Revealing the correlation between physiology and physical chemistry of microcalcification in connective tissue using AI supported tissue characterization.

About the Project

A fundamental component of human body is connective tissue which serves various structural and supportive functions including skin integrity, abdominal wall enforcement and bone formation. It comprises of diverse cell types, fibers, and an extracellular matrix, which provides structural strength and flexibility to organ systems. Various types of cells are part of that tissues like immune response cells, fat cells, mast cells etc. An essential component here is extracellular matrix, which is a combination of fibers embedded in a gel-like structure.

Though connective tissue cannot directly help in diagnosing cancer, but it can provide valuable information related to changes in the surrounding microenvironment. Research suggests that calcification (which is non-stoichiometric hydroxyapatite mineral) of connective tissue are associated with tissue damage and complications (like atherosclerosis, chronic kidney disease, or certain genetic disorders).

There are instances where mineralization or calcification can be observed in tumors. Breast, ovarian, and thyroid cancers, among others, may show calcifications in diagnostic imaging such as mammograms or ultrasounds. These calcifications are often associated with benign conditions or certain types of tumors but are not necessarily indicative of cancerous behavior or progression on their own. Similarly abnormal bone formation and mineralization in osteosarcoma is tumor characteristic and not directly caused by mineralization. Thus, there are certain association between mineralization of connective tissue and certain diseases. But there is little evidence to suggest direct causal relationship between mineralization and prognosis of cancer. The proposed research focusses to dive deeper into understanding potential interactions between mineralization and cancer through Raman spectroscopy.

Raman spectroscopy uses light scattering to provide information about the molecular composition of tissues. It can be employed to quantitatively analyze the chemical composition of connective tissues and detect changes associated with various diseases or pathological conditions. The above spectroscopic will be augmented with following analysis:

1. Electron Microscopy is proposed to be used for tissue analysis. This technique will allow high resolution imaging through the beam of electrons instead of photonic light. Transmission electron microscopy (TEM at Nano center) will be used to pass electrons through a thin tissue section to produce a high-resolution image, revealing detailed structures such as cell organelles (e.g., mitochondria, endoplasmic reticulum), nuclei, membrane structures, and fine details of tissues. Another similar technique, scanning electron microscopy (SEM at Biology Department) will be used to get a 3-D view of tissue surface by scanning the specimen with a focused beam of electrons. SEM is valuable for examining surface features, textures, and structures of tissues, including cells, tissues, and extracellular matrices.
2. Magnetic resonance imaging, computer tomography and ultrasound. These modalities can visualize both tumor and surrounding connective tissues and help in understanding changes in density, vascularity, as well as cancerous growth near connective tissues. The proposed project involves development of a diagnostic model for cancer through AI-driven systematic analysis of Raman spectrum to obtain physiological and chemical changes in cancerous and connective tissues. This could realize projects aiming to establish the link from tissue characteristics to cancer prognosis and accelerating structural biology research. AI/ML has the potential to revolutionize bio-photonic identification of cancer withbenefits involving increased productivity, accuracy, efficiency, and efficacy. The project will involve interdisciplinary research in:

1. Development of robot assisted in-situ stages for simulating physiological conditions like temperature, pH, moisture level, protein rich fluidic environment etc.
2. Bio photonic fingerprinting of connective tissues, cancerous tissues and subcellular components like proteins, lipids, nucleic acids, and carbohydrates, resulting chemical composition of biological fluids.
3. Cross-validation of results using histological testing.
4. 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. Bio photonic characterisation of biological tissues.
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:

1. Exploring spectroscopic data and relating it with chemical bonding of biological samples,
2. Raman spectroscopy training will be provided.
3. 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.

How to Apply:

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.