We thank Dr. Tanja Maritzen and Dr. Fabian Feutlinske (Leibniz Institute of Molecular Pharmacology, Berlin, Germany) for sharing with us their version of the Fuji ImageJ Clock Scan plugin and inspiring us to develop this version of the program. We are also grateful to Dr. Fritz Melchers (Department of Lymphocyte Development, Max Planck Institute for Infection Biology) for his kind permission to use the images from the database of his department for the purpose of testing and improving the plugin. Support: Center for Translational Neurosciences; NIH grant: P30-GM110702-03.

1. Software Installation
<ol>
	<li>Install the latest versions of bundled Java and either ImageJ or Fiji ImageJ as recommended on the respective websites (see materials table for links to the corresponding websites). In the text below, both programs are referred to as &#34;ImageJ&#34;.</li>
	<li>Copy &#34;Clock_Scan-1.0.1. jar&#34; and &#34;Multi_Clock_Scan-1.0.1.jar&#34; plugin files using the link provided in materials table and paste them into the ImageJ plugin directory. Alternatively, use &#34;Plugins | Install plugin&#34; menu option to install these files after they have been saved on the computer hard drive.</li>
</ol>
2. Clock Scan analysis
<ol>
	<li>Standard Clock Scan plugin (Figure 1):
	<ol>
		<li>Use the ImageJ &#34;File | Open&#34; menu command to open an image of interest.</li>
		<li>Click on the &#39;polygon&#39; tool, or &#39;segmented line selection&#39; tool, and then draw on the image to outline the entire ROI or a segment of this region. See Figure 1A for an example of polygon selection (inner dashed outline). 
		NOTE: Other selection tools, available in the software (rectangular, oval and freehand line selection), may also be used.</li>
		<li>Select &#34;Plugins | Clock Scan&#34; from the menu to open the standard clock scan protocol pop-up option window. Note that this command will also open the ROI Manager window with the outline automatically added to it.</li>
		<li>Use the plugin option window to do the following.
		<ol>
			<li>Review and change the X and Y coordinates of the ROI center (automatically-calculated as coordinates of the physical mass center) by using scroll bars or changing the values in the corresponding input boxes. See Figure 1B.</li>
			<li>Depending on how much of the background region outside of the object should be covered by scanning, adjust the scan limits by using the &#34;scan limit&#34; scroll bar. See Figure 1A. 
			NOTE: Scan limit is the fractional number representing how far the scan should proceed beyond the objects&#39; border in any given direction; the default value is 1.20, indicating that the scan length will be 20% longer than the object radius in the direction of scan; see Figure 1A, outer dashed line).</li>
			<li>Modify the output of the plugin using &#34;real radius&#34;, &#34;subtract background&#34;, &#34;polar transform&#34; and/or the &#34;plot with standard deviation&#34; check-boxes.</li>
			<li>Click &#34;OK&#34; to run the plugin. See Figure 1C-H. 
			NOTE: Examples of the output of the protocol with &#34;plot with standard deviation&#34; and &#34;polar transform&#34; or &#34;real radius&#34; and &#34;polar transform&#34; options selected are shown in Figure 1C and 1D and Figure 1E and 1F, respectively. Note that the calculated standard deviation (SD) values represent the variation between individual radial pixel intensity scans of the object. Also note the &#34;ROI selection length&#34; line in the plugin window, which displays the information on the ROI outline length measured in pixels.</li>
		</ol>
		</li>
		<li>In the generated &#34;Clock Scan Profile Plot&#34;, use the &#34;List&#34; command to plot values displayed in two, X and Y columns of data for grey scale images and in X and four Y columns of data for RGB images, of which Y0, Y1, Y2 and Y3 columns will be filled with integral and individual (red, green and blue) color channel pixel intensity values.</li>
	</ol>
	</li>
	<li>Multiple ROI Clock Scan plugin - working with multiple ROI (Figure 2):
	<ol>
		<li>Open an image containing multiple ROI.</li>
		<li>Open the ROI Manager by clicking &#34;Analyze | Tools | ROI Manager&#34;.</li>
		<li>Sequentially outline (see step 2.1.2) and add each ROI to the ROI Manager by clicking &#34;Add&#34; in the ROI Manager window; do this for all ROIs within the image. Use the &#34;Analyze| Measure&#34; command if ROI metrics are of interest.
		<ol>
			<li>See Figure 2A for an example of multiple segmented line selections and Figure 2E for an example of multiple polygon selections.</li>
		</ol>
		</li>
		<li>Select &#34;Multi Clock Scan&#34; in the &#34;Plugins&#34; menu to open the protocol options pop-up window.</li>
		<li>Use the protocol option window to do the following.
		<ol>
			<li>If needed, reset the scan limit as per step 2.1.4.2; default value is 1.20.</li>
			<li>If needed, select the option to plot the mean clock scan profile with SD bars by checking the &#34;Plot with standard deviation&#34; box. See Figure 2C and D. 
			NOTE: The calculated SD values will represent variation between integral clock scan profiles of different objects. Also, note the line in the plugin window displaying information on the &#34;number of selected ROIs&#34;.</li>
			<li>Click &#34;OK&#34; to run the protocol.</li>
		</ol>
		</li>
		<li>In the generated &#34;Clock Scan Profile Plot&#34;, use the &#34;List&#34; command to plot the values displayed in the &#34;Plot Values&#34; window. See the &#34;Multi Clock Scan Profile Plot&#34; window legend for column designation by color channel.</li>
		<li>Note that the ROIs are numbered and their clock scan profiles for any given color channel are plotted in the same sequence in which the ROIs were outlined and added to the &#34;ROI Manager&#34;.</li>
	</ol>
	</li>
	<li>Multiple ROI Clock Scan plugin - working with an image stack (Figure 3):
	<ol>
		<li>Open an image-stack of interest.</li>
		<li>Open the ROI Manager by clicking &#34;Analyze | Tools | ROI Manager&#34;.</li>
		<li>Outline the ROI of the images within the stack and add it to ROI manager as described in steps 2.1.2 and 2.2.3. Use the &#34;Analyze | Measure&#34; command if the ROI metrics are of interest.</li>
		<li>Select &#34;Multi Clock Scan&#34; in the &#34;Plugins&#34; menu to open the protocol options pop-up window.</li>
		<li>Use the protocol option window to do the following.
		<ol>
			<li>Reset the scan limit as described in step 2.1.4.2; default value is 1.20.</li>
			<li>Select the option to plot the mean clock scan profile with SD bars by checking the &#39;Plot with standard deviation&#34; box. 
			NOTE: The calculated SD values will represent variation between different instances of the object selected in the image stack. Also, note the line in the plugin window displaying information on the &#34;number of images in the stack&#34;.</li>
			<li>Click &#34;OK&#34; to run the protocol.</li>
		</ol>
		</li>
		<li>In the &#34;Clock Scan Profile Plot&#34; window, click &#34;List&#34; to plot the values displayed in the &#34;Plot Values&#34; window, where the Y column number represents the image position within the stack - 1.</li>
	</ol>
	</li>
</ol>

<ol><li>Dobretsov, M., Romanovsky, D. "Clock-scan" protocol for image analysis. Am J Physiol Cell Physiol. 291, 869-879 (2006).</li>
<li>Feutlinske, F., Browarski, M., Ku, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Stonin1+mediates+endocytosis+of+the+proteoglycan+NG2+and+regulates+focal+adhesion+dynamics+and+cell+motility%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Stonin1 mediates endocytosis of the proteoglycan NG2 and regulates focal adhesion dynamics and cell motility.</a> Nat Commun. 6, 8535(2015).</li>
<li>Schneider, C. A., Rasband, W. S., Eliceiri, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((NIH+Image+to+ImageJ%3A+25+years+of+image+analysis%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">NIH Image to ImageJ: 25 years of image analysis.</a> Nat Methods. 9, 671-675 (2012).</li>
<li>Schindelin, J., Arganda-Carreras, I., Frise, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fiji%3A+an+open-source+platform+for+biological-image+analysis%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Fiji: an open-source platform for biological-image analysis.</a> Nat Methods. 9, 676-682 (2012).</li>
<li>Dobretsov, M., Hastings, S. L., Stimers, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Non-uniform+expression+of+alpha+subunit+isoforms+of+the+Na%2B%2FK%2B+pump+in+rat+dorsal+root+ganglia+neurons%5BTitle%5D)+AND+%22Brain+Res%22%5BJournal%5D)">Non-uniform expression of alpha subunit isoforms of the Na+/K+ pump in rat dorsal root ganglia neurons.</a> Brain Res. 821, 212-217 (1999).</li>
<li>Hayar, A., Gu, C., Al-Chaer, E. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+improved+method+for+patch+clamp+recording+and+calcium+imaging+of+neurons+in+the+intact+dorsal+root+ganglion+in+rats%5BTitle%5D)+AND+%22J+Neurosci+Methods%22%5BJournal%5D)">An improved method for patch clamp recording and calcium imaging of neurons in the intact dorsal root ganglion in rats.</a> J Neurosci Methods. 173, 74-82 (2008).</li>
<li>Dobretsov, M., Pierce, D., Light, K. E., Kockara, N. T., Kozhemyakin, M., Wight, P. A. Transgenic mouse model to selectively identify alpha3 Na,K-ATPase expressing cells in the nervous system. Society for Neuroscience. , Online Program No. 123.01/B54 1(2015).</li>
<li>Lein, E. S., Hawrylycz, M. J., Ao, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genome-wide+atlas+of+gene+expression+in+the+adult+mouse+brain%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Genome-wide atlas of gene expression in the adult mouse brain.</a> Nature. 445, 168-176 (2007).</li>
<li>Romanovsky, D., Mrak, R. E., Dobretsov, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Age-dependent+decline+in+density+of+human+nerve+and+spinal+ganglia+neurons+expressing+the+alpha3+isoform+of+Na%2FK-ATPase%5BTitle%5D)+AND+%22Neuroscience%22%5BJournal%5D)">Age-dependent decline in density of human nerve and spinal ganglia neurons expressing the alpha3 isoform of Na/K-ATPase.</a> Neuroscience. 310, 342-353 (2015).</li>
<li>Campbell, J., Singh, D., Hollett, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Spatially+selective+photoconductive+stimulation+of+live+neurons%5BTitle%5D)+AND+%22Front+Cell+Neurosci%22%5BJournal%5D)">Spatially selective photoconductive stimulation of live neurons.</a> Front Cell Neurosci. 8, 142(2014).</li>
<li>Yuryev, M., Pellegrino, C., Jokinen, V., et al. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701926/">In vivo Calcium Imaging of Evoked Calcium Waves in the Embryonic Cortex.</a> Front Cell Neurosci. 9, 500(2015).</li>
<li>Qiao, M., Sanes, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genetic+Method+for+Labeling+Electrically+Coupled+Cells%3A+Application+to+Retina%5BTitle%5D)+AND+%22Front+Mol+Neurosci%22%5BJournal%5D)">Genetic Method for Labeling Electrically Coupled Cells: Application to Retina.</a> Front Mol Neurosci. 8, 81(2015).</li>
</ol>

The authors declare that they have no competing financial interests or other conflicts of interest.

Clock Scan Protocol: The Clock Scan protocol is a fast and simple tool of image analysis. The advantages of this protocol, compared to existing common approaches of image analysis (such as linear pixel intensity scans or calculation of mean pixel intensity of the ROI), have been described in details in previous publications1,9. Briefly, this protocol allows the generation of integral radial pixel-intensity profiles by quantifying the intensity of...

The goal of this work is to present a Clock Scan protocol that is platform-free and freely available to any researcher interested in this type of image analysis. The Clock Scan protocol was originally developed in 20061, with the goal of improving existing methods of pixel intensity quantification within convex-shaped regions of interest (ROI), a method which has better integrative capacity and improved spatial resolution. During the acquisition, the protocol sequentially collects multiple radial pixel-intensity profiles, scanned from the ROI center to its border, or to a predetermined distance outside the ROI for the purpose of measuring the &#34;background&#34; pixel intensity. The protocol scales these profiles according to the cell radius, measured in the direction of the scan. Thus, the distance from the center to the ROI border of each individual radial scan is always 100% of the X scale. Finally, the program averages these individual profiles into one integral radial pixel-intensity profile. Because of scaling, the mean pixel-intensity profile, produced by the &#34;Clock Scan&#34; protocol, depends neither on the ROI size nor, within reasonable limits, on the ROI shape. This method allows direct comparison or, if required, averaging or subtraction of profiles of different ROIs. The protocol also allows correction of the integral pixel intensity profiles, of any object for background noise, by a simple subtraction of the average intensity of pixels located outside the object. Although it has only been tested in biological samples, our protocol provides a valuable addition to other existing image analysis tools used in studies of images of physical or chemical processes that are arranged around a point of origin (such as diffusion of substances from a point source)1.
However, the major limitation of the original image analysis method was that the protocol was developed as a Visual Basic 6 (VB6) (code, and therefore, it was platform-dependent and difficult to distribute (requiring VB6). To address this problem and to join similar recent efforts by other investigators2, we converted the VB6 Clock Scan program code into two Java-based plugins, compatible with the NIH-sponsored and freely-available open-source and platform-independent image analysis programs, ImageJ3 and Fiji ImageJ4. Furthermore, these plugins have now several new functions that expand the capability of the original protocol to process multiple ROIs and image stacks. Many image analysis applications are not user-friendly, with regards to performing statistical analysis of multiple objects, and thus, often only representative data are shown. With the multi Clock Scan ImageJ plugin, it is possible to facilitate the analysis of multiple objects simultaneously. A robust statistical evaluation of microscopy data, with regards to signal intensity distribution in single cells/objects, is now possible with this plugin extension. Here, we describe the Clock Scan plugins and show examples of their applications in image analysis.

<table><tbody><tr><td>Computer</td><td>Any</td><td></td><td>compatible with software listed below</td></tr><tr><td>ImageJ or Fiji ImageJ</td><td>NIH</td><td>https://imagej.nih.gov/ij/ or https://fiji.sc/</td><td>bundled with Java 1.8 or higher</td></tr><tr><td>Clock-scan plugins</td><td>freeware</td><td>https://sourceforge.net/projects/clockscan/</td><td>Clock_Scan-1.0.1 jar and Multi_Clock_Scan-1.0.1/ jar</td></tr><tr><td>Origin 9.0</td><td>OriginLab</td><td>Northampton, MA, USA</td><td>This program was used to generate some graphs of the original Clock Scan data. Any other graphic software can be used to perform this function</td></tr></tbody></table>

clock scan protocol for image analysis: imagej plugins

The clock scan protocol for image analysis is an efficient tool to quantify the average pixel intensity within, at the border, and outside (background) a closed or segmented convex-shaped region of interest, leading to the generation of an averaged integral radial pixel-intensity profile. This protocol was originally developed in 2006, as a visual basic 6 script, but as such, it had limited distribution. To address this problem and to join similar recent efforts by others, we converted the original clock scan protocol code into two Java-based plugins compatible with NIH-sponsored and freely available image analysis programs like ImageJ or Fiji ImageJ. Furthermore, these plugins have several new functions, further expanding the range of capabilities of the original protocol, such as analysis of multiple regions of interest and image stacks. The latter feature of the program is especially useful in applications in which it is important to determine changes related to time and location. Thus, the clock scan analysis of stacks of biological images may potentially be applied to spreading of Na+ or Ca++ within a single cell, as well as to the analysis of spreading activity (e.g., Ca++ waves) in populations of synaptically-connected or gap junction-coupled cells. Here, we describe these new clock scan plugins and show some examples of their applications in image analysis.

The images that are used here for illustration purpose, are taken from databases created during our previous cell and tissue biological studies5,6,7 and from the Allen Mouse Brain Atlas8. Both plugins were successfully tested using ImageJ 1.50i/Java 1.8.0_77, ImageJ 2.0.0-rc-44/1.50e/ Java 1.8.9_66 and Fiji ImageJ 2.0.0-rc54/1.51g/Java 1.8.0_66 program environment.
<p ...

Watch this Scientific Journal Video about Clock Scan Protocol for Image Analysis: ImageJ Plugins at JoVE.com

Clock Scan Protocol for Image Analysis: ImageJ Plugins

This paper describes two novel ImageJ plugins for &#39;Clock Scan&#39; image analysis. These plugins expand the functionality of the original visual basic 6 program and, most importantly, make the program available to a large research community by bundling it with the ImageJ free image analysis software package.

The clock scan protocol for image analysis is an efficient tool to quantify the average pixel intensity within, at the border, and outside (background) a ...

clock-scan-protocol-for-image-analysis-imagej-plugins

Research

JoVE Journal

Biology

17.3K Views.  University of Arkansas for Medical Sciences.  Dit artikel beschrijft twee nieuwe ImageJ-plugins voor 'Clock Scan' beeldanalyse. Deze plugins vergroten de functionaliteit van het oorspronkelijke Visual Basic 6-programma en, belangrijker, maakt het programma beschikbaar voor een grote onderzoeksgemeenschap door het te bundelen met het ImageJ gratis beeldanalysesoftwarepakket. 

Video: Clock Scan Protocol for Image Analysis: ImageJ Plugins

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro when stem cells are spatially confined onto micropatterns, thus setting up experimental models which offer unique opportunities to identify, in vitro, the fundamental principles of biological organisation.
Here we describe our own implementation of the methodology. We adapted a photo-patterning technique to reduce the need for specialized equipment to make it easier to establish the method in a standard cell biology laboratory. We also developed a free, open-source and easy to install image analysis framework in order to precisely measure the preferential positioning of sub-populations of cells within colonies of standard shapes and sizes. This method makes it possible to reveal the existence of patterning events even in seemingly disorganized populations of cells. The technique provides quantitative insights and can be used to decouple influences of the environment (e.g., physical cues or endogenous signaling), on a given patterning process.

The method presented here uses micropatterning together with quantitative imaging to reveal spatial organization within mammalian cultures. The technique is easy to establish in a standard cell biology laboratory and offers a tractable system to study patterning in vitro.

A fundamental goal in biology is to understand how patterns emerge during development. Several groups have shown that patterning can be achieved in vitro ...

Developmental Biology

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Micro-computed tomography (micro-CT) is routinely used to assess bone quantity and trabecular microstructural properties in small animals under different bone loss conditions. However, the standard approach for trabecular analysis of micro-CT images is slice-by-slice semi-automatic hand-contouring, which is labor intensive and error prone. Described here is an efficient method for automatic segmentation of trabecular bones according to the bone's outer boundaries, where trabecular bones can be identified and segmented automatically with accuracy with less operator bias when appropriate segmentation parameters are set. To profile satisfactory segmentation parameters, an image stack of segmentation results is displayed, where all possible combinations of the segmentation parameters are changed one by one in sequence, and segmentation results with associated parameters can easily be visually checked. As a quality-control feature of the plugin, simulated standard objects are quantified where the measured quantities can be compared with theoretical values. Layer-by-layer quantification of trabecular properties and trabecular thicknesses are reported by such a plugin, and the distributions of such properties within the selected regions can be profiled easily. Although layer-by-layer quantification retains more information about trabecular bones and facilitates further statistical analysis of structural changes, such measures are unavailable from the output of current commercial software, where only a single quantified value for each parameter is reported for each sample. Therefore, the described workflows are better approaches for analyzing trabecular bones with accuracy and efficiency.

We present a workflow for segmenting and quantifying trabecular bones for 2D and 3D images based on the bone&#39;s outer boundary using an ImageJ plugin. This approach is more efficient and accurate than the current manual hand-contouring approach, and provides layer-by-layer quantifications, which are not available in current commercial software.

Micro-computed tomography (micro-CT) is routinely used to assess bone quantity and trabecular microstructural properties in small animals under different ...

Bioengineering

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows across a large spatiotemporal range. However, its implementation typically requires the use of expensive and specialized instrumentation, which limits its broader utility. Moreover, within the field of bioengineering, in vitro flow visualization studies are also often further limited by the high cost of commercially sourced tissue phantoms that recapitulate desired anatomical structures, particularly for those that span the mesoscale regime (i.e., submillimeter to millimeter length scales). Herein, we present a simplified experimental protocol developed to address these limitations, the key elements of which include 1) a relatively low-cost method for fabricating mesoscale tissue phantoms using 3-D printing and silicone casting, and 2) an open-source image analysis and processing framework that reduces the demand upon the instrumentation for measuring mesoscale flows (i.e., velocities up to tens of millimeters/second). Collectively, this lowers the barrier to entry for nonexperts, by leveraging resources already at the disposal of many bioengineering researchers. We demonstratethe applicability of this protocol within the context of neurovascular flow characterization; however, it is expected to be relevant to a broader range of mesoscale applications in bioengineering and beyond.

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro

Here we present simplified methods for fabricating transparent neurovascular phantoms and characterizing the flow therein. We highlight several important parameters and demonstrate their relationship to field accuracy.

Particle image velocimetry (PIV) is used in a wide variety of fields, due to the opportunity it provides for precisely visualizing and quantifying flows ...

ReAsH/FlAsH Labeling and Image Analysis of Tetracysteine Sensor Proteins in Cells

Fluorescent proteins and dyes are essential tools for the study of protein trafficking, localization and function in cells. While fluorescent 
proteins such as green fluorescence protein (GFP) have been extensively used as fusion partners to proteins to track the properties of a protein of interest1, recent 
developments with smaller tags enable new functionalities of proteins to be examined in cells such as conformational change and protein-association 2, 3. One small 
tag system involves a tetracysteine motif (CCXXCC) genetically inserted into a target protein, which binds to biarsenical dyes, ReAsH (red fluorescent) and FlAsH 
(green fluorescent), with high specificity even in live cells 2. The TC/biarsenical dye system offers far less steric constraints to the host protein than 
fluorescent proteins which has enabled several new approaches to measure conformational change and protein-protein interactions 4-7. We recently developed 
a novel application of TC tags as sensors of oligomerization in cells expressing mutant huntingtin, which when mutated aggregates in neurons in Huntington 
disease 7. Huntingtin was tagged with two fluorescent dyes, one a fluorescent protein to track protein location, and the second a TC tag which only 
binds biarsenical dyes in monomers. Hence, changes in colocalization between protein and biarsenical dye reactivity enabled submicroscopic oligomer 
content to be spatially mapped within cells. Here, we describe how to label TC-tagged proteins fused to a fluorescent protein (Cherry, GFP or CFP) 
with FlAsH or ReAsH in live mammalian cells and how to quantify the two color fluorescence (Cherry/FlAsH, CFP/FlAsH or GFP/ReAsH combinations).

The biarsenical dyes FlAsH and ReAsH bind specifically to tetracysteine motifs in proteins and can selectively label proteins in live cells. Recently this labeling strategy has been used to develop sensors for different protein conformations or oligomeric states. We describe the labeling approach and methods to quantitatively analyze binding.

Fluorescent proteins and dyes are essential tools for the study of protein trafficking, localization and function in cells.  While fluorescent 
proteins ...

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

In cardiac muscle, intracellular Ca2+ transients activate contractile myofilaments, causing contraction, macroscopic shortening, and geometric&#160;deformation. Our understanding of the internal relationships between these events has been limited because we can neither &#39;see&#39; inside the muscle nor precisely track the spatio-temporal nature of excitation-contraction dynamics. To resolve these problems, we have constructed a device that combines a suite of imaging modalities. Specifically, it integrates a brightfield microscope to measure local changes of sarcomere length and tissue strain, a fluorescence microscope to visualize the Ca2+ transient, and an optical coherence tomograph to capture the tissue&#39;s geometric&#160;changes throughout the time-course of a cardiac cycle. We present here the imaging infrastructure and associated data collection framework. Data are collected from isolated rod-like tissue structures known as trabeculae carneae. In our instrument, a pair of position-controlled platinum hooks hold each end of an ex vivo muscle sample while it is continuously superfused with nutrient-rich saline solution. The hooks are under independent control, permitting real-time control of muscle length and force. Lengthwise translation enables the piecewise scanning of the sample, overcoming limitations associated with the relative size of the microscope imaging window (540 &#181;m by 540 &#181;m) and the length of a typical trabecula (&#62;2000 &#181;m). Platinum electrodes at either end of the muscle chamber stimulate the trabecula at a user-defined rate. We exploit the stimulation signal as a trigger for synchronizing the data from each imaging window to reconstruct the entire sample twitching under steady-state conditions. Applying image-processing techniques to these brightfield imaging data provides tissue displacement and sarcomere length maps. Such a collection of data, when incorporated into an experiment-modeling pipeline, will provide a deeper understanding of muscle contractile homogeneity and heterogeneity in physiology and pathophysiology.

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

This protocol presents a collection of sarcomere, calcium, and macroscopic geometric&#160;data from an actively contracting cardiac trabecula ex vivo. These simultaneous measurements are made possible by the integration of three imaging modalities.

In cardiac muscle, intracellular Ca2+ transients activate contractile myofilaments, causing contraction, macroscopic shortening, and ...

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

Research in neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging is a widely used technique that requires sophisticated software to obtain reliable results, but many laboratories struggle to adopt computational methods when updating protocols to meet modern standards. Difficulties arise due to a lack of programming knowledge and paywalls for software. In addition, cells of interest display movements in all directions during calcium imaging. Many approaches have been developed to correct the motion in the lateral (x/y) direction.
This paper describes a workflow using a new ImageJ plugin, TrackMate Analysis of Calcium Imaging (TACI), to examine motion on the z-axis in 3D calcium imaging. This software identifies the maximum fluorescence value from all the z-positions a neuron appears in and uses it to represent the neuron&#39;s intensity at the corresponding t-position. Therefore, this tool can separate neurons overlapping in the lateral (x/y) direction but appearing&#160;on distinct z-planes. As an ImageJ plugin, TACI is a user-friendly, open-source computational tool for 3D calcium imaging analysis. We validated this workflow using fly larval thermosensitive neurons that displayed movements in all directions during temperature fluctuation and a 3D calcium imaging dataset acquired from the fly brain.

TrackMate Analysis of Calcium Imaging (TACI) is an open-source ImageJ plugin for 3D calcium imaging analysis that examines motion on the z-axis and identifies the maximum value of each z-stack to represent a cell's intensity at the corresponding time point. It can separate neurons overlapping in the lateral (x/y) direction but on different z-planes.

Research in neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging ...

Neuroscience

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis algorithms which can be used effectively to count various biological particles. When counting large numbers of cell samples, the hemocytometer presents a bottleneck with regards to time. Likewise, counting membranes from migration/invasion assays with the ImageJ plugin Cell Counter, although accurate, is exceptionally labor intensive, subjective, and infamous for causing wrist pain. To address this need, we developed two plugins within ImageJ for the sole task of automated hemocytometer (or known volume) and migration/invasion cell counting. Both plugins rely on the ability to acquire high quality micrographs with minimal background. They are easy to use and optimized for quick counting and analysis of large sample sizes with built-in analysis tools to help calibration of counts. By combining the core principles of Cell Counter with an automated counting algorithm and post-counting analysis, this greatly increases the ease with which migration assays can be processed without any loss of accuracy.

This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis ...

Applications of Spatio-temporal Mapping and Particle Analysis Techniques to Quantify Intracellular Ca2+ Signaling In Situ

Ca2+ imaging of isolated cells or specific types of cells within intact tissues often reveals complex patterns of Ca2+ signaling. This activity requires careful and in-depth analyses and quantification to capture as much information about the underlying events as possible. Spatial, temporal and intensity parameters intrinsic to Ca2+ signals such as frequency, duration, propagation, velocity and amplitude may provide some biological information required for intracellular signalling. High-resolution Ca2+ imaging typically results in the acquisition of large data files that are time consuming to process in terms of translating the imaging information into quantifiable data, and this process can be susceptible to human error and bias. Analysis of Ca2+ signals from cells in situ typically relies on simple intensity measurements from arbitrarily selected regions of interest (ROI) within a field of view (FOV). This approach ignores much of the important signaling information contained in the FOV. Thus, in order to maximize recovery of information from such high-resolution recordings obtained with Ca2+dyes or optogenetic Ca2+ imaging, appropriate spatial and temporal analysis of the Ca2+ signals is required. The protocols outlined in this paper will describe how a high volume of data can be obtained from Ca2+ imaging recordings to facilitate more complete analysis and quantification of Ca2+ signals recorded from cells using a combination of spatiotemporal map (STM)-based analysis and particle-based analysis. The protocols also describe how different patterns of Ca2+ signaling observed in different cell populations in situ can be analyzed appropriately. For illustration, the method will examine Ca2+ signaling in a specialized population of cells in the small intestine, interstitial cells of Cajal (ICC), using GECIs.

Applications of Spatio-temporal Mapping and Particle Analysis Techniques to Quantify Intracellular Ca2+ Signaling In Situ

Genetically encoded Ca2+ indicators (GECIs) have radically changed how in situ Ca2+ imaging is performed. To maximize data recovery from such recordings, appropriate analysis of Ca2+ signals is required. The protocols in this paper facilitate the quantification of Ca2+ signals recorded in situ using spatiotemporal mapping and particle-based analysis.

Ca2+ imaging of isolated cells or specific types of cells within intact tissues often reveals complex patterns of Ca2+ signaling. This activity requires ...

Time-lapse Imaging of Bacterial Swarms and the Collective Stress Response

Swarming is a form of surface motility observed in many bacterial species including Pseudomonas aeruginosa and Escherichia coli. Here, dense populations of bacteria move over large distances in characteristic tendril-shaped communities over the course of hours. Swarming is sensitive to several factors including medium moisture, humidity, and nutrient content. In addition, the collective stress response, which is observed in P. aeruginosa that are stressed by antibiotics or bacteriophage (phage), repels swarms from approaching the area containing the stress. The methods described here address how to control the critical factors that affect swarming. We introduce a simple method to monitor swarming dynamics and the collective stress response with high temporal resolution using a flatbed document scanner, and describe how to compile and perform a quantitative analysis of swarms. This simple and cost-effective method provides precise and well-controlled quantification of swarming and may be extended to other types of plate-based growth assays and bacterial species.

We detail a simple method to produce high-resolution time-lapse movies of Pseudomonas aeruginosa swarms that respond to bacteriophage (phage) and antibiotic stress using a flatbed document scanner. This procedure is a fast and simple method for monitoring swarming dynamics and may be adapted to study the motility and growth of other bacterial species.

Swarming is a form of surface motility observed in many bacterial species including Pseudomonas aeruginosa and Escherichia coli. Here, dense populations ...

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers (MADM)

Beginning from a limited pool of progenitors, the mammalian cerebral cortex forms highly organized functional neural circuits. However, the underlying cellular and molecular mechanisms regulating lineage transitions of neural stem cells (NSCs) and eventual production of neurons and glia in the developing neuroepithelium remains unclear. Methods to trace NSC division patterns and map the lineage of clonally related cells have advanced dramatically. However, many contemporary lineage tracing techniques suffer from the lack of cellular resolution of progeny cell fate, which is essential for deciphering progenitor cell division patterns. Presented is a protocol using mosaic analysis with double markers (MADM) to perform in vivo clonal analysis. MADM concomitantly manipulates individual progenitor cells and visualizes precise division patterns and lineage progression at unprecedented single cell resolution. MADM-based interchromosomal recombination events during the G2-X phase of mitosis, together with temporally inducible CreERT2, provide exact information on the birth dates of clones and their division patterns. Thus, MADM lineage tracing provides unprecedented qualitative and quantitative optical readouts of the proliferation mode of stem cell progenitors at the single cell level. MADM also allows for examination of the mechanisms and functional requirements of candidate genes in NSC lineage progression. This method is unique in that comparative analysis of control and mutant subclones can be performed in the same tissue environment in vivo. Here, the protocol is described in detail, and experimental paradigms to employ MADM for clonal analysis and lineage tracing in the developing cerebral cortex are demonstrated. Importantly, this protocol can be adapted to perform MADM clonal analysis in any murine stem cell niche, as long as the CreERT2 driver is present.

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM

A protocol to perform lineage tracing and functional genetic analysis of candidate genes at a single cell level using mosaic analysis with double markers (MADM) is presented. MADM clonal analysis provides a quantitative framework to measure the proliferative behavior, cellular output, and lineage relationship of individual progenitors and their daughter cells.

Beginning from a limited pool of progenitors, the mammalian cerebral cortex forms highly organized functional neural circuits. However, the underlying ...

Clock Scan Protocol for Image Analysis: ImageJ Plugins

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