Images were processed using Fiji
70 (link) and
MATLAB 2020b (MathWorks) using custom codes that are available on request. For visualization purposes, the PopRed lookup table from the J. Manton collection (
https://github.com/jdmanton/ImageJ_LUTs) was applied to most monochrome images after the dynamic range was adjusted between minimum and maximum grey values of each image (note that the dynamic range was not kept identical between images when presenting different conditions). Figures were assembled in Adobe
Illustrator 2021. Videos were edited in Adobe
Premiere 2021.
Where appropriate, spatial drift during acquisition of videos was corrected using a custom GPU-accelerated registration code based on cross-correlation between successive frames. For representation purposes, a wavelet ‘à trous’ denoising filter was applied to Extended Data Fig.
8c (custom GPU-accelerated MATLAB port of a code originally developed by F. Cordeliere for the Improve Kymo ImageJ plugin
81 (link)). The raw images were averaged with the filtered video. Both codes are available at our GitHub page (
https://github.com/deriverylab), as well as the codes for quantification of protein condensation and nuclear segmentation described below.
Watson J.L., Seinkmane E., Styles C.T., Mihut A., Krüger L.K., McNally K.E., Planelles-Herrero V.J., Dudek M., McCall P.M., Barbiero S., Vanden Oever M., Peak-Chew S.Y., Porebski B.T., Zeng A., Rzechorzek N.M., Wong D.C., Beale A.D., Stangherlin A., Riggi M., Iwasa J., Morf J., Miliotis C., Guna A., Inglis A.J., Brugués J., Voorhees R.M., Chambers J.E., Meng Q.J., O’Neill J.S., Edgar R.S, & Derivery E. (2023). Macromolecular condensation buffers intracellular water potential. Nature, 623(7988), 842-852.