JUL 16, 2020 8:00 AM PDT

SRRF 'n' TIRF - simultaneous spatiotemporal super-resolution and multi-parametric fluorescence microscopy

Sponsored by: Andor

Event Date & Time
DATE:  July 16, 2020
TIME:   8am PT, 11am ET, 4pm BST, 5pm CEST
Super-resolution microscopy and single molecule fluorescence spectroscopy often require mutually exclusive experimental strategies optimizing either time or spatial resolution. While the measurement of biomolecular dynamics on the single molecule level requires fast measurements on the millisecond scale, super-resolved images require acquisition times on the second scale to achieve the required signal-to-noise ratio. These complementary requirements render the combination of spatiotemporal super-resolution microscopy challenging. Past solutions either restricted time resolution, limited the field of view and number of recorded points, or they required specialized instrumentation or fluorescent labels, restricting access to the techniques. To achieve high spatiotemporal resolution, we implemented a GPU-supported, camera-based measurement strategy that resolves high spatial structures (~60 nm), temporal dynamics (≤ 2 ms), and molecular brightness analysis from the exact same data almost in real time. 
In this webinar, we investigated the connection between the epidermal growth factor receptor (EGFR) dynamics, its oligomerization state and the cytoskeleton. For this purpose, we acquired images of mApple labeled EGFR and LifeAct, an actin binding protein labelled with EGFP, on whole cells with high sensitivity and high-speed using EMCCD or sCMOS cameras and GPU-based processing with spatial or temporal binning to optimize extraction of various parameters. The resulting single datasets are evaluated by a combination of spectroscopy and super-resolution techniques that include: imaging fluorescence correlation spectroscopy (imaging FCS) to measure dynamics; Number and Brightness analysis (N&B) to determine oligomerization or aggregation states; and super resolved radial fluctuation microscopy (SRRF) to obtain super-resolution images. The simultaneous acquisition of these multiple fluorescence parameters allows a direct cross-correlation analysis, which would not be possible in sequential measurements, that allows us to determine how EGFR diffusion is dependent on its oligomerization state, and whether the cytoskeleton has any influence on the receptor diffusion mode and dynamics.  This approach is easily extendable to other fluorescence parameters, does not require specialized instrumentation, and thus is immediately applicable to a wide range of situations. 
Learning Objectives:
This webinar will demonstrate how participants can extract more information from their fluorescence images by combining computational super-resolution microscopy with fluorescence fluctuation techniques using only existing, freely available ImageJ plugins and widely existing setups of commercially available TIRF/SPIM microscope and cameras (EMCCDs or sCMOS). For that purpose, the webinar will discuss imaging fluorescence correlation spectroscopy and number and brightness analysis and how it benefits from computational super-resolution techniques.
Questions Answered:
  1. Is it possible to extract from a single measurement structure with a resolution below 100 nm and with dynamics on the millisecond scale?
  2. Given the huge amounts of data and the size of single data sets (~GB) is data treatment in real time possible?
  3. What is the advantage of combining multiple fluorescence techniques in a single measurement?
  4. Can one optimise the signal-to-noise ration of multiple fluorescence techniques simultaneously in a single measurement?
  5. What is the maximum information content of an image? 
Webinars will be available for unlimited on-demand viewing after live event.
LabRoots is approved as a provider of continuing education programs in the clinical laboratory sciences by the ASCLS P.A.C.E. ® Program. By attending this webinar, you can earn 1 Continuing Education credit once you have viewed the webinar in its entirety.

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