Towards robotic 2D-IR spectroscopy for biomolecular fingerprinting of cancer

Ultrafast 2D-IR spectroscopy is a new variant of Infrared spectroscopy which offers a unique analytical method for characterizing complex biomolecular samples. The bonds between atoms in molecular structures absorb IR light at specific wavelengths, so IR spectroscopy provides valuable information about the functional groups, chemical composition, and molecular structure of compounds. 2D-IR spectroscopy measures a two dimensional ‘molecular fingerprint’ which is unique to a given molecule and which has been shown to be extremely sensitive to protein structure. Thus 2D-IR has shown considerable promise as a tool for cancer detection and diagnosis because it is the only method with the potential to measure and quantify diagnostically relevant changes to protein levels in patient blood serum. New laser technology means that a 2D-IR spectrum can be measured in ~ 1 min per sample, which makes sample preparation the rate determining step. Robotic automation sample preparation, presentation and measurement for protein-rich fluids could pave the way to

1. Biomolecular fingerprinting of proteins, lipids, nucleic acids, and carbohydrates, resulting chemical composition of biological fluids. This helps in cancer detection and characterization.

2. Liquid biopsy analysis via IR and 2D-IR spectroscopy could provide insights to guide accurate diagnosis of cancer and determination of its stage/grade.

3. Monitoring treatment efficacy via infrared spectroscopy of biofluids could help in monitoring effects of cancer treatment (like chemotherapy).

4. Preparation and measurement of protein samples in combination with candidate drug molecules could be used to facilitate high throughput screening of the structural impact of drugs binding to proteins, with benefit to the pharmaceutical sector.

5. Extending beyond liquids to tissue characterization could allow visualization of tissue sections at microscopic level. This technique can identify cancerous regions with tissue samples.

Hence, infrared, and 2D-IR spectroscopy can offer valuable insights into the characterization of proteinaceous samples, leading to understanding of molecular problems relevant to biomedical applications. Despite its advantages, 2D-IR spectroscopy suffers from limitations like:

1. Need for high sample concentrations (millimolar range), which implies preparation for infrared and 2D-IR spectroscopy of water-rich protein-containing fluids is time consuming and requires specialized training.

2. As a result of high sampling time, data acquisition for samples is time consuming. Other technical challenges involve phase distortion, vibrational mode coupling, solvent effects, which makes spectrum analysis difficult as it is computationally demanding.

The proposed research aims to integrate 2D-IR spectroscopy with robotic automation for biomolecular fingerprinting of cancer. This could realize projects aiming to establish the link from 2D-map to protein 3D structure, accelerating structural biology research and improving our ability to quantitatively analyze patient biofluid samples. Robotics has the potential to revolutionize bio-photonic identification of cancer with benefits involving increased productivity, accuracy, efficiency, and efficacy. The PhD project will involve interdisciplinary research in:

1. Finding robotic solutions for automated biological sample preparation (liquid or tissues) and delivery. These robotic solutions are important for achieving high throughput and limiting effect on potential sample contamination or degradation through handling.

2. Developing robotic techniques for collecting high-quality spectral data. This ability to make and measure samples from constituent components (e.g., drugs, proteins) before passing the samples for measurement could lead to fully automated drug-screening protocols.

3. Developing Machine learning methods for automating data analysis and overcoming computational limitations. Automation would remove human error in sample preparation, which would enable reliable collection of large datasets that could be compared using ‘big data’ methods such as Machine Learning.

Successful candidate will work with an interdisciplinary team of researchers from Chemistry, Robotics, Physics and Biology department at the University of York. Clinical partners will help in validating results during the research work.

Key areas of research:

1. Design, control of robotic automation for spectroscopic sample preparation.

2. Developing Machine learning algorithm for analysing spectroscopic data.

3. Utilizing AI/ML algorithms by co-relating them with micro-biological information for cancer diagnosis.

4. Data analysis using mathematical modelling.

Possible areas of impact:

Novel Collaboration for synergizing interdisciplinary research (robotics, optical science, spectroscopy, biology, AI, and ML) for cancer diagnosis.

Training provided:

Research will include working with synthetic biochemical compound, cancerous tissues, mechanical and electronic circuit designs, prototyping, and pre-clinical in-vitro testing of robotic devices:

1. Exploring spectroscopic data and relating it with chemical bonding of biological samples,

2. Integration of sensors, camera interfaces into robotic systems,

3. 2D-IR spectroscopy training will be provided.

4. AI/ML modelling and data analysis using CNN.

Candidates with the following skills are desirable:

Proficiency in Data Analysis/AI/Machine learning (Python, MATLAB), programming (Micro-controller/processors), mechanical CAD design (SolidWorks, hands-on experience of 3D printing, laser cutting, 3D scanning). Candidate should be able to understand chemistry from basic concepts especially fundamentals of spectroscopy.

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.