How-to-guide: Temporal grouping tool
In localisations-based super-resolution microscopy, each fluorescent molecule can emit ("blink") multiple times, depending on its properties and the super-resolution technique used. Those blinking events are localized accurately using advanced algorithms over the frames of one single-molecule acquisition. The photo-switching kinetic, also called blinking kinetic, defined by the fluorophores off-state/on-state duration ratio impacts the quality of the final reconstruction. Thus, each blinking event can be localized across multiple frames leading to the multiplication of localisations and overcounting. Temporal grouping is a powerful tool which aims to reduce noise and clean your data by combining duplicated localisations from a single blinking event.
Each fluorophore blinking event can be localized across multiple frames leading to multiplication of localisations and overcounting.
Temporal grouping tool will aggregate duplicated localisations from a single blinking event.
The temporal grouping tool is now included in the analysis workflows of CODI. Access Temporal grouping tool from the analysis workflow menu ("clock" icon). The tool is divided into two widgets : Temporal grouping (top) and duration filtering (bottom).
Temporal grouping can be run with default parameters by clicking “run” in the widget. This will apply any pre-processing steps loaded in the workflow to the localisations before temporarily grouping localisations by channel.
For more fine-grained control, a menu can be accessed by clicking the three dot icon to reveal further options. In this menu you will be able to change the maximum "frame gap" and “maximum distance” (nm) parameters.
Localisations are regrouped into a temporal group only if it satisfies a maximum frame gap and maximum distance (search radius) conditions (see figure below). Note that these parameters are not informed by blinking kinetics. The "maximum frame gap" criterion allows one blinking event that is captured un-continuously over the frame (e.g. due to poor signal noise ratio) to be combined as one temporal group. While the "maximum distance" criterion is driven by localisation precision.
All events belonging to one group will be replaced by a single point, here named as one temporal group, its coordinates are calculated based on all the grouped localisations (barycenter).
Localisations are regrouped into a temporal group if it satisfies a maximum “frame gap” and “distance” conditions.
By channel, each localisation event will regroup into temporal groups depending on the input parameters and user defined criteria. Only temporal groups will appear in the dataset view and in the visualization panel. It results in a cleaner dataset and allows more accurate quantitative analysis of biological structures.
One sub-diffracted EV’s tetraspanins structure imaged using 3-color dSTORM (a) before and (b) after temporal grouping correction. (a) Each point represents one localisations event. (b) each point represents one temporal group.
Once the temporal grouping tool is run, each localisations will be gathered into temporal groups by channel.
Only temporal groups will appear in the dataset view and in the visualization panel.
Duration filtering: Refinement of temporal groups
Using duration filtering you will be able to filter out short or long temporal groups for each channel, and thus to discard fiducial markers or artifacts from your dataset as an example.
The duration filtering tool allows to filter the temporal groups by channel based on their duration.
The temporal groups distribution can be visualised by channel on this panel.
How to guide: Filtering tool
Tool view Purpose of tool Enable user to filter out points based on their properties and save the output for further analysis in workflow. Contents Input parameters Multiple channels Output results Input parameters The points in dSTORM acquisitions ...
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How to guide: Counting tool
Tool view Purpose of tool Counts the number of localisations or ‘bins’ by channel around a set of centre points - currently the centroids of a clustering result. Contents Inputs Results Outputs Inputs The centre points for counting come from a loaded ...
How to guide: Clustering tool
Tool view Purpose of tool The tool performs clustering on localisation data to find biological structures and quantify their statistical and morphological properties. The default clustering method is HDBSCAN. The cluster results can then be ...
How to guide: CODI Apps and Batch Analysis
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