eSRRF (enhanced Super-Resolution Radial Fluctuations) is an extension of the SRRF method developed by the Henriques lab, described in Gustafsson et al. (2016). For more details check out our preprint on bioRXiv.
eSRRF aims at improving the fidelity of SRRF images with respect to the underlying true structure. Below is shown a representative dataset obtained from high-density emitters for which the underlying structure was obtained via DNA-PAINT (SMLM).
The (e)SRRF approach is based on
Some of the new features available in eSRRF include:
The latest stable version of eSRRF can be directly obtained from our Fiji update site: https://sites.imagej.net/NanoJ-LiveSRRF/
Information about update sites can be found here.
Video guide: Installation
There have been some issues reported with OpenCl and running NanoJ-Squirrel and NanoJ-eSRRF on Windows10/11. You can find an instruction with the temporal fix in the wiki.
:sparkles: :sparkles: Update November 2023 :sparkles: :sparkles::
eSRRF is now also available in Python :snake:! You'll find the code and notebooks here: :point_right: https://github.com/HenriquesLab/NanoPyx
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We have published test datasets including eSRRF parameter suggestions on Zenodo. Download and get started right away!
Video guide: Getting started
eSRRF comes packed with useful Tools plugins to perform a range of things, such as (but not limited to):
Many people are involved in developing and testing this method, here are some of the key players:
Exciting news! enhanced Super-Resolution Radial Fluctuations (eSRRF) is now accessible in Python through the NanoPyx package. This integration brings the power and versatility of eSRRF to Python users, opening up new possibilities for analysis and integration within Python-based workflows.
NanoPyx seamlessly integrates eSRRF capabilities into Python environments. With NanoPyx, users can now leverage eSRRF's high-performance analytical approach within their Python scripts, pipelines, and interactive sessions. Through NanoPyx, eSRRF is also available as "codeless" Jupyter Notebooks and a napari plugin.