About the Project
Cell Biologist with Image analysis background
Computational imaging expert with life science interest
The Schools of Computer Science, Electrical and Electronic Engineering, and Engineering Maths (SCEEM) and Biochemistry at the University of Bristol, UK are looking for a PhD student interested in the area of bioimage analysis of background fluorescence in cells.
All cells display some degree of autofluorescence. Although the molecular structures that display autofluorescence are characterised, a quantitative link between autofluorescence and cell state is not currently known [1]. By combining high-end microscopy techniques to detect the fluorescence pattern and intensity in cells with advanced image processing and machine learning tools [3,4,5], we aim to make precisely that correlation between the autofluorescence pattern and the physiological state of that cell.
This project will provide a novel image analysis tool for monitoring cell state. Whereas biosensors measure chemical biomarkers indicative, for example, of apoptosis (cell death), optical/imaging methods can better assess cell integrity and activity of individual cells (e.g., via morphology and autofluoresence) and detect “special” cells. In addition, by quickly measuring and analysing many cells, a statistically representative indication of the cell population in real time can be obtained.
We are looking for a person that thrives in an interdisciplinary environment. Either someone with a cell biological training but with a strong interest and expertise in image analysis or a person with a computational imaging / image processing background with exposure to life sciences research would fit the role profile. To highlight the interdisciplinary nature of the project the aims of the research project are as follows:
1. To detect the spectra, intensities, and localisation of the autofluorescence in cells over time and during at different (induced) physiological states (e.g. oxygen stress, temperature).
2. To develop automated analysis tools for the detection changes in fluorescence pattern, signature, and/or intensities and classification into categories.
3. To (co-)localise the autofluorescence categories with known cellular markers.
4. To correlate the classification groups identified in goal 2 with the physiological state as described in goals 1 and 3.
5. To design an imaging patch that could be used on cell culture bags
The successful candidate will be based either in Biochemistry or SCEEM, depending on the background of the student. Part of the studies will be conducted at University College London (UCL) and the student will be part of the EPSRC funded CDT for Innovative Manufacturing at UCL.
If you would like to discuss this project or have any questions please contact:
- Prof. Paul Verkade (Biochemistry): [Email Address Removed]
- Dr. Alin Achim (SCEEM): [Email Address Removed]
Closing date: 31 July 2018
Interviews: mid August 2018
References
[1] Miranda-Lorenzo et al., "Intracellular autofluorescence: a biomarker for epithelial cancer stem cells," Nature Meth., 11, 1161-69, 2014
[2] H. Sadreazami et al., "A study on image denoising in contourlet domain using the alpha-stable family of distributions," Sig. Proc., 128, 459-73, 2016
[3] P. Hill et al., "Undecimated dual-tree complex wavelet transforms," Sig. Proc.: Im. Comm, 35, 61-70, 2015
[4] J. Chen et al., "Bayesian Video Super-Resolution with Heavy-Tailed Prior Models," IEEE Trans. Circ. Sys. Video Tech., 24, 905-14, 2014
[5] T. Trzcinski et al., "Learning Image Descriptors with Boosting," IEEE Trans PAMI., 37, 597-610, 2015
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