Imaging FCS :microscope:

Imaging FCS is an ImageJ plugin featuring post-processing tools to calculate and view spatio-temporal correlation functions from 16 bit grey tiff stack files as well as data acquisition software for real-time image analysis. It was written as a FIJI plugin (ImageJ 1.53f; Java 1.8.0_281).

Imaging FCS provides a comprehensive software tool to calculate and evaluate spatiotemporal correlation functions. It includes the calculation of all auto- or cross-correlation functions for arbitrary pixel binning and regions of interest within an image, provides fit functions for total internal reflection fluorescence (TIRF) and single plane illumination microscopy (SPIM) based FCS measurements, can calculate the FCS diffusion laws and contains an essential simulator to create simulated data for different diffusive modes.

ImagingFCS runs under ImageJ, FIJI and Micromanager, and it runs on PC, Linux, and Mac OS. We will always use FIJI in the following text, but it should be understood that the same is true for ImageJ and Micromanager.

Installation

Installation from the ImageJ update site

The easiest way to install Imaging FCS is by using the ImageJ update site. In ImageJ chose Help->Update. This opens the ImageJ Updater window. Click on Manage update sites.

Please tick both Image Science and ImagingFCS. Then close the Manage update site window and click Apply Changes in the ImageJ Updater window.

Manual installation

Download on Github Releases

You can directly download the jar file from the GitHub releases page and move the file in fiji_root/plugins/ (here fiji_root is the location of your fiji root). It will work on Windows, Linux and MacOS without additional features (GPU and camera readout).

If you want to add these features, follow these steps:

• Linux: Download linux-libs.zip from the last release. In this archive, you can find the file libagpufit.so that you can move in fiji_root/jars/gpufitImFCS-cublas to support GPU operations. The camera readout features are not compatible with Linux.

• Windows: For the camera readout features, you can download windows-libs.zip from the last release and move the .dll file to fiji_root/jars/liveImFCS-SDK. For the GPU operations you have to compile the library yourself and move it to fiji_root/jars/gpufitImFCS-cublas.

• MacOS: MacOS does not support camera readout or GPU features.

Manual compilation

To compile the .jar file, you need to have Maven and JDK 8 installed and then you can simply run from the root of the project:

mvn clean package

You can then find the .jar file in the target folder and move it to fiji_root/plugins.

If you need to compile the libs, you have to install CMake and CUDA Toolkit (only to compile gpufit). If the CMake doesn't find CUDA it will not run this part. Moreover, if you're not on Windows, it will not compile the camera readout part.

If everything is installed on your side, you can run:

mvn clean package -DcompileLibs=true

It will build the libraries and output it in src/main/cpp/build. You can find your library files there and move them to fiji_root/jars/gpufitImFCS-cublas and fiji_root/jars/liveImFCS-SDK.

Updating the codebase

C++ / CUDA

If you want to add files, you will need to update the CMakeLists.txt in the current folder. Moreover, if you want to add a JNI function, you will need to update the Java code as well and generate a new header file for your code.

To generate the header files, run:

mvn clean package -DgenerateJniHeaders=true

The output files will directly be at the right location in the project.

Java

You can create or update files and the compilation should not change and work the same way as before.

What's new?

This version includes ImFCSNet and FCSNet inference. ImFCSNet predicts diffusion coefficient directly from intensity traces. FCSNet predicts diffusion coefficient from autocorrelation function.

ImFCS documentation 1_62.pdf

This manual contains the basic instructions on using the program, the definition of all items in the control and fit panels, the file formats of the saved data, and the theoretical functions used for fitting.

References

(Deep learning) Tang WH, et al. "Deep learning reduces data requirements and allows real-time measurements in Imaging Fluorescence Correlation Spectroscopy." bioRxiv. 2023. https://doi.org/10.1101/2023.08.07.552352

(Direct camera readout) Aik DYK, et al. "Microscope alignment using real-time Imaging FCS." Biophys J. 2022. https://doi.org/10.1016/j.bpj.2022.06.009

(GPU capabilities) Sankaran J, et al. "Simultaneous spatiotemporal super-resolution and multi-parametric fluorescence microscopy." Nat Commun. 2021. https://doi.org/10.1038/s41467-021-22002-9

(Correlator scheme) Sankaran K, et al. "ImFCS: a software for imaging FCS data analysis and visualization." Opt Express. 2010. https://doi.org/10.1364/OE.18.025468

Disclaimer

The software and data on this site are provided for personal or academic use only and may not be used in any commercial venture or distributions. All files have been virus scanned, however, for your own protection; you should scan these files again. You assume the entire risk related to your use of this software and data. By using the software and data on this site your expressly assume all risks of data loss or damage alleged to have been caused by the software and data. The Biophysical Fluorescence Laboratory at NUS is providing this data "as is," and disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose. In no event will the Biophysical Fluorescence Laboratory at NUS and/or NUS be liable to you or to any third party for any direct, indirect, incidental, consequential, special or exemplary damages or lost profit resulting from any use or misuse of this software and data.