iXon SRRF-Stream Applications
With its ability to smash through the classical diffraction limit, and furthermore, to accomplish this in real time, with non-complex sample labelling, conventional equipment and with low intensity illumination, SRRF-Stream paves the way to unlock previously unseen cellular structure and behaviour, at unprecedented spatio-temporal resolution in a low-photodamage friendly manner.
Observational Capabilities of SRRF-Stream
- Elucidation of protein structure analysis at a sub-organelle level
- Tracking of single molecules inside cells
- Using this tracking to gain insight into individual molecular machinery underpinning cellular physiology
- With this new information, update models of cell function
Applications of SRRF-Stream
- Membrane fusion involving individual SNARE protein machinery
- Dynamic changes of and within synaptic vesicles
- Dendritic spines reformation due to synaptic plasticity and learning
- Signal transduction processes and cell-to-cell communication and differentiation
iXon SRRF-Stream Workflow Advantage
Adopting the recently developed SRRF technology from the lab of Dr Ricardo Henriques, University College London (UCL), and working in close collaboration with Dr Henriques, Andor have enhanced the technology to run optimally on iXon EMCCD cameras. Andor are also expert in advanced GPU processing optimization techniques, employed in this instance to execute the SRRF algorithm up to 30x faster than the existing ImageJ-based post processing implementation of SRRF (NanoJ-SRRF). This significant acceleration enables workflow enhancement, by allowing data acquisition and SRRF processing to operate in parallel.
Processing Speed Comparison - SRRF Stream vs NanoJ-SRRF
- Input image pixels - 1024 x 1024
- # input images per SRRF image - 100
- Output SRRF pixels - 4096 x 4096
- NVidia GTX 1080 GPU card
This graph compares the rate of processing of blocks of 100 raw input images (1024 x 1024 pixels), to yield resultant SRRF super-resolution images of 4096 x 4096 pixels. SRRF-Stream is compared to NanoJ-SRRF, the processing occurring on the same Nvidia GTX 1070 GPU card. The SRRF-Stream acceleration subsequently allows data acquisition and processing to happen in parallel, yielding a further workflow improvement over NanoJ-SRRF.
Since processing is now much faster than the camera can acquire data, ‘SRRF-Stream enabled’ cameras now accomplish real time super-resolution, with large field of view super-resolution images.
Having thoroughly tested SRRF-Stream in our own lab, we are very impressed by both the workflow and also the ability to now utilise larger fields of view for live cell super-resolution. By seamlessly combining the SRRF algorithm with the high-performance of the iXon, we have accomplished the world’s first super-resolution camera for fluorescence microscopy.
Dr Ricardo Henriques, Quantitative Imaging and Nanobiophysics Group, UCL
The Learning Center hosts a wide range of tutorial videos, technical articles and webinars to guide you through the range of products for all your imaging needs. We have provided some links below which will get you started on some of our most recent uploads.
- Technical Article: SRRF-Stream
- Webinar: NanoJ-SRRF & SRRF-Stream: Fast Live-Cell Conventional Fluorophore Super-Resolution for Most Modern Microscopes
- Product Memo: Optimizing SRRF-Stream Performance