The ERCC2 published 50 manuscripts during this past funding year (Aug 2021 – July 2022): 34 from ERCC2 (16 from exRNA carrier grants, 17 from single EV grants, and 2 from the DMRR), and 17 from ERCC1 grants. (Some manuscripts acknowledge multiple grants.)
The Coffey lab at Vanderbilt discovered supermeres, small RNA-rich particles in the supernatant of EV preps (1) and reviewed their role and that of other EVs in colorectal cancer (2).
The Weaver lab at the Vanderbilt Center for EV Research released a fundamental new paper describing aspects of the mechanism of biogenesis of RNA-containing EVs (3). The Vickers, Weaver, Coffey, and Patton labs at Vanderbilt examined the role of m(6)A methylation on export of miRNAs into EVs, showing that depletion of METTL3, an m(6)A writer enzyme, decreased the spread of cancer phenotypes usually transferred via EVs (4).
The Vickers lab reported on the biophysics of binding between HDL and small RNAs, an important aspect of the mechanism by which lipids can protect RNAs in circulation (5).
Robert Raffai and colleagues engineered a fluorescent reporter into E.coli to show that bacterial EVs –– outer membrane vesicles (OMVs) –– deliver cargo into host cells when introduced into mice (6). The Raffai group showed in mice and cell culture that exosomes exposed to IL4 reduce the level of cardiometabolic disease and obesity and outlined the roles of several miRNAs in the process (7). They also identified a population of adipose cells that improve muscle growth in surrounding tissue (8). They worked with the Van Keuren-Jensen and others to show that EVs exposed to high glucose conditions communicate detrimental properties of hyperglycemia to accelerate atherosclerosis in diabetes, and that miR-486-5p plays a role (9). The Jensen and Das labs together published a new human tissue atlas for small RNAs that can be used for deconvolution of circulating exRNAs to determine tissue of origin (10). Mesoscale Diagnostics and the Das lab worked together to identify biomarkers of neural injury in patients with COVID (11).
Witwer and Zivkovic used ultracentrifugation and size exclusion chromatography to improve the purity of HDL isolated from plasma (12). Accuracy was high enough for them to show that large and small HDLs carry different protein complements. Zivkovic studied the effect of lipid-based nutrient supplementation on HDL (13). Ken Witwer’s group also wrote reviews on EV biology (14), LCAM1-associated EVs (15), and on developments in EV imaging (16), as well as coordinating an update to the MISEV standards for reporting on EV experiments (17).
The Chang lab at Notre Dame built a sensitive nanopore-based DNA sensor (18).
The Huang lab at Duke continues its development of acoustofluidic and microfluidic methods for purifying and characterizing EVs. They released several papers describing technologies that underpin their HANDS particle manipulation platform (19). HANDS stands for Harmonic Acoustics for Non-contact, Dynamic, Selective particle manipulation. Components of the platform include an acoustofluidic droplet sorter (20), ring tweezers for single particle manipulation (21), and an intelligent nanoscope that uses microsphere array-based image magnification to provide high-resolution images and machine learning to classify nanomaterials with high accuracy after training (22).
Beyond basic technology development, the Huang lab tailored instruments for diagnosing specific diseases like Alzheimer’s (23) and traumatic brain injury (24). The Huang lab summarized all these developments in a review on biomedical acoustics (25).
The lab of Ionita Ghiran reported on their development of molecular beacons for miRNA detection (26). It’s a rapid and affordable method based on delayed electrophoretic mobility of miRNA-beacon complexes, with a limit of detection around 100pM. The Das laboratory published a murine model for tracking tissue origin of EVs and their cellular targets, a model that is in use by several laboratories now (27). The Reategui group at Ohio State developed a microfluidic device that enables extraction of EVs from 3D-cultured breast cancer tumor spheroids. The system can measure tetraspanin (CD63 and CD81) concentrations on single EVs (28). The Talisman lab at City of Hope made improvements in imaging at the single vesicle level with the development of the touch prep-qSMLM technique (29).
The Jones lab (NCI) and others worked to establish the MIFlowCyt-EV standards for the minimum information about EV flow cytometry experiments (30) and developed the MPAPASS software application for flow-based multi-dimensional EV analysis (31). The DMRR reviewed open problems in exRNA data analysis (32).
Crislyn D’Souza-Schorey showed that tumor membrane vesicles (TMVs) – which shed directly from the cell membrane, contain double-stranded DNA (33). The Wong lab at UCLA reviewed saliva diagnostics (34), while the Wang lab from the Institute for Systems Biology contributed to a large study identifying risk factors associated with severity of COVID-19 symptoms after the acute phase (35).
Of 8 ERCC2 webinars hosted this year, 6 were recorded and have been viewed 560+ times on YouTube (85 hours total viewing). The exRNA Portal featured 12 blogs this year that focused on RNA and EV therapeutics and exRNA biomarkers, as well as a blog from the Vanderbilt EV Research Center covering their new findings about the biogenesis of exRNA-containing EVs.
The ERCC hosted a two-day online workshop last year (April 19-20, 2021) on the unique challenges of exRNA data analysis. This year the workshop lecturers published an overview of the workshop in Frontiers in Genetics (29).
The exRNA Atlas is a core ERCC resource for exRNA studies as highlighted by work that culminated in the submission of two manuscripts.
The ERCC capstone analysis examined the extracellular RNA (exRNA) expression profiles of 6,907 ERCC samples from various human biofluids. Each profile was sequenced and uniformly processed to quantify the expression of detected RNAs (e.g., miRNAs, piRNAs, tRNAs, longer RNAs) as well as potential exogenous RNAs. Machine learning models were developed that effectively distinguish between biofluids and identify the exRNA features, frequently biofluid-specific, that are most informative for classification. The paper also showed how to construct machine learning models that can differentiate between biological conditions, such as disease state, using the exRNAs present in human plasma. Furthermore, the study demonstrated how the small exRNA profiles of human biofluids can be related to those of a compendium of human tissues to infer the tissue-of-origin for exRNAs present in cell-free biofluids. Lastly, the authors performed dimensionality reduction on the dataset through a pipeline incorporating PCA, tSNE, VAE, UMAP, and PHATE. The paper also describes a customizable tool for interactively exploring and visualizing low-dimensional representations of the dataset. The manuscript was submitted to PLOS Biology (Rozowsky et al., “Integrative Analysis of Extracellular RNA Profiles”). The Atlas was also highlighted by DMRR during a workshop on Extracellular Vesicles, Exosomes, and Cell-Cell Signaling in Response to Environmental Stress on September 27 -28, 2021 hosted by the National Institute of Environmental Health Sciences and the National Center for Advancing Translational Sciences. A white paper on the workshop is in preparation.
The exRNA Atlas can also be leveraged to examine exRNA binding proteins (RBPs). The role of exRNA binding proteins remain poorly understood due to the large number of exRBPs and lack of well-developed methods to isolate and characterize exRBPs and their cargo. Although the role of RBPs in exRNA biology is now well established, their exRNA cargo and distribution across biofluids is largely unknown. To address this gap, researchers extended the exRNA Atlas resource by a map of exRNA carried by RBPs (exRBPs). The map was developed through an integrative analysis of ENCODE eCLIP data (150 RBPs) and human exRNA (6,930 samples) in the exRNA Atlas. Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, and CSF and in cell conditioned media. exRBPs carry exRNA transcripts from small non-coding RNA biotypes including miRNA, piRNA, tRNA, snRNA, snoRNA, and Y RNA, and fragments from protein-coding genes. Computational deconvolution of exRBP RNA cargo revealed associations of exRBPs with extracellular vesicles, lipoproteins, and classes of RNPs across human biofluids. In summary, ERCC2 researchers mapped exRBPs in human biofluids and generated a new resource for the community. The DMRR and ERCC colleagues collaborated on this study and a manuscript has been submitted to Cell Genomics (LaPlante et al., “A map of human RNA binding proteins that carry into human biofluids RNA cargo representing small non-coding RNA biotypes and human protein-coding genes”).
The DMRR released several new features and capabilities to the NanoFlow Repository. It is now being used by ERCC2 benchmarking labs and contains 400+ data files and is available to the broader scientific community. The NanoFlow Repository provides the first implementation of the MIFlowCyt-EV framework. It enables sharing of nanoflow cytometry data and standards-compliant metadata about experiment design, specimens, instrument configuration, and analysis parameters. The Repository will improve rigor and reproducibility of EV research by enabling researchers to reproduce published results and validate previous findings. We anticipate that it will also catalyze collaboration and data reuse for meta-analyses. The Repository will enable EV researchers to develop Data Management and Sharing Plans per recently articulated NIH Policy for Data Management and Sharing. Josh Welsh discussed the NanoFlow Repository as part of his presentation during the Annual Course in Cytometry in June, 2022 at the University of Wisconsin, Madison.
ERCC seeks to share ERCC data more broadly with the scientific community. The ERCC DMRR is also funded by the Common Fund Data Ecosystem (CFDE) project that is focused on integrating data across twelve different CF projects. DMRR DCC worked with the CFDE Coordinating Center to integrate RNA-seq and qPCR data from 14 ERCC projects (1,421 biosamples) with with the CFDE data portal. This brings the total number of projects and biosamples shared with CFDE to 54 and 4,721, respectively. This ERCC data is now available in the CFDE portal and available for cross-project queries. The successful integration of ERCC data provides greater access and visibility for ERCC data that can now be considered in a much broader context of biological and scientific questions.
The ERCC Scientific Outreach working group supported a grant writing workshop at the first annual meeting of the newly formed American Society for Intercellular Communication (ASIC) in October 2021 and has been working to build support and find appropriate datasets for an exRNA data analysis challenge.
ERCC welcomed four associate members this year. Associate members are expected to be actively engaged in ERCC activities (i.e., participate in working groups as appropriate, may attend the ERCC biannual meetings at their own expense) and agree to abide by all policies approved by the consortium and any other pertinent NIH policies. Associate members must sign the ERCC2 Confidentiality Disclosure Agreement to participate in all ERCC activities. The new associate members are:
Technology development and benchmarking efforts to improve methods for identifying, isolating, and characterizing EVs and their cargo remain a major focus of the consortium. A recent position paper summarizes these and other efforts (iScience, 2022 Jun 23;25(8):104653. doi: 10.1016/j.isci.2022.104653. eCollection 2022 Aug 19). The abstract of the paper is provided here:
The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2’s current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.
DiFi, a colorectal cell line with an amplified EGFR locus, was chosen as the cell line to use for ERCC benchmarking studies. DiFi was chosen due to the abundance of EGFR on individual EVs where EGFR is an analyte of interest for several ERCC groups and where there are clinically relevant antibodies that are well characterized that bind EGFR. Furthermore, there is substantial data on secreted RNAs and EV and nanoparticle proteins from DiFi cells from the Coffey group. DiFi cells have also been adapted to a hollow fiber bioreactor that produces abundant material that can be freshly distributed to ERCC groups. A large pool of DiFi conditioned media was created for distribution of a uniform standard to ERCC2 members and was sent to the Nolan group for distribution to other groups performing flow cytometric studies as well as to the Laurent and Raffai groups. Additionally, DiFi material was distributed to 8 ERCC groups to test their various technologies using a uniform sample and various methods including affinity purification, RBP analysis, other specific flow cytometric analysis, filtration and new RNA and protein sensor technologies. Samples were also further purified by the Raffai and Mateescu groups for distribution of pure DiFi EVs to ERCC groups and production of nanobodies to DiFi EV proteins. DiFi cells were distributed to 5 other groups for various studies. There are several papers that have used the benchmark material and it is anticipated that these studies will be completed in the middle of next year.
The exRNA field continues to grow and the ERCC has a unique opportunity to educate the broader community about exRNAs and related topics. The Scientific Outreach Working Group (SOWG) within the DMRR is developing an exRNA-themed online course. The course launched publicly on the exRNA Portal in May 2022 with the release of Xandra Breakefield’s introduction to the history of vesicular exRNA research. Three more lectures have been released since, with 4 more planned in the coming weeks. Core lectures on class of exRNA and on exRNA associated with non-vesicular protein carriers remain to be completed.
DMRR Scientific Outreach is presenting a workshop on exRNA data analysis at the Keystone Vesicles meeting in Santa Fe, NM (Oct-Nov, 2022). Discussions are underway with Ionita Ghiran and colleagues for a workshop on molecular beacons in Spring 2023.
Webinars planned for next year include Guoping Li from Saumya Das’ lab who will discuss the role of transfer RNA-derived small RNAs (tDRs) in stress and Ionita Ghiran highlighting his lab’s work on molecular beacons.
A September 2022 blog post will explore the supermere nanoparticle and its characterization by the Coffey lab (Vanderbilt). We will also record “Lessons Learned” interviews with ERCC2 researchers at the ERCC18 investigators’ meeting in October 2022. Those interviews will be posted throughout the final year of the consortium, sometimes accompanied by blogs highlighting the technology developed by the associated ERCC2 lab.
DMRR DCC will continue to accept and process into the Atlas ERCC small and long RNA-seq and qPCR data generated during the year. DMRR will also continue to expand the preliminary RNA binding protein (RBP) studies initiated this past year as summarized above. DMRR DCC will also be facilitating the integration of new ERCC2 RNA-seq and qPCR data into the CFDE Data Portal as it becomes available.
DMRR will continue to work with consortium members to facilitate their ongoing EV Flow benchmarking studies. DMRR will also continue to engage the broader community by presenting an ERCC webinar and participating in the ISEV x Tech Conference, both in Nov. DMRR is currently preparing for submission an application note that describes the NanoFlow repository.
The EV Antibody Database (https://exRNA.org/EVAbdb/) now includes links to the vendor webpage and Antibodypedia (https://www.antibodypedia.com/) entry for each antibody. Beyond the core module on antibodies for Western blots, two new modules are under development: one for antibodies for use in EV flow cytometry, and one for characterizing target protein specificity in a supplemental project led by Mesoscale Diagnostics.
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