Why?

Extracellular vesicles (EVs) have emerged as having important (patho)physiological roles, and as such they have been the focus of intense study over the past decade. Despite substantial progress, the complexity and challenges associated with EV research remain considerable. EVs released from different cell types (and even from a single cell type) are heterogeneous in size and in protein, nucleic acid and lipid content. The separation of EV populations is frequently complicated by the presence of other extracellular macromolecular structures with comparable features. Different separation methods enrich for single or multiple EV subtypes with diverse composition and variable purity, thus identifying method-dependent EV content and function. Each detection and characterization method has its own accuracy and precision in measuring EVs.

In EV research, as in any field, the implementation of different methods requires validated controls and adequate reporting of experimental parameters. Failure to follow these principles can result in data that are difficult to interpret and reproduce. Similar to minimal information checklists in other fields, the International Society for Extracellular Vesicles (ISEV) introduced minimal informations for studies on EVs (MISEV) guidelines. Nevertheless, EV research is unfolding at a rate that impedes widespread adoption of these guidelines.

What?

We developed the EV-TRACK knowledgebase to record experimental parameters of EV-related studies with the aim to:

The final goal of the EV-TRACK consortium is to facilitate standardization of EV research through increased systematic reporting on EV biology and methodology.

How?

The EV-TRACK online toolset consists of seven enabling features to coach researchers through the use of the EV-METRIC, to centralize data on EV characteristics and methods, to query research articles and to involve researchers in decision-making on future improvements to EV-TRACK and its EV-METRIC.

The EV-METRIC is a key feature, designed to reflect the level of transparency in reporting of experimental parameters. It is presented as a percentage of fulfilled components from a list of nine, which were argued by the EV-TRACK consortium to be indispensable for unambiguous interpretation and independent reproduction of EV experiments. Researchers are encouraged to obtain this EV-METRIC prior to submitting their manuscript for peer review. After uploading requested experimental parameters, an EV-TRACK ID will be assigned and a preliminary EV-METRIC is calculated. The authors can implement this metric to improve the transparency of their experiments. When including the EV-TRACK ID in the material and methods section of a manuscript, journal editors and reviewers will also be able to access the corresponding EV-TRACK data entry, which provides them with a comprehensive overview of the presented data. Upon publication, the data submitted in EV-TRACK are curated by the EV-TRACK administrators, the final EV-METRIC is calculated, and the experiment(s) is(are) included and searchable in the public knowledgebase.

EV-TRACK also allows uploading of experimental parameters of already published experiments. A unique feature of EV-TRACK is the possibility to add unpublished experimental parameters of an already published EV experiment. For example, since the publication of EV-TRACK, researchers might realize that they forgot to include important experimental information. Since this information is generally available in the lab, it can be added post-publication to increase the reporting transparency.

Publications loaded in the EV-TRACK knowledgebase can be queried for specific experimental parameters that are not easily searchable in any current biomedical literature database. By centralizing this information, the EV-TRACK knowledgebase contributes to creating a better understanding of EV biology and methodology, which is needed to develop the next generation of experimental guidelines, if and when they are required.

The EV-METRIC

Conclusion

EV-TRACK and the EV-METRIC are tools to enhance transparency and interpretation of EV experiments. An increase in reporting transparency will benefit overall reproducibility in EV studies. Most importantly, EV-TRACK is driven by both the input and consensus of researchers that perform the experiments.

Contact

Please direct questions to evtracksubmissions@gmail.com

Reference

If you make use of the EV-TRACK platform, please cite it as:
Van Deun J, et al. EV-TRACK: transparent reporting and centralizing knowledge in extracellular vesicle research. Nature methods. 2017;14(3):228-32.

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