/LEXY_Toolbox

Small collection of image quantitation scripts for analysis of repurposed versions of the light-inducible nuclear export system (LEXY) optogenetic toolkit

Primary LanguagePythonMIT LicenseMIT

LEXY Toolbox

Summary

This small collection of processing scripts was created for image analysis and quantitation of repurposed versions of the light-inducible nuclear export system (LEXY) optogenetic toolkit (Niopek D, Wehler P, Roensch J, Eils R, Ventura B Di (2016) Optogenetic control of nuclear protein export. Nat Commun, doi:10.1038/ncomms10624)

ImageJ/Jython functionality

Briefly, the bounding region of interest (ROI) for all cells in a field of view is assigned on brightfield images using the GDSC Cell Outliner plugin. Nuclei ROIs are assigned via automatic thresholding of the Hoechst image at each timepoint. Pixel-wise intensity information for both the cells and nuclei was then exported from all fluorescent channels for further analysis.

Python functionality

Transfected cells are selected via thresholding the mean fluorescence intensity per cell and the corresponding nuclei automatically assigned via a maximum Euclidean distance threshold. These nuclei were then tracked over time via similar distance thresholdis. The mean fluorescence intensity of each nuclei 'track' over time is then exported to a single excel file for later plotting and analysis in graphical plotting software.

This work will be prepared for publication, and additional details included here when available.

Prerequisites

Fiji/ImageJ

Use of this toolkit requires Fiji equiped with the Bio-Formats plugin and GDSC Cell Outliner plugin. For additional details on installing ImageJ plugins, refer to the relevant documentation.

Python

The python processing scripts assumes a standard installation of Python 3.7. For specific package requirements, see the requirements.txt file.

Authors

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments and additional citations

  • Templates for this package were adapted from PurpleBooth
  • Kudos to the 2018 Asia-Pacific Advanced Scientific Programming in Python (#ASPP) Summer School for giving me the condfidence and tools to tackle my first python package!
  • ImageJ: Schneider, C. A.; Rasband, W. S. & Eliceiri, K. W. (2012), "NIH Image to ImageJ: 25 years of image analysis", Nature methods 9(7): 671-675, PMID 22930834 (on Google Scholar).
  • Fiji: Schindelin, J.; Arganda-Carreras, I. & Frise, E. et al. (2012), "Fiji: an open-source platform for biological-image analysis", Nature methods 9(7): 676-682, PMID 22743772, doi:10.1038/nmeth.2019 (on Google Scholar).