Locus Tag Quantification (FIJI/ImageJ Macro)
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Jones, M. (2022). Locus Tag Quantification (FIJI/ImageJ Macro) [Dataset]. https://doi.org/10.17863/CAM.21856
This is a FIJI macro that assists with quantifying nuclear size and shape, generating 3D distance maps of nuclei and locating locus tag puncta within the map thus producing a relative radial distance measurement. Example files and updated code will be available here: https://github.com/mjthreesixfive/image-analysis Feel free to contact me if you need help getting this code working, see the comments at the start for instructions regarding the key parameters to change as it is likely your images and nuclei of interest differ from those used in my thesis.
//This macro is designed to segment nuclei and detect locus tags. It requires microscopy images in tif format with at least two //channels, one for the nuclear marker and one for the locus tag, by default, these channels are assumed to be channels 3 (locus tag) // and 4 (nuclear marker, e.g. Dpn), but this can be modified at the import step (lines 228 - 248) //Start by placing the input images into a new folder and title it 'Input' (case sensitive). //Place this inside another folder and name this something sensible e.g. DD-MM-YYYY-Neuroblast-Gene-Position-Analysis //Open Fiji and click Plugins>Macros>Run and navigate to this file. When prompted choose the folder within which the //'Input' folder lies. Various output folders should be created automatically. To get the EVF value look in EVFResults (the output is .txt but can open in excel). Column AN. //It does this in a two step process, with manual validation in each step. //In step one, a nuclear marker, in this case Deadpan staining applied to L3 larval Drosophila brains //is used to determine the approximate edge of the nucleus. //In step two, images with a validated nucleus detection are masked so that only //locus tag signal within the validated nucleus volume are detected. //The same script can be used to detect locus tags in Salivary gland nuclei using DNA stains //such as Hoechst or DAPI. //Several parameters will likely need to be optimised to get good results, primarily //The sigma value of the gaussian blur on the nuclear segmentation step (line 278) and the median filter radius //Also within the nucleus segmentation step (line 279). //Similarly, there is a gaussian filter step which also needs a different sigma value for different locus tag sizes //(line 590). For setting sigma values, try measuring the nucleus or locus tag in microns on an image with the scale set //in ImageJ>Analyse>Set Scale (possibly set by default if your microscope metadata is accurate). //Citations and thanks: //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). //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). // Many thanks are owed to Thomas Boudier and other members of the ImageJ mailing list for their advice and suggestions. //This script relies heavily on the 3D ImageJ Suite by Thomas Boudier, J Ollion and colleagues: http://imagejdocu.tudor.lu/doku.php?id=plugin:stacks:3d_ij_suite:start //Including the 3D ROI manager which forms most of the user interface. //Any updates and much needed improvements to this code will be posted here: https://github.com/mjthreesixfive/image-analysis , test images will also be uploaded here if possible. //Feel free to contact me (firstname.lastname@example.org) if you are attempting to use any of this and it does not work as expected. //The code may need some adaption if the images differ significantly from that found in my thesis. //In addition to the filters mentioned above, the 3D object counter and 3D simple segmentation have size filters and some //other parameters that might need to be altered.
FIJI, ImageJ, genome organisation, 3D image segmentation, locus tag, locus tag quantification
Publication Reference: https://doi.org/10.17863/CAM.22060
This record's DOI: https://doi.org/10.17863/CAM.21856
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Licence URL: https://creativecommons.org/licenses/by-nc/4.0/