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Updated guidelines on Minimal Information for Studies of Extracellular Vesicles have now been published in the Journal of Extracellular Vesicles (JEV, Taylor & Francis) as MISEV2018.

The original MISEV2014 guidelines were released in 2014 by the Board of Directors of the International Society for Extracellular Vesicles (ISEV) to provide guidance in standardization of protocols and reporting in the EV field. Accumulating more than 800 citations since its release, the MISEV2014 guidelines have achieved the aim of becoming a guiding standard for researchers. A 2016 survey of ISEV members reaffirmed the need for guidelines and recommended that they be updated regularly…but with broad community input to accommodate and shape the quickly developing field.

MISEV2018 updates the topics of nomenclature, separation, characterization, and functional analysis, integrating the contributions of over 380 ISEV members, a strong tribute to the commitment of ISEV members. A two-page checklist summarizing the main points is also included.

So what’s new? MISEV2018 recommends the use of ‘extracellular vesicle’ as the preferred generic terminology for use in publications, in part due to challenges in confirming the biogenesis mechanisms of exosomes, microvesicles, and other particles, and in part due to the vague and varied uses of other terms. Separation and concentration options are now many and diverse; researchers should pick the methods most fit for downstream purpose and, more importantly, report these clearly and accurately. The EV-TRACK database (van Deun et al., Nature Methods, 2017) is supported as a means to record these details in order to improve clarity and reproducibility. To establish presence of EVs, examples of EV-enriched markers are provided, but the need for “negative” (better: “depleted”) markers is also highlighted. MISEV2018 adds topology as a recommended form of EV characterization, for example identifying where in or on a vesicle your favorite protein or RNA resides. It also recommends functional analysis of the ‘non-EV’ fractions to confirm EV-specific function (or not!). An appreciation of EV heterogeneity is included with a reminder that ‘larger EVs matter’ and a request to explore a range of EV subtypes in functional studies. Finally, although some of the specific details contained in MISEV2018 are focused on mammalian components, it is appreciated that the guidelines are applicable to non-mammalian and non-eukaryote research.

Please contact the corresponding authors, Clotilde Théry and Kenneth Witwer with any questions or comments.

For more information on the process of writing and publishing MISEV2018, see this white paper and Witwer et al., J. Extracell. Vesicles, 2017.

References

Lotvall J, Hill AF, Hochberg F, et al. Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the international society for extracellular vesicles. J. Extracell. Vesicles. (2014) 3: 26913. doi:10.1080/20013078.2018.1535750. PMID:25536934.

Witwer KW, Soekmadji C, Hill AF, et al. Updating the MISEV minimal requirements for extracellular vesicle studies: building bridges to reproducibility. J. Extracell. Vesicles. (2017) 6: 1396823. doi:10.1080/20013078.2017.1396823. PMID:29184626.

Théry C, Kenneth W Witwer KW, Aikawa E, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J. Extracell. Vesicles. (2018) 7: 1535750. doi:10.1080/20013078.2018.1535750.

Despite being one of the earliest known classes of non-coding RNA molecules, tranfer RNAs (tRNAs) are still notoriously difficult to study. The challenge is largely due to this molecule’s secondary structure, chemical modifications to its constituent nucleotides (see figure), and the multiplicity of tRNA genes. As the number of non-coding RNA datasets proliferates, it is becoming increasingly important for tRNA genes to be accurately annotated. In a recent study, Thomas Tuschl from Rockefeller University and colleagues tackled this problem by developing a new protocol for sequencing tRNAs. The new method enabled them to assemble an atlas of human tRNAs for other researchers to use in analyzing their non-coding RNA data.

Hydro-tRNA Sequencing
Transfer RNAs have thermodynamically stable secondary and tertiary structures, and their constituent nucleotides are highly modified by RNA editing. Both of these characteristics are problematic for traditional RNA sequencing methods. The key to the Tuschl lab’s protocol, called hydro-tRNA sequencing (hydro-tRNAseq), is a partial alkaline hydrolysis step that breaks the 60-100 nucleotide-long tRNA into smaller fragments with fewer RNA modifications. These fragments, 19-35 nucleotides in size, have weaker secondary structure and fewer RNA modifications per fragment than the parent tRNA.

Applying the method to short RNA extracted from human embryonic kidney (HEK293) cells resulted in an increase in the fraction of reads mapped to tRNA between 2% and 40%, depending on the depth of sequencing. The short fragment length also improved read accuracy per base compared to standard tRNA sequencing.

To develop a thorough and representative reference set of human tRNAs, the HEK293 dataset was subjected to iterative cycles of mapping to existing reference tRNAs followed by manual curation. In each round, all transcripts with an error distance (number of mismatches, insertions, and deletions) of 1-2 from a given tRNA were kept as candidate reference sequences if they could be attributed to a tRNA isoacceptor (i.e. a different tRNA that binds to the same amino acid). If not, assuming that other mismatches were caused by misidentifying a modified base, transcripts with more than 10% mismatches compared to reference were expanded into a set of all possible combinations of RNA modifications and included in the reference pool (see figure). This mapping and selection process was repeated until there were no longer any modified positions left with a mismatch frequency over 10% compared to reference.

Candidate pre-tRNA genes were obtained by mapping the final tRNA reference sequences back to the genome. Altogether, this analysis was able to account for 93% of the 114 million reads in the deepest library of HEK293 cells’ tRNAs.

tRNA Modification Sites

tRNA Modification Sites
The team identified sites of modification from the high frequency of mismatches during mapping caused by read errors there during reverse transcription. Here the reference nucleotide is at ring center, known modification outside the ring, and frequency of each nucleotide read at that site inside the ring.
Source: Cell Reports

The Added Power of SSB PAR-CLIP
Though hydro-tRNAseq greatly improved the reference dataset of human tRNAs, there was still a risk that it alone would miss pre-tRNAs expressed at low levels or processed quickly into mature tRNA. Previous efforts to assay that ephemeral population employed ChIP-seq of POLR3, the polymerase that transcribes all tRNA genes, but doing so assumed that polymerase binding always led to expression and complete processing. The Tuschl lab focused instead on SSB, a protein that binds to the 3′ end of pre-tRNAs, immunoprecipitating tRNAs crosslinked to SSB using a method called PAR-CLIP. As predicted, almost half of the reads from their SSB PAR-CLIP experiments mapped to pre-tRNAs. Combining SSB PAR-CLIP with hydro-tRNAseq allowed the team to better identify mature and pre-tRNAs with improved, accurate, nucleotide-level resolution.

This study supplies the community with several new and useful resources. Hydro-tRNAseq provides a new method to overcome many of the struggles of tRNA sequencing analyses. Combining this method with SSB PAR-CLIP enabled the construction of a comprehensive atlas of pre-tRNAs and mature tRNAs in humans. This methodology can now be applied to study the tRNA complement in other species to further dissect tRNA biology.

Reference
Tasos Gogakos T, Brown M, Garzia A, Meyer C, Hafner M, & Tuschl T. Characterizing Expression and Processing of Precursor and Mature Human tRNAs by Hydro-tRNAseq and PAR-CLIP. Cell Reports (2017) 20: 1463-1475. doi: 10.1016/j.celrep.2017.07.029

Quantitative measurements of the number, size, and cargo of extracellular vesicles (EVs) are essential to both basic research on how EVs are produced and function, and to application of this knowledge to the development of EV-based biomarkers and therapeutics. Flow cytometry is a popular method for analyzing EVs, but their small size and dim signals have made this a challenge using the conventional flow cytometry approaches developed for analysis of cells (1). Moreover, established flow cytometry calibrators, standards, and experimental design considerations for cell studies are not regularly used in EV studies. As a result, there is significant variation in instrument set up, sample preparation, and data reporting for flow cytometric measurements of EVs. These issues are increasingly appreciated (1-5), but much needs to be done to develop consensus on best practices. To address these issues, members of the International Society for Extracellular Vesicles (ISEV), the International Society for Advancement of Cytometry (ISAC), and the International Society on Thrombosis and Hemostasis (ISTH) are participating in a tri-Society Working Group, which includes several ERCC members, to improve the reporting of methods and results for FC-based EV measurements.

Reporting of EV Measurement Methods

A flow cytometer is an instrument, not a method. An EV analysis method that uses a flow cytometer involves many instrument setup, sample preparation, and data analysis decisions, including: 1) what signal to use for EV detection (light scatter or fluorescence); 2) how to resolve single EVs from the simultaneous occurrence of many EVs in the laser at the same time (aka coincidence or “swarm”); 3) how to gate the data to focus on EVs versus background events (without introducing artifacts or mis-representing the data); 4) how to estimate the size of the particles detected; 5) how to estimate the brightness of the particles detected; 6) how to verify that the particles detected are EVs and not other particles present in the sample, to name just some of the many decisions involved.

Several years ago, ISAC developed and introduced the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) (6), a set of guidelines to promote the sharing, reproducibility, and proper interpretation of flow cytometry data. These guidelines were developed with cell analysis, and particularly high parameter immunophenotyping, in mind, but they also apply to multiparameter EV analysis. However, there are several additional details about an EV measurement that are essential to include. The ISEV-ISAC-ISTH EV FC Working Group has been conducting a series of standardization studies to develop a consensus on the essential elements of an FC-based EV measurement that should be reported. These studies will be reported, along with the consensus reporting guidelines, in a paper planned for the coming year.

Standards and Calibrators for EV Analysis

Standards and calibration are essential components of any analytical method. These standards, and their use, are well established for flow cytometry and include 1) counting beads that can be used to calibrate sample flow rates for reporting of absolute particle concentrations, 2) fluorescence intensity standards that enable particle brightness to be expressed in NIST-traceable absolute units of mean equivalent soluble fluorochromes (MESF) (7) or equivalent reference fluorochromes (ERF) (8); 3) antibody-capture standards that can be used to estimate antibody binding in immunofluorescence measurements; and 4) NIST-traceable particle size standards.

Most of these standards and calibrators, and their methods of use, can be applied to EV measurements, with some caveats and cautions. Particle size standards, in particular, are often mis-used in FC-based EV measurements due to a lack of understanding of the effect on light scatter of refractive index (RI), which is different for polystyrene, silica, and lipids. With care, however, these differences can be used in conjunction with Mie scattering theory to enable estimates of EV size based on FC light scatter measurements. Commercially available fluorescence intensity and antibody-capture standards are generally designed for cell measurements, and tend to be brighter than EVs, but still have value for facilitating comparison of measurements between labs or instruments. EV-scaled intensity and antibody-binding standards will be a useful addition to the EV analysis toolbox, and are in development by several groups and companies.

A major unmet need is for EV standards, which will have use not only in FC-based EV measurements, but across the EV field. This is a challenging prospect, as an ideal EV standard will reflect not only the size and number of EVs, but also cargo, including surface molecules (for immunophenotyping) and intra-vesicular cargo, including nucleic acids, soluble proteins, and small molecules. Moreover, EVs are themselves quite diverse, raising the question of what type of EV, if any, might represent a universal standard. EV preparations for various cultured cell lines are commercially available from a number of sources but, in general, these have not been subjected to rigorous, independent characterization of these essential features or their uniformity, stability, or reproducibility. Such characterization is essential for validation of any putative standard and may be the subject of future activities by the ISEV-ISAC-ISTH EV FC Working Group.

Conclusions and Prospects

As EV research expands to impact every area of biology, issues with rigor and reproducibility are front and center. Translating observations made in the basic research lab into mechanistic understanding of EV actions and clinically actionable knowledge requires robust and validated analytical methods. Careful attention to the description of methods, standardization and calibration of analytical instrument and methods, and reporting of results are essential. Community efforts by the ERCC and relevant international societies will be key to helping researchers maximize the value of their work to the broader community.

In future blog posts we will discuss the controversial issue of whether to use light scatter or fluorescence to detect EVs, as well as new EV detection methods we’ve developed using fluorogenic membrane probes.

References

1. Nolan JP. Flow cytometry of extracellular vesicles: potential, pitfalls, and prospects. Curr. Protoc. Cytom. (2015) 73:13.14.1-13.14.16. PMID: 26132176. doi: 10.1002/0471142956.cy1314s73.
2. Chandler WL. Measurement of microvesicle levels in human blood using flow cytometry. Cytometry B Clin. Cytom. (2016) 90:326-336. PMID: 26606416. doi: 10.1002/cyto.b.21343.
3. Coumans FA, et al. Methodological guidelines to study extracellular vesicles. Circ. Res. (2017) 120:1632-1648. PMID: 28495994. doi: 10.1161/CIRCRESAHA.117.309417.
4. Nolan JP, Duggan E. Analysis of individual extracellular vesicles by flow cytometry. Methods Mol. Biol. (2018) 1678:79-92. PMID: 29071676. doi: 10.1007/978-1-4939-7346-0_5.
5. Nolan JP, Jones JC. Detection of platelet vesicles by flow cytometry. Platelets (2017) 28:256-262. PMID: 28277059. doi: 10.1080/09537104.2017.1280602.
6. Lee JA, et al. MIFlowCyt: the minimum information about a flow cytometry experiment. Cytometry A. (2008) 73:926-930. PMID: 18752282 doi: 10.1002/cyto.a.20623.
7. Wang L, Gaigalas AK, Abbasi F, Marti GE, Vogt RF, Schwartz A. Quantitating fluorescence intensity from fluorophores: practical use of MESF values. J. Res. Natl. Inst. Stand. Technol. (2002) 107:339-354. PMID: 27446735. doi: 10.6028/jres.107.027.
8. Wang L, Gaigalas AK. Development of multicolor flow cytometry calibration standards: Assignment of equivalent reference fluorophores (ERF) unit. J. Res. Natl. Inst. Stand. Technol. (2011) 116:671-83. PMID: 26989591. doi: 10.6028/jres.116.012.


Scientists from the ERCC have joined forces to create a CSF Consortium to pool resources and establish standard practices in the study of cerebrospinal fluid (CSF).

One of the goals of the ERCC is not only to understand the fundamental biology of extracellular RNA (exRNA), but to develop exRNA-based biomarkers of disease. When such biomarkers have been found, studied, and cleared for clinical use, liquid biopsy of blood and other biofluids can enable earlier disease detection and less invasive tracking of disease progression. For neurological disorders, drawing CSF from the spinal cord has clear benefits over a more invasive brain biopsy. Progress in our technical understanding of how to accurately assess biomarkers in CSF will increase our basic understanding and promote clinical advancements in the diagnosis and treatment of neurological disease. Unfortunately, there are many inconsistencies between the processing of CSF in current studies. Data replication is often difficult, in large part due to variability across laboratories and institutions in protocols for sample isolation, purification, and analysis. Thus, the CSF Consortium, spearheaded by Dr. Fred Hochberg (https://fredhhochbergmd.com), was designed to be a resource for researchers to help minimize these discrepancies.

The CSF consortium plan calls for CSF researchers and clinicians to work together to improve standard practices. A major focus is transparency through open sharing of their work. Researchers are encouraged to establish collaborations, share in-depth details of experimental designs and reagents (including batch/lot numbers), and release any details of in-house protocol modifications. Working with the same biosamples shared through the Virtual Biorepository (VBR) enables multiple labs to compare and synchronize their protocols with one source of variability removed. The expectation is that sharing of detailed information will enable future researchers to avoid common pitfalls and plan their own experiments appropriately. Ultimately, the goal is to have open-access information available from each stage of every project: from biofluid, RNA, and extracellular vesicle (EV) collection, isolation, and storage to downstream analyses such as RT-qPCR and RNA sequencing.

If you are a CSF researcher, please contact us so that we can work with you as well!

Highlights of recent CSF Consortium efforts
Saugstad et al. (2017) recently demonstrated the strength of the CSF consortium. In a collaboration between three institutions (UC San Diego, Oregon Health & Science University, and the Translational Genomics Research Institute), researchers worked together to characterize the EV and RNA composition of identical pools of CSF at each institute from patients with five different neurological disorders. This work in parallel allowed the groups to identify potential sources of variability in protocols including sample preparation, RNA isolation, and quantification of RNA via RNA sequencing and RT-qPCR. The study identified changes in EVs and RNA in the disease CSF samples and detected an enrichment of microRNAs and mRNAs related to disease in both EV and total RNA. The paper highlights the importance of stringent standard operating procedures, including the use of common standard sample collection and data analysis protocols across institutions.

In other work, Figueroa et al., 2017 performed a multi-institutional study of RNA extracted from CSF-derived EVs of patients with glioblastoma (GBM), a very aggressive form of brain cancer. (See this related blog on glioblastoma.) A key diagnostic biomarker in classical GBM is the functional status of the Epidermal Growth Factor Receptor (EGFR). This cell-surface receptor is the starting point of a series of signaling pathways related to cell growth. When its expression surges or it folds incorrectly, the result is cells with hyper-active signaling that never stop growing. This study involved the development of a liquid biopsy that scans RNA extracted from CSF EVs for tumor-associated amplifications and mutations in EGFR. The test has very high specificity and fair sensitivity: it almost never incorrectly flags a healthy patient as having GBM and correctly identifies almost two thirds of GBM sufferers. The clinical standard for diagnosis of GBM is magnetic resonance imaging (MRI), which correctly classifies most brain tumors, but in too many cases incorrectly suggests that healthy brain tissue might be cancerous. The complementarity of highly sensitive MRI and highly specific RNA liquid biopsy argues that updating the standard of care to include collection of CSF and brain images at the same time would better separate healthy from diseased brain tissue.

The CSF Consortium is casting its nets wider in its fight against glioblastoma, looking at molecules beyond EGFR in the attempt to develop an RNA-based diagnosis tool for GBM. Akers et al., 2017 developed a diagnostic panel of 9 miRNA biomarkers by analyzing the EV RNA from 135 CSF samples in 3 cohorts, followed by validation of the miRNA panel in 60 CSF samples from 2 cohorts. The researchers found that even with that fairly large sample size, the miRNA profiles in lumbar and cisternal CSF — fluid collected from the spine or the base of the neck, respectively — are significantly different, which is problematic, since cisternal CSF is much more difficult to collect. On the other hand, they also found that RNAs extracted from raw CSF had a similar profile and diagnostic power as RNAs extracted from vesicles after an initial EV purification step, which might simplify the translation of this biomarker research into the clinic.

References
Akers, J.C. et al. A cerebrospinal fluid microRNA signature as biomarker for glioblastoma. Oncotarget (2017) 8: 68769-68779.
Figueroa, J.M et al. Detection of wtEGFR amplification and EGFRvIII mutation in CSF-derived extracellular vesicles of glioblastoma patients. Neuro. Oncol. (2017) Advance online publication. doi: 10.1093/neuonc/nox085
Saugstad, J.A. et al. Analysis of extracellular RNA in cerebrospinal fluid. J. Extracellular Vesicles (2017) 6: 1317577.

The Extracellular RNA Communication Consortium (ERCC) has developed a Virtual Biorepository (VBR) to facilitate the sharing of biological materials between researchers. As of September 1, 2017, the VBR hub is now available for use by the global extracellular RNA research community (both ERCC and non-ERCC members) at https://genboree.org/vbr-hub. This Phase 1 (beta) release currently provides access to metadata on more than 10,000 biosamples. Specifically, there are 7,651 cerebrospinal fluid (CSF) and 2,356 hepatobiliary samples from the Translational Genomics Research Institute, Phoenix Children’s Hospital, Oregon Health and Science University, and the University of California, San Diego. Another 50,000 hepatobiliary samples from the Mayo Clinic are planned to be available before the end of 2017. Most participant institutions have agreed to a common framework for biosample exchange, including common Institutional Review Board (IRB) protocols and Material Transfer Agreements (MTA).

The Virtual Biorepository originally arose from the needs of investigators within the ERC consortium to share biofluid samples across institutions for the purpose of collaborative protocol development and biomarker discovery. To enable efficient sample sharing, the ERCC Resource Sharing Working Group worked with the Data Coordination Center (DCC) and Administrative Core to initiate VBR development. The initial goal was to enable the sharing of cerebrospinal fluid (CSF) samples among members of the ERCC-based CSF consortium. The types of shared resources available in the VBR have since extended to include hepatobiliary samples, tissue, cell, and macromolecular samples, and even sample slides. These resources may be useful for catalyzing collaborations during the next stage of the Extracellular RNA Communication project.

The VBR is a distributed database system consisting of a hub and a set of local or cloud-hosted nodes. The VBR hub provides an overview of the types and number of biosamples present at the nodes. The hub supports sample queries based on consortium (CSF, hepatobiliary), institution, and on publicly shared metadata about anonymized VBR biosamples, including clinical, radiographic, pathologic, and accession metadata. Lists of samples that satisfy search criteria are placed in a shopping cart for ordering from sample providers. Search criteria and results can be saved for later retrieval and modification. In the current implementation phase (Phase 1) of the biorepository, after selecting samples, researchers communicate directly with each other to make specific arrangements for sharing biosamples. Future improvements (Phase 2) of the shopping cart feature will allow end-to-end tracking of the biosample ordering and exchange process.

VBR nodes are set up independently of the hub and are under the control of sample providers. The ERCC DCC provides assistance regarding maintenance of data within individual VBR nodes using pre-defined metadata templates. Investigators potentially interested in setting up a VBR node to share metadata about their samples may contact the VBR administrator (thistlew@bcm.edu).

The first version of the miRandola database has been published in 2012. It contained 89 papers and miRNA data. Now, we have updated the database with 272 papers and we redesigned the website!

We are starting to add more RNA molecules such as lncRNAs and circRNAs.

The miRandola database 2017 includes:

  • 272 articles
  • 2704 entries
  • 6 extracellular RNA forms
  • 673 microRNAs
  • 12 long non-coding RNAs
  • 8 circular RNAs
  • 21 drugs
  • 9 organisms and animal models
  • 173 diseases and cell lines
  • More features are coming soon!

The exrna.org Research Portal is linked in our web page.

Website: http://mirandola.iit.cnr.it/

For researchers who have just begun studying extracellular vesicles (EVs) and their contents, including extracellular RNA, the Extracellular RNA Communication consortium (ERCC) published a protocols decision tree today, designed to help select a set of protocols for isolating EVs and exRNA from several biofluids of interest. Some of the protocols in the decision tree have been developed by ERCC researchers, but many relevant methods were published before the ERCC existed, and the versions on the ExRNA Portal include modifications and comments made by ERCC members in the course of their experiments, and are being periodically updated. In this blog, we introduce the ERCC protocols decision tree and discuss some of the nuanced differences between classes of EV isolation methods, highlighting methods that have existed in the field for some time.

There are several methods and commercial kits available to isolate extracellular vesicles (EVs) and extracellular RNA (exRNA) from human biofluids. Multiple studies have reported on a variety of these methods, but to date there is not one method or kit that suits all studies. The type of biofluid, the sample volume, and the fraction of exRNAs of interest are some of the criteria used to determine what method should be used for a particular study. Here, we have compiled a list of methods and kits most widely reported in the literature for isolation of EVs, exRNA or other components of biofluids. Broadly speaking, all methods can be classified into five categories (see Table), the most widely used being ultracentrifugation.

Drawbacks of ultracentrifugation include the need for expensive instrumentation (ultracentrifuges and rotors) and the belief that the EV population after ultracentrifugation will be contaminated with cell-free DNA and proteins. Depending on the speed and time of ultracentrifugation, some ribonucleoprotein (RNP) and lipoprotein (LPP) complexes may also sediment. After ultracentrifugation, some researchers further purify the EV population using density gradients or size exclusion chromatography. Sucrose gradients have been used widely for several years but are being replaced more recently by iodixanol (OptiPrep) gradients because some groups have reported that sucrose may inhibit the biological effects of EVs, while EVs prepared with OptiPrep better retain their biological activity.

Size exclusion chromatography is also widely used and is suitable for fractionation of sedimented EVs, as well as unprocessed biofluids. Recently Izon introduced a commercial kit to speed up this method, with relatively good results. Size exclusion chromatography yields a very clean population of EVs with the drawback being loss of EVs during the multi-step purification process.

The first commercial EV isolation kit (ExoquickTM) was launched on the market about six years ago, and is based on the principle of polyethylene glycol/sodium chloride precipitation, which has long been used for concentration of viruses. Since that time, several other kits using a precipitation strategy were launched by other manufacturers. Each of these kits (SBI, Life Technologies, Norgen Biotek, and Exiqon) have slightly different proprietary approaches to EV precipitation. Drawbacks to EV precipitation kits include co-precipitation of other unwanted molecules found in the biofluids and the difficulty of isolating EVs from large volumes of starting material. Ultrafiltration (e.g. using Millipore Amicon filters) is often used by researchers to concentrate large volumes, either before or after EV purification.

Filtration based methods which isolate specific size ranges of EVs can also be performed using commercially available devices. In addition, several kits and protocols for affinity purification have been developed by biotechnology companies and academic research laboratories. Antibody-based affinity methods (ExoCap, Microfluids, µNMR), and heparin-coated agarose or magnetic beads have been shown to bind subpopulations of EVs from cell culture media, plasma and serum efficiently. Another affinity kit, the METE kit, includes a proprietary peptide that, according to the manufacturer, binds to heat shock proteins found on the surface of the plasma membrane, suggesting a possible method to enrich for EVs with high levels of heat shock proteins. All of these methods yield a pure sample of EVs and can be scaled up, although scale-up costs can be significant, particularly in the case of antibody-based methods. These methods are limited to isolating a subset of EVs that express a specific antigen. For researchers who are interested in targeting a specific population EVs that displays one of these antigens, this may be a good option. However, at this point, for most cases, the biology is unclear on the diversity of EVs released by cells. Therefore, one antibody, or a pool of three to four antibodies, may not isolate all relevant EVs present in the sample.

ExoRNeasy isolates EVs based on their affinity to a proprietary membrane. ERCC members using this kit have reported that it can efficiently separate EVs (they bind to the membrane with high affinity) from other exRNA-containing particles, such as RNP complexes, which can be collected in the flow through, so this kit offers the extra advantage of efficiently separating EVs from RNP complexes. The ExoRNeasy/exoEasy kit yields a pure EV population and can isolate EVs from a volume as low as 200 μL or as high as 4 mL of plasma or serum with one loading per column, and up to 100 mL of cultured media per column by loading the column 3-4 times. Larger volumes require loading multiple filters.

Two kits available on the market are based on a two-step procedure where the sample is first either filtered (e.g. PureExo) or concentrated (e.g. Exo-Spin) and then resuspended and allowed to bind to proprietary beads. Intact EVs are then eluted in PBS and can be used for a variety of downstream assays. These kits do not offer feasible approaches to scale up to larger volumes.

Many other combinations and variations on these methods have also been reported in the literature, and this list is not meant to comprehensively encompass all reports in the field. It is a simple overview of the major classes of EV and RNA isolation methods present in the EV isolation field.

EV protocols table

Thery C. et al. 2006 Lobb et al. 2015 Boing et al. 2014 Lobb et al. 2015 Taylor DD. et al. 2011 Vlassov AV. et al. 2012 Hudson MB. et al. 2014 Lasser C. et al. 2012 Bryant, R. J. et al. 2012 jsrmicro.com Chen C. et al. 2010 Shao H. et al. 2012 Balaj et al. 2015 Enderle et al. 2015 Korbelik et al. 2015 cellgs.com Ghosh et al. 2014
Sample output from Target Interaction Finder

Sample output from Target Interaction Finder

We recently developed two tools for the Genboree Workbench: Target Interaction Finder and Pathway Finder. Given a set of miRNAs, these tools find miRNA-target interactions and pathway targets from public databases. To support consortium scientists in using these tools, we have created two training videos which describe the necessary input file, how to use the tool, and downstream network visualization in Cytoscape for Target Interaction Finder and Pathway Finder. The videos are available in the collection of ERC consortium videos at YouTube and in the Resources/Presentations section of this website.

The tools are open to anyone with a (free) Genboree account and can be used with any arbitrary input list of miRNA identifiers or with public datasets available in the exRNA Atlas. Target Interaction Finder generates a network of miRNA and protein target interactions, which is returned as a tabular summary and an XGMML formatted network file. The network file can be imported into network visualization and analysis tools like Cytoscape. Pathway Finder generates a table of pathways containing the miRNA and/or their protein targets based on information from WikiPathways. Embedded in the results window of Pathway Finder is an interactive pathway viewer.

Sample Pathway Finder results file

Sample Pathway Finder results file

exRNA_WP_blog_tools-PF-interactive

Interactive Pathway Finder viewer on Genboree Workbench

Non-coding RNAs (ncRNAs), for example microRNAs (miRNAs), are frequently dysregulated in cancer and other diseases, and have shown great potential as tissue-based markers for cancer classification and prognostication. ncRNAs are present in membrane-bound vesicles, such as exosomes, in extracellular human body fluids. Circulating miRNAs are also present in human plasma and serum and cofractionate with the Argonaute2 (Ago2) protein and high-density lipoprotein (HDL). Since miRNAs and other ncRNAs circulate in the bloodstream in highly stable forms, they may be used as blood-based biomarkers for cancer and other diseases. A knowledge base of non-invasive biomarkers is a fundamental tool for biomedical research in this field.

In 2012, miRandola was developed as the first database of circulating extracellular miRNAs (Russo et al., 2012). miRandola is a comprehensive, manually curated collection and classification of circulating extracellular miRNAs. We recently updated miRandola with 271 papers, 2695 entries, 673 miRNAs and 12 long non-coding RNAs. The future direction of the database is to be a resource for all potential non-invasive circulating nucleic acid biomarkers.

miRandola_schema

miRandola is the first online resource which gathers all the available data on circulating RNAs into one environment (see Figure). It represents a useful reference tool for anyone investigating the role of extracellular RNAs as biomarkers, as well as their physiological function and their involvement in pathologies.

The database is constantly updated as soon as new data is available, and the online submission system is a crucial feature which helps to ensure that the system is always up-to-date. We are working on a second version of the database to increase the amount of data and to improve usability. miRandola is available online at http://mirandola.iit.cnr.it/.