Here we provide test datasets and a training manual for the parameter optimization with eSRRF. The training manual will guide users through an eSRRF paramenter optimization routine and quantiative image quality assesment with both, the ImagJ-Plugin NanoJ-eSRRF (Chapter 1) and the python implementation NanoPyx-eSRRF (Chapter 2). By showcasing the optimization routine on three differnt test dataset (Chapter 3), providing intermediate results and expected outcome, the users can eaisily learn how to find the optimal processing parameters for eSRRF processing.Three samples are provided to showcase the eSRRF reconstruction process:1. Microtubules sample: Set01_DNA-PAINT_Microtubules.tifDNA-PAINT microscopy measurement of immunolabeled microtubules in fixed COS-7 cells, showing 0.121 localizations per frame and µm^2 (data published in Laine and Heil et al.)108x90 pixels, 500 frames, pixel size: 160 nm 2. Kidney sample: Set02_KidneySDNephrinExM.tifHuman kidney biopsies stained with nephrin (data published in Kylies et al.)150x150 pixels, 200 frames, pixel size: 102 nm 3. Single emitters simulation: Set03_simulation_groundTruth_2p5Sigma - Fluorescence stack_Avg5.tifSimulated individual molecules emitting placed on concentric rings with radii increasing by 220 nm steps. On each ring the molecules are separated by 57.5, 115, 173, 230, 288 and 345 nm, respectively (data published in Laine and Heil et al.)33x33 pixels, 100 frames, pixel size: 100 nm  Jupyter-Notebook: ridge_detection.ipynbWith this notebook qantitative image analyis of sturctures resolved with ExSRRF can be performed.Such as: calculation of the target structure density. identifying areas with high inter-ridge spacing by maping the distance to the nearest ridge based on Euclidean distance transform. measuring the spatial uniformity of the structure of interest by examining the distribution of the local densities and the distances to the nearest ridge.

Here we provide test datasets and a training manual for the parameter optimization with eSRRF. The training manual will guide users through an eSRRF paramenter optimization routine and quantiative image quality assesment with both, the ImagJ-Plugin NanoJ-eSRRF (Chapter 1) and the python implementation NanoPyx-eSRRF (Chapter 2). By showcasing the optimization routine on three differnt test dataset (Chapter 3), providing intermediate results and expected outcome, the users can eaisily learn how to find the optimal processing parameters for eSRRF processing.Three samples are provided to showcase the eSRRF reconstruction process:1. Microtubules sample: Set01_DNA-PAINT_Microtubules.tifDNA-PAINT microscopy measurement of immunolabeled microtubules in fixed COS-7 cells, showing 0.121 localizations per frame and µm^2 (data published in Laine and Heil et al.)108x90 pixels, 500 frames, pixel size: 160 nm 2. Kidney sample: Set02_KidneySDNephrinExM.tifExM of human kidney biopsies stained with nephrin (data published in Kylies et al.)150x150 pixels, 200 frames, pixel size: 102 nm 3. Single emitters simulation: Set03_simulation_groundTruth_2p5Sigma - Fluorescence stack_Avg5.tifSimulated individual molecules emitting placed on concentric rings with radii increasing by 220 nm steps. On each ring the molecules are separated by 57.5, 115, 173, 230, 288 and 345 nm, respectively (data published in Laine and Heil et al.)33x33 pixels, 100 frames, pixel size: 100 nm 4. Test dataset for drift/vibration correction: Set04_ExSRRF_eSRRF_vibration_correction_practice_dataset.tifEsM of human kidney biopsies stained with nephrin (data published in Kylies et al.)100x100 pixels, 200 frames, pixel size: 102 nm5. Test dataset for photobleaching: Set05_Photobleaching.tifExM of 120 nm Nanorulers (data published in Kylies et al.)150x150 pixels, 75 frames, pixel size: 64 nm Jupyter-Notebook: ridge_detection.ipynbWith this notebook qantitative image analyis of sturctures resolved with ExSRRF can be performed.Such as:calculation of the target structure density. identifying areas with high inter-ridge spacing by maping the distance to the nearest ridge based on Euclidean distance transform. measuring the spatial uniformity of the structure of interest by examining the distribution of the local densities and the distances to the nearest ridge.

Here we provide test datasets and a training manual for the parameter optimization with eSRRF. The training manual will guide users through an eSRRF paramenter optimization routine and quantiative image quality assesment with both, the ImagJ-Plugin NanoJ-eSRRF (Chapter 1) and the python implementation NanoPyx-eSRRF (Chapter 2). By showcasing the optimization routine on three differnt test dataset (Chapter 3), providing intermediate results and expected outcome, the users can eaisily learn how to find the optimal processing parameters for eSRRF processing.Three samples are provided to showcase the eSRRF reconstruction process:1. Microtubules sample: Set01_DNA-PAINT_Microtubules.tifDNA-PAINT microscopy measurement of immunolabeled microtubules in fixed COS-7 cells, showing 0.121 localizations per frame and µm^2 (data published in Laine and Heil et al.)108x90 pixels, 500 frames, pixel size: 160 nm 2. Kidney sample: Set02_KidneySDNephrinExM.tifHuman kidney biopsies stained with nephrin (data published in Kylies et al.)150x150 pixels, 200 frames, pixel size: 102 nm 3. Single emitters simulation: Set03_simulation_groundTruth_2p5Sigma - Fluorescence stack_Avg5.tifSimulated individual molecules emitting placed on concentric rings with radii increasing by 220 nm steps. On each ring the molecules are separated by 57.5, 115, 173, 230, 288 and 345 nm, respectively (data published in Laine and Heil et al.)33x33 pixels, 100 frames, pixel size: 100 nm 4. Test dataset for drift/vibration correction: Set04_ExSRRF_eSRRF_vibration_correction_practice_dataset.tifHuman kidney biopsies stained with nephrin (data published in Kylies et al.)100x100 pixels, 200 frames, pixel size: 102 nm Jupyter-Notebook: ridge_detection.ipynbWith this notebook qantitative image analyis of sturctures resolved with ExSRRF can be performed.Such as:calculation of the target structure density. identifying areas with high inter-ridge spacing by maping the distance to the nearest ridge based on Euclidean distance transform. measuring the spatial uniformity of the structure of interest by examining the distribution of the local densities and the distances to the nearest ridge.

Here we provide test datasets and a training manual for the parameter optimization with eSRRF. The training manual will guide users through an eSRRF paramenter optimization routine and quantiative image quality assesment with both, the ImagJ-Plugin NanoJ-eSRRF (Chapter 1) and the python implementation NanoPyx-eSRRF (Chapter 2). By showcasing the optimization routine on three differnt test dataset (Chapter 3), providing intermediate results and expected outcome, the users can eaisily learn how to find the optimal processing parameters for eSRRF processing.Three samples are provided to showcase the eSRRF reconstruction process:1. Microtubules sample: Set01_DNA-PAINT_Microtubules.tifDNA-PAINT microscopy measurement of immunolabeled microtubules in fixed COS-7 cells, showing 0.121 localizations per frame and µm^2 (data published in Laine and Heil et al.)108x90 pixels, 500 frames, pixel size: 160 nm 2. Kidney sample: Set02_KidneySDNephrinExM.tifExM of human kidney biopsies stained with nephrin (data published in Kylies et al.)150x150 pixels, 200 frames, pixel size: 102 nm 3. Single emitters simulation: Set03_simulation_groundTruth_2p5Sigma - Fluorescence stack_Avg5.tifSimulated individual molecules emitting placed on concentric rings with radii increasing by 220 nm steps. On each ring the molecules are separated by 57.5, 115, 173, 230, 288 and 345 nm, respectively (data published in Laine and Heil et al.)33x33 pixels, 100 frames, pixel size: 100 nm 4. Test dataset for drift/vibration correction: Set04_ExSRRF_eSRRF_vibration_correction_practice_dataset.tifEsM of human kidney biopsies stained with nephrin (data published in Kylies et al.)100x100 pixels, 200 frames, pixel size: 102 nm5. Test dataset for photobleaching: Set05_Photobleaching.tifExM of 120 nm Nanorulers (data published in Kylies et al.)150x150 pixels, 75 frames, pixel size: 64 nm Jupyter-Notebook: ridge_detection.ipynbWith this notebook qantitative image analyis of sturctures resolved with ExSRRF can be performed.Such as:calculation of the target structure density. identifying areas with high inter-ridge spacing by maping the distance to the nearest ridge based on Euclidean distance transform. measuring the spatial uniformity of the structure of interest by examining the distribution of the local densities and the distances to the nearest ridge.