NCATS is administering six NIH Extracellular RNA (exRNA) Communication program projects that are supported by the NIH Common Fund. These projects will establish new tools, technologies and methodologies to sort, isolate and analyze extracellular vesicles (EVs) and their cargo, including exRNA. These projects will clarify the role of EVs in exRNA transport and cellular communication and accelerate the development of EV-based disease diagnosis and tracking protocols.
- Single Extracellular Vesicle Sorting and Analysis
- Molecular Dissection and Imaging of Extracellular Vesicles to Define Their Origin and Targets
- Integrative, Multi-parametric Characterization of the EV Surface Protein and Nucleic Acid Landscape by Nano-flow and Sorting Cytometry
- Microfluidics Array-Based Sorting, Isolation and RNA Analysis in Single Extracellular Vesicles
- Enabling Isolation and Characterization of Single Extracellular Vesicles and Their Molecular Contents Using Multi-marker Surface Signatures
- Acoustofluidic Separation, Purification and Raman Spectral Fingerprinting of Single EVs: From Cell of Origin to Target Cell and Biofluids
Single Extracellular Vesicle Sorting and Analysis
Investigator: Daniel T Chiu, Ph.D., University of Washington, Seattle
Grant Number: UG3-TR002874
EVs are membrane-enclosed particles that are secreted from cells into the extracellular space. They are highly diverse and carry different kinds of cargo, including exRNA. Effectively studying the subtypes of EVs and their cargo requires isolating them with high precision and sensitivity. Current EV isolation methodologies are prone to contamination. Moreover, established methodologies work in bulk, collecting many different EVs and reporting on the average, which obscures differences that could provide insight into EV subpopulations. This study team will develop and apply new technologies to study EVs at the single-EV level. These technologies include methods that sort individual EVs with high sensitivity and throughput and use a digital imaging platform that will help to quantify the RNA contents in both single and grouped EVs. These new technologies will enable better sorting and analysis of individual EVs, which will provide a deeper understanding of their biological role and lead to better utilization of EVs in diagnostic and therapeutic applications.
Molecular Dissection and Imaging of Extracellular Vesicles to Define Their Origin and Targets
Investigator: Saumya Das, M.D., Ph.D., Massachusetts General Hospital, Boston; Tijana Talisman, Ph.D., Beckman Research Institute at City of Hope, Duarte, California; and Kendall Van Keuren-Jensen, Ph.D., Translational Genomics Research Institute: An Affiliate of City of Hope, Phoenix
Grant Number: UG3-TR002878
EVs can contain RNA (EV-RNA) that can be used to diagnose or predict many different diseases. Isolating and characterizing EV-RNA with respect to specific tissues and disease states, however, is challenging, preventing the widespread use of EV-RNA as a diagnostic tool in the clinic. This project team will develop technology that enables the isolation and characterization of EVs from hematopoietic cells (cells in the bone marrow and surrounding blood that can develop into any kind of blood cell), the brain and the heart. The team also will identify differences in EVs from human subjects with acute diseases, such as a heart attack or stroke, and from subjects who are undergoing physiological stress processes, such as exercise. This work will provide insight into how different tissues contribute to EV-RNA in standard, disease and stress conditions. This understanding will facilitate the discovery and development of EV biomarkers and the identification of tissue-specific EV-RNA, which can sensitively and accurately provide insight into disease progression and regression.
Integrative, Multi-parametric Characterization of the EV Surface Protein and Nucleic Acid Landscape by Nano-Flow and Sorting Cytometry
Investigator: Ionita Calin Ghiran, M.D., Beth Israel Deaconess Medical Center, Boston; Jennifer Jones, M.D., Ph.D., National Cancer Institute, Bethesda; and Aleksandar Milosavljevic, Ph.D., Baylor College of Medicine, Houston
Grant Number: UG3-TR002881
Cells use EVs to communicate with other cells. This cellular communication can change in response to varying conditions, because EVs differ greatly in their cargo and membrane proteins. EV-based communication can trigger differences in gene expression and alter cell function in a cell-specific manner. In diseases like cancer, abnormal EV-based communication can even alter the host immune response and synchronize the behavior of secondary tumor development and growth. By isolating and comparing EVs in both healthy and diseased states, the components of EVs that signal and exacerbate disease status can be identified and used as biomarkers for diagnosing and tracking disease. Using EVs as biomarkers, however, is restricted by the lack of effective EV sorting methods, thereby forcing bulk-analysis strategies that are biased against low-abundance EV subsets. This study team will focus on the reliable and reproducible characterization of EVs with single-EV resolution. Specifically, the team will develop new ways to sort EVs, better detect components of EV subpopulations, and establish protocols that will promote standardization across laboratories and clinics. These methods will enable the sorting and characterization of unique EVs based on tissue type and disease state, which will greatly facilitate the utilization of EVs as disease biomarkers.
Microfluidics Array-Based Sorting, Isolation and RNA Analysis in Single Extracellular Vesicles
Investigator: Eduardo Reategui, Ph.D., The Ohio State University, Columbus, and Yon Son Betty Kim, M.D., Ph.D., The University of Texas MD Anderson Cancer Center, Houston
Grant Number: UG3-TR002884
EVs are present in all biological fluids and contain different biomolecules, including DNA, RNA, proteins and metabolites. These biomolecules enable EVs to be used in fine-tuned cell-to-cell communication. The role of EVs in cellular communication is biologically valuable, but EV communication is difficult to study, because isolating and characterizing EVs is still technically challenging. Much of the current EV-analysis technology requires that many different EVs be consolidated and broken down to their internal contents, which are subsequently tested as a whole. This obscures individual EV information, showing RNA, DNA, proteins and metabolite content only in aggregate. The specialized role of EVs in cell-to-cell communication, however, is made possible by their differences. Thus, EVs must be studied on an individual level to establish the effects of different EVs on cellular communication, including how they relate to disease states. This study team will analyze the molecular content of individual EVs using multilevel sorting methods to isolate EV subpopulations based on size and molecular composition. In addition, the team will study EV-based cell-to-cell communication to determine whether and how certain EV subpopulations are involved in immune regulation and disease states. This EV analysis will be done using clinical samples from patients with glioblastoma, a cancer that can occur in the brain or spinal cord, to identify one or more subpopulations of EVs that may be involved in immunosuppression or associated with worse clinical outcomes in glioblastoma patients.
Enabling Isolation and Characterization of Single Extracellular Vesicles and Their Molecular Contents Using Multi-marker Surface Signatures
Investigator: David Aaron Routenberg, Meso Scale Diagnostics, LLC, Rockville
Grant Number: UG3-TR002886
EVs are important for cell-to-cell communication and serve different roles in normal physiologic function and disease states. To serve these different roles and transmit different signals between cells, the EVs themselves must be different. EVs carry different signaling cargo, and they differ greatly in the proteins on their surfaces. This study team will develop a new way to isolate specific types of EVs based on these differing surface proteins. The team will target two or more cell type-specific EV surface molecules to isolate EV subsets. Once highly purified populations of EVs have been isolated, their molecular contents will be measured. By determining the molecular contents and surface proteins specific to EVs from defined cell types, the team can develop a new, scalable approach to identify multi-marker surface signatures for cell types. EVs with these surface signatures can then be isolated with greater specificity, which will enable EV studies that are more targeted than those possible with current methods. These new methods can then be applied to high-throughput systems to automate single-EV characterization, thereby enabling high-resolution studies that would otherwise be too large or too time consuming to be cost-effective.
Acoustofluidic Separation, Purification and Raman Spectral Fingerprinting of Single EVs: From Cell of Origin to Target Cell and Biofluids
Investigator: David T Wong, D.M.D., D.M.Sc., University of California, Los Angeles; Tony Jun Huang, Ph.D., Duke University, Durham; Sung Kim, M.D., Samsung Medical Center, Seoul; Yong Kim, Ph.D., Dental Research Institute, Los Angeles; and Ya-Hong Xie, Ph.D., University of California, Los Angeles, Samueli School of Engineering, Los Angeles
Grant Number: UG3-TR002978
Gastric, or stomach, cancer can be a particularly painful and devastating disease, and diagnosing the disease can be difficult and invasive. A prior project, however, discovered and validated exRNA markers that can be used to effectively detect gastric cancer. Biomolecule cargo, including exRNA, can be contained in different types of EVs. The study team for this project will develop technology to isolate single EVs and characterize their exRNA cargos so that these cargos can be associated with different cells of origin, target cells and biofluids. In particular, the investigators of this project seek to identify salivary exRNA markers in EVs that are indicative of the presence and progress of gastric cancer. The completion of this project will result in a set of novel technologies enabling rapid, high-yield, single-EV-level isolation that facilitates the detection and tracking of gastric cancer.