MRI: Acquisition of a Nikon SIM & STORM capable super-resolution fluorescent microscope as a shared instrument for the Penn State research community

Project: Research project

Project Details

Description

An award is made to The Pennsylvania State University, University Park campus to purchase a super-resolution microscope that will enable the capture of images of plant and animal cells, as well as complex chemical samples, at the scale of single molecules. This microscope will reveal new insights into how living and chemical systems are organized and work. The project will also generate new image analysis tools for the scientific community. The microscope will enable interdisciplinary research training and enhance education through coursework and outreach to other Penn State campuses and other institutions. Integration of this microscope into a core microscopy facility will make it available to undergraduate, graduate and postdoctoral trainees, and regular imaging workshops will be offered by Penn State. New teaching modules for K-12 and undergraduate educators demonstrating the science of size and the potential of super-resolution microscopy will be developed. Access and training will be assured for underrepresented students through programs including the Summer Experience in the Eberly College of Science, McNair Scholars, Women in Science and Engineering Research, and Minority Undergraduate Research Experience. Public understanding of super-resolution microscopy and its advantages will be catalyzed by multiple outreach activities and venues, including The Franklin Institute (science museum) and Penn State's Ag Progress Days, which together will expose this cutting-edge imaging technology to tens of thousands of people. The discoveries enabled by this microscope will advance the study of plant and animal development, sustainable agriculture and energy production, and the chemical interactions that define our physical environment.

Super-resolution microscopy is leading to dramatic new research opportunities in the life sciences, and has emerging capabilities in chemistry and materials research. Penn State will advance our optical imaging-based research through the purchase of a super-resolution microscope with both Structured Illumination Microscopy (SIM) and Stochastic Optical Reconstruction Microscopy (STORM) capabilities. Both imaging modules are combined on a single microscope platform, and use high-magnification total internal reflection fluorescence (TIRF) objectives, an automated focus retention system, and an integrated multi-channel solid state laser launch. The ease of use of this commercial super-resolution microscope is a particularly appealing feature for equipment residing within Penn State's Huck Institutes of the Life Sciences Microscopy & Cytometry Facility, a multi-user core facility. Research and training with this microscope will focus on four main areas: (a) plant cell biology; (b) animal cell biology; (c) chemistry; and (d) advanced image processing and pattern recognition. Researchers in groups (a)-(c) will leverage the increased resolution offered by this system to push their research into hitherto unreachable areas of fine-scale biological processes and molecular interactions. In parallel, group (d) will collaborate with those generating super-resolution imaging data to advance our ability to recognize structures and patterns from molecular localization data. The combined SIM/STORM microscope will maximally benefit the largest number of researchers in the life and chemical sciences at Penn State by offering a high degree of flexibility for sample labeling and imaging. As an integral component of this project, faculty with research interests in advanced image analysis and pattern recognition will generate new algorithms for analyzing SIM and STORM imaging data that will be widely applicable to super-resolution microscopy and will be freely disseminated to the global scientific community.

StatusFinished
Effective start/end date9/1/168/31/19

Funding

  • National Science Foundation: $649,261.00

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