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Precision Medicine DTP - A step change in sensitivity of Raman spectroscopy for accurate and rapid analysis of living cells

  • Full or part time
  • Application Deadline
    Wednesday, January 08, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description


Most methods of cell characterization require a destructive or invasive test, without any real-time readout. There is therefore an unmet need for a highly sensitive non-destructive and reporter-free analytical technique, for rapid real-time phenotyping of both microbial cultures and their environment (including secreted products). It should be equally applicable to small-scale cell cultures and in-line analysis of larger bioreactors, with single cell sensitivity and sub-ppm sensitivity for products in solution. Its general applicability to analysis of any type of biomolecule or cell type would also be highly desirable.

Raman spectroscopy involves focussing laser light onto molecules, and exciting vibrational modes at a range of characteristic frequencies. A small proportion of photons lose energy in exciting these vibrational modes, so are red-shifted. These red-shifted photons are dispersed by a spectrometer, to reveal a characteristic fingerprint of the molecule from its set of vibrational frequencies, hence chemical groups (e.g. CH2, PO42-, amide). Raman spectroscopy is used for quantitative chemical analysis of biomolecules, but acquisition times for good quality spectra are long, due to the weak signal levels: typically only 1 in 108 - 1010 photons incident on molecules is Raman-shifted.

Raman spectroscopy is inherently quantitative – it can measure the concentration of a given product in solution (after acquiring a spectrum of product at a known high concentration, as a calibration). For an aqueous solution of one single product, this can be calculated by measuring the area under peaks, but to determine the concentration of a product in a complex mixture, multivariate curve resolution [DOI: 10.1039/C4AY00571F] is used.

Project aims

We present a novel [confidential] illumination scheme with a factor of 500x increase in signal levels compared to normal Raman spectrometer systems, which will allow accurate and rapid disease screening of biofluids such as blood plasma [DOI: 10.1039/C6AN02100J], and also allow accurate and rapid characterization of large numbers of cells.

We will implement the simple yet effective illumination scheme on a full-size spectrometer for comparison with standard illumination, measure signal enhancement, and characterize samples of bacteria (MRSA vs controls). Raman spectroscopy has recently classified cells as anti-microbial resistant or not [DOI: 10.1038/s42003-018-0093-8], but with accuracies too low and acquisition times too long for the technique to have practical uptake. With our scheme, a whole population of bacteria can be accurately classified in the time it took to assess one bacterium.

The illumination scheme requires processing the raw data into separate spectra of individual cells, spectral processing to remove unwanted signals and reduce noise, and machine learning / artificial intelligence, along with more traditional data processing techniques to classify cell type and phenotype.

The aims of the project and timeplan [for a 3.5 year PhD] are as follows:

Year 1

1A. Design and implement novel illumination scheme onto Raman spectrometer (Edinburgh).

1B. Measure signal enhancement for Raman spectra of liquid solutions.

1C. Develop AMR strain of staphylococcus aureus (MRSA) with increasing concentration of methicillin.

Year 2

2A: Investigate bacterial samples.

2B: Characterize MRSA samples.

Year 2/3

2C: Data processing: converted recorded data into separate spectra for individual cells.

2D: Data processing: determination of concentrations of liquids using multivariate curve resolution [DOI: 10.1021/acs.est.8b01388] and determine the limit of detectability for ethanol in water. Collaborators in Engineering will advise on spectral unmixing [DOI: 10.1117/12.2241834].

2E. Data processing: classification of cell types with PLS-LDA, and various artificial intelligence / machine learning algorithms.

2F: Design & implementation of novel illumination scheme into microfluidic device (Glasgow).

Year 3

3A: Characterize and classify MRSA with optimized experimental setup and data processing.

3B: [time permitting] Apply to bacteria for synthetic biology applications (two strains of E. Coli) for production of fluorinated drugs for PET imaging.

3C: [time permitting] Apply to blood plasma samples for disease diagnosis of biofluids.

Year 4:

4A: Over-running (risk mitigation)

4B: Thesis writing

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.

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:

Funding Notes

Start: September 2020

Qualifications criteria: Applicants applying for a 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.
Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £15,009 (RCUK rate 2019/20) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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