning the adaptation of living architectures, particularly in plants. They study how plants modify their construction and architecture in response to physical environments and external forces. Their work employs interdisciplinary approaches, combining cell and developmental biology with mechanical engineering techniques such as microfluidics and micro-3D scanning. They are developing plant single cell platforms and synthetic biology toolkits to understand cellular responses to mechanical stressors. The research aims to improve agricultural practices by addressing issues like lodging in plants and exploring the potential of plant cells in industrial biotechnology. Additionally, they investigate the design principles of biological forms and their plasticity, contributing to the development of sustainable materials and innovative biotechnological solutions.

View Naomi Nakayama's profile 
Career overview

Ramon Grima obtained a B.Sc (Hons) in Physics and Pure Mathematics from the University of Malta in 2000, followed by an M.A. in Physics from the University of Virginia in 2002. He completed a Ph.D. in Physics at Arizona State University in 2005. After his doctoral studies, he was a Postdoctoral Fellow at the School of Informatics, Indiana University, from 2005 to 2006. He then held the position of Mathematical Institute Fellow at Imperial College London from 2006 to 2008. Grima joined the University of Edinburgh in 2008 as a Lecturer, progressed to Reader in 2013, and was promoted to Professor in 2019. His research focuses on the chemical master equation in biochemical systems, particularly gene regulatory networks, and he has developed interests in the reaction-diffusion master equation and parameter estimation methods for gene regulatory networks.


Research interests

Ramon Grima's research focuses on the exact or approximate solution of the chemical master equation describing biochemical systems, particularly gene regulatory networks. They are also interested in the approximate solution of the reaction-diffusion master equation, considering the complex nature of the cytoplasm, including phenomena such as macromolecular crowding. A main aim is to obtain closed-form solutions for the approximate distributions of molecule numbers, which can provide insights into stochastic intracellular dynamics and how living cells have evolved to manage inherent noise. Recently, there has been a growing interest in developing efficient methods for estimating parameter values for gene regulatory networks from single cell and population snapshot data.

View Professor Ramon Grima's profile