The EV-TRACK summary add-on: integration of experimental information in databases to ensure comprehensive interpretation of biological knowledge on extracellular vesicles

摘要

Introduction Released in multiple body fluids, EVs protect their content from degradation and are increasingly considered for the development of novel clinical applications such as liquid biopsy tests [ 1 – 3 ]. The development of these tests is currently mainly driven by in-depth analysis of the protein and RNA cargo of EVs using omics approaches to identify biomarkers for disease. In addition, researchers aim to delineate EV functions in (patho)physiological conditions by integrating this knowledge on EV cargo [ 4 ]. Detection of EV-associated protein and RNA is highly valuable [ 5 – 7 ] but remains a challenge. Not all extracellular RNA and proteins are associated with EVs. Other extracellular macromolecular structures overlapping in size and/or density with EVs, such as protein aggregates, ribonucleoproteins and lipoprotein particles, contain RNA and proteins and are frequent contaminants in EV preparations [ 8 – 11 ]. Both the EV source and the method of choice determine the degree of specificity to which these contaminants can be separated from EVs [ 12 ]. A multitude of methods have become available to separate EVs from biofluids but each method achieves this with different specificity and efficiency, resulting in method-dependent identification of EV cargo [ 13 , 14 ]. To allow for the interpretation of contaminant-induced bias and to ensure reproducibility, transparent reporting of EV separation and characterization is crucial. To promote transparent reporting and reproducibility we released the open-source knowledgebase EV-TRACK that centralizes (meta) data of EV separation and characterization [ 13 ]. Currently, EV-TRACK includes experimental parameters of 2165?EV experiments from 1355 publications. For each experiment, the completeness of reporting the generic and method-specific information that facilitates interpretation and reproduction of the experiment is assessed by a checklist, summarized into the EV-METRIC (13; evtrack.org/about.php). Supported by the community, EV-TRACK has been included in the 2018 update of the MISEV guidelines (Minimal Information for Studies of Extracellular Vesicles) [ 15 ]. To enhance validation of EV-associated biomarkers and functions and, in general, to centralize knowledge on EV cargo, a multitude of databases have been created. EV-contained RNA and/or proteins are accessible on specialized databases such as EVpedia, Vesiclepedia, Exocarta, and more recently ExoRbase and EVmiRNA [ 16 – 20 ]. These databases are driven by community annotation and present cargo information retrieved using a variety of separation methods. Dependent upon the specificity of the method, this cargo thus associates with differential likelihood to EVs or extracellular macromolecular structures. As such, one of the main challenges of these databases is to ensure access to unbiased experimental information to interpret the EV content and thus to fit the purpose of biological knowledge discovery. By providing users the EV-METRIC linked to the EV-TRACK entry for reported studies, the 2019 update of Vesiclepedia was a first step towards integrating EV-TRACK knowledge in EV-related databases [ 21 ]. We present here the importance, development and integration of the EV-TRACK summary add-on to further integrate experimental information relevant to the interpretation of knowledge in databases and thus facilitate true EV cargo and function discovery using publicly available data. Development of the EV-TRACK summary add-on Currently, experimental information on EV separation and characterization provided by EV-related databases is limited and heterogeneously reported between platforms. To address this issue, we developed an EV-TRACK summary add-on ( Figure 1 ), which can easily be integrated in EV-related databases using the following hyperlink http://www.evtrack.org/study_summary.php?PMID= ?completed with the PubMed ID of the specific study. The summary add-on provides an instant overview of the nine experimental parameters that form the EV-METRIC, a measure for transparent reporting of separation and characterization methods (13; evtrack.org/about.php). Doughnut charts indicate the proportion of reporting adherence to each of the nine experimental parameters ( Figure 1 ). Where applicable, the study tree provides a schematic overview of different EV-related experiments and indicates the EV-METRIC for each individual study experiment. Additional experimental information can be viewed by clicking the EV-TRACK ID hyperlink, which redirects to the full entry in the EV-TRACK knowledgebase. For studies that have not yet been recorded in the EV-TRACK knowledgebase, users following this hyperlink will be invited to connect to the My EV-TRACK page and submit the publication for annotation. Once curation has been completed by EV-TRACK administrators, the EV-TRACK study summary will be automatically generated and become available on all EV-related databases providing the hyperlink to the EV-TRACK summary