To Cut Through the Noise in Extracellular Vesicle Analysis, Reproducible Isolation is Essential
Why the signal-to-noise ratio is important for EV-based diagnostics.
As is done for traditional assays, candidate EV-based diagnostic tests must be systematically and critically assessed to ensure they are fit for clinical utility. This is achieved by measuring assay performance characteristics such as precision, linearity, and analytical sensitivity.<super-script>1<super-script> Before an EV-based biomarker reaches this stage of rigorous assessment, its potential can be considered on a more conceptual level: by considering the ‘signal-to-noise ratio’. Here, we discuss the value of looking through the lens of signal-to-noise in EV biomarker development and explore how it has been leveraged to produce the qEV isolation platform.
Signal-to-noise as a guiding ‘big picture’ concept
The signal-to-noise ratio can be defined as the ratio between the desired information and the background noise associated with various unwanted signals. While it is used to check the validity of a physical measurement<super-script>2<super-script>, the principle can also be used to guide protocols prior to analysis. In other words, the quality of a measurement depends not only on the analytical method in question, but on efforts to minimise sources of variation associated with pre-analytical variables – including EV isolation.
Even though it is difficult (and perhaps impossible) to accurately quantify the signal-to-noise ratio across an entire EV workflow of sample collection, isolation, storage and analysis (where there are more than 100 variables<super-script>3<super-script>) the concept is no less important. Implementing practices that reduce noise and thereby maximise the signal-to-noise ratio is critical to obtaining a meaningful measurement.
Signal-to-noise considerations for EV researchers and instrument manufacturers
Collectively, there are many sources of variation which affect the performance of an EV-based biomarker test, both prior to and during analysis. ‘Zooming out’ and looking at the big picture can help assess how meaningful a measurement is likely to be and can also enable researchers to identify further opportunities to minimise noise and subsequently maximise the relative signal. To achieve this, researchers can ask the following questions when seeking EV-based biomarkers:
- What are the sources of noise in this EV isolation workflow?
- How reproducible is this isolation/analytical method?
- What are the sources of noise in this EV analytical method?
- What assumptions does this analytical method rely on, and how do these assumptions affect measurement accuracy?
- What factors can I control to minimise noise and maximise reproducibility? E.g., choice of instrument, study design, sample collection and storage protocols.
- Would this level of signal-to-noise be acceptable in a clinical setting?
- What are the potential implications of using an isolation method with low reproducibility?
Similarly, considering the signal-to-noise ratio is useful when developing instruments for EV separation and analysis. As is the case for researchers, instrument manufacturers can help maximise the final signal-to-noise ratio, thereby helping to increase the chance of developing a reliable and clinically useful EV-based biomarker. Developers of these instruments can ask themselves:
- What are the potential sources of noise in this instrument?
- How can we reduce noise at every stage of design and manufacturing to reduce noise and subsequently provide EV researchers with the best chance of obtaining a more meaningful and reproducible signal?
The qEV platform: Reproducible EV separation essential to minimising noise
While our qEV isolation platform is used by many research groups involved in EV-based biomarker discovery, the qEV isolation platform is also being used by developers who have their eyes set on clinical diagnostic applications. With clinical application being the end goal, we have therefore kept the concept of signal-to-noise at front of mind when developing solutions for EV isolation and analysis.
Izon’s approach to separating EVs from other components is built on a foundation of automation, ease-of-use, and size-exclusion chromatography. Collectively, the Automatic Fraction Collector (AFC), and AFC-compatible, size-exclusion chromatography qEV columns comprise a platform that provides rapid and reproducible EV isolation. Noise is reduced by maximising not only the removal of contaminants, but by maximising the consistency of the whole process. Overall, the reproducibility of qEV isolation substantially helps to reduce noise during analysis, as each sample is isolated in a highly consistent manner.
Automation-enabled reproducibility and precision: The AFC introduces a level of automation which minimises manual handling as a source of error. While manual SEC collection techniques rely on the user manually collecting the volumes and changing the collection tube at the correct moment, the AFC ensures samples are separated in the same manner every time. This is achieved by measuring void and sample collections based on weight. Also, the in-built rotational carousel holds collection tubes and automatically advances to the next fraction when the programmed volume has been reached.
Ease-of-use: The AFC is straightforward to use, defined fractions can be easily obtained, and multiple AFCs can be run concurrently. The potential for manual error is reduced through the user-friendly setup menu which allows key parameters to be easily checked before hitting ‘start’. Once isolation is initiated via the integrated touch screen, the user is free to walk away. Ease-of-use helps reduce noise by minimising the potential for human error.
Maximised separation: The separation of EVs from proteins and other components is achieved on qEV columns through size-exclusion chromatography, via built-in agarose bead technology. This approach allows users to consistently select fractions with the largest proportion of EVs, relative to protein content. Selecting a column size appropriate to the sample volume further optimises separation and minimises the extent of sample dilution.
Smart column management: qEV columns are tagged with RFID chips which store critical information about the number of uses. This helps with traceability and ensures columns are not overused.
EV isolation can ‘make or break’ EV-based diagnostics
The reproducible isolation of EVs from plasma, cell culture, and other sample types is critical to the development of EV-based biomarkers. All EV-related isolation protocols and analytical methods will have some inherent level of noise; however, some will be noisier than others, due to the assumptions that measurements are based on. The level of noise accepted in early stages of biomarker discovery – and in the therapeutics space – often exceeds what would be suitable at later stages of validation or application.
Taking a step back and looking at the big picture can help guide decisions that align with goals of EV biomarker development. By zooming out with this wider perspective, it’s easy to see that the selection of reproducible and scalable tools is one way to minimise noise across the EV isolation-analytical workflow, thereby amplifying the signal in downstream analysis.
On the other hand, zooming in on opportunities to minimise error is another important part of the puzzle – both for researchers and instrument manufacturers alike. Focusing on ways to minimise noise has led to the precise isolation of the qEV isolation platform, which is now facilitating meaningful advances towards clinical applications.
- Ayers L, Pink R, Carter DRF, Nieuwland R. Clinical requirements for extracellular vesicle assays. Journal of Extracellular Vesicles. 2019;8(1):1593755. doi:10.1080/20013078.2019.1593755
- Chuo ST-Y, Chien JC-Y, Lai CP-K. Imaging extracellular vesicles: current and emerging methods. Journal of Biomedical Science. 2018;25(1). doi:10.1186/s12929-018-0494-5
- Van Deun J, Mestdagh P, Agostinis P, et al. EV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research. Nature Methods. 2017;14(3):228-232. doi:10.1038/nmeth.4185