Grant: Alliance Grants
École Polytechnique de Montréal
$439,000.00
Raman spectroscopy platform integrating highly sensitive photonic devices, nanostructured surfaces and machine learning for on-site detection of multiple diseases
EF 2024-25
Résumé
Many diagnostic tests for disease presence, progression and severity are invasive, expensive, time-consuming or lack accuracy. Meanwhile, there is a lack of biofluid-based assessment for many cancers, as well as for infectious diseases. The goal of this project, in partnership with the company Exclaro-Tridan, is to develop the first point-of-care biofluid system relying on Raman spectroscopy and fluorescence spectroscopy, coupled with machine learning and plasmonic technology. Optical spectroscopy techniques are non-destructive and non-ionising for molecular identification using low-power lasers. In this project, optical spectroscopy will be developed as a platform for rapid diagnostic test development using label-free and/or ligand-based interrogation, to rapidly provide a reading at the time of sample acquisition.
This new technology has the potential to detect multiple diseases from the same biospecimen, including cancer (brain, breast, prostate, colorectal, lung) and infectious diseases (COVID-19, influenza, and respiratory syncytial virus). The proposal has four objectives: 1) to develop a new instrument relying on label-free Raman and fluorescence spectroscopy imaging techniques, 3) to assemble large datasets of biofluid samples, 4) to develop machine learning tools leading to diagnostic test development for rapid disease screening and treatment efficacy assessment, 4) to address anticipated limitations of that system, resulting in system improvements in photon sensitivity, background suppression and disease detection specificity.
This work will result in significant advances in biophysics and data science research, leading to the development and maturation of a new class of diagnostic technologies. The downstream impact on society will be the availability of new point-of-care devices allowing to rapidly detect the onset of cancer and infectious diseases, as well as to assess treatment efficacy. This device would be the first of its kind and could be implemented in doctor's offices, pharmacies, or remote communities.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
585226-2023
Name nom
Leblond, Frederic F
Department département
Génie physique
Organization id
64
Institution établissement
École Polytechnique de Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2023
Award amount
439000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1102
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Biomedical engineering
Area of application fr
Génie biomédical
Research subject code
1900
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical engineering
Research subject fr
Génie biomédical
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Raman spectroscopy platform integrating highly sensitive photonic devices, nanostructured surfaces and machine learning for on-site detection of multiple diseases
Keywords
biofluids; biomedical engineering; infectious diseases; machine learning; metrology; molecular assay; nanomaterials; oncology; optics; raman spectroscopy
Application summary
Many diagnostic tests for disease presence, progression and severity are invasive, expensive, time-consuming or lack accuracy. Meanwhile, there is a lack of biofluid-based assessment for many cancers, as well as for infectious diseases. The goal of this project, in partnership with the company Exclaro-Tridan, is to develop the first point-of-care biofluid system relying on Raman spectroscopy and fluorescence spectroscopy, coupled with machine learning and plasmonic technology. Optical spectroscopy techniques are non-destructive and non-ionising for molecular identification using low-power lasers. In this project, optical spectroscopy will be developed as a platform for rapid diagnostic test development using label-free and/or ligand-based interrogation, to rapidly provide a reading at the time of sample acquisition.
This new technology has the potential to detect multiple diseases from the same biospecimen, including cancer (brain, breast, prostate, colorectal, lung) and infectious diseases (COVID-19, influenza, and respiratory syncytial virus). The proposal has four objectives: 1) to develop a new instrument relying on label-free Raman and fluorescence spectroscopy imaging techniques, 3) to assemble large datasets of biofluid samples, 4) to develop machine learning tools leading to diagnostic test development for rapid disease screening and treatment efficacy assessment, 4) to address anticipated limitations of that system, resulting in system improvements in photon sensitivity, background suppression and disease detection specificity.
This work will result in significant advances in biophysics and data science research, leading to the development and maturation of a new class of diagnostic technologies. The downstream impact on society will be the availability of new point-of-care devices allowing to rapidly detect the onset of cancer and infectious diseases, as well as to assess treatment efficacy. This device would be the first of its kind and could be implemented in doctor's offices, pharmacies, or remote communities.
