SuperResNET supports loading SMLM point cloud data from various microscopes (e.g., dSTORM, PALM, DNA-PAINT) with different file formats (e.g., non-text binary, text, and ASCII formats). See the microscopy data formats that SuperResNET supports in the video

Note 1. SuperResNET will inform you when trying to load unsupported file format. You might need to prepare your file into one of the supported microscopy formats (e.g., .3dlp, .bin, .ascii) or generate a file with the localization coordinates in X, Y, Z and save it as one of the supported general formats (e.g., .txt, .xyz). You can save the file with or without header information. SuperResNET can load both types of files

Please contact Ismail Khater if you have trouble loading your data

General Notes When using SuperResNET

  1. SuperResNET can be used to process 2D data by including a zero-valued, the third Z coordinate for each (X,Y) 2D coordinate. SuperResNET can be used to process/visualize the 2D data. However, some of the features will be calculated to zeros in the Blob Features Module/Tab (e.g., volume) as these features are designed for 3D data only.

  2. SuperResNET is installed on personal computer to process the whole field of view (FOV) or a cropped region of interest (ROI). For big SMLM data with a relatively large number of localizations (e.g. > 1millions from a large field of views), then we recommend using a machine with large memory (e.g., 32GB, 64GB, or even larger). SuperResNET can be used to crop ROIs to improve runtimes.

  3. SuperResNET is a relatively fast software. However, when segmenting big-data using the mean-shift algorithm [1, 2], it might take minutes to hours depending on the size of the data and the computer specifications (CPU speed and RAM size). SuperResNET also supports the use of the DBSCAN [3] algorithm to segment biological structures (i.e., clusters) of various shapes.

[1] Comaniciu, D. & Meer, P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002).

[2] Fukunaga, K. & Hostetler, L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21, 32–40 (1975).

[3] Ester, M., Kriegel, H.-P., Sander, J., Xu, X. & Others. A density-based algorithm for discovering clusters in large spatial databases with noise. in Kdd vol. 96 226–231 (, 1996).

See the step-by-step video guide for using SuperResNET or read the details below

Sample Datasets

After you complete the installation, run SuperResNET to load and process your 3D SMLM point cloud data. SuperResNET comes with sample SMLM datasets, simulated [1] and experimental [2], which are found under the installation folder selected during the installation.

[1] Levet F, Julien G, Galland R, Butler C, Beghin A, Chazeau A, Hoess P, Ries J, Giannone G, Sibarita JB. A tessellation-based colocalization analysis approach for single-molecule localization microscopy. Nature communications. 2019 May 30;10(1):2379.

[2] Clathrin heavy chain dataset (labeled with Alexa647) using an Abbelight SAFe360 microscope (one cell).

SuperResNET GUI Tabs

Launching SuperResNET brings up the main windows that implement the different modules of the 3D SMLM Network Analysis [1] computational pipeline as well as many additional functionalities. SuperResNET modules appear as tabs, as shown in the figure, where the user should start with the first tab: Load Data, go to the next tab: Merge & Network Analysis, and so on, in sequence. Note: re-launch the software to load new data.

[1] Khater IM, Meng F, Wong TH, Nabi IR, Hamarneh G. Super resolution network analysis defines the molecular architecture of caveolae and caveolin-1 scaffolds. Scientific reports. 2018 Jun 13;8(1):9009.

Load Data Tab

Data Loading

SuperResNET main window shows the Load Data tab. Click the Load button to browse and locate your data file. The current implementation supports many popular SMLM data formats, e.g., GSD Leica microscopy data (ASCII, binary), STORM data (3dlp), localization data in XYZ format stored in xyz files, and localization data in XYZ format stored in txt files. The Load module loads the data and the meta-data (descriptors: photon count, sigma X, etc.) that comes along with the 3D localizations. Once the file with a specific format is selected, click the Open button to start loading the data. When the data is loaded successfully, the #Localization and #Descriptors will be shown. In the following explanations, we will assume the sample experimental data (Abbelight clathrin-coated pits), provided with SuperResNET is loaded.

Data Viewing

The current version of SuperResNET (version 3.5) supports one color SMLM data. Future versions will support double (multi-color) labeling.

Once the data is loaded, #Localization and #Descriptors will be displayed. #Descriptors may vary according to the microscope used in data acquisition (i.e., file extension). Visualize the SMLM data by selecting/modifying/ticking any of the Color, Size, Log-scale, Colorbar, or Colormap GUI widgets.

To show the histogram/statistics for the different localization descriptors, select the descriptor from the drop-down component of the Histograms panel. The figure shows, as an example, the distribution of the Z coordinates of the localizations.

ROI Cropping

Optionally crop an ROI from the field point cloud image by moving the Xmin, Xmax, Ymin, and Ymax sliders (Default method). Select an ROI with either rectangular, elliptical, or polygonal shapes (see figure). The Preview shows the change in real-time. Click the View button to visualize the localizations according to the selected parameters. If needed, save the pre-processed data by clicking on the Save ROI Localizations button.

Rectangular ROI

Elliptical ROI

Polygonal/Free-hand ROI

External View

The View button visualizes the data in a separate window that provides additional viewing features, e.g., zoom-in/out, rotate, pan, and save. See below the steps for rendering/visualizing the SMLM data.

Quick Toolbar

Hovering the mouse on the figure shows a quick toolbar to control the visualization of the plot (e.g. save the plot in many formats such as SVG).

See the following snapshots for the quick toolbar of the figure.

View Settings

The user can change the background color, the colormap, view point, etc. To do so, select the rotate icon from the quick toolbar and then go to the figure and right-click on it to select from the displayed menu.

Merge & Network Analysis Tab

Use this tab to alleviate the multiple blinking effects of a single fluorophore artifact by selecting a proper merging threshold [1]. Select the merging threshold = 0 nm then click on the Merge button to skip this multiple blinking correction step and only remove the duplicate localizations that share the exact same coordinates. Next, select a proximity threshold (PT) to construct a network graph from the localizations and calculate the degree of the localizations. Select the PT parameter according to the size of clustering then click on the Calculate button. SuperResNET visualizes the histograms of the network degree before and after merging as well as the localizations before and after the merging.

Optionally, calculate Ripley’s H-function to provide a heuristic for setting PT. Ripley's H-function can be used to return the global scale for the clustering (r). As a rule of thumb, we can construct a network to extract the homogenous clusters in the data by using a proximity threshold PT as (r/2 ≤ PT ≤ r ). For heterogeneous clusters, PT ≅ r/2.

Calculating Ripley's H-function may take a long time to compute depending on the r_min and r_max parameters and the size of the data.

Ripley's H-function can also provide insight for setting the kernel bandwidth when segmenting the blobs using mean-shift (see the Segment Tab).

Note 1: In SuperResNET Ripley’s H-function is calculated for the 2D projected SMLM data because the 3D data has a lateral resolution of ~20 nm and an axial resolution of ~40-50 nm. Don’t use this Ripley’s H-function if you have deep Z (e.g. more than 1 µm).

Note 2: Every time you use a new merging threshold value and click on the Merge button, you have to click on the Draw button after it to find the new parameters.

[1] Khater IM, Meng F, Wong TH, Nabi IR, Hamarneh G. Super resolution network analysis defines the molecular architecture of caveolae and caveolin-1 scaffolds. Scientific reports. 2018 Jun 13;8(1):9009.

Filter Tab

The next module is noise filtering. Click on the Filter button to calculate the filtering parameter [1]. After that, the user can move the Alpha Slider to control noise removal in real-time by noticing both the scatter plot and the histograms.

[1] Khater IM, Meng F, Wong TH, Nabi IR, Hamarneh G. Super resolution network analysis defines the molecular architecture of caveolae and caveolin-1 scaffolds. Scientific reports. 2018 Jun 13;8(1):9009.

Segment Tab

The aim of the segmentation is to find the membership of every localization to one and only one cluster or blob. The Segment module supports two methods for segmenting the SMLM localizations:

  1. The mean-shift algorithm [1] [2] is suitable for segmenting blob-like structures. It requires the user to specify the kernel bandwidth. As a rule of thumb, select the kernel bandwidth to be less than or equal to the r value found using Ripley’s H-function (see the Merge tab above).

  2. DBSCAN [3] is suitable for segmenting clusters with non-spherical-like shapes. However, it requires setting two parameters, Epsilon and Min. Pnts. Setting both parameters depends on the size of the clusters, spacing between the clusters, density of the clusters, and the amount of noise. It may not always be straightforward to set both parameters for the desired results.

After selecting the segmentation method, click on the Segment button. Optionally, set the Min. Blob Size to visually filter the clusters/blobs according to their number of localizations.

[1] Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on information theory. 1975 Jan;21(1):32-40.

[2] Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis & Machine Intelligence. 2002 May 1(5):603-19.

[3] Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. InKdd 1996 Aug 2 (Vol. 96, No. 34, pp. 226-231).

Blob Features Tab

The Blob Features module used to extract a set of features/descriptors for every segmented blob. SuperResNET extracts 28 features that capture the shape, size, volume, network measures, etc. for every blob [1]. The user can show the histogram for some of the extracted features by selecting the feature name from the drop-down component.

[1] Khater IM, Meng F, Wong TH, Nabi IR, Hamarneh G. Super resolution network analysis defines the molecular architecture of caveolae and caveolin-1 scaffolds. Scientific reports. 2018 Jun 13;8(1):9009.

Blob Groups Tab

Unsupervised machine learning can estimate the groups in the data based on their features. SuperResNET uses the K-means method for grouping the blobs. Set the number K (an integer between 1 and 14) of groups (or classes) of blobs then click Group.

SuperResNET implements the Silhouette Criterion, which can help the user evaluate the number of groups when clustering the blobs. Calculating the Silhouette Criterion uses Linkage as the clustering algorithm and the correlation (one minus the sample correlation between points) as the distance metric.

Group Features Tab

Visualizing Individual Features

After grouping the blobs into classes, we can visualize the features of the blobs of every identified group using the individual feature histogram.

Visualizing Feature Pairs

Pair-wise features visualization is used to examine the relationship between a pair of selected features. Select which feature should be plotted along the x-axis and which other feature to be plotted along the y-axis.

Individual Blobs Tab

This module provides interactive visualization of blobs. Sort/filter the blobs based on a selected feature and visualize the blob localizations and the blob network by clicking on the row of the table. Scroll the blobs in the table and select them for visualizations.

Exploring blob from class 1

Exploring blob from class 2

Exploring blob from class 3

For example, retrieve the most spherical or linear blobs and visualize them. We show an example of visualizing the most spherical blob. Move the sliders and edit fields to change the transparency, the localization/node size, proximity threshold/connectivity of the localizations, and the shrinkage factor for the boundary visualization.

Blob Modules Tab

This module used to do more analysis for the selected blob. The modularity analysis allows studying the modules of interacting molecules within the blob [1]. The user can interactively visualize the modules in various ways.

[1] Khater IM, Liu Q, Chou KC, Hamarneh G, Nabi IR. Super-resolution modularity analysis shows polyhedral caveolin-1 oligomers combine to form scaffolds and caveolae. Scientific reports. 2019 Jul 8;9(1):1-0.

Blob Retrieval Tab

This module is used for finding representative blobs from every group. Specify the number of blobs with features most similar to the mean features of every individual group. These top blobs are then overlaid and visualized in various ways.

About Tab

In the About tab, you can update your license key, as described in the Installation documentation. The About tab also includes contact information. We are pleased to receive your questions and feedback about SuperResNET. Please contact Ismail Khater