An integrated microfluidic – single cell Raman technology for rapid diagnosis of pathogens and their antibiotic resistance
Lead Participant:
EPIGEM LIMITED
Abstract
The global spread of anti-microbial resistance (AMR) is one of the biggest threats to human health.
A rapid diagnosis of AMR within 1-3 hours to infectious disease will not only reduce uncertainty in diagnosis,
saving millions of lives (e.g. those lost through sepsis), but also enable an effective help to clinical doctors
making the best use of antibiotics: preserving the usefulness of existing antibiotics for longer and reducing the
urgency of discovering new ones. This project will develop such a diagnostic tool by integration of advances in
single-cell Raman spectroscopy, microfluidics and lab-on-a-chip and world-leading clinical expertise. Our new
methodology is based on the detection of the general metabolic activity of cells at the single cell level. It
overcomes inherent limitations in the existing growth-based and DNA-based technologies, providing both
speed and phenotype information needed for data-informed prescription. We anticipate that the
implementation of this new diagnostic tool in healthcare will transform current approaches based on
“empirical” rules, bringing significant benefits to patients and public health.
A rapid diagnosis of AMR within 1-3 hours to infectious disease will not only reduce uncertainty in diagnosis,
saving millions of lives (e.g. those lost through sepsis), but also enable an effective help to clinical doctors
making the best use of antibiotics: preserving the usefulness of existing antibiotics for longer and reducing the
urgency of discovering new ones. This project will develop such a diagnostic tool by integration of advances in
single-cell Raman spectroscopy, microfluidics and lab-on-a-chip and world-leading clinical expertise. Our new
methodology is based on the detection of the general metabolic activity of cells at the single cell level. It
overcomes inherent limitations in the existing growth-based and DNA-based technologies, providing both
speed and phenotype information needed for data-informed prescription. We anticipate that the
implementation of this new diagnostic tool in healthcare will transform current approaches based on
“empirical” rules, bringing significant benefits to patients and public health.
Lead Participant | Project Cost | Grant Offer |
---|---|---|
EPIGEM LIMITED | £431,545 | £ 302,082 |
  | ||
Participant |
||
UNIVERSITY OF OXFORD | ||
UNIVERSITY OF OXFORD | £120,000 | £ 120,000 |
UNIVERSITY OF GLASGOW | £326,426 | £ 326,426 |
People |
ORCID iD |
Timothy Ryan (Project Manager) |