https://github.com/henriqueslab/nanoj-esrrf
https://github.com/henriqueslab/nanoj-esrrf
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/henriqueslab/nanoj-esrrf
- Owner: HenriquesLab
- License: apache-2.0
- Created: 2021-06-16T14:50:07.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2025-05-30T10:51:27.000Z (about 1 year ago)
- Last Synced: 2025-05-30T13:22:18.512Z (about 1 year ago)
- Language: Java
- Size: 22.4 MB
- Stars: 20
- Watchers: 3
- Forks: 1
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NanoJ-eSRRF
## Adaptive image reconstruction for high-fidelity, fast and easy-to-use 3D live-cell super-resolution microscopy
eSRRF (enhanced Super-Resolution Radial Fluctuations) is an extension of the SRRF method developed by the Henriques lab, described in **[Laine & Heil _et al._ (2023)](https://www.nature.com/articles/s41592-023-02057-w)**. For more details you can also check out the **[preprint](https://doi.org/10.1101/2022.04.07.487490)** on bioRXiv, or the publicaton on SRRF: **[Gustafsson _et al._ (2016)](http://www.nature.com/articles/ncomms12471)**.
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
* **A spatial analysis** of the high-density emitter data using Radial symmetry transform;
* **A temporal analysis** of the obtained temporal stack using similar appraoch to SOFI.
## Features of eSRRF
Some of the new features available in eSRRF include:
* Improved fidelity of reconstructions;
* Adaptive reconstruction schemes allowing to explore the compromise between **fidelity** and **resolution**. This is enabled by an integration of our SQUIRREL approach, described in **[Culley _et al._ (2018)](https://doi.org/10.1038/nmeth.4605)**;
* Estimation of the maximum number of frames to use for eSRRF analysis from a dataset, based on SSIM (or optical flow magnitude) calculation;
* Rolling analysis;
* Integrated drift correction;
* Better memory management;
* Full OpenCL integration, enabling GPU acceleration;
* Direct saving to disk for large dataset analysis;
* **eSRRF 3D** reconstruction!
## Getting the eSRRF plugin on Fiji
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](https://imagej.net/update-sites/).
Video guide: Installation
[
](https://www.youtube.com/watch?v=3Oa2ADEa-qY)
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](https://github.com/HenriquesLab/NanoJ-eSRRF/wiki#nanoj-esrrf-opencl-issues).
: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](https://github.com/HenriquesLab/NanoPyx)
:sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles:
## Test datasets
We have published test datasets including eSRRF parameter suggestions on [Zenodo](https://doi.org/10.5281/zenodo.6466472). Download and get started right away!
Video guide: Getting started
[
](https://www.youtube.com/watch?v=VScGXvdKoNg))
## Tools included in the eSRRF plugin
eSRRF comes packed with useful Tools plugins to perform a range of things, such as (but not limited to):
* Fluorescence fluctuation simulator;
* Rescale individual slices within a stack and convert it to RGB (useful to visualise the parameter sweep output);
* Save all current open images as Tiff files;
* Perform linear rescaling on stack;
## People involved
Many people are involved in developing and testing this method, here are some of the key players:
* Romain F. Laine ([@LaineBioImaging](https://twitter.com/LaineBioImaging))
* Ricardo Henriques ([@HenriquesLab](https://twitter.com/HenriquesLab))
* Guillaume Jacquemet ([@guijacquemet](https://twitter.com/guijacquemet))
* Christophe Leterrier ([@christlet](https://twitter.com/christlet))
* Siân Culley ([@SuperResoluSian](https://twitter.com/SuperResoluSian))
* Bassam Hajj ([@Bassam_A_HAJJ](https://twitter.com/Bassam_A_HAJJ))
* Hannah S. Heil ([@Hannah_SuperRes](https://twitter.com/hannah_superres))
* Simao Coelho ([@simaopc](https://twitter.com/simaopc))
* Jonathon Nixon-Abell ([@AbellJonny](https://twitter.com/AbellJonny))
* Angélique Jimenez
* Tommaso Galgani
* Aki Stubb ([@akistub](https://twitter.com/akistub))
* Gautier Follain ([@Follain_Ga](https://twitter.com/Follain_Ga))
* Samantha Webster
* Jesse Goyette
## eSRRFing in Python
Exciting news! enhanced Super-Resolution Radial Fluctuations (eSRRF) is now accessible in Python through the [NanoPyx](https://github.com/HenriquesLab/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](https://github.com/HenriquesLab/napari-NanoPyx).