Year: 2014

Alzheimer’s disease (AD) is the most common form of dementia, a general term for loss of memory and other intellectual abilities serious enough to interfere with daily life. AD accounts for 60 – 80% of dementia cases, and is the sixth leading cause of death in the United States. The greatest known risk factor for AD is increasing age; most people with AD are 65 and older. As AD is a progressive disease, and dementia symptoms gradually worsen over several years, costs increase in parallel with dementia and behavioral disturbances, which require increased caregiving time and intensity. The cost of AD in the United States today is estimated to be $100 billion each year; the greatest costs are for long-term care by health care professionals and institutionalization, with significant family costs for direct caregiving and lost earnings. Current AD treatments do not stop disease progression, but they can slow the progression of dementia symptoms and improve quality of life for those with AD and their caregivers, and thus provide tremendous economic and social benefits. However, there is no clinical diagnostic test for AD, nor any that distinguishes early AD from age-related dementia. Such a tool would be invaluable in guiding clinicians towards early interventional efforts.

The existence of extracellular RNAs (exRNAs) in biofluids represents a fertile molecular landscape from which diagnostic and prognostic biomarkers may be accessed, characterized, and exploited. The mission of the Extracellular RNA Communication Program (ERCP) is to further basic and translational studies on exRNAs in a wide range of biofluids and human diseases. Our studies are focused on the identification of exRNAs in cerebrospinal fluid (CSF) that can be used as biomarkers for AD. CSF is produced in the brain and circulates through the ventricles and spinal cord, providing a buffer against chemical imbalances and mechanical injury, and a means to clear metabolic waste from the brain. The brain produces ~500 mL of CSF per day, with ~150 mL present at any time. Biological components in CSF provide a sensitive indicator of changes in the brain. For example, prominent protein biomarkers for AD include beta-amyloid and tau. Combined measures of beta-amyloid fragment and phosphorylated tau show high sensitivity for AD (90%), but low specificity (64%), which limits their clinical utility. To date there are no clinical biomarkers that can predict the onset of AD, distinguish AD from mild cognitive impairment, or distinguish the individual stages of AD (latent, mild, moderate, severe). Further, there are no studies to date to examine the utility of exRNAs in CSF from living human donors as AD biomarkers.

MicroRNAs (miRNAs) are one example of exRNAs that are increasingly found in circulating biofluids such as CSF, plasma, serum, and placental tissue, where their expression is correlated with several diseases including brain injury (traumatic brain injury, ischemia), degenerative disease (multiple sclerosis and AD), and mental health disorders (bipolar disorder). MiRNAs are members of the non-protein-coding family of RNAs that serve as regulators of post-transcriptional gene expression. Importantly, miRNAs are stable in circulating fluids, presumably because they are contained within vesicles or ribonucleoprotein complexes, which likely affords them protection against ribonuclease digestion. MiRNAs bind to specific proteins to form RNA-induced silencing complexes (RISCs), and together they can bind messenger RNA (mRNA) to either repress mRNA translation, or degrade mRNA transcripts, to ultimately regulate the expression of target proteins in the cell. Given that AD is generally characterized by an over-accumulation of proteins, we proposed that a loss of miRNAs might lead to overexpression and accumulation of proteins in AD brain.

To examine this hypothesis, we obtained miRNAs in CSF from 47 AD and 47 control living human donors from the Oregon Alzheimer’s Disease Center (OADC). The OADC uses standardized methods for CSF collection that are consistent with other dementia research centers. Total RNA was isolated from CSF, and miRNA detection was performed using PCR based methods that are in widespread clinical use. Each miRNA cycle threshold (Ct) value was normalized to U6 snRNA internal controls (ΔCt). Of 332 CSF miRNAs, we found that 19 had significant differential expression between AD and control, and these 19 miRNAs were detected less often in AD versus control CSF. Logistic regression models and receiver operating characteristic (ROC) curves were then used to evaluate combinations of the 19 top-performing miRNA to accurately classify subjects into AD and control. Figure 1 shows that area under the ROC curves demonstrate 63% classification accuracy for 1 miRNA, 73% for two miRNAs, and 81% for 4 miRNAs. Thus, combinations of miRNAs show better discrimination between AD and control CSF. Together, our studies support that miRNAs in human CSF show potential utility as biomarkers for AD, and suggest that CSF exRNAs may be used as biomarkers for other dementia and neurodegenerative diseases.

Figure 1: Combinations of miRNAs better discriminate between AD and control CSF.

Figure 1: Combinations of miRNAs better discriminate between AD and control CSF.

Saliva is a highly desirable body fluid for biomarker development for clinical applications as it provides a non-invasive, simple and low-cost method for disease detection and screening. Disease detection from a saliva sample has been addressed as one of the so-called Grand Challenges of the 21st century in President Barack Obama’s Strategy for American Innovation. In the last decade, the potential use of salivary RNA has been demonstrated for detecting various local and systemic diseases such as oral cancer, Sjögren syndrome, pancreatic cancer and breast cancer. We believe that the ability to characterize salivary exRNA with next-generation sequencing can further strengthen the advantages of using saliva as a clinical diagnostic biofluid for biomarker discovery.

Compared with other biofluids, saliva can be collected easily and noninvasively. However, low RNA abundance, small sample volumes, highly fragmented RNA and high abundance of bacterial contents create challenges for downstream RNA sequencing assays. Our experience in the field reveals that salivary exRNAs need different processing methods from other biofluids. RNA extracted from cell-free saliva (CFS) from low speed spinning contains significant levels of bacterial RNA. A subsequent cell removal step with high speed centrifugation resulted in all of the intact ribosomal RNAs (rRNA) being detected by Bioanalyzer in the pellet rather than in the cleared supernatant (SN). This observation, combined with the migration of the rRNA peaks (which is more rapid than typically seen for eukaryotic 28S and 18S rRNAs) support the notion that these rRNAs are of bacterial origin (Figure 1, SN & pellet bioanalyzer profile). These findings demonstrate that rRNA contamination, which for most biofluids would be presumed to be of cellular origin, was likely due to the high bacterial load in saliva. This conclusion was further proved by analysis of the resulting NGS data. Only 6-14% of reads mapped to the human genome and 60-70% of reads were from the microbiome, with the majority of sequences representing bacterial rRNAs. These results indicate the need to develop additional technical capabilities to rid samples of microbial rRNA sequences where the final goal is to ascertain the comprehensive salivary exRNA profile.

Figure 1: SN & pellet bioanalyzer profile

Figure 1: SN & pellet bioanalyzer profile

We have systematically tested RNA isolation efficiency with six commercially available kits with optimized protocols and compared their performance on mimic clinical saliva samples. We compared the RNA yield from saliva samples using six RNA isolation methods: (1) organic extraction method (Trizol LS); (2) spin filter based method (QIAamp Viral (Qiagen), NucleoSpin (Clontech) and miRVana (Life Technologies)); and (3) combined method of organic extraction and spin filter clean up (miReasy micro (Qiagen), Quick-RNA micro (Zymo). The quantity and size distributions of the resulting RNA samples were assessed using the RiboGreen reagent and the Bioanalyzer, respectively (Figure 2, Kits comparison), with the best yields from the NucleoSpin and miRNeasy micro kits.

Figure 2: Kits comparison

Figure 2: Kits comparison

Quantification of exRNAs is particularly challenging, given that they are typically at low concentrations and have a wide range of lengths, with a prominent population of small RNAs. It is important to keep in mind that different measurement techniques will yield very different total amounts of RNA. Agilent Bioanalyzer profile (Eukaryotic RNA Pico Chip Bioanalyzer) and High sensitive Ribogreen reagent were used as quality controls (QCs) to evaluate the quality and quantity of total RNA yield, respectively. The Ribogreen reagent (available as the Quant-iTTM RiboGreen RNA Assay Kit and Reagent, Life Technologies) can be used to quantify samples at a concentration as low as 50 pg/ul, but is affected by the presence of DNA since it binds to DNA as effectively as it binds to RNA. Although this method can be used to quantify RNA, only the Agilent Bioanalyzer, which has a lower range limit of 50 pg/ul for the RNA Pico and Small RNA Chips, can evaluate the integrity of the RNA molecules. However, this method is not as reproducible in terms of RNA quantification as Ribogreen-based assays and does not distinguish between RNA and DNA. The Bioanalyzer methods are also affected by impurities that can quench the fluorescent signal. Variability in the height of the internal marker peak, an uneven baseline, and an imperfect size standard ladder are indicators that there may be factors present that compromise the accuracy of Bioanalyzer quantification. Therefore, it is important to consider the characteristics of each quantification method, in terms of the limit of detection, dynamic range, and specificity for nucleic acid type, so the most accurate method can be used for the expected yield of RNA, since the measured total RNA yield will vary based on the quantification method used (Figure 3, Ribogreen vs Bioanalyzer quantification).

Figure 3: Ribogreen vs Bioanalyzer quantification

Figure 3: Ribogreen vs Bioanalyzer quantification

With all these factors in mind, we set up several thresholds for the QCs that the exRNA saliva samples should achieve to go further with the NGS analysis. Thus, all the samples should yield higher than 5 ng of total RNA measured by Ribogreen (RNA yield is about 20-80 ng/mL), and no intact ribosomal RNA peaks should appear in the Bioanalyzer profile.

Besides the RNA isolation and quantification setup, we performed qPCR/ddPCR assays to determine the efficiency of long and small RNA isolation from each kit, showing that RNeasy micro Kit and NucleoSpin are the best kits in yielding small RNAs and long RNAs simultaneously (Figure 4, ddPCR/qPCR data).

Figure 4: ddPCR/qPCR data

Figure 4: ddPCR/qPCR data

Among the commercially available RNA-Seq library construction methods that we tested, the NEB library preparation kit resulted in the highest number of human genes and small RNAs species. Generally, 23-36% of reads from long-RNA libraries are from bacterial ribosomal RNA. We tried to increase the sensitivity to human transcripts by using a protocol to selectively remove bacterial rRNA (Ribo-Zero(TM) Magnetic Kit for Bacteria, Epicentre). We tried several conditions, combining different proportions of biotinylated beads and rRNA-depletion-probes, to maximize rRNA depletion while avoiding the loss of human species by overloading the reaction with too many beads and/or probes. The mapping results showed many fewer microbial reads (0.8%-4.6%), and at least 48% more genes can be detected in rRNA-depleted samples compared to the control (Figure 5, rRNA-seq results).

Figure 5: rRNA-seq results

Figure 5: rRNA-seq results

This indicates the rRNA removal step could improve the comprehensiveness of human exRNA profile in saliva. Although the rRNA levels were reduced around 95%, we found a high loss in total RNA yield in the depleted samples compared to the controls (checked by Ribogreen assay). We found higher numbers of human genes in the depleted samples, which means that we were able to go deeper in the sequencing process, but we observed a reduction in quantitation for several mRNA, miRNAs and piRNAs in the depleted samples compared to the controls (Figure 6, Total yield, qPCR/ddPCR depleted vs control samples).

Figure 6: Total yield, qPCR/ddPCR depleted vs control samples

Figure 6: Total yield, qPCR/ddPCR depleted vs control samples

Want to share your knowledge of a particular exRNA gene with the broader scientific community? Want to reach readers through both the “traditional” peer-reviewed literature as well as the sixth-most accessed website in the world? If so, continue reading about our three-way partnership between the journal GENE, the Gene Wiki project at Wikipedia, and the Extracellular RNA Communication Program (ERCP):


  1. What is it? The goal of the Gene Wiki is to create a comprehensive Wikipedia article for every human gene. To incentivize authors to improve Wikipedia content, GENE is now soliciting new gene-specific review articles under a new dual-publication model. ExRNA genes are especially desirable and authors are invited to create two separate versions of their review (one for the journal, and one in wikipedia). More on the partnership here: Gene Wiki Reviews: Marrying crowdsourcing with traditional peer review.
  2. How long should the review article be? The length of the review article is up to you! Since you are the expert on the exRNA gene you’re writing about, the length is based on whatever you think is necessary to describe the current state of the field.
  3. How long should the wikipedia article be? We are targeting a final length of approximately 1200 words (though longer and more detailed articles are certainly welcome)
  4. How are the two versions different? One version is targeted at professional scientists following typical academic and editorial standards. The second version is written for the Wikipedia audience and includes a slightly heavier emphasis on a general audience. Both versions will be peer-reviewed together, but for copyright reasons, these two versions must be separate works that have no substantial similarity. Some examples of review articles and wikipedia entries published under this model include:
  5. I am busy but intrigued, what is the time line? We generally suggest a 2-3 month deadline, but since this is an ongoing series in the journal, the time line is flexible and can be worked around your schedule. Don’t be discouraged from participating because you are busy now. Make the commitment to submit when your schedule permits.
  6. Do I have to go at this alone? Absolutely not! If you have colleagues who would make good co-authors for the review, feel free to solicit their assistance.
  7. Do I have to write the wiki article all at once? Nope. Our goal is to incentivize you, the expert, to make your knowledge about your exRNA gene accessible. If it’s easier for you to write the wiki article in pieces, go ahead and do so! As long as the wiki entry is complete by the time you submit your manuscript, we will be happy to accept your review article.
  8. The gene I work on doesn’t make much sense to write about alone, how should I contribute? Genes that work in concert can be tackled as a pair as with this example:
  9. Why should I do this? By publishing an exRNA gene-specific review article, you help your scientific colleagues stay abreast of the current literature on your favorite gene. By publishing under the dual publication model (ie- on wikipedia), you help make your favorite exRNA gene more accessible to everyone allowing more people to understand the importance of your field of research. Everyone wins!
  10. How do I get in on this? Check to see whether or not your favorite exRNA gene could use some serious contributions on wikipedia. If so, contact me. Include your exRNA gene of interest in the email, and your preferred deadline for the manuscript submission.

Looking forward to hearing from you!

The isolation and characterization of subclasses of extracellular vesicles (EVs) has been an important goal of the field for many years. Measurements based on nanoparticle tracking analysis (NTA) (Dragovic et al, Nanomedicine 2011) and resistive pulse sensing (RPS) (Vogel et al, Anal Chem 2011) properties of these EVs are in common use in the field to measure various particle characteristics, including size and concentration. Flow cytometry is a well-developed technology that offers several unique advantages to characterize EVs, including high speed and quantitative analysis of individual cells or particles, with the added advantage of subsequent purification of discrete subsets of particles of interest. Our group has designed a robust flow cytometry-based technique capitalizing on recent improvements in light collection optics, electronic detectors, and computer sub-systems to develop FAVS (Fluorescence-Activated Vesicle Sorting). This technique requires at a minimum a modern digital sorter with highly efficient light collection optics. EVs need to be disaggregated into individual particles by shearing them sequentially through 22-, 27-, and 30-gauge syringes. Properly suspended EVs can then be stained, washed, and analyzed using sorter-optimized configurations. The sorter must be calibrated and set up so that the signals from the EVs are processed as fast as the electronics will allow, and the EVs must be diluted to no more than 10 μg/ml total protein to avoid swarm effects by which multiple particle events are measured together rather than individually (van der Pol et al J Thromb Haemost 2012). We have demonstrated the ability to purify vesicles below the diffraction limit of light to a post-sort purity of greater than 99% double-positive fluorescence and size range of 40 to 70 nm in diameter (Cao et al, Mol Cell Proteomics 2008). To achieve this result, we started with iodixanol gradient-enriched intracellular vesicles that averaged less than 70% double-positive fluorescence and a vesicle size range of 30-300 nm in diameter. We have gone on to use FAVS to analyze EVs in various contexts, including those secreted from cells in culture (Higginbotham et al, Curr Biol 2011 and Demory et al, Mol Cell Proteomics 2013) and to isolate specific subclasses of secreted vesicles in vivo (McConnell et al, J Cell Biol 2009 and Shifrin et al, Curr Biol 2012) to analyze their constituents. Other groups have employed similar strategies to measure secreted vesicles by flow methods (Nolte-‘t Hoen et al, J Leukoc Biol 2013 and Momen-Heravi et al, Front Physiol 2012). Strict adherence to the parameters described above and judicious gating can allow comparable results.

Negative-stain transmission electron micrograph (TEM) of vesicles present in sucrose gradient fraction 3. The considerable size variation observed before FAVS (i) is notably decreased after sorting (ii). Bars, 0.5 μm.

Negative-stain transmission electron micrograph (TEM) of vesicles present in sucrose gradient fraction 3. The considerable size variation observed before FAVS (i) is notably decreased after sorting (ii). Bars, 0.5 μm.

The following is a press release from Rockefeller University. The original can be found here.

Like clues to a crime, specific molecules in the body can hint at exposure to toxins, infectious agents or even trauma, and so help doctors determine whether and how to treat a patient. In recent years, tiny pieces of RNA called microRNAs have captured scientific attention for their potential as markers of health and disease.

New research at Rockefeller University and Columbia University suggests these small molecules may be able to relay valuable information about damage to the heart: Scientists in Thomas Tuschl’s Laboratory of RNA Molecular Biology have linked an increase in certain microRNAs circulating in the blood with injury to cardiac muscle. The researchers hope that, one day, these molecules might provide the basis for a more sensitive diagnostic tool than those currently available.

“When we profiled the small RNAs circulating in the blood of healthy people versus heart failure patients, we found increases in levels of certain microRNAs expressed by muscle, including three known to originate only in the heart,” says Kemal Akat, the first author and a postdoc in the lab. “Our research suggests these three microRNAs could be used as indicators of injury produced by anything from heart attack to an impact in a car accident.”

Postdoc Kemal Akat use sensitive RNA sequencing techniques

Signs of trouble: Postdoc Kemal Akat and colleagues used sensitive RNA sequencing techniques to profile the abundance of microRNAs in samples from healthy people and those suffering from heart failure. They found three RNAs with potential for use as markers of heart injury.

The findings were published online July 10 in the Proceedings of the National Academy of Sciences and add to a growing body of knowledge about microRNAs. These small RNA molecules are encoded in the genome, and they fine-tune the expression of genes in the cells that produce them. They also show up in the blood stream, outside the protective environment of the cell. Although microRNAs are present in blood at extremely low levels, the highly sensitive RNA sequencing techniques used by the Rockefeller team can detect them.

The researchers profiled the microRNA in samples of blood and heart tissue from healthy people and people suffering from one of two types of heart failure, which develops when the heart’s pumping action weakens, making it unable to deliver blood throughout the body.

They compared their microRNA results with those of a protein currently used to diagnose injury to heart muscle: cardiac troponin. This protein occurs within healthy heart muscle cells, but when injured, these cells leak cardiac troponin out into the blood stream, causing its levels to spike in circulation. Something similar appears to happen with microRNAs. The researchers found elevated levels of muscle-associated microRNAs in the blood but not in the heart tissue samples of the heart failure patients.

This similarity, as well as other evidence, suggests microRNAs could also serve as biomarkers for heart injury, and the researchers hope microRNAs could even have an advantage over cardiac troponin. “Cells contain a small pool of soluble cardiac troponin, but the majority is bound to heart muscle filaments. By contrast, the protein complexes that contain microRNA within the cell are fully soluble. For this reason, we suspect microRNAs may be more readily released into circulation and their levels may increase faster than cardiac troponin upon tissue injury,” Akat says.

“RNA sequencing can capture a wide spectrum of microRNAs and other potentially interesting RNA molecules from a tiny sample,” Tuschl says. “This opens the possibility of finding many promising biomarkers like those we found from heart muscle, leading to a more universal test than the current monitoring of single proteins. Some technological barriers must still be overcome before tests based on RNA biomarkers like these can be brought into the clinic, but the potential is there for an entirely new type of clinically important diagnostic tool.”

Secreted extracellular vesicles (EVs) have been proposed to play a role in many processes, including HIV transmission and neuronal function. In some cases these nanovesicles appear to travel through the body and fuse with specific cell types to deliver nucleic acid or protein cargoes that may alter cellular phenotypes. EVs from body fluids such as blood, cerebrospinal fluid, urine, saliva, semen, breast milk, and amniotic fluid could provide useful biomarkers for a variety of human diseases including brain disorders. Similarly, EVs could be exploited for in vivotargeting of cargoes (e.g. nucleic acids or small molecule therapeutics) to specific organs or cell types.
Some viruses can exploit the endogenous EV machinery during budding and infection. However, the extent to which EVs and the cellular EV machinery contribute to HIV/AIDS progression is not fully understood. In the nervous system, EVs may function in neuronal-glial communication, synaptic plasticity, and/or immune surveillance. However the role of EVs in psychiatric disorders and substance abuse is not well characterized. There have been some investigations into the role of EVs and their associated machinery in HIV/AIDS infection or progression. We have very limited information regarding how HIV infection might modulate normal EV function in HIV reservoirs such as the brain, gut, or lymph nodes. The possible utility of EVs as biomarkers of HIV progression and/or substance abuse exposure or as potential therapeutic agents for these disorders has also not been well explored.

The purpose of this FOA is to encourage research projects that investigate extracellular vesicles in HIV infection/progression or as potential HIV/AIDS biomarkers or therapeutics. Proposed projects must also explore the potential impact of exposure to substances of abuse.

For complete information, see the full funding announcements below:

Extracellular Vesicles in HIV/AIDS and Substance Abuse (R01)
(RFA-DA-15-011) National Institute on Drug Abuse
Application Receipt Date(s): December 15, 2014
Extracellular Vesicles in HIV/AIDS and Substance Abuse (R21)
(RFA-DA-15-012) National Institute on Drug Abuse
Application Receipt Date(s): December 15, 2014

The consumption of alcohol is prevalent in the United States. In a recent survey, 87.6 percent of people aged 18 or older reported that they drank alcohol at some point in their lifetime; 71 percent reported that they drank in the past year; 56.3 percent reported that they drank in the past month. In view of these statistics, understanding the effects of alcohol on the human body is of relevance to a large fraction of the US population.

When people drink alcohol, it distributes along with water in the body, reaching everywhere in the body. Drinking too much alcohol results in multiple health effects, including alterations in brain function, damage to the liver, heart, immune system, and pancreas, as well as an increased risk of certain types of cancer. Also, drinking alcohol during pregnancy can result in fetal alcohol effects. In contrast to these detrimental health effects of alcohol abuse, there is also evidence that moderate alcohol drinking may have some health benefits, particularly on the cardiovascular and immune systems. Thus, the effects of alcohol on the human body are complex and diverse.

For the most part, studies of the effects of alcohol have focused on one tissue or target at a time. However, it is likely that in addition to effects on specific organs and cell types, alcohol also affects communication between different organs, and relatively little work has been done on this topic. The NIH Common Fund program on Extracellular RNA Communication reflects the fact that the role of extracellular RNA in mediating communication between different cell types is one of the most exciting areas of biology today. In view of this program, as well as the importance of a full understanding of the effects of alcohol on the human body, we wrote program announcement PA-13-197 to stimulate the alcohol research community to investigate the role of extracellular RNA in mediating the health effects of alcohol. We have been pleased with the response to this RFA to date, and look forward to receiving additional applications.

We have launched a new portal at WikiPathways to highlight the mechanisms of exRNA signaling and regulation. Check it out at The goal of this portal is to build a collection of pathway models for exRNA researchers to use for illustration, data visualization, and analysis. Each pathway is a self-contained data model that connects to identifier and annotation databases. So, in addition to providing static images for figures and presentations, these pathways can also be used by bioinformatics and network analysis packages such as Cytoscape and PathVisio. Furthermore, as a wiki, anyone can sign up to improve and grow the content. We invite you all to edit, fix, and add to the pathway model in the exRNA portal at WikiPathways.

We have curated a starting set of pathways for the portal based on existing miRNA research and the growing collection of exRNA consortium papers (see Related Pathway links). As the findings pour in, we will continue to organize curation efforts to add to this pathway set. If your research involves exRNA interactions with signaling or regulatory pathways, and it is not represented, let us know! Please send us a link to the relevant information and/or publications.

This portal was developed by the Data Management and Resource Repository group, serving the data coordination and scientific outreach needs of the consortium. If you have questions and/or feedback on ways to improve the exRNA portal at WikiPathways, please contact or Alex Pico at

The extracellular RNA communication program (ERCP) has established an ontology group to work towards standardization and classification of the terms used to describe extracellular vesicles (EVs). As part of this ontology subcommittee effort, we have worked with domain experts and ontology experts to propose new terms and relationships that can be added to Gene Ontology (GO) for describing EVs. Thus far, we have created three versions shown in Figures 1, 2 and 3 with different levels of detail (click on each figure for a higher resolution image).

In the most detailed version (Figure 1), each term (node) and relationship (edge) is required to have a minimal amount of evidence, potentially consisting of a single publication. As the knowledge evolves, some terms/relationships may be tentative, as in cases where the population of vesicles is heterogeneous and/or the biogenesis of the vesicles is uncertain. It is anticipated that there will be future revisions of the ontology structure (e.g., terms and relationships can be added, removed, and rearranged based on new knowledge). The main advantage of this approach is that it reflects the current state of the literature. Since it has more comprehensive coverage, including specific subtypes of EVs identified by subgroups of researchers, it can support more detailed annotation. The disadvantage, however, is that it is prone to changes. Some of the terms and relationships are likely to be revised in future iterations.

In the simplified/incremental approach (Figure 2 and Figure 3), each term and relationship must have strong evidence with a high level of community consensus to be included into the ontology. Because of the early stage of this field, using this approach, the current iteration of the ontology will include only terms that are quite general. It is anticipated that future iterations of the ontology will include additional terms for more specific types of extracellular vesicles/particles as they are better defined in terms of biophysical properties, functional characteristics, and macromolecular composition. The main advantage of this approach is the less anticipated need to remove or correct terms/relationships in the future. The disadvantage, however, is the lack of specificity in terminology limiting the ability to annotate datasets with precise terms.

We are in the process of building community consensus. We submitted our proposals to several consortiums and societies including the External RNA Controls Consortium (ERCC), International Society for Extracellular Vesicles (ISEV), and American Society for Exosomes and Microvesicles (ASEMV) for their review and approval. In addition, we will engage the broader community in the discussion.

The proposed versions of EV classification correspond to what we perceive as the lumper vs. splitter approaches. In the context of biological taxonomy, lumpers are those who prefer to classify organisms into larger groups based on substantial differences, resulting in fewer species overall, while the splitters prefer to use minute differences to classify living organisms into a greater number of separate groups. These two different approaches may not create significant conflicts for a well-understood domain. However, for a new or partially understood domain like extracellular RNA (exRNA) communications, it is difficult to decide which approach is better. Domain experts often contend vigorously about where the boundary ought to lie in terms of classification. For example, should there be separate terms for vesicles that are formed in endosomes/multivesicular bodies (sometimes termed exosomes) and vesicles that bud directly from the cell surface (sometimes termed microvesicles or shedding microvesicles)?

Finally, there are two major goals of the ontology subcommittee: i) publish a manuscript on the current considerations regarding the ontology of exRNA-containing particles, and ii) develop a recommended vesicle ontology to be submitted to Gene Ontology. We would like these documents to reflect the opinions of the exRNA community, so we need your input.  Please post comments on the following questions, or send your thoughts to

What we have here is not a new debate. It is just another example of a classic argument between lumpers and splitters, one that may not be resolved any time soon. That lack of resolution is fine, because understanding grows from these debates. We should not be frustrated when scientists cannot agree with each other. Instead we should encourage their debates. New ideas and innovations are forged from debates, and are stronger because of them.

Which version of the three draft ontologies do you favor, and why? Specifically:

  • Should there be separate terms for vesicles that are formed in endosomes/multivesicular bodies (sometimes termed exosomes) and vesicles that bud directly from the cell surface (sometimes termed microvesicles or shedding microvesicles)?
  • How much literature support does a specific particle type need to be included in the ontology?
  • Are there use cases associated with each version?

Figure 1. Most detailed version. Note that we have qualified the term Exosome, as Extracellular Exosome (as “Exosome” is already defined in GO for the intracellular protein complex)

Figure 2. Less detailed version.

Figure 3. Least detailed version.

Mark your calendars for two upcoming conferences on extracellular vesicles. In early May, the International Society for Extracellular Vesicles (ISEV) will hold its fourth annual meeting in Rotterdam, the Netherlands. If you can’t make it to Europe, the American Society for Exosomes and Microvesicles is holding its annual meeting in northern California in October. Hope to see you all there!


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