Trajectory analysis

ELEPHANT is a platform for 3D cell tracking, based on incremental and interactive deep learning.
It implements a client-server architecture. The server is built as a web application that serves deep learning-based algorithms. The client application is implemented by extending Mastodon, providing a user interface for annotation, proofreading and visualization.

This plugin ships automated methods for extracting trajectories of multiples objects in a sequence of 2D or 3D images. Up to version 2 it was known as the ‘Probabilistic particle tracker’ plugin.

Mean square displacement (MSD) analysis is a technique commonly used in colloidal studies and biophysics to determine what is the mode of displacement of particles followed over time. In particular, it can help determine whether the particle is:

• freely diffusing;
• transported;
• bound and limited in its movement.

On top of this, it can also derive an estimate of the parameters of the movement, such as the diffusion coefficient.

@msdanalyzer is a MATLAB per-value class that helps performing this kind of analysis. The user provides several trajectories he measured, and the class can derive meaningful quantities for the determination of the movement modality, assuming that all particles follow the same movement model and sample the same environment.

Classification of trajectoire: need tracking results as input and will then classify the trajectories as  brownian motion, confined brownian or directed.

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 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).

A standalone cell tracking software for single cell migration. Tracking of cells in tissue was also done in Drosophila germband.

The workflow consists of firstly identifying spot (which can be also gravity center of cells identified by another method), and then secondly compute trajectories by linking these spots by global optimisation with a cost function. This method is part of the methods evaluated in Chanouard et al (2014) as "method 9" and is described in detail in its supplementary PDF (page 65).

Dependencies

Following plugins are required.

1. JAR to be placed under IJ plugin directory
2. A pdf file with instructions and output description is also available in the zip .
3. MTrackJ : Used for visualization of tracks. Preinstalled in Fiji.
4. Imagescience.jar: This library is used by MTrackJ. Use update site to install this plugin.
5. jama.jar. Preinstalled in Fiji.

##Advantages:

• support blinking (with a parameters allowing it or not)
• fast,
• can be used in batch, some analysis results provided.
• No dynamic model.
• The tracking part is not dependent of ImageJ.

Pitfalls:

• does not support division
• the optimization algorithm used is a simulated annealing, so results can be slightly different between two runs.
• No Dynamic model (so less good results but can be used for a first study of the kind of movements)

##The sample data

The parameters used for this example data Beads, were

1. detection: 150
2. the max distance in pixels: 20
3. max allowed disappearance in frame: 1

This macro and plugins suite for ImageJ (and Fiji) serves to measure the velocity of moving structures and visualize them, from image time series (2D over time).

The module can be installed in ImageJ as a Macro Menu and each function/component can be called separately. The full workflow consists in calling some, or all, the functions sequentially in order to get from the image preparation (e.g. filtering and visualization of tracks) to the production of the kymographs (time vs. distance plot) and their analysis (retrieving the velocities).

Here is the full workflow sequence:

• Load image sequence
• Crop and time-filter the image sequence ("Walking average" plugin)
• Generate tracks by z-projection ("Stack difference" plugin)
• Select tracks and restore them in the original stack.
• execute plugin "multiple kymograph"
• Analyse: select edges of moving tracks graphically and quantify movement in a table.

input: 8-bit, 16-bit stacks, 2D in time. Calibrated is better for meaningful velocity measurements.

ouput: the kymograph image, the velocity measurements tables.

Requires ImageJ version: 1.33.n minimum.

Example of applications:

• velocity of moving objects/ structures with sharp edges, incl. the velocity of microtubules (and their plus ends),
• the velocity of vesicles or particles along a 2D path
• the velocity of migration of the edge of a cell or a multicellular group
• retraction velocity of contractile bundles (e.g. actin fibers) or multicellular tissues after mechanical disruption (e.g. laser surgery)