Difference between revisions of "MicrotubuleTracker"

 
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[[Image:logoline.jpg|right|250px]]
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[[Image:MTracker_NewLogo-04.png|right|250px]]
 
{{ComponentStats:net.imagej:MTrack}}
 
{{ComponentStats:net.imagej:MTrack}}
  
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MTrack is a tool, which detects, tracks, and measures the behavior of fluorescently labeled microtubules imaged by TIRF (total internal reflection fluorescence) microscopy. In such an in vitro reconstitution approach, stabilized, non-dynamic microtubule seeds serve as nucleation points for dynamically growing microtubules.

  
 +
MTrack is a bi-modular tool. The first module detects microtubule seeds, tracks the growing microtubule ends and creates trajectories. The second module uses these trajectories to fit models of dynamic behavior (polymerization and depolymerization velocities, catastrophe and rescue frequencies) and is able to compute population statistics such as length distributions.
 +
 +

To make yourself familiar with MTrack, please go to the [[#Example|Example section]], where you are able to download an example TIRF movie and you will find detailed instruction for running it.
  
MTrack is a tool, which detects, tracks, and measures the behavior of fluorescently labeled microtubules imaged by TIRF (total internal reflection fluorescence) microscopy. In such an in vitro reconstitution approach, stabilized, non-dynamic microtubule seeds serve as nucleation points for dynamically growing microtubules.
+
For using MTrack on movies which have very low signal to noise ratio you should create a denoised image to be used for segmentation and upload it along with the original movie. In this setting the microtubules pixels are identified from the segmentation movie while the actual measurement is always done on the original movie. To make yourself familiar with this setting please go to the [[#Low SNR Example|Low SNR Example section]] where we detail this approach with a demo movie.
 
 
MTrack is a bi-modular tool. The first module detects and tracks the growing microtubule ends and creates trajectories. The second module uses these trajectories to fit models of dynamic behavior
 
(polymerization and depolymerization velocities, catastrophe and rescue frequencies). It also computes statistics such as length and lifetime distributions when analyzing more than one
 
movie (batch mode).
 
 
 
 
== Installation ==
 
== Installation ==
  
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# Click the ''Manage update sites'' button.
 
# Click the ''Manage update sites'' button.
 
# Select the ''MTrack'' update site in the list.
 
# Select the ''MTrack'' update site in the list.
# Click Close and then click ''Apply changes''.
+
# Click ''Close'' and then click ''Apply changes''.
 
# Restart Fiji.
 
# Restart Fiji.
 
# Launch the plugin with ''Plugins ▶ MTrack''.
 
# Launch the plugin with ''Plugins ▶ MTrack''.
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=== Module 1 - Microtubule Detection & Tracking===
 
=== Module 1 - Microtubule Detection & Tracking===
A typical dataset consists of a single two-dimensional (2d) image of the non-dynamic microtubule seeds followed by a 2d time-lapse of the dynamically growing microtubules (Note1). The file format
+
A typical dataset consists of a single two-dimensional (2d) image of the non-dynamic microtubule seeds followed by a 2d time-lapse of the dynamically growing microtubules. The file format
can be any format readable by Fiji/Bioformats (.tif, .nd2, … ). To run the tracker select {{bc|Plugins|MTrack|Tracking}}
+
can be any format readable by Fiji/Bioformats (.tif, .nd2, … ). To run the tracker select {{bc|Plugins|MTrack|Microtubule Detection and Tracking}}
  
 
The welcome panel will open.
 
The welcome panel will open.
  
[[Image:MTrack.png|500px]]
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[[Image:WelcomeA.png|500px]]
  
 
==== Choose Mode====
 
==== Choose Mode====
For a first analysis of your data, we suggest using the simple mode, in which we have pre-selected a number of parameters. In case you are unsatisfied with the outcome of the tracking you can use the
+
For a first analysis of your data, we suggest using the simple mode, in which we have pre-selected a number of parameters. In case you are unsatisfied with the outcome of the tracking, you can use the
[[advanced mode(MTrack)]] to fine-tune settings. When analyzing more than one movie, you can select [[batch mode(MTrack)]] and run many movies simultaneously. However, before running the program in batch mode, you
+
[[advanced mode(MTrack)]] to fine-tune settings. When analyzing more than one movie, you can select [[batch mode(MTrack)]] and run many movies simultaneously. However, before running the program in batch mode, you have to at least run the program once in simple or advanced mode to select and save the required parameters.  
have to at least run the program once in single or advanced mode to select and save the required parameters. The following intro is on simple mode.
+
 
 +
The following intro is on simple mode.
  
 
==== Select Movie====
 
==== Select Movie====
  
Next, the user selects the movie. The movie to be uploaded in the original movie coming out of the microscope. Object recognition of Microtubules requires a preprocessed movie  and such a preprocessed movie is only used for locating where the microtubules are in the image and not for end-points detection, which is always performed on the original image. The user has the option to either perform a flat-field correction and apply a median filter of chosen radius or user can also upload their own preprocessed movie.
+
Next, the user selects the movie. The movie to be uploaded is the original movie coming out of the microscope. In simple mode, the program will do a pseudo flat-field correction by default. This preprocessed movie will only be used for object recognition of seeds, not for end-point detection. End point detection will always be performed on the original image.  
 +
 
 +
In the advanced mode, the user has the option to either perform a flat-field correction and apply a median filter of a chosen radius. Alternatively, the user can upload their own preprocessed movie.
 
(Read more about [[Preprocessing(MTrack)]]).
 
(Read more about [[Preprocessing(MTrack)]]).
For the movie type choose one of the three supported options:
+
 
* Two channel image as hyper stack (both channels in one image)
+
For the movie type, choose one of the three supported options:
 +
* Two channel image as hyper-stack (both channels in one image)
 
* Concatenated seed image followed by time-lapse images
 
* Concatenated seed image followed by time-lapse images
 
* Single channel time-lapse images
 
* Single channel time-lapse images
Please choose an output file name and directory. The trajectory files will be written as .txt files. By default trajectories will be saved in the current working directory with the name of the movie.
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Please choose an output file directory. The trajectory files will be written as .txt files. By default, trajectories will be saved in the current working directory with the name of the movie.
  
 
==== Microscope Parameters====
 
==== Microscope Parameters====
The program automatically reads the metadata shown as pixel size (micrometer in x and y) and frame rate (in seconds). If the metadata could not be read properly, the user can change the values. In addition, the user is asked to enter the Sigma(X) and Sigma(Y) of the Point-Spread-Function (PSF) of the microscope in pixel units see here for [[more explanation]]. For your convenience, our software comes with an inbuilt PSF analyzer tool, which
+
The program automatically reads the metadata shown as pixel size (micrometer in x and y) and frame rate (in seconds). If the metadata can not be read properly, the user can manually add the values. In addition, the user is asked to enter the Sigma (X) and Sigma (Y) of the Point-Spread-Function (PSF) of the microscope in pixel units (see here for [[more explanation]]). For your convenience, our software comes with an inbuilt PSF analyzer tool, which
 
can optionally determine the PSF of your microscope from bead images by fitting a Gaussian function.
 
can optionally determine the PSF of your microscope from bead images by fitting a Gaussian function.
  
==== Preprocessing====
+
When you input any parameters, please ensure that you use decimal number formatting only.
As mentioned before, for recognizing microtubules in the image, preprocessing is essential, we provide the user with an option to do pseudo-flat field correction and to apply a median filter of certain radius (e.g. r=2 pixels). Optionally user can load their own preprocessed movie.
 
Please note that this blurring will not be used during subsequent tracking.  
 
  
Press Next to proceed, and 3 screens and one panel will open. They show the original movie, the preprocessed movie, and the “active image”, which representing the seeds and is typically the first frame of the movie. Every successfully recognized seed will be marked with a red ellipse.
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Press Next to proceed. Three screens and one panel will open. They show the original movie, the preprocessed movie, and the “active image”, which represents the seeds and is typically the first frame of the movie. Every successfully recognized seed will be marked with a red ellipse.
  
 
==== MSER parameters====
 
==== MSER parameters====
The default algorithm to identify the seeds as objects is called Maximally Stable Extremal Regions (MSER) <ref>   
+
The default algorithm to identify the seeds as objects is called Maximally Stable Extremal Regions (MSER)<ref>   
Robust wide-baseline stereo from maximally stable extremal regions, J Matas, O Chum, M Urban, T Pajdla Image and vision computing 22 (10), 761-767.  </ref>.Read more about [[MSER parameters]]. If seeds are not recognized or two very close seeds are recognized as one, the user can change the MSER parameters using the adjustable sliders. The effect will be displayed live on the “active image”. Once most seeds are correctly recognized as objects, click “Find endpoints” to detect the ends of each seed with sub-pixel accuracy.  
+
Robust wide-baseline stereo from maximally stable extremal regions, J Matas, O Chum, M Urban, T Pajdla Image and vision computing 22 (10), 761-767.  </ref>. Read more about [[MSER parameters]]. If a single seed is not recognized or two very close seeds are recognized as one, the user can change the MSER parameters using the adjustable sliders. The effect will be displayed live on the “active image”. Once most seeds are correctly recognized as objects, click “Find endpoints” to detect the ends of each seed with sub-pixel accuracy.  
  
The end-points will be displayed as green circles and a “Next” button appears on the panel which allows the user to flip to the next panel.
+
The end-points will be displayed as green circles. A “Next” button appears on the panel, which allows the user to flip to the next panel.
  
 
[[Image:MserSimple.png|300px]]
 
[[Image:MserSimple.png|300px]]
  
====1.6 Options====
+
==== Options====
  
In this panel the user will see some options before starting the tracking. They are described in this section
+
Before starting the actual tracking of the dynamically growing microtubules, the program will give you several options:
  
 
[[Image:Options.png|300px]]
 
[[Image:Options.png|300px]]
  
Deselect and select ends
+
'''Deselect and select ends'''
  
Before starting the actual tracking of the dynamically growing microtubules, the program will give you several options. Before choosing endpoints to remove or add you check all frames of the movie by moving the slider in the “Deselect and select ends” dialog. Select or deselect ends. If an end was wrongly recognized or two points are too close to each other you can deselect an end by left clicking on it in the image (will appear as pink circle instead of green). The program will remember and allow to re-select this end by clicking left click again near to the end point of interest (the pink circle becomes green again). In case an end has not been recognized or a randomly nucleated end has to be selected, use Shift + left click to select a user defined end (orange circle will mark the end). Making a left click near the user defined seed end deselects it and marks it as red, the user can make as many clicks to select the ends they want to. Read more on [[microtubule polarity and (+) end vs. (-) end tracking]].
+
In case an end has been wrongly recognized, the user can deselect an end by left
 +
clicking on it in the image. The program will remember and allow to re-select this end by clicking Shift + left click (pink circle will mark the end). In case an end has not been recognized, use Shift + Alt + left click to select a user defined end (orange circle will mark the end). Read more on [[microtubule polarity and (+) end vs. (-) end tracking]].
  
 +
'''Select time'''
  
Select time
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The user can select the start and end time over which the tracking will be performed by entering the frame numbers.
 +
Click “Confirm ends and track” to perform the actual tracking, which will be performed “live” (progress bar will show).
  
The tracking is performed from the first frame till the end frame by default, if the user however wishes to alter the start and the end frame of tracking they can do so here. The tracking will then be performed from the user chosen start frame till the user chosen end frame.
+
Yellow ellipses mark seeds to be tracked, red ellipses mark seeds which won’t be tracked. Green circles mark ends to be tracked. Orange circles mark user defined ends that will be tracked. During tracking, a yellow crosshair will show the current position of the tracking
 +
on each marked microtubule. A "Success" frame will let you know about the end of the tracking. Two movies will be displayed, the “Track ID” movie, which can be used to link the trajectories to individual microtubules and an “Overlay movie”, in which the user can recapitulate the tracking. The trajectory of each end is individually saved as .txt file and numbered according to the track ID. Each trajectory will contain the following information: frame number, total microtubule length (in px and μm), track ID, x and y position (px and μm) and the length increment from the previous frame (px and µm).
 +
After successful tracking, the user has the option to save the selected ends, so that the movie can be run (again) in batch mode.
  
Click on confirm seed ends and track button to start the tracking. The program displays yellow ellipses showing the  marked seeds that are being tracked, red ellipses mark seeds, which won”t be tracked. Green circles mark ends to be tracked. Orange circles mark user defined ends and will be tracked.
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===Module 2 - Microtubule dynamics===
The actual tracking is  performed “live” (progress bar will show the current time in relation to the end time-point). A crosshair will show the current position of the found end on each marked microtubule end. A faint grey line is also drawn along the detected track enabling the user to ensure the correctness of the tracks obtained by the program.
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Microtubules show a dynamic behavior known as dynamic instability, which is characterized by four parameters (1) polymerization velocity (vp, nm/sec), (2) depolymerization velocity (vd, nm/sec), (3) catastrophe frequency (fcat, sec-1), and (4) rescue frequency (fres, sec-1). Module 2 derives these dynamic parameters by fitting models using RANSAC. (Read more on [[MTrack-RANSAC models]]).
  
At the end of tracking a Log file will appear with the message to inform the user about the end of tracking and about the text files written with the track information.  The results are also displayed as two movies, the “Track ID” movie, which can be used to link the trajectories to individual microtubules and an “Overlay movie”, in which the user can recapitulate the tracking. The trajectory of each end is individually saved as .txt file and numbered according to the track ID.
+
If not forwarded by Module 1, Module 2 can be selected by  {{bc|Plugins|MTrack|Microtubule Dynamics Analyzer}}
  
Save parameters
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The panel that opens will allow the user to select individual files containing trajectories, which were generated in the first module. The trajectory will be displayed as length versus time plot, on which RANSAC fits a model of microtubule dynamics using
 +
the default parameters. Read more about the [[MTrack-RANSAC parameters]].
  
After successful tracking, the user has the option to save the program parameters, so that the movie can be run (again) in batch mode. This choice can also be made without doing the tracking after just making the parameter selection. Clicking on this button triggers the close of the program and saves the program parameters in IJ.log file of FIJI, this close of program is done to ensure that the log file is properly updated by FIJI.
+
Clicking "Auto Compute Velocity and Frequencies" auto computes the polymerization/depolymerization velocities and catastrophe and rescue frequency for all the files. If a file is empty, a warning message will show up with a blank plot.
  
Click here for some example [[tracks]].
+
In addition, the user can obtain microtubule length distribution for a certain time point or a time-averaged distribution. In the length distribution plot, the mean length, and the standard deviation will be displayed after fitting an exponential decay curve to the obtained distribution.
  
===Module 2- Microtubule dynamics===
 
Microtubules show a dynamic behavior known as dynamic instability, which is characterized by four parameters (1) polymerization velocity (vp, nm/sec), (2) depolymerization velocity (vd, nm/sec), (3) catastrophe frequency (fcat, sec-1), and (4) rescue frequency (fres, sec-1). Using the trajectories created in Module 1 (The .txt file contains frame number, total length (in px and μm), track ID, x and y position
 
(pixel and μm) and the length increment from the previous frame (px and micrometer). Module 2 derives these dynamic parameters by fitting models using RANSAC. (Read more on [[MTrack-RANSAC models]]).
 
  
If not forwarded by Module 1, Module 2 can be selected by  {{bc|Plugins|MTrack|Rate Analyzer}}
+
[[Image:RansacPanel.png|600px]]
 +
Click here to see some examples of the [[MTrack-Ransac fits]].
  
The panel that opens will allow the user to select individual files containing trajectories displayed as length versus time plots on which RANSAC fits a model of microtubule dynamics using
+
== Example ==
the default parameters. Read more about the [[MTrack-RANSAC parameters]]. If the default parameters work well, the user can save the estimated dynamic parameters of the microtubule, which will appear as a results table.
 
  
After the user has gone through certain files in the table shown in the panel below they can click "compile results till current file" in which case average growth/shrink rates and a count of each growth and shrink event along with catastrophe and rescue frequency for each trajectory is computed and stored in the current directory. Program creates two files labelled "Allrates.txt" and "Allaverages.txt", in the first file it contains the information about the start and end time and rates of each growth and shrink events along with the trajectory filename for easy identification of to which movie does the computation belongs to. In the second file it contains the averaged rate and frequency information of each trajectory analyzed up to then.
+
An example movie with several dynamic microtubules is available for download [http://preibischlab.mdc-berlin.de/download/MTrack/MTrack_Demo.tif.zip here]. To perform the analysis of this movie:
  
If the user wishes to use the same parameter selection for all the files and obtain the "Allrates" and "Allaverages" text files without individually selecting the parameters for each they can then simply click "Auto fit and compile results" button, which would do the Ransac fits on all the trajectory files listed on the table and save the two results file in the current directory.  
+
# Put the demo movie '''MTrack_Demo.tif''' into an empty directory, the results will also be stored here.
 +
# To run the MicroTubule Tracker:
 +
# Select '''Plugins''' ▶ '''MTrack''' ▶ '''Microtubule Detection and Tracking''',
 +
# Select '''Simple Mode''', '''Concatenated Seed Image followed by time-lapse images''', choose the file, the microscope parameters will be automatically loaded, finally click '''Next>''' to continue.
 +
# Using the default MSER parameters ''7 microtubule seeds will be identified'', click '''Find Endpoints''' to continue.
 +
# The correct endpoints of 6 microtubule seeds will be identified (one is too short and can be added manually), click '''Next>''' to continue.
 +
# Click '''Confirm the end(s) and track''' to track the microtubules over all 241 time-points. The expected runtime is around 6-7 min. ''Note: the few warnings of missed assignments can be safely ignored, these timepoints will simply be missing, which does not create any further problems as long as it is not happening in the majority of cases.''
 +
# Each microtubule trajectory will be saved.
 +
# You are now able to review the tracking results in the ImageJ windows, click '''Enter RANSAC stage>''' to continue.
 +
# Click '''Select directory of MTrack generated files''' and select the directory that now contains all the text files with the tracking results. The assignment which file belongs to which seed the user can get via the labelled seed end points in the 'Display Tracks' window which appears after tracking the microtubules in module 1.
 +
# To adjust the parameters to automatically derive microtubule dynamics '''select one of the microtubule seed end points''' in the panel Ransac velocity and Statistics Analyzer. The selected file  will be displayed in the '''Microtubule Length Plot''' window and some parameters will be shown in an extra window. For the example file '''MTrack_DemoSeedLabel1Plus.txt''' all growth events are successfully detected. The catastrophes are shorter than the default setting, therefore the user needs to click 'Detect catastrophies without fit' to ensure a working data evaluation. Furthermore the user can adjust  parameters like "Minimum number of timepoints" manually or can change the fitting function to achieve a better fit of the data.
  
After completing the analysis for one directory of trajectory files the user can again select another directory and all the tracker generated files will be updated in the table. Now the user can repeat the same process as above for analysis of these set of files and continue to compile results for as many directory of files as they wish to.
+
All dynamic parameters are saved in a .txt file call 'Allaverages'. For a better presentation the user can copy and paste the data from the .txt file to Excel or an equivalent software.
  
[[Image:ransacfirst.png|500px]]
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==Low SNR Example ==
Click here to see some examples of the [[MTrack-Ransac fits]].
+
 
 +
An example movie with a single simulated microtubule at low SNR and the same microtubule without the noise is available for download [http://preibischlab.mdc-berlin.de/download/MTrack/LowSNRMTrack_Demo.tif.zip here]
 +
To perform the analysis of such microtubules:
 +
 
 +
# Put the demo movies '''LowSNRMTrack_Demo.tif''' and '''DenoisedMTrack_Demo.tif''' into an empty directory, the results will also be stored here.
 +
# To run the MicroTubule Tracker:
 +
# Select '''Plugins''' ▶ '''MTrack''' ▶ '''Microtubule Detection and Tracking''',
 +
# Select '''Advanced Mode''', '''Concatenated Seed Image followed by time-lapse images''', choose the LowSNR movie , the microscope parameters will be automatically loaded, finally click '''Next>''' to continue.
 +
#In the second panel load the denoised movie and click on '''Load preprocessed movie and go next'''
 +
#In the panel labelled  '''Object recognition methods''' select MSER from the drop down menu. The microtubule would be correctly identified, click  ''' next''' to go to the second panel where MSER parameters are displayed. Now click '''Find Endpoints''' to continue.
 +
#Click '''next''' to go to the next panel. Using the slider in the panel '''Deselect and select ends''' identify the non-growing end of the microtubule and deselect it from further calculations by doing a left click near it, the green circle should turn pink implying that the end has been deselected. 
 +
# Select '''Do MSER based segmentation''' from the '''Select segmentation method'''  as the choice of segmentation over time. Now click '''Next''' to go to the next panel. 
 +
#To track the microtubules over all 100 time-points click on '''Start tracking''' from the '''Tracker options''' panel  . The expected runtime is around 5 min.
 +
# The microtubule trajectory will be saved.
 +
 
 +
 
 +
==Citation==
 +
Please note that MTrack is available through Fiji, and is based on a publication. If you use it
 +
successfully for your research please be so kind to cite our work:
 +
 
 +
Varun Kapoor, William G. Hirst, Christoph Hentschel, Stephan Preibisch and Simone Reber,
 +
“MTrack: Automated Detection and Tracking of Dynamic Microtubules” <ref>https://www.biorxiv.org/content/early/2018/07/13/368191 </ref>
 +
 
 +
== References ==

Latest revision as of 09:38, 2 November 2018

MTracker NewLogo-04.png
Microtubule Tracker
Project Fiji
URL http://imagej.net/MicrotubuleTracker
Source on GitHub
License GPLv3
Date Wed Jul 19 12:00:45 CDT 2017
Development status Active
Support status Active
Team
Founders SimoneR, Stephan Preibisch, Varun Kapoor
Leads Varun Kapoor
Developers Varun Kapoor, Stephan Preibisch
Debuggers Varun Kapoor, Stephan Preibisch, SimoneR, William Hirst, Christoph Hentschel
Reviewers Varun Kapoor
Support Varun Kapoor
Maintainers Varun Kapoor
Contributors Varun Kapoor, Stephan Preibisch, SimoneR, William Hirst, Christoph Hentschel


MTrack is a tool, which detects, tracks, and measures the behavior of fluorescently labeled microtubules imaged by TIRF (total internal reflection fluorescence) microscopy. In such an in vitro reconstitution approach, stabilized, non-dynamic microtubule seeds serve as nucleation points for dynamically growing microtubules.


MTrack is a bi-modular tool. The first module detects microtubule seeds, tracks the growing microtubule ends and creates trajectories. The second module uses these trajectories to fit models of dynamic behavior (polymerization and depolymerization velocities, catastrophe and rescue frequencies) and is able to compute population statistics such as length distributions.


To make yourself familiar with MTrack, please go to the Example section, where you are able to download an example TIRF movie and you will find detailed instruction for running it.

For using MTrack on movies which have very low signal to noise ratio you should create a denoised image to be used for segmentation and upload it along with the original movie. In this setting the microtubules pixels are identified from the segmentation movie while the actual measurement is always done on the original movie. To make yourself familiar with this setting please go to the Low SNR Example section where we detail this approach with a demo movie.

Installation

  1. Click Help ▶ Update....
  2. Click the Manage update sites button.
  3. Select the MTrack update site in the list.
  4. Click Close and then click Apply changes.
  5. Restart Fiji.
  6. Launch the plugin with Plugins ▶ MTrack.

Usage

Module 1 - Microtubule Detection & Tracking

A typical dataset consists of a single two-dimensional (2d) image of the non-dynamic microtubule seeds followed by a 2d time-lapse of the dynamically growing microtubules. The file format can be any format readable by Fiji/Bioformats (.tif, .nd2, … ). To run the tracker select Plugins ▶ MTrack ▶ Microtubule Detection and Tracking

The welcome panel will open.

WelcomeA.png

Choose Mode

For a first analysis of your data, we suggest using the simple mode, in which we have pre-selected a number of parameters. In case you are unsatisfied with the outcome of the tracking, you can use the advanced mode(MTrack) to fine-tune settings. When analyzing more than one movie, you can select batch mode(MTrack) and run many movies simultaneously. However, before running the program in batch mode, you have to at least run the program once in simple or advanced mode to select and save the required parameters.

The following intro is on simple mode.

Select Movie

Next, the user selects the movie. The movie to be uploaded is the original movie coming out of the microscope. In simple mode, the program will do a pseudo flat-field correction by default. This preprocessed movie will only be used for object recognition of seeds, not for end-point detection. End point detection will always be performed on the original image.

In the advanced mode, the user has the option to either perform a flat-field correction and apply a median filter of a chosen radius. Alternatively, the user can upload their own preprocessed movie. (Read more about Preprocessing(MTrack)).

For the movie type, choose one of the three supported options:

  • Two channel image as hyper-stack (both channels in one image)
  • Concatenated seed image followed by time-lapse images
  • Single channel time-lapse images

Please choose an output file directory. The trajectory files will be written as .txt files. By default, trajectories will be saved in the current working directory with the name of the movie.

Microscope Parameters

The program automatically reads the metadata shown as pixel size (micrometer in x and y) and frame rate (in seconds). If the metadata can not be read properly, the user can manually add the values. In addition, the user is asked to enter the Sigma (X) and Sigma (Y) of the Point-Spread-Function (PSF) of the microscope in pixel units (see here for more explanation). For your convenience, our software comes with an inbuilt PSF analyzer tool, which can optionally determine the PSF of your microscope from bead images by fitting a Gaussian function.

When you input any parameters, please ensure that you use decimal number formatting only.

Press Next to proceed. Three screens and one panel will open. They show the original movie, the preprocessed movie, and the “active image”, which represents the seeds and is typically the first frame of the movie. Every successfully recognized seed will be marked with a red ellipse.

MSER parameters

The default algorithm to identify the seeds as objects is called Maximally Stable Extremal Regions (MSER)[1]. Read more about MSER parameters. If a single seed is not recognized or two very close seeds are recognized as one, the user can change the MSER parameters using the adjustable sliders. The effect will be displayed live on the “active image”. Once most seeds are correctly recognized as objects, click “Find endpoints” to detect the ends of each seed with sub-pixel accuracy.

The end-points will be displayed as green circles. A “Next” button appears on the panel, which allows the user to flip to the next panel.

MserSimple.png

Options

Before starting the actual tracking of the dynamically growing microtubules, the program will give you several options:

Options.png

Deselect and select ends

In case an end has been wrongly recognized, the user can deselect an end by left clicking on it in the image. The program will remember and allow to re-select this end by clicking Shift + left click (pink circle will mark the end). In case an end has not been recognized, use Shift + Alt + left click to select a user defined end (orange circle will mark the end). Read more on microtubule polarity and (+) end vs. (-) end tracking.

Select time

The user can select the start and end time over which the tracking will be performed by entering the frame numbers. Click “Confirm ends and track” to perform the actual tracking, which will be performed “live” (progress bar will show).

Yellow ellipses mark seeds to be tracked, red ellipses mark seeds which won’t be tracked. Green circles mark ends to be tracked. Orange circles mark user defined ends that will be tracked. During tracking, a yellow crosshair will show the current position of the tracking on each marked microtubule. A "Success" frame will let you know about the end of the tracking. Two movies will be displayed, the “Track ID” movie, which can be used to link the trajectories to individual microtubules and an “Overlay movie”, in which the user can recapitulate the tracking. The trajectory of each end is individually saved as .txt file and numbered according to the track ID. Each trajectory will contain the following information: frame number, total microtubule length (in px and μm), track ID, x and y position (px and μm) and the length increment from the previous frame (px and µm). After successful tracking, the user has the option to save the selected ends, so that the movie can be run (again) in batch mode.

Module 2 - Microtubule dynamics

Microtubules show a dynamic behavior known as dynamic instability, which is characterized by four parameters (1) polymerization velocity (vp, nm/sec), (2) depolymerization velocity (vd, nm/sec), (3) catastrophe frequency (fcat, sec-1), and (4) rescue frequency (fres, sec-1). Module 2 derives these dynamic parameters by fitting models using RANSAC. (Read more on MTrack-RANSAC models).

If not forwarded by Module 1, Module 2 can be selected by Plugins ▶ MTrack ▶ Microtubule Dynamics Analyzer

The panel that opens will allow the user to select individual files containing trajectories, which were generated in the first module. The trajectory will be displayed as length versus time plot, on which RANSAC fits a model of microtubule dynamics using the default parameters. Read more about the MTrack-RANSAC parameters.

Clicking "Auto Compute Velocity and Frequencies" auto computes the polymerization/depolymerization velocities and catastrophe and rescue frequency for all the files. If a file is empty, a warning message will show up with a blank plot.

In addition, the user can obtain microtubule length distribution for a certain time point or a time-averaged distribution. In the length distribution plot, the mean length, and the standard deviation will be displayed after fitting an exponential decay curve to the obtained distribution.


RansacPanel.png Click here to see some examples of the MTrack-Ransac fits.

Example

An example movie with several dynamic microtubules is available for download here. To perform the analysis of this movie:

  1. Put the demo movie MTrack_Demo.tif into an empty directory, the results will also be stored here.
  2. To run the MicroTubule Tracker:
  3. Select PluginsMTrackMicrotubule Detection and Tracking,
  4. Select Simple Mode, Concatenated Seed Image followed by time-lapse images, choose the file, the microscope parameters will be automatically loaded, finally click Next> to continue.
  5. Using the default MSER parameters 7 microtubule seeds will be identified, click Find Endpoints to continue.
  6. The correct endpoints of 6 microtubule seeds will be identified (one is too short and can be added manually), click Next> to continue.
  7. Click Confirm the end(s) and track to track the microtubules over all 241 time-points. The expected runtime is around 6-7 min. Note: the few warnings of missed assignments can be safely ignored, these timepoints will simply be missing, which does not create any further problems as long as it is not happening in the majority of cases.
  8. Each microtubule trajectory will be saved.
  9. You are now able to review the tracking results in the ImageJ windows, click Enter RANSAC stage> to continue.
  10. Click Select directory of MTrack generated files and select the directory that now contains all the text files with the tracking results. The assignment which file belongs to which seed the user can get via the labelled seed end points in the 'Display Tracks' window which appears after tracking the microtubules in module 1.
  11. To adjust the parameters to automatically derive microtubule dynamics select one of the microtubule seed end points in the panel Ransac velocity and Statistics Analyzer. The selected file will be displayed in the Microtubule Length Plot window and some parameters will be shown in an extra window. For the example file MTrack_DemoSeedLabel1Plus.txt all growth events are successfully detected. The catastrophes are shorter than the default setting, therefore the user needs to click 'Detect catastrophies without fit' to ensure a working data evaluation. Furthermore the user can adjust parameters like "Minimum number of timepoints" manually or can change the fitting function to achieve a better fit of the data.

All dynamic parameters are saved in a .txt file call 'Allaverages'. For a better presentation the user can copy and paste the data from the .txt file to Excel or an equivalent software.

Low SNR Example

An example movie with a single simulated microtubule at low SNR and the same microtubule without the noise is available for download here To perform the analysis of such microtubules:

  1. Put the demo movies LowSNRMTrack_Demo.tif and DenoisedMTrack_Demo.tif into an empty directory, the results will also be stored here.
  2. To run the MicroTubule Tracker:
  3. Select PluginsMTrackMicrotubule Detection and Tracking,
  4. Select Advanced Mode, Concatenated Seed Image followed by time-lapse images, choose the LowSNR movie , the microscope parameters will be automatically loaded, finally click Next> to continue.
  5. In the second panel load the denoised movie and click on Load preprocessed movie and go next
  6. In the panel labelled Object recognition methods select MSER from the drop down menu. The microtubule would be correctly identified, click next to go to the second panel where MSER parameters are displayed. Now click Find Endpoints to continue.
  7. Click next to go to the next panel. Using the slider in the panel Deselect and select ends identify the non-growing end of the microtubule and deselect it from further calculations by doing a left click near it, the green circle should turn pink implying that the end has been deselected.
  8. Select Do MSER based segmentation from the Select segmentation method as the choice of segmentation over time. Now click Next to go to the next panel.
  9. To track the microtubules over all 100 time-points click on Start tracking from the Tracker options panel . The expected runtime is around 5 min.
  10. The microtubule trajectory will be saved.


Citation

Please note that MTrack is available through Fiji, and is based on a publication. If you use it successfully for your research please be so kind to cite our work:

Varun Kapoor, William G. Hirst, Christoph Hentschel, Stephan Preibisch and Simone Reber, “MTrack: Automated Detection and Tracking of Dynamic Microtubules” [2]

References

  1. Robust wide-baseline stereo from maximally stable extremal regions, J Matas, O Chum, M Urban, T Pajdla Image and vision computing 22 (10), 761-767.
  2. https://www.biorxiv.org/content/early/2018/07/13/368191