tubulin_block_repetition.zip Data used in Fig. 1a and Fig. S2 Folder "tubulin_line_blocks" contains the input image - a STORM image reconstruction of a HeLa cell with labeled microtubules (from Jimenez et al. (2020). About samples, giving examples: optimized single molecule localization microscopy. Methods. 174:100-114.). A Gaussian blur (2 px) was applied and the image was scaled to 0.25x the original size. The full field-of-view and the crop used in the manuscript are provided. The folder also contain files "blocks_lines.tif" and "block_crossings.tif", which are image stacks containing the simulated blocks used in the analysis. The folder "line_sliding_test" contains the input (full size and a crop) and the simulated reference block used in Fig. S2c (i.e., specificity test). jurkat_gag-egfp_hilo.tif Data used in Fig. 1c and Fig. S4. A Jurkat cell expressing an TetOn-Optigag-(i)EGFP. The cell was cultured in an activation surface containing anti-CD3 antibody and 1 µM of doxycycline. Imaging was done in a Nanoimager (ONI) using the 488 nm laser at 10% and channel 0 (two-band dichroic: 498-551 nm and 576-620 nm). The HILO angle was optimised manually and images were acquired at 100 ms exposure. rpe_eb3.tif Data used in Fig. 4, Fig. S1 and Fig. S13. An RPE1 cell stably expressing EB3-GFP Seeded into a Lab-Tek 8-well chamber (Thermo Fisher) and allowed to adhere for 24 hours. Data acquired in a Nanoimager (ONI), using the 488 nm laser at 10% and channel 0 (two-band dichroic: 498-551 nm and 576-620 nm); 75 ms exposure, one frame every 3 seconds. jurkat_gag-EGFP_epi.tif Data used in Fig. 3 and Fig. S12. A Jurkat cell expressing an TetOn-Optigag-(i)EGFP. Imaging was performed on an inverted microscope ECLIPSE Ti2-E (Nikon Instruments) equipped with a Fusion BT (Hamamatsu Photonics K.K., C14440-20UP) and a Plan Apo λ 100x (NA 1.45) Oil objective. The sample was illuminated with LED light at 515 nm (CoolLED pe800) and acquisition was done at 75 ms exposure with an active Nikon Perfect Focus system and the NIS-Elements AR 5.30.05 software (Nikon Instruments). Volumes were captured by acquiring frames at different depths (z-step size: 0.5 µm). Image deconvolution was performed using a custom Python script based on the Richardson-Lucy method.

A.M. and R.H. acknowledge the support of the Gulbenkian Foundation (Fundação Calouste Gulbenkian), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001332) (to R.H.) and funding from the European Union through the Horizon Europe program (AI4LIFE project with grant agreement 101057970-AI4LIFE and RT-SuperES project with grant agreement 101099654-RTSuperES to R.H.). Funded by the European Union. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This work was also supported by a European Molecular Biology Organization (EMBO) installation grant (EMBO-2020-IG-4734 to R.H.), a Chan Zuckerberg Initiative Visual Proteomics Grant (vpi-0000000044 with https://doi.org/10.37921/743590vtudfp to R.H.) and a Chan Zuckerberg Initiative Essential Open Source Software for Science (EOSS6-0000000260). This study was also supported by the Research Council of Finland (338537 to G.J.), the Sigrid Juselius Foundation (to G.J.), the Cancer Society of Finland (Syöpäjärjestöt; to G.J.), the Solutions for Health strategic funding to Åbo Akademi University (to G.J.), the InFLAMES Flagship Programme of the Academy of Finland (decision numbers: 337530, 337531, 357910, and 357911) and the CNRS ATIP program (AO2016 to C.L.). This research was also supported by the National Institutes of Health (NIH) with grants K22AI140963, K61DA058348 and subcontract R01AI50998 (to J.I.M).