Name: sample drift correction following 4d confocal time-lapse imaging
Uploaded: 2014-04-12T22:35:00
Description: Watch this Scientific Journal Video about Sample Drift Correction Following 4D Confocal Time-lapse Imaging at JoVE.com

We would like to thank Gaby Martins and the organizers of the EMBO2010 3D Developmental Imaging workshop where this work began and all of the contributors to the Fiji and ImageJ projects.

1.&#160;4D Time-lapse Imaging Experiments
The settings used for image acquisition will differ depending on the equipment used. The ability of confocal microscopy to optically section a sample depends on a number of factors: the wavelength of excitation, pinhole size, numerical aperture of the objective, the refractive index of the sample and the medium in which the sample is embedded. The size of the confocal pinhole selected will determine the thickness of the optical section collected. A small...

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Our ability to use post-processing software to correct sample drift of datasets derived from extended time-lapse microscopy experiments is restricted by a number of factors. The ability to discern drift versus migratory movement of a sample is dependent on the cellular markers used. Cellular markers that are either widely expressed within a sample or are not involved in migratory events during image acquisition provide the best source for drift correction. The plugin uses a single channel to register the movement between...

Confocal imaging is widely used in cell and developmental biology to follow cell movements and changes in morphology. Capturing a series of optical sections at different focal planes allows the generation of a three-dimensional (3D) model of a sample, which can then be extended into four-dimensions (4D) by creating a time-lapse series of 3D datasets. The generation of 4D datasets allows detailed measurement of cell movements and behaviors. In long-term time-lapse experiments it is common to observe sample movement. This can be caused by slight inaccuracies in the hardware controlling stage and focal positions. While in others cases, drift is a result of movements induced by sample growth or flexibility within the sample mounting media. Methods exist to compensate or limit these movements including improvements to hardware focusing systems and increased rigidity of the mounting medium. However, these approaches cannot be applied in many cases due to the imaging set up required to provide suitable conditions for the samples maintenance and growth. Open source software solutions do exist for the correction of movement in 2D over time, through the use of the StackReg and TurboReg (http://bigwww.epfl.ch/thevenaz/stackreg/) 5 plugins in ImageJ or Fiji, but these cannot be applied to 4D datasets.
To correct for the sample drift we have developed a plug-in (Correct 3D drift) to utilize the open-source imaging-processing platform, Fiji 1. Our plug-in is able to perform phase correlation registration to correct movement that occurs as a result of sample drift in three-dimensional time-lapse experiments. Phase correlation 6 is a computational efficient method to determine translation between images. The plug-in described here utilizes the phase-correlation library developed by Preibisch et al.&#160;7. In multi-channel experiments, the plug-in utilizes one channel to determine the required correction. This correction is then applied to any additional channels resulting in registration of the 4D dataset. &#160;
In the zebrafish model system it is possible to carry out time-lapse imaging over a period of many hours, or even several days 8. A common method for mounting the zebrafish is to embed the anaesthetized live embryo in low melting point agarose (0.8-1.5%), restricting its movement 9-11. Whilst movement is restricted growth of the sample still occurs, resulting in the cells within the field of view shifting position. In order to follow movement of the cells within the embryo it is necessary to first correct for movement of the entire sample. This protocol was developed with zebrafish specimens, and has been utilized to image somite development12 but can be applied to any 4D confocal dataset.

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			<td height="43" style="height:43px;width:216px;">Ethyl 3-aminobenzoate methanesulfonate (Tricaine)</td>
			<td style="width:71px;">Sigma-Aldrich</td>
			<td style="width:119px;">A5040</td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Low gelling temperature agarose</td>
			<td style="width:71px;">Sigma-Aldrich</td>
			<td style="width:119px;">A9414-25G</td>
		</tr>
		<tr height="85">
			<td height="85" style="height:85px;width:216px;">Dumont #4 Forceps</td>
			<td style="width:71px;">Electron Microscopy Sciences</td>
			<td style="width:119px;">0208-4-PO</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Disposable 3mL graduated</td>
			<td rowspan="2" style="width:71px;">Samco</td>
			<td rowspan="2" style="width:119px;">212</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:216px;">Polyethylene transfer pipette</td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">9cm bacterial grade Petri dishes</td>
			<td style="width:71px;">Greiner Bio One</td>
			<td style="width:119px;">632180</td>
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		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Fluorinated ethylene propylene (FEP) tubing</td>
			<td style="width:71px;">Bola</td>
			<td style="width:119px;">S1815-04</td>
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		<tr height="43">
			<td height="43" style="height:43px;width:216px;">Zeiss LSM-710 Confocal microscope</td>
			<td style="width:71px;">Zeiss</td>
			<td style="width:119px;"></td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:216px;">W Plan-Apochromat 20x/1.0 DIC Objective</td>
			<td style="width:71px;">Zeiss</td>
			<td style="width:119px;">421452-9600-000</td>
		</tr>
	</tbody>
</table>

sample drift correction following 4d confocal time-lapse imaging

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture cellular behaviors involved in developmental processes.&#160; The ability to track and follow cell movements is limited by sample movements that occur due to drift of the sample or, in some cases, growth during image acquisition. Tracking cells in datasets affected by drift and/or growth will incorporate these movements into any analysis of cell position. This may result in the apparent movement of static structures within the sample. Therefore prior to cell tracking, any sample drift should be corrected. Using the open source Fiji distribution 1 &#160;of ImageJ 2,3 and the incorporated LOCI tools 4, we developed the Correct 3D drift plug-in to remove erroneous sample movement in confocal datasets. This protocol effectively compensates for sample translation or alterations in focal position by utilizing phase correlation to register each time-point of a four-dimensional confocal datasets while maintaining the ability to visualize and measure cell movements over extended time-lapse experiments.

In the developing zebrafish, fast muscle cells fuse into multinucleated fibers from 19 hours post-fertilization (20- somite stage) 13. In order to visualize the movement of nuclei and fusion of muscle cells we carried out 4D confocal time-lapse imaging using a transgenic strain that expresses green fluorescent protein (GFP) under the control of the skeletal &#945;-actin promoter to label all of the muscle cells 14 and injected RNA encoding the red fluorescent protein mCherry tagged with a nuclear lo...

Watch this Scientific Journal Video about Sample Drift Correction Following 4D Confocal Time-lapse Imaging at JoVE.com

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Time-lapse microscopy allows the visualization of developmental processes. Growth or drift of samples during image acquisition reduces the ability to accurately follow and measure cell movements during development. We describe the use of open source image processing software to correct for three dimensional sample drift over time.

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture ...

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