| ID | Title | Body | Facebook Description | Facebook image | Facebook Title | Facebook Video | Twitter Description | Twitter Image | Twitter Title | Meta tags |
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| 10840 | Translational Science Education & Training | Discover the programs, activities and resources available to further your knowledge of and advance your career in translational science. @media (min-width: 768px){ .page-links ul { column-count: 1; } } | NCATS is committed to increasing understanding of translational science through the development, demonstration, and dissemination of educational and training resources to stakeholder communities. | Translational Science Education & Training | NCATS is committed to increasing understanding of translational science through the development, demonstration, and dissemination of educational and training resources to stakeholder communities. | Translational Science Education & Training | ||||
| 10756 | Work with the 3-D Tissue Bioprinting Group | Kristy Derr with a 3-D printer. Through the 3-D Tissue Bioprinting program, NCATS collaborates with academic investigators in the basic and clinical research communities who provide disease and cell biology expertise and materials. NCATS also works with private-sector partners, such as Organovo, to access technology and engineering solutions. NCATS is interested in establishing new collaborations with pharmaceutical and biotechnology companies. These efforts would focus on benchmarking and establishing translational capacity for tissue-in-a-well assays to be integrated into the discovery and development process for new therapies. Contact Marc Ferrer or Sam Michael if you are interested in working with the NCATS 3-D Tissue Bioprinting team. | Learn how to work with the 3-D Tissue Bioprinting group. | Work with the 3-D Tissue Bioprinting Group | Learn how to work with the 3-D Tissue Bioprinting group. | Work with the 3-D Tissue Bioprinting Group | ||||
| 10753 | 3-D Tissue Bioprinting Team | Full-Time Staff Marc Ferrer, Ph.D., director (assay development, screening, 3-D cell models) Sam Michael, chief information officer (screening, automation, IT) Molly Boutin, Ph.D. (bioengineering) YuChi Chen, M.S. (genetics, cell imaging) Kristy Derr (cell biology, histology) Paige Derr, Ph.D. (physics, bioengineering) Srikanya Kundu, Ph.D. (neuroscientist) Min Jae Song, Ph.D. (bioengineering) Hoda Zarkoob, Ph.D., M.Sc. (biomedicine) Xiaohu Zhang (molecular biology) Affiliated Investigators Kapil Bharti, Ph.D. (cell development, eye physiology) Ty Voss, Ph.D. (cell imaging) Postdoctoral Fellow Lucia Liu, Ph.D. (bioengineering, skin physiology) Post-Baccalaureate Trainee Andy Vo Project Management Deborah Ngan Anna Rossoshek, M.S., M.B.A. | 3-D Tissue Bioprinting Team | 3-D Tissue Bioprinting Team | ||||||
| 10750 | 3-D Tissue Bioprinting Lab Resources | The 3-D Tissue Bioprinting program builds on internal expertise at NCATS in assay development for drug screening, automation technologies, stem cell biology, tissue engineering, laboratory automation engineering and advanced high-content microscopy.Bioprinting Core• 2 RegenHU 3DDiscovery® bioprinters• 1 RegenHU 3DDiscovery® R-GEN 200 bioprinter• 1 CellInk BIONOVA X DLP printer Histology Core• Leica VT1200 S fully automated vibrating blade microtome• Leica Aperio VERSA brightfield, fluorescence and FISH digital pathology scanner• Leica BOND RXm fully automated advanced staining system• Thermo HistoStar™ embedding workstation• Thermo Excelsior™ AS tissue processor• Thermo CryoStar NX50 Cryostat• Thermo Gemini AS automated slide stainer• Thermo ClearVue™ Coverslipper• Akoya Biosciences Phenoimager HT slide imagerMicroscopy Core• Fluorescence Microscopy: EVO5000 and EVO7000• Confocal Fluorescence Microscopy: Nikon Eclipse Ti with a modular microscope system; Nikon D-Eclipse C1; Leica TCS SP8 MP multiphoton microscope; Zeiss LSM710 with Airyscan• High-Content Cell Imaging Readers: Molecular Devices ImageXpress micro confocal high-content imaging system and PerkinElmer Opera Phenix high-content screening system• Laser Microdissection System: Leica LMD6500Access to Additional High-Throughput Screening Assay Technologies• Molecular Devices FLIPR Tetra high-throughput cellular screening system• Meso Scale Diagnostics MESO QuickPlex SQ 120• Luminex FLEXMAP 3D®• Axion BioSystems Maestro microelectrode array system• Bruker rapifleX MALDI PharmaPulse• WPI Automated TEER Measurement System | Learn more about 3-D Tissue Bioprinting program scientific capabilities. | 3-D Tissue Bioprinting Technical Capabilities | Learn more about 3-D Tissue Bioprinting program scientific capabilities. | 3-D Tissue Bioprinting Technical Capabilities | ||||
| 10747 | 3-D Tissue Bioprinting Program Goals | Pictures of 3-D bioprinted tissues in a 12-well transwell plate showing reproducible tissue shape from well to well. The goal of the 3-D Tissue Bioprinting Program is to speed up the process of discovering and developing new medicines by creating new assay models that better predict the effects of drugs in humans. 3-D tissue models that mimic characteristics of live human tissues are produced on microplates to test the effectiveness and toxicity of small molecules or other therapeutics. Access to 3-D tissue models in microplate format leverages tissue engineering/organogenesis, stem cell and disease biology, and use of in situ detection technologies (technologies that can be used to detect something in intact tissue) for tissue characterization and testing of drugs’ effects.The 3-D Tissue Bioprinting Program’s primary focus is developing “disease-relevant tissue models” to reduce the predictability gap between results from current 2-D cell-based assays and results from testing in humans. The program team focuses on many different treatment methods and aims to overcome translational barriers toward the development of urgently needed treatments for unmet medical needs.While historically the program used bioprinting technologies to make 3-D tissue models, it is now expanding the repertoire of methods used to create 3-D organotypic models for therapeutics discovery and development. Program Objectives• Create a portfolio of validated and clinically benchmarked normal and disease 3-D tissue models using a broad range of technologies, including spheroids, organoids, biofabricated tissues and tissue-on-chip, based on a fit-for-purpose approach.• Apply the use of each 3-D organotypic cellular models as predictive assays in different stages in drug discovery and development, from early discovery to preclinical development.• Apply biological assay technologies to develop quantitative phenotypic measurements for automated medium throughput screening with 3-D organotypic cellular models.• Apply laboratory automation and bioengineering solutions to develop and operationalize the use of 3-D organotypic assay platforms for automated medium throughput screening.• Implement efficacy, toxicology and metabolism screening of small molecules, antibodies, gene therapies and cell-based therapies with 3-D organotypic cellular models in support of therapeutics development projects.• Operationalize the development and use of “personalized” 3-D organotypic models using patient-derived cells for advancing research on gender, racial and ethnic health disparities (precision medicine).• Operationalize the use of 3-D organotypic cellular models for ex vivo clinical trials to guide clinical trial design, with special emphasis on rare diseases.• Work in collaboration with the wide biomedical community to advance, demonstrate and disseminate the usefulness and impact of 3-D tissue models in accelerating therapeutics development.Our vision is to integrate the use of 3-D organotypic cellular models in the drug discovery and development pipelines and effectively implement the principles of the 3Rs (Replacement, Reduction and Refinement) in the humane use of animals in research, and make the process of therapeutic development personalized, faster, more efficient and less costly by decreasing the overall failure rates in clinical trials. | Learn more about 3-D Tissue Bioprinting program goals. | /sites/default/files/3d_tissues_900x600.jpg | 3-D Tissue Bioprinting Program Goals | Learn more about 3-D Tissue Bioprinting program goals. | /sites/default/files/3d_tissues_900x600.jpg | 3-D Tissue Bioprinting Program Goals | ||
| 10744 | 3-D Tissue Bioprinting Operational Model | Histology is the gold standard to determine the architecture of tissues. These panels showing histological images of 3-D bioprinted skin (top row) and skin taken from patient samples (bottom row) staining for different markers relevant to skin cells and extracellular matrix composition. Our projects are partnerships with members of the broad biomedical community, who provide expertise on disease physiology, access to patient-derived induced pluripotent stem cells and primary cells, and access to disease animal models. In turn, we provide expertise in tissue engineering, assay development and drug screening.We work collaboratively with NIH intramural and extramural investigators and clinicians, regulatory agencies, biopharma industry groups and technology providers to advance, demonstrate and disseminate the development, application and use of 3-D tissue models for advancing therapeutics discovery and development. History of the Program2013: A research collaboration agreement was signed between NIH — including NCATS and the National Eye Institute (NEI) — and Organovo, a company dedicated to pushing through the development of new therapies through the design and creation of functional human tissues using proprietary 3-D bioprinting technologies. As part of the partnership, NCATS installed an Organovo NovoGen MMX Bioprinter® to develop 3-D bioprinted retinal (jointly with NEI) and skin tissue.2016: The 3-D Tissue Bioprinting program at NCATS received funding through the Cures Acceleration Network, which allowed the program to expand its bioprinting technical capabilities, create a histology core, and expand its microscopy imaging capabilities. It also enabled requests for applications (RFAs) to fund extramural labs to collaborate with NCATS to implement drug screens with 3-D bioprinted tissue models.2017: NCATS published an RFA, RFA-TR-17-007, titled NCATS Pilot Program for Collaborative Drug Discovery Research Using Bioprinted Skin Tissue (U18). Two teams at Columbia University (Angela Christiano, Ph.D.) and Rockefeller University/New York University (Dan Gareau, Ph.D.) received funding through this opportunity.2018: The 3-D Tissue Bioprinting Laboratory (3DTBL) was formally established in the Division of Preclinical development at NCATS.2018: The 3DTBL received funding from NIH’s Helping to End Addiction Long-term (HEAL) initiative to develop biofabricated 3-D tissue models of nociception, opioid use disorder and overdose for drug screening.2019: NCATS published an RFA, RFA-TR-19-020, titled Drug Screening With Biofabricated 3-D Skin Disease Tissue Models (U18). Two teams at Columbia University (Angela Christiano, Ph.D.) and University of Washington (Jia Zhu, Ph.D.) received funding through this opportunity.2020: The 3DTBL received funding from the Coronavirus Aid, Relief, and Economic Security (CARES) Act (2020) to develop biofabricated 3-D tissue models for COVID-19 drug screening.2021: NCATS published an RFA, RFA-TR-21-015, titled Intramural-Extramural Collaboration for Drug Screening with Biofabricated 3-D Disease Tissue Models (UH2/UH3). Two teams at the University of Pittsburgh (Dr. Mark Miedel) and University of Texas Medical Branch Galveston/Texas A&M (Drs. Ram Menon and Arum Han) received funding through this opportunity. | Learn more about the 3-D Tissue Bioprinting program operational model. | /sites/default/files/3d-Histology_900x600.jpg | 3-D Tissue Bioprinting Operational Model | Learn more about the 3-D Tissue Bioprinting program operational model. | /sites/default/files/3d-Histology_900x600.jpg | 3-D Tissue Bioprinting Operational Model | ||
| 10693 | 2017 Translator Investigators | View the 2017 Translator investigators in the table below. Organization Investigator(s) Broad Institute of MIT and Harvard Joshua Bittker, Ph.D. Paul Clemons, Ph.D. Jason Flannick, Ph.D. Columbia University Aris Floratos, Ph.D. George Hripsack, M.D., M.S. Nicholas Tatonetti, Ph.D. Chunhua Weng, Ph.D. Institute for Systems Biology Gustavo Glusman, Ph.D. Sui Huang, M.D., Ph.D. Jackson Laboratory Peter Robinson, Ph.D. Johns Hopkins University Christopher Chute, M.D., Dr.P.H. Kim Doheny, Ph.D. Ada Hamosh, M.D., M.P.H. Casey Overby, Ph.D. Lawrence Berkeley National Laboratory Christopher Mungall, Ph.D. Maastricht University Michel Dumontier, Ph.D. Mayo Clinic Guoquian Jian, M.D., Ph.D. Hongfang Liu, Ph.D. Oregon Health & Science University Melissa Haendel, Ph.D. Maureen Hoatlin, Ph.D. David Koeller, M.D. Shannon McWeeney, Ph.D. Scripps Research Institute Benjamin Good, Ph.D. Andrew Su, Ph.D. Chunlei Wu, Ph.D. St. Jude Children’s Research Hospital Jinghui Zhang, Ph.D. University of Alabama James Ciminio, M.D. University of California, San Diego Trey Ideker, Ph.D. University of Montreal Michael Tyers, Ph.D. University of North Carolina at Chapel Hill Stanley Ahalt, Ph.D. Alexander Tropsha, Ph.D. | 2017 Translator Investigators | 2017 Translator Investigators | ||||||
| 10690 | 2016 Translator Investigators | View the 2016 Translator investigators in the table below. Organization Investigator(s) Broad Institute of MIT and Harvard Paul Clemons, Ph.D. Columbia University Nicholas Tatonetti, Ph.D. Chunhua Weng, Ph.D. Institute for Systems Biology Gustavo Glusman, Ph.D. Sui Huang, M.D., Ph.D. Johns Hopkins University Christopher Chute, M.D., Dr.P.H. Lawrence Berkeley National Laboratory Christopher Mungall, Ph.D. Oregon Health & Science University Melissa Haendel, Ph.D. St. Jude Children’s Research Hospital Jinghui Zhang, Ph.D. Stanford University Michel Dumontier, Ph.D. University of California, San Diego Trey Ideker, Ph.D. University of Montreal Michael Tyers, Ph.D. University of North Carolina at Chapel Hill Stanley Ahalt, Ph.D. Alexander Trosha, Ph.D. | 2016 Translator Investigators | 2016 Translator Investigators | ||||||
| 10687 | 2018 Translator Awardees | View the 2018 Translator awardees in the table below. Organization Investigator(s) Broad Institute of MIT and Harvard Paul Clemons, Ph.D. Jason Flannick, Ph.D. Institute for Systems Biology Eric Deutsch, Ph.D. The Ohio State University Arnab Nandi, Ph.D. Oregon State University David Koslicki, Ph.D. Stephen Ramsey, Ph.D. Stanford University Russ Altman, M.D., Ph.D. Scripps Research Institute Andrew Su, Ph.D. University of Alabama Jake Chen, Ph.D. Matthew Might, Ph.D. University of North Carolina at Chapel Hill Chris Bizon, Ph.D. Alexander Tropsha, Ph.D. | 2018 Translator Awardees | 2018 Translator Awardees | ||||||
| 10684 | Translator: Unique Collaborative Approach to Advancing Biomedical Data Sharing | Translational Science Highlight NCATS is leading a collaboration that builds on the diverse expertise of academic and private-sector partners to create a unique data-mining, computational resource that will integrate many different types of biomedical information. When completed, broad access to this resource will help facilitate translational innovation in disease prevention, diagnosis and treatment. In September 2017, Stephen Ramsey, Ph.D., saw an intriguing Twitter post about a new funding opportunity announcement (FOA). He clicked on the link, expecting to find a traditional government FOA. Instead, he learned that he would first have to solve a math puzzle and complete a series of computational tasks; only then could he gain access to the instructions to begin the application process. Intrigued, Ramsey rounded up colleagues at Oregon State University and other institutions to get started on the challenge. Eight puzzles and a few days later, they finally accessed the FOA. NCATS was seeking applicants who could build reasoning tool prototypes — a “brain” — for the Biomedical Data Translator program. Translator is NCATS’ unprecedented effort to create a computational resource that connects many kinds of biomedical information from many sources to help researchers generate new ideas for preventing, diagnosing and treating diseases. Once completed, Translator will be able to draw on data sources ranging from air quality measurements to electronic health records (EHRs) to answer questions such as “What diseases could aspirin treat?” and “What genetic conditions reduce your risk for osteoporosis?” and subsequently ask, “How is it doing that?” Creating Translator requires exceptional collaboration. Eleven teams of scientists, scattered across the country at 20 institutions, constantly check in with each other about their progress as well as potential solutions to the obstacles they face. The idea behind the FOA puzzle challenge was to have teams demonstrate they had the skills to build a reasoning tool before they could apply for funding. One of the puzzles was a problem in number theory, which is a branch of mathematics. “Not being a number theorist, I was not sure how to efficiently solve the problem,” Ramsey said. “It would have taken me days.” Ramsey decided to email David Koslicki, Ph.D., a mathematician colleague at Oregon State University. Koslicki sent back a solution within an hour. Now, Ramsey and Koslicki are co-principal investigators (co-PIs) for one of the five teams that are working to build Translator’s prototype reasoning tool. Connecting Data and People Scientists collaborate at the January 2018 NCATS Data Hackathon. From left to right: Greg McInnes, Stefano Rensi, Margaret Guo, Adam Lavertu, Matt Brush and Tyler Peryea. (OHSU) Building Translator is an enormous task with many problems yet to be solved. “We’re trying to figure out how to connect all the disparate knowledge we have,” said Melissa Haendel, Ph.D., Oregon Health & Science University, another Translator PI. Biomedical knowledge comes in many forms, from individual patients’ records to abstracts of scientific journal articles. Even the same types of data can be represented in different ways. For example, different EHR software packages might record blood pressure differently. Each researcher in Translator brings particular skills and expertise. Haendel’s team specializes in figuring out how to make information from diverse sources comparable, much like how Translator team members must figure out how to work together. One way the teams connect is through “hackathons,” events at which members gather in small groups to work on specific problems and periodically report to the other groups. A team might brainstorm how to make patient data available publicly without compromising privacy, for example. Stanley Ahalt, Ph.D., a Translator PI from the University of North Carolina at Chapel Hill, is part of a team working on demonstration projects to show how Translator could find connections between different kinds of data. “It’s really fun to be a part of something that’s intellectually stimulating, collaborating with people you like and respect, where you’re all making progress together,” Ahalt said. Learning from Each Other Translator not only needs to be able to understand the user’s query, it must then be able to find the relevant knowledge sources, extract the right information and piece the information together into a narrative that the user can understand. Each of those tasks is a difficult problem on its own, and each needs the contributions of people from many backgrounds. “I did not have any inkling of how difficult it is to define a disease,” Ahalt said. “The fact that we don’t have a clean way of describing what a disease is was kind of a shock to me.” Ahalt’s team is working on identifying different types of asthma, based on whether the condition is related to genetics, to environmental exposures such as air pollution, or perhaps to some other medical problem that the patient is experiencing. Understanding these underlying differences should make it possible to tailor treatments to specific patients. Koslicki, the mathematician who solved the number theory puzzle challenge, works mostly on developing better methods to analyze biological data. “Translator requires many technical, engineering and scientific problems to be addressed in one integrative system,” he said. The project needs people who understand web design, people who work with application programming interface solutions and people who understand biology. Koslicki gave an example of one problem that the Translator collaboration solved: His team was interested in a database that contains information based on scientific results published in journal articles. The database contains 20 million different relationships, such as the fact that high salt intake is associated with high blood pressure. Koslicki’s team spent a while trying to figure out how to work with the information before realizing that another Translator team had already done the work. Had he followed the usual scientific path, with individual teams working alone on their projects, Koslicki would have had to duplicate that work, he said. Instead, his team could move on to the next problem. “Typically, we only get to learn from other scientists’ work through a publication or a presentation,” Koslicki said. “You don’t get to dig in with them and learn how they did what they did. Our collaborations allow things to go very fast and enable us to share different parts of reasoning tools and try to integrate them into our own.” A New Way of Advancing Science? The Translator teams are completing feasibility assessments on an ambitious timeline of just a few short years. The kind of tight, unusual collaboration that the team has created seems to be the most efficient way to develop a reasoning tool in such a compressed time frame. Beyond the efficiencies created by the unique collaborations, Translator may offer a new approach to advancing science. When the tool identifies connections that scientists haven’t thought of before, research teams can evaluate them, choose promising hypotheses and design studies to investigate further. Ahalt has introduced the idea to colleagues at his university, and they are considering whether a Translator-like project could help them find new ways to tackle complex issues, such as the opioid epidemic. “I believe Translator can change the way science is being done,” Ahalt added. Posted May 2018 | NCATS is connecting academic and private-sector partners to create a data-mining resource to speed translational innovation in disease prevention, diagnosis and treatment. | /sites/default/files/translator-awards_1260x630.jpg | Unique Collaborative Approach to Advancing Biomedical Data Sharing | NCATS is connecting academic and private-sector partners to create a data-mining resource to speed translational innovation in disease prevention, diagnosis and treatment. | /sites/default/files/translator-awards_1260x630.jpg | Unique Collaborative Approach to Advancing Biomedical Data Sharing |
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