Extracellular vesicles (EVs) are biological nanoparticles released by cells into body fluids, such as blood. Annually, thousands of studies claim that EVs are biomarkers for diseases. However, the results are often not reproducible for several reasons:
(1) unknown detection limits and uncertainties thereof,
(2) the smallest EVs cannot be detected, and
(3) the rarest EVs are missed.
The last decade, we have developed a flow cytometry method to determine the concentration of EVs in blood plasma within known diameter and fluorescent intensity ranges. Although we achieved a high level of standardization, the complexity of the assay and technology as well as the thriving research field require continuous improvements of the used hardware (electronics, fluidics, optics). Hence, there are still many open questions, like:
- Which factors contribute most to the uncertainty of measured EV concentrations?
- How can we detect the smallest EVs in body fluids at a high throughput?
- What is the optimal balance between sensitivity and speed?
- Could optical improvements, like spectral flow cytometry or interferometric detection, push the limits further?
Any improvement can be directly applied to EV samples in clinical biobanks, such that an EV-based biomarker for diseases is within reach.
The main aim of this research is to improve the hardware (electronics, fluidics and optics) of a flow cytometer to realize an EV-based biomarker for diseases. This research requires hardware and software innovations and hence there is plenty of space for your own initiatives. The approach includes:
- Making the first uncertainty budget of EV concentration measurements with flow cytometry;
- Developing hardware and software solutions to improve the sensitivity and speed of a flow cytometer as well maximizing the information that can be retrieved from a single particle, for example by implementing spectral flow cytometry or interferometric detection. A flow cytometer at the optical bench is available and ready for optical modifications;
- Finding the optimum between sensitivity and speed in the context of rare EV detection.