Comparison: Motility of actin filaments before adding EDTA Vs. after adding EDTA in channel 2 which was applied with EDTA

As shown in this figure, in the experiment, we injected EDTA into the channel 2 to explore the effect of EDTA on the motility of actin filaments.

After we got the video showing the motility of actin filaments before EDTA and after EDTA recorded by EMCCD camera, we use ImageJ to analyze the motility of these actin filaments. By using imageJ, we have traced the moving of actin filaments and obtained some numerical analysis results.

Figure 1: Upper image showing the initial state of actin filaments in channel2 before applying EDTA; Lower image showing the initial state of actin filaments in this channel after applying EDTA

Figure 1 indicates that after we applied EDTA, most of actin filaments were killed by EDTA.

The following images show the tracing result of the motility of actin filaments before applying EDTA and after applying EDTA, the blue tails indicate that the tracked filaments are moving leftward; the yellow tails indicate that the tracked filaments are moving rightward.

Figure 2: Upper image shows the motility of actin filaments at 2.01s before EDTA;

Lower image shows the motility of actin filaments at 2.01s after EDTA

Figure 3: Upper image showing the motility of actin filaments at 3.01s before applying EDTA;

Lower image showing the motility of actin filaments at 3.01s after applying EDTA;

Figure 4: Upper image showing the motility of actin filaments at 4.01s before applying EDTA;

Lower image showing the motility of actin filaments at 4.01s after applying EDTA;

By observing the above tracing results, it can be found that EDTA can stop the movement of most actin filaments, only few of these actin filaments survived after applying EDTA.

Comparison of the numerical analysis results obtained:

Figure 5: Difference tracking results before EDTA

Figure 6: Difference tracing results after EDTA

By comparing the moving intensity, the moving intensity of actin filaments before applying EDTA is about 91372 which is much higher than the moving intensity 323 after EDTA. In addition, by comparing the data of moving count, it can easily find that the number of moving actin filaments is much smaller after applying EDTA. The numerical results identically correspond to the results obtained from the above fluorsecent images.

Description and Specifications of the EMCCD camera(used to visualize the motility）

iXon^EM+

ANDOR's pioneering iXon^EM+ is a revolutionary camera range that provides single photon detection sensitivity, highest QE and -100°C cooling at rapid frame rates, utilizing ANDOR's pioneering and award-winning EMCCD technology. The iXon^EM+ is ideal for dynamic applications such as Lucky Astronomy and Adaptive Optics. The low-noise photon counting capabilities of the DU-897 can be harnessed to overcome multiplication noise. The DU-888 back illuminated, with 1024 x 1024 13µm pixels, offers an excellent combination of sensitivity and field of view. ANDOR-exclusive Real Gain™ sets the new standard in day-to-day EMCCD usage.

Description

Electron Multiplying CCD Camera (EMCCD Camera):MISS NOTHING, with the new DU-888.

• EMCCD – single photon sensitive & >90% QE - Extremely Sensitive
• 1024x1024 pixels (13.3x13.3mm sensor) - Large Field of View
• 13 x 13µm pixel size - Optimal balance between NyQuist resolution and Signal to Noise (S/N)

With a 1024 x 1024 sensor format, 13 x13µm pixel size and back- illuminated QE (> 90%), the DU-888 is an excellent combination of field of view (13.3 x 13.3mm sensor), resolution and ultra-sensitivity. The DU-888 is ideal for low-light imaging applications such as live cell imaging, astronomy, luminescence or microtitre plate reading. Andor’s vacuum-housed back-illuminated iXonEM+ EMCCD range is designed to ensure the absolute highest sensitivity from a quantitative scientific digital camera, particularly under dynamic measurement conditions (faster frame rates). Andor’s vacuum-housing is critical to ensure complete protection of the back-illuminated sensor, such that it will not suffer QE performance degradation.

The architecture of EMCCD sensors also render them extremely flexible. When harnessed effectively in the iXonEM+, EMCCD technology can be applied in an entirely quantitative fashion to meet a wide variety of experimental requirements, ranging from single photon counting experiments through to slower scan, true 16-bit dynamic range measurements. The iXonEM+ is also equipped with patented pioneering technology that ensures the longest quantitative service life of any EMCCD, offering ultimate anti-ageing protection of the sensor and automated user-initiated recalibration of EMCCD gain (EMCAL™).

Specifications

Data Transfer:
10MHz

Quantum Efficiency:
Up to > 90%

Megapixels:
1

Cooling:
–95°C

Dynamic Range:
16-bit

Frame Rate (frames/sec):
9

Features and Benefits of the iXon^EM+

Features and Benefits of the iXon^EM+ include:

• Single photon sensitivity + high QE (> 90% available)
• RealGain™: easily tune gain multiplication factor to balance signal amplification vs dynamic range.
• Single photon counting capability from industry: leading minimization of dark noise events.
• Faster frame rates: 1000 x 1000 @ 30 frames/sec to 128 x 128 @ > 500 fps.
• Minimized darkcurrent from unparalleled -100°C TE cooling
• EMCCD and Conventional amplifiers: operate at fast frame rate with EM gain, or as classic slow readout CCD for longer exposures

Improve the quality of fluorescent images

One of the jobs done by using ImageJ is to enhance the contrast between the fluorescent actin filaments and dark background ( to make fluorescent filaments brighter, to make dark background darker).

The function used to achieve this is called 'contrast enhancer' as shown in Figure1.

Figure 1: Enhance Contrast

Meanwhile, an important assistant plugin used is called 'background subtractor' as shown in Figure2.

Another assistant function can be used to achieve our purpose is shown in Figure3. 

Figure 3: Assistant function

The processing results:

Figure 5: Before processing

Figure 6: After processing

Both of the fluorescent images record the motility of actin filaments at 4.01s. By comparing the two fluorescent images, it is obvious to see that the image after processing is much clear than the image before processing.  

Results：Measure the motility of actin filaments using difference tracker

The experimental data used is the video number 4 posted in a previous blog, the time duration of the video is 20s. Figure 1 shows the situation of actin filaments at 9.63s,  with tracker on(blue tracker means the filament is moving leftward, yellow tracker means the filament is moving rightward.)

Figure 1: The motility of actin filaments at 9.63s

Figure 2 shows the motility of actin filaments at 16.45s.

Figure 2: The motility of actin filaments at 16.45s

Figure 3 shows the motility of actin filaments at 19.86s.

Figure 3: The motility of actin filaments at 19.86s

Numerical analysis results obtained:

Introduction of a ImageJ plugin called 'Difference Tracker' what we use to measure motility of actin filaments

Difference tracker was originally developed to analyse the movement of mitochondria through axons. This type of data features a large number of moving particles which often exhibit low contrast against the background and can move behind stationary objects.

The aim for this kind of data was not to get the most complete track possible for any individual particle, but to collect relevant aggregate statistics for the movie as a whole.

Difference tracker contains two ImageJ plugins. Difference filter extracts the moving parts of an image to enhance the contrast of the motile particles. From there the Mass Particle Tracker tracks this large set of particles and works out a set of statistics for the number, size and rate and direction of movement for the particles.

Difference Filter Options:

Minimum Difference

This is the minimum absolute intensity difference which must be observed for a pixel to be counted as moving. Increasing this value will cut down on the noise of misidentified moving pixels, at the expense of a reduction in sensitivity for moving particles with low signal to noise.

Difference frame offset

This value says how many frames apart from the source frame the pluin will look to compare images to indentify moving pixels. You should aim to specify the minimum number of frames needed for a particle to move a distance greater than its length.

Mass Particle Tracker Options

Produce full output

If you want to do manual analysis of the tracked regions identified then selecting this option will cause the program to write out a file alongside your image which contains the full tracked positions for every particle.

Minimum tracked intensity

Sets a lower cutoff for the amount of signal which the program will use when tracking.

Minimum feature size

Sets the minimum number of adjacent pixels which will count as a particle to be tracked.

Initial flexibility

Says how far apart two particles can be in adjacent frames to be connected to start a new track. You should set this to the maximum distance which could be covered by a particle in one frame.

Subsequent flexibility

Says how far from its predicted position a particle can be to allow it to still be associated with an existing track.

Min Track Length

Particles which have been tracked for fewer frames than this value will not be included in the aggregate statistics calculated for the movie.

Introduction of ImageJ: The software we use to process the obtained fluorescent videos

ImageJ is a public domain Java image processing and analysis program inspired by NIH Image
for the Macintosh. It runs, either as an online applet or as a downloadable application, on any
computer with a Java 1.5 or later virtual machine. Downloadable distributions are available for
Windows, Mac OS, Mac OS X and Linux. It can display, edit, analyze, process, save and print
8–bit, 16–bit and 32–bit images. It can read many image formats including TIFF, GIF, JPEG,
BMP, DICOM, FITS and ‘raw’. It supports ‘stacks’ (and hyperstacks), a series of images that
share a single window. It is multithreaded, so time-consuming operations such as image file
reading can be performed in parallel with other operations.
It can calculate area and pixel value statistics of user-defined selections. It can measure distances
and angles. It can create density histograms and line profile plots. It supports standard image
processing functions such as contrast manipulation, sharpening, smoothing, edge detection and
median filtering.
It does geometric transformations such as scaling, rotation and flips. Image can be zoomed up to
32 : 1 and down to 1 : 32. All analysis and processing functions are available at any magnification
factor. The program supports any number of windows (images) simultaneously, limited only by
available memory.
Spatial calibration is available to provide real world dimensional measurements in units such as
millimeters. Density or gray scale calibration is also available.
ImageJ was designed with an open architecture that provides extensibility via Java plugins.
Custom acquisition, analysis and processing plugins can be developed using ImageJ’s built in
editor and Java compiler. User-written plugins make it possible to solve almost any image
processing or analysis problem.
ImageJ is being developed on Mac OSX using its built in editor and Java compiler, plus the BBEdit
editor and the Ant build tool. The source code is freely available. The author, Wayne Rasband
(wsr@nih.gov), is a Special Volunteer at the National Institute of Mental Health, Bethesda,
Maryland, USA.

The experimental data got from the experiment

There were two parts of experiments have been implemented. The first part of the experiment is to observe the motility of actin filaments. The second part is to observe the effect of a chemical called EDTA(Ethylene Diamine Tetraacetic Acid) to the motility of actin filaments. The following five videos show the results.

1. demonstration of the motility of actin filaments

2. The motility before applying EDTA

3. The motility of actin filaments in channel 2 after applying EDTA

4. After applying EDTA, the motility of actin filaments in channel 1 and channel 3 which are two channels beside the channel 2