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AverageIntensityAndCount

Laura Cooper, [email protected]

InFocusAndMaxProjAverages.ijm

This script takes .nd2 files with filenames of the form 2024MMDD_text_CellLine_text_Sx_y.nd2, where x is 1,2,5 or 8 and y is 1 to 5. Two tif stacks are output: 1) the most in focus plane from each channel and a text file noting which plane was selected and 2) the max projection for each channel. Two tables are created one for each stack measuring the average intensity in the first and third channels, except for S8 files, where only channel 1 is measured. The number of nuclei is counted in the max projection image only but the value is included in both tables.

MaxProjAverages.ijm

This script takes .nd2 files with filenames of the form 2024MMDD_text_CellLine_text_Sx_y.nd2, where x is 1,2 or 5 and y is 1 to 5. Two tables are created: "MaxProj Averages.csv" max, min, mean and standard deviation of the intensity in the first and third channels and MaxProj Thresholded Averages.csv measuring the max, min, mean and standard deviation of the instensity of the first and third channels after otsu thresholding.

positiveNuclei.ijm

This script takes .nd2 files with filenames of the form 2024MMDD_text_CellLine_text_S8_y.nd2, where y is 1 to 5. It uses Stardist to find the nuclei and thresholds the alpha actin. It then states if the nuclei are positive for alpha actin if the nuclei region contains IntDen>2000 for the alpha actin channel. It output the ROIs and the counts and printed to the log.

References

Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. doi:10.1038/nmeth.2019

David Legland, Ignacio Arganda-Carreras, Philippe Andrey, MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ, Bioinformatics, Volume 32, Issue 22, November 2016, Pages 3532–3534, https://doi.org/10.1093/bioinformatics/btw413

Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018). Cell Detection with Star-Convex Polygons. In Lecture Notes in Computer Science (pp. 265–273). Springer International Publishing. doi:10.1007/978-3-030-00934-2_30

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