Periodic Reporting for period 2 - ENDOMICS (Raman Endoscopic Proteo-lipidomics of Bladder Cancer)
Reporting period: 2020-09-01 to 2022-02-28
The goal of ENDOMICS is to drive forward a new paradigm of Raman technology that enables proteomic and lipidomic analysis for diagnosis of bladder cancers in vivo. Raman spectroscopy is a label-free optical technique that can provide a point-wise vibrational molecular fingerprint of tissue “optical biopsy” for cancer diagnosis in vivo. State-of-the-art Raman endoscopy, however, does not offer specific compositional analysis or insights into molecular biology of tissue. This is because the vibrational Raman bands are overlapping and cannot be deciphered into the myriad of biomolecules in complex tissue. We are introducing a ground-breaking new methodology to enable Raman proteomic and lipidomic analysis in vivo. To this end, heterospectral co-registered Raman and mass spectrometry imaging will be used to develop a multivariate regression model for translating vibrational structural information (Raman spectroscopy) into compositional information. To meet the unmet clinical needs in we will tailor the first fibre-optic Raman endoscopic technology that can measure depth-dependent molecular profiles to simultaneously enable detection, grading and staging of cancers. We will finally apply the technique to measure a comprehensive molecular database of tissue pathologies. The latter will allow for the identification of proteomic and lipidomic biomarkers to develop novel algorithms for real-time diagnosis of cancers. The synergy between scientific and technological advances in ENDOMICS will break ground for shedding new light on the molecular biology of bladder cancer in vivo including new insights into clinical diversity and identification of biomarkers for diagnostics, prognostics, and novel therapeutic targets.
To this point we have developed an integrated Raman and mass spectrometry instrument. We have identified strategies to co-register and analyse Raman and mass spectrometry data with high accuracy. We have developed a comprehensive software for heterospectral data analysis. We have developed a rigid and flexible Raman endoscope technique that offers simultaneous morphological and molecular information. Finally, we have developed algorithms to speed up spectroscopy measurements using deep learning.
We have established a new instrument for molecular analysis of tissues using heterospectral Raman/mass spectrometry imaging. We have developed new methods and protocols to analyse spectral data combining both structural vibrational and composition information. We have developed a novel Raman probe for internal organs. Finally, we have developed algorithms to speed up data acquisition. We expect further novelties in data analysis and instrumentation to be developed until the end of the project.