A Zeiss LSM 880 in the Fralin Imaging Center was used to collect the calcium imaging data. We acknowledge Dr. Michelle L Olsen and Yuhang Pan for their assistance with the IMARIS software. We acknowledge Dr. Lenwood S. Heath for constructive comments on the manuscript and Steven Giavasis for comments on the GitHub README file. This work was supported by NIH R21MH122987 (https://www.nimh.nih.gov/index.shtml) and NIH R01GM140130 (https://www.nigms.nih.gov/) to L.N. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1. Calcium imaging
<ol>
	<li>Fly larvae preparation 
	NOTE: Flies and larvae are maintained at 25 &#176;C under a 12 h:12 h light:dark cycle.
	<ol>
		<li>Anesthetize the flies with CO2. Sort 20-45 males and 20-45 females into each fly vial, and give them at least 24 h to 48 h to recover from the CO2 exposure. 
		NOTE: Fly exposure to CO2 should last for the shortest amount of time possible.</li>
		<li>To synchronize the larvae age, tap over the flies into new vials containing yeast granules, and allow them 4-8 h to lay eggs. Remove the flies by flipping them into new vials.</li>
		<li>Collect the larvae at 72 h using 10 mL of 20% w/v sucrose solution.</li>
	</ol>
	</li>
	<li>Microscope and temperature control setup
	<ol>
		<li>Perform the imaging on a confocal microscope (see the Table of Materials) and a z-axis piezo stage with a stage insert using the following settings: laser, Argon; scan mode, frame; frame size, 512 x 512; speed, maximum; channels/bit depth, 1/8 Bit; zoom, 1.5; z-stack, slice =15, Keep = slice; focus devices and strategy, Definite focus; focus, Definite focus on.</li>
		<li>Attach a Peltier cooling module to a heat sink by the heat transfer compound to build a thermoelectric cooler. The Peltier is powered by a 2 A power supply.</li>
		<li>Attach a thermocouple microprobe to a data acquisition device to record the temperature.</li>
	</ol>
	</li>
	<li>Calcium imaging
	<ol>
		<li>Rinse the larvae 3x in 1x phosphate-buffered saline (PBS).</li>
		<li>Pipette 75 &#956;L of 1x PBS onto the center of a glass slide.</li>
		<li>Put one or two larvae in 1x PBS and place the thermocouple microprobe near the larvae. 
		NOTE: The distance between the larvae and the microprobe should be ~5 mm. The larvae may move if the microprobe is positioned too close to them. A great distance could result in inaccurate temperature readings.</li>
		<li>Cover the larvae and thermocouple microprobe with a glass coverslip. Seal the coverslip with nail polish.</li>
		<li>Place the slide on the microscope stage, find the focus using a 25x objective, and place the thermoelectric cooler on the slide. 
		NOTE: The Peltier is placed directly on the slide where the larvae are to deliver the temperature stimuli.</li>
		<li>In confocal software, focus on the fluorescent cells of interest. Adjust the laser power to avoid oversaturation. Set the first and last slice positions in the z-stack setting. 
		&#8203;NOTE: Use the lowest possible laser power before recording to prevent photobleaching.</li>
		<li>Start the z-stack scanning and temperature recording at the same time.</li>
		<li>Control the power supply that powers the Peltier to change the Peltier surface temperature. Turn on the power supply to decrease the temperature and turn it off to increase the temperature.</li>
		<li>Stop the z-stack scanning and temperature recording.</li>
	</ol>
	</li>
</ol>
2. Analysis of the 3D calcium imaging data
<ol>
	<li>Export the calcium imaging data to TIFF files and save them in a folder with the same name as the base name of the TIFF files inside. 
	NOTE: The filename must not contain any commas.
	<ol>
		<li>Use the following parameters to export the calcium imaging data from ZEN (black edition): file type, TIFF; compression, LZW; channels, 2; Z-position, all; time, all; phase, 1; region, full.</li>
	</ol>
	</li>
	<li>Installation
	<ol>
		<li>Download TACI-Calcium_Imaging.jar from Github (https://github.com/niflylab/TACI_CalciumImagingPlugin/releases).</li>
		<li>Install the plugin in FIJI by clicking on Plugins in the menu bar and then clicking on Install in the dropdown menu (Plugins | Install). Then, restart FIJI. 
		NOTE: Do NOT use Plugins | Install Plugin.</li>
		<li>Run the plugin by clicking on Plugins and then choosing TACI-Calcium Imaging (Plugins | TACI-Calcium Imaging).</li>
	</ol>
	</li>
	<li>Use the RENAME function to convert the TIFF filenames to the required structure. 
	NOTE: The tool uses filename_h#t#z#c#.tif as the default structure (h# and c# are optional; #: a positive integer). If the image filenames are not in the default structure, the RENAME function must be executed.
	<ol>
		<li>Choose the folder in which the TIFF images need renaming by clicking Browse Folders.</li>
		<li>Fill in the parameter information. Five parameters are listed, including Filename, Phase, Max T-Position, Max Z-Position, and Channel. Each parameter has three values: Preceding Text, Parameter Value, and Order. 
		NOTE: The information is case-sensitive. If the image filenames contain a phrase before a parameter, fill in the phrase as the Preceding Text of the corresponding parameter. If the image filenames contain a phrase after all parameters, fill in the phrase as the Post Text.
		<ol>
			<li>Be sure to enter Filename, Max T-Position, and Max Z-Position and that the parameter values for Max T-Position and Max Z-Position include all digits.</li>
			<li>Wait for the Filename to be automatically filled using the folder name.</li>
			<li>If the TIFF filenames do not include the phase and channel, leave the corresponding parameter value blank, and choose Na in Order.</li>
		</ol>
		</li>
		<li>Click Rename to create a folder with the same name and _r. Observe that in the folder, the TIFF filenames have been restructured to be compatible with the ORGANIZE function. 
		NOTE: _r has been added to the filenames of the TIFF images.</li>
	</ol>
	</li>
	<li>Use the ORGANIZE function to save the TIFF images from the same z-position in one folder. 
	NOTE: The folder name must have the same name as the base name of the TIFF files inside and must NOT have any commas.
	<ol>
		<li>Choose the folder in which the TIFF images need organizing by clicking Browse Folders.</li>
		<li>If the parameter CSV file (param.csv) exists, wait for the parameter values to be filled in automatically. 
		NOTE: The param.csv file has a required format. The parameters, including filename, phase, position_t, position_z, channel, and is_gray, must be filled in in row 1 from left to right starting from column A. Corresponding values for each parameter must be filled in in row 2.</li>
		<li>If the parameter CSV file (param.csv) does not exist, manually fill in the parameter values. Ensure that the parameter values of Phase and Channel include letters, while the parameter values of T Position and Z Position should be the largest numbers of the t- and z-positions. If the image filenames do not include the phase or channel, enter Na.</li>
		<li>Create grayscale TIFF images when needed. Leave the box of Are images gray? unchecked to grayscale the images.</li>
		<li>Click Organize to create a folder with the same name and _gray_stacks and to generate folders with the same name and _# (#: z-positions) in the folder. Observe that the TIFF files are sorted into corresponding folders by z-positions and that a file named param.csv is generated, in which the parameters and their values can be found.</li>
	</ol>
	</li>
	<li>Use TrackMate in FIJI to extract the fluorescence intensities of the cells of interest from each z-position. 
	NOTE: This step can also be accomplished by other imaging software.
	<ol>
		<li>Open the TIFF images in a z-position folder by FIJI.</li>
		<li>Run TrackMate by clicking on Plugins | Tracking | TrackMate, and adjust the following parameters if necessary.
		<ol>
			<li>Use DoG or LoG detectors.</li>
			<li>Change the blob diameter, threshold, and median filter. Adjust the blob diameter to be similar to the diameter of the cells. If the cells are oval, adjust the blob diameter to be similar to the minor axis. 
			NOTE: Increasing the threshold helps avoid background noise being picked up as signals without affecting the signal intensities (Supplementary Figure 1). However, an increase in the threshold may miss true signals (Supplementary Figure 1). If the signals are strong, use the median filter to decrease the Salt and Pepper noise.</li>
			<li>Set the filters to remove some, if not all, of the irrelevant signals. Filters X and Y are spatial filters. Use filters X and Y to remove the irrelevant signals that are distant from the real signals. 
			NOTE: When filters are set on one image, it is crucial to check all the other images to ensure that the real signals are not removed.</li>
			<li>Set the linking max distance, gap-closing max distance, and gap-closing max frame gap. Set the linking max distance and gap-closing max distance to be 3x-5x the blob diameter, especially when the samples move significantly over time, to help decrease the number of tracks. Set the gap-closing max frame gap to the number of images in the stack.</li>
			<li>Export the fluorescence intensities of the regions of interest (ROIs) to a CSV file. If an old TrackMate version is used, choose Export all spots statistics in the Select an action window. If the TrackMate version is 7.6.1 or higher, choose the Spots in the Display options window. Export or save as the interactive files to CSV files. 
			NOTE: Both files are interactive with the image window; highlighting an ROI displays the corresponding ROI in the image window. These files include the mean intensities (MEAN_INTENSITY or MEAN_INTENSITY_CH1) of the cells of interest at corresponding time points (POSITION_T). The same TRACK_IDs should represent the same ROIs at different time points. However, this is not always true and may need to be corrected manually, when necessary. If TrackMate does not recognize the ROIs at some time points, those time points are not displayed.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Extract the background fluorescence intensity, and create the Background_list.csv file. 
	NOTE: In this study, the background intensity for each z-position was estimated by using the average value of three to five neighboring same-size blobs that did not contain fluorescence signals and were from different time points.
	<ol>
		<li>The Background_list.csv file has a required format: each column contains the information of one neuron, starting from Neuron 0. Fill in the neuron numbers, such as Neuron 0, in row 1. Then, provide the background intensity for each z-position analyzed-if five z-positions are analyzed for Neuron 0, fill in five background intensity values below Neuron 0. 
		NOTE: The Background_list.csv file is required for the TACI EXTRACT function. If the background is negligible, the Background_list.csv with the zero-background intensity of every neuron must be provided.</li>
	</ol>
	</li>
	<li>Use the EXTRACT function to identify the maximum fluorescence intensities at each t-position and calculate &#916;F/F0&#160;as shown in equation (1). 
	<img alt="Equation 1" src="/files/ftp_upload/64953/64953eq02.jpg" style="margin: auto;" />&#160; &#160; (1)
	<ol>
		<li>Create a folder and name it using the neuron number, starting from Neuron 0. Save the CSV files containing the fluorescence information of the corresponding neuron in the folder. Each CSV file contains the information of one z-position, so the number of CSV files equals the number of z-positions analyzed.&#160;Name the CSV files as Mean_Intensity#.csv (# represents the z-position)&#160;and each CSV file includes at least two columns: POSITION_T and MEAN_INTENSITY or MEAN_INTENSITY_CH1. 
		NOTE: Create a folder for every cell of interest.</li>
		<li>Save the Background_list.csv file and folders created in step 2.7.1 in one folder. Choose this folder by clicking on Browse Files. 
		NOTE: The number of background values in the Background_list.csv file must match the number of the Mean_Intensity#.csv files for each neuron.</li>
		<li>The Background File is automatically filled in. Fill in the largest number of t-positions for the Number of T Positions.</li>
		<li>Click Extract to create a results folder, including the CSV files and plots for each neuron. The CSV files include information on the maximum fluorescence intensity and &#916;F/F0 at each t-position. The plots are line charts of &#916;F/F0 over t-positions. 
		NOTE: In this study, &#916;F/F0 was calculated using equation (1). The first value of each z-position was used as F0. If this F0 is not appropriate20, the plugin provides files including raw data for each neuron in the python_files folder.</li>
	</ol>
	</li>
	<li>Use the MERGE function to average each neuron&#39;s &#916;F/F0, calculate the SEM, and plot the average &#916;F/F0 over t-positions.
	<ol>
		<li>Choose the results folder created by the EXTRACT function by clicking on Browse Files.</li>
		<li>Fill in the Number of T Positions with the largest number of t-positions.</li>
		<li>Click Merge to create a merged_data folder, including a merged_data.csv file and an Average_dF_F0.png plot. The CSV file includes the information on the average and SEM &#916;F/F0 at each t-position. The plot is a line chart of the average &#916;F/F0 over t-positions.</li>
	</ol>
	</li>
</ol>

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The authors have no conflicts of interest to disclose.

This study developed a new ImageJ plugin, TACI, and described a workflow analyzing 3D calcium imaging. Many currently available tools focus on correcting the x/y motion, although motion on the z-axis also needs to be explicitly diagnosed or corrected6. During image acquisition in a live organism, movement on the z-axis is unavoidable even when the organism is immobilized, and some stimuli, such as temperature change, often cause significant z-drift. Increasing the height of the z-stacks will allow for recording the cells of interest during the whole imaging process; however, it is not trivial to analyze motion on the z-axis, especially when individual cells appear in multiple z-positions. If such movement is ignored, researchers will not obtain the precise calcium responses of these cells. TACI corrects the z-drift by extracting the fluorescence signals from every z-position. A critical step of TACI is to sort the maximum value at each time point and use it to represent a cell&#39;s intensity. Therefore, it is key to include all the z-positions of the cells of interest during the imaging process. Additionally, we recommend allowing a cell appearing in five or more z-positions so that the z-distances and z-positions do not affect the maximum value (Supplementary Figure 2A,B). In addition, TACI allows for the separation of cells that overlap in the lateral (x/y) direction but appear in different z-positions.
Z-drift can also be corrected by extracting fluorescence intensities from 3D ROIs8,11,29. We compared TACI with two 3D-ROI methods. First, Klein et al. created a custom software written in IGOR Pro that uses the brightest 100 pixels in each 3D ROI to generate the raw signal29. This method and TACI produced similar results (Supplementary Figure 3A). Second, we applied IMARIS (version 9.8.2), a commercial software, to model the 3D ROIs and extract their mean intensities. Although the results from TACI and IMARIS displayed similar trends, the fold changes were different (Supplementary Figure 3B). This discrepancy may be due to algorithms. Of note, the maximum value extracted by TACI is proportional to the ground truth intensity (Supplementary Figure 2C).
Although this workflow is semi-automatic and still requires manual efforts from researchers, it provides a computational approach for 3D calcium imaging analysis. Importantly, this workflow is based on ImageJ and does not require commercial software or programming knowledge. The limitations and potential solutions for this workflow are as follows. First, TACI only accepts TIFF files and a specific file name structure: filename_h#t#z#c#.tif (h# and c# are optional). However, other file formats compatible with ImageJ can be easily converted to TIFF files by ImageJ. Moreover, the plugin has a RENAME function that converts the image names to the required structure so that it is compatible with calcium imaging data obtained from different systems.
Second, the plugin is designed for calcium imaging data with constant backgrounds. Subtracting the corresponding background information from the ROI intensities at each time point is one way to correct fluctuating backgrounds. The tool provides ROI intensities at each time point in the python_files folder. The background intensities could be represented by (1) the images&#39; mean intensities or (2) the mean intensities of the ROIs with no active cells. ImageJ provides methods to obtain the images&#39; mean intensities (by clicking on Image | Stack | Measure Stack) and the mean intensities of random ROIs (by clicking on Analyze | Tools | ROI Manager | Multi Measure). If photobleaching happens during calcium imaging, the Bleach Correction function (by clicking on Image | Adjust | Bleach Correction) may be run before TrackMate.
Finally, cell registration across z-positions needs to be included in this workflow if using TACI to analyze a large number of neurons simultaneously. Accordingly, an additional function needs to be developed that organizes the information on fluorescence intensities into the data structure required by the MERGE function.

The level of intracellular calcium is a precise marker of neuronal excitability. Calcium imaging measures the changes in intracellular calcium to understand neuronal activity1. Studies in neuroscience have increasingly used this method due to the development of techniques for measuring intracellular calcium concentration, including genetically encoded calcium indicators (GECIs), such as GCaMP2,3, which can be noninvasively expressed in specific sets of neurons through genetic approaches. The lower costs of lasers and microscope components have also increased the use of calcium imaging4. Importantly, calcium imaging allows for recording and studying single neurons as well as large neuron populations simultaneously in freely moving animals5.
Nevertheless, the analysis of calcium imaging data is challenging because (1) it involves tracking the changes in fluorescence of individual cells over time, (2) the fluorescence signal intermittently disappears or reappears with neuronal responses, and (3) the neurons may move in all directions, specifically in and out of a focal plane or appearing on multiple planes4,6. Manual analysis is time-consuming and becomes impractical as the length of recordings and the number of neurons increases. Various software programs have been developed to accelerate the process of analyzing calcium imaging. Previously, software was designed in a limited experimental context, making it difficult for other laboratories to adopt it. Recent efforts to meet modern standards for software sharing have led to the development of several tools that can consistently analyze calcium imaging data across different groups7,8,9,10,11,12,13,14,15,16,17,18,19. However, most of these tools require programming knowledge and/or depend on commercial software. A lack of programming knowledge and software paywalls deter researchers from adopting these methods. Moreover, many of these tools focus on correcting the x/y motion, although motion on the z-axis also needs to be explicitly diagnosed and corrected6. There is a need for a computational tool to analyze 3D calcium imaging that focuses on neurons exhibiting z-drift and appearing on multiple z-planes. Ideally, this tool should use open-source software and not require programming knowledge to allow other laboratories to readily adopt it.
Here, we developed a new ImageJ plugin, TACI, to analyze 3D calcium imaging data. First, the software renames, if needed, and organizes the 3D calcium imaging data by z-positions. The cells of interest are tracked in each z-position, and their fluorescence intensities are extracted by TrackMate or other computational tools. TACI is then applied to examine the motion on the z-axis. It identifies the maximum value of a z-stack and uses it to represent a cell&#39;s intensity at the corresponding time point. This workflow is suited to analyzing 3D calcium imaging with motion in all directions and/or with neurons overlapping in the lateral (x/y) direction but appearing in different z-positions. To validate this workflow, 3D calcium imaging datasets from fly larval thermosensitive neurons and mushroom neurons in the brain were used. Of note, TACI is an open-source ImageJ plugin and does not require any programming knowledge.

<table><tbody><tr><td>Blunt Fill Needel</td><td>BD</td><td>303129</td><td></td></tr><tr><td>Calcium chloride dihydrate</td><td>Fisher Scientific&amp;nbsp;</td><td>10035-04-8</td><td>Fly food ingredient</td></tr><tr><td>Carbon dioxide</td><td>Airgas</td><td>UN1013</td><td>Size 200 High Pressure Steel Cylinder</td></tr><tr><td>CO&lt;sub&gt;2&lt;/sub&gt; bubbler kit</td><td>Genesee</td><td>59-180</td><td></td></tr><tr><td>Confocal microscope LSM880</td><td>Zeiss</td><td>4109002107876000</td><td>An inverted Axio Observer Z1, equipped with 5 lasers, 2 standard PMT detectors, 32-channel GaAsP dectectors, an Airyscan detector, and Definite Focus.2.</td></tr><tr><td>DAQami software</td><td>Measurement Computing</td><td></td><td></td></tr><tr><td>Dextrose</td><td>Genesee</td><td>62-113</td><td>Fly food ingredient</td></tr><tr><td>Drosophila Agar</td><td>Genesee</td><td>66-111</td><td>Fly food ingredient</td></tr><tr><td>Ethanol</td><td>Decon Labs, Inc.</td><td>64-17-5</td><td>Fly food ingredient</td></tr><tr><td>Fly line: &lt;em&gt;Ir21a-Gal4&lt;/em&gt;</td><td>Dr. Paul Garrity lab</td><td></td><td>A kind gift</td></tr><tr><td>Fly line: &lt;em&gt;Ir21a-Gal80&lt;/em&gt;</td><td>Dr. Lina Ni lab</td><td></td><td></td></tr><tr><td>Fly line: &lt;em&gt;Ir68a-Gal4&lt;/em&gt;</td><td>Dr. Aravinthan DT Samuel lab</td><td></td><td>A kind gift</td></tr><tr><td>Fly line: &lt;em&gt;Ir93a-Gal4&lt;/em&gt;</td><td>Dr. Paul Garrity lab</td><td></td><td>A kind gift</td></tr><tr><td>Fly line: &lt;em&gt;UAS-GCaMP6&lt;/em&gt;</td><td>Bloomington Drosophila Stock Center</td><td>42750</td><td></td></tr><tr><td>Flypad</td><td>Genesee</td><td>59-114</td><td></td></tr><tr><td>General purpose forged brass regulator</td><td>Gentec</td><td>G152</td><td></td></tr><tr><td>Gibco PBS pH 7.4 (1x)</td><td>Thermo Fisher Scientific</td><td>10010-031</td><td></td></tr><tr><td>Green Drosophila tubing</td><td>Genesee</td><td>59-124</td><td></td></tr><tr><td>Heat transfer compound</td><td>MG Chemicals</td><td>860-60G</td><td></td></tr><tr><td>Heatsink</td><td>Digi-Key Electronics</td><td>ATS2193-ND</td><td>Resize to 12.9 x 5.5 cm</td></tr><tr><td>Illuminator</td><td>AmScope</td><td>LED-6W</td><td></td></tr><tr><td>Inactive Dry Yeast</td><td>Genesee</td><td>62-108</td><td>Fly food ingredient</td></tr><tr><td>Incubator</td><td>Pervical</td><td>DR-41VL</td><td>Light: dark cycle: 12h:12h; temperature: 25 &amp;deg;C; humidity: 40-50% RH.</td></tr><tr><td>Methyl-4-hydroxybenzoate</td><td>Thermo Scientific</td><td>126965000</td><td>Fly food ingrediete</td></tr><tr><td>Micro cover glass</td><td>VWR</td><td>&amp;nbsp;48382-126</td><td>22 x 40 mm</td></tr><tr><td>Microscope slides</td><td>Fisher Scientific&amp;nbsp;</td><td>12-544-2</td><td>25 x 75 x 1.0 mm</td></tr><tr><td>Nail polish</td><td>Kleancolor</td><td></td><td></td></tr><tr><td>Narrow Drosophila vials</td><td>Genesee</td><td>32-113RL</td><td></td></tr><tr><td>Objective&amp;nbsp;</td><td>Zeiss</td><td>420852-9871-000</td><td>LD LCI Plan-Apochromat 25x/0.8 Imm Corr DIC M27</td></tr><tr><td>Peltier cooling module</td><td>TE Technology</td><td>TE-127-1.0-0.8</td><td>30 x 30 mm</td></tr><tr><td>Plugs</td><td>Genesee</td><td>49-102</td><td></td></tr><tr><td>Power Supply</td><td>Circuit Specialists</td><td>CSI1802X</td><td>10 volt DC 2.0 amp linear bench power supply</td></tr><tr><td>Princeton Artist Brush Nepture</td><td>Princeton Artist Brush Co.</td><td></td><td>Series 4750, size 2</td></tr><tr><td>Sodium potassium L-tartrate tetrahydrate</td><td>Thermo Scientific</td><td>033241-36</td><td>Fly food ingredient</td></tr><tr><td>Stage insert&amp;nbsp;</td><td>Wienecke and Sinske</td><td>432339-9030-000</td><td></td></tr><tr><td>Stereo Microscope</td><td>Olympus</td><td>SZ61</td><td>Any stereo microscope works</td></tr><tr><td>T-Fitting</td><td>Genesee</td><td>59-123</td><td></td></tr><tr><td>Thermocouple data acquisition device</td><td>Measurement Computing</td><td>USB-2001-TC</td><td>Single channel</td></tr><tr><td>Thermocouple microprobe</td><td>Physitemp</td><td>IT-24P&amp;nbsp;</td><td></td></tr><tr><td>Yellow Cornmeal</td><td>Genesee</td><td>62-101</td><td>Fly food ingredient</td></tr><tr><td>Z-axis piezo stage</td><td>Wienecke and Sinske</td><td>432339-9000-000</td><td></td></tr></tbody></table>

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.

Workflow of 3D calcium imaging analysis 
In this study, we developed a new ImageJ plugin, TACI, and described a workflow to track z-drift and analyze 3D calcium imaging that pinpoints the responses of individual cells appearing in multiple z-positions (Figure 1). This tool has four functions: RENAME, ORGANIZE, EXTRACT,&#160;and MERGE. First, if the image names are not compatible with the ORGANIZE function, the RENAME function can convert the image names to the required structure. Then, the ORGANIZE function grayscales (if needed) and organizes the 3D calcium imaging TIFF data by z-positions. Images from the same z-position are saved in one folder. Next, different imaging analysis tools can be used to detect and track the ROIs and extract their fluorescence intensities over time for every z-position. An ImageJ plugin, TrackMate, was used to accomplish this step. TrackMate is an open-source ImageJ plugin for tracking single particles21. It has been widely used to track particles in various biological studies involving live-cell imaging, including calcium imaging11,21,22,23. TrackMate, in an automated manner, tracks cells in the lateral (x/y) direction, detects ROIs, and extracts signals from a live-imaging data set4,12. For every cell of interest, the EXTRACT function sorts the fluorescence intensities from all the z-positions by t-positions, identifies the maximum values of each t-position, subtracts the background, and calculates and plots &#916;F/F0. Last, the MERGE function calculates and plots the average of &#916;F/F0 of multiple cells.
Calcium responses of fly larval cool neurons to temperature changes 
We validated this method using the calcium changes in response to temperature fluctuations in fly larval cool neurons. A genetically encoded calcium indicator, GCaMP6m24, was expressed in larval cool neurons by Ir21a-Gal425. When exposed to approximately 27 &#176;C, the neurons had low intracellular calcium levels (Figure 2A and Figure 3). When the temperature was decreased to approximately 10 &#176;C and held there, the intracellular calcium levels rapidly increased and were sustained (Figure 2B and Figure 3). The calcium levels rapidly dropped when the temperature was increased (Figure 3).
Analyzing a fly brain 3D calcium imaging dataset 
We also validated this method using a fly brain 3D calcium imaging dataset11. The imaged transgenic flies (VT50339-Gal4;UAS-GCaMP6f) expressed GCaMP6f in the mushroom body in the brain11. Data from 45 z-positions (spaced at 1.5 &#956;m intervals) were collected at 50 Hz for 225 s (250 time points), and the first half of the dataset (125 time points) was analyzed. When the recording began, seven neurons had obvious fluorescence, and four of them were analyzed (Figure 4A). The intensities in these neurons decreased over time (Figure 4B). When octanol was applied (Figure 4C), multiple neurons brightened. The fluorescence of 10 neurons was found to increase simultaneously at time point 92 (Figure 4D), suggesting that these mushroom neurons respond to octanol odor. Although octanol was applied for 5 s, high fluorescence in these neurons was observed in only one time point (0.9 s) and then quickly dropped, suggesting that the response is phasic and transient.
Separation of overlapping cells 
Figure 5A presents a maximal projection image of three neurons. The white arrowhead points to two neurons that overlapped in the x/y plane but were separate in the ortho view (blue and orange arrowheads in Figure 5B), indicating that these neurons appeared in different z-positions; the orange cell had the strongest signal on z7 (Figure 5C), while the blue cell had the strongest signal on z10 (Figure 5D). The tool distinguished these two cells and revealed the delayed but strong activation of the orange cell (Figure 5E).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64953/64953fig01.jpg" /> 
Figure 1: Workflow using TACI to analyze 3D calcium imaging. TACI has four functions: RENAME, ORGANIZE, EXTRACT, and MERGE.&#160;First, if the TIFF names are not compatible with the ORGANIZE function, the RENAME function can convert the image names to the required structure. Then, the ORGANIZE function grayscales (if needed) and organizes the 3D calcium imaging TIFF data by z-positions. Images from the same z-position are saved in one folder. Next, different imaging analysis tools can be used to detect and track the ROIs and extract their fluorescence intensities in every z-position. For every cell of interest, the EXTRACT function sorts the fluorescence intensities by the corresponding time points, identifies the maximum values of each t-position, subtracts the background, and calculates and plots &#916;F/F0. Last, the MERGE function calculates and plots the average of &#916;F/F0 of multiple cells. Abbreviations: TACI = TrackMate Analysis of Calcium Imaging; ROIs = regions of interest; &#916;F/F0 = ratio of change in fluorescence to initial fluorescence intensity. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/64953fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64953/64953fig02.jpg" /> 
Figure 2: Calcium imaging of fly larval cool cells in inactive and active states. (A) The cells are barely visible in the inactive state. (B) The cells are strongly fluorescent in the active state. Different color arrowheads indicate different cells. The genotype is Ir21a-Gal4;UAS-GCaMP6m. z5-13: images at z-positions from 5 to 13. In B, the cell indicated by the white arrowheads is shown on z5 to z8; the cells indicated by orange and blue arrowheads are shown on z8 to z13. Scale bar = 10 &#956;m. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/64953fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64953/64953fig03.jpg" /> 
Figure 3: Quantification of fluorescence as the change in fluorescence intensity (F) compared to the initial intensity (F0). The genotype is Ir21a-Gal4;UAS-GCaMP6m. n = 7 cells from three animals. Traces = mean &#177; SEM. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/64953fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/64953/64953fig04.jpg" /> 
Figure 4: Analyzing a fly brain 3D calcium imaging dataset. (A) The maximal projection image at time point 1 (t1). The numbers 0-3 indicate the four analyzed neurons. (B) Fluorescence changes of neurons 0-3 in A during time points 0 to 125. (C) The maximal projection image at time point 92 (t92). The numbers 0-9 indicate the 10 analyzed neurons. (D) Fluorescence changes of neurons 0-9 in C during time points 0 to 125. Either 5 s of air or 5 s of octanol were applied, as shown in gray. Scale bar = 10,000 units of raw fluorescence intensity. Abbreviation: OCT = octanol. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/64953fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/64953/64953fig05.jpg" /> 
Figure 5: Separation of overlapping cells based on the maximum value. (A) Two neurons are overlapping (white arrowhead) in a maximal projection image (m). The genotype is Ir21a-Gal4;UAS-GCaMP6m. (B) These neurons are separate in the ortho view (blue and orange arrowheads). (C,D) The orange cell appears on z7 (C), while the blue cell appears on z10 (D). Scale bar = 10 &#956;m. (E) The fluorescence changes of the orange and blue cells are quantified using the maximum values from individual z-positions. Abbreviation: &#916;F/F0 = ratio of change in fluorescence to initial fluorescence intensity. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/64953fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Two neurons with weak calcium signals are analyzed using DoG and LoG detectors at different thresholds. (A-C) The first neuron (Ir21a-Gal8026/UAS-GCaMP6;Ir93a-Gal427) is analyzed by DoG and LoG at different thresholds. (A) The threshold is set at 0.1. Both DoG and LoG detect all 36 time points. (B) The threshold is set at 0.3. Among the 36 time points, DoG detects 36, and LoG detects 35. (C) The threshold is set at 1.0. Among the 36 time points, DoG detects 34, and LoG detects 15. (D-F) The second neuron (UAS-GCaMP6;Ir68a-Gal428) is analyzed by DoG and LoG at different thresholds. (D) The threshold is set at 0.1. DoG detects all 36 time points, and LoG detects 34. (E) The threshold is set at 0.3. Among the 36 time points, DoG detects 33, and LoG detects 18. (F) The threshold is set at 1.0. Among the 36 time points, DoG detects 14, and LoG detects only one. Of note, increasing the threshold does not affect its intensity reading if an ROI is recognized. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/FigS1.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2:&#160;The maximum value is a good representative of a cell&#39;s intensity. (A) Z-stacks with different z-distances are simulated. (B) Z-stacks with different z-positions are simulated. (C) Z-stacks are created from simulated cells with different intensities. Abbreviations: max = the maximum value of a z-stack; ground_truth = the product of the simulated cell&#39;s volume and its filled intensity.&#160;<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/FigS2.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Comparison between TACI and 3D-ROI methods. (A) Fluorescence changes in two cells indicated by orange and blue are quantified using TACI and a custom software written in IGOR Pro. The genotype type is R11F02-Gal429;UAS-GCaMP6m. (B) Fluorescence changes in two cells indicated by orange and blue are quantified using TACI and IMARIS. The genotype type is Ir21a-Gal4;UAS-GCaMP6m. Abbreviation: &#916;F/F0 = ratio of change in fluorescence to initial fluorescence intensity.&#160;<a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/64953/FigS3.pdf" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis at JoVE.com

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

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 ...

taci-an-imagej-plugin-for-3d-calcium-imaging-analysis

Universidad San Sebastian

Research

JoVE Journal

Neuroscience

3.9K Views.  Virginia Polytechnic Institute and State University. 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. 

Video: TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

Focal Ca2+ Transient Detection in Smooth Muscle

Ca2+ imaging of smooth muscle provides insight into cellular mechanisms that may not result in changes of membrane potential, such as the release of Ca2+ from internal stores, and allows multiple cells to be monitored simultaneously to assess, for example, coupling in syncytial tissue. Subcellular Ca2+ transients are common in smooth muscle, yet are difficult to measure accurately because of the problems caused by their stochastic occurrence, over an often wide field of view, in an organ that it prone to contract. To overcome this problem, we've developed a series of imaging protocols and analysis routines to acquire and then analyse, in an automated fashion, the frequency, location and amplitude of such events. While this approach may be applied in other contexts, our own work involves the detection of local purinergic Ca2+ transients for locating transmitter release with submicron resolution. 
ATP is released as a cotransmitter from autonomic nerves, where it binds to P2X1 receptors on the smooth muscle of the detrusor and vas deferens. Ca2+ enters the smooth muscle, resulting in purinergic neuroeffector Ca2+ transients (NCTs). The focal Ca2+ transients allow the optical monitoring of neurotransmitter release in a manner that has many advantages over electrophysiology. Apart from the greatly improved spatial resolution, optical recording has the additional advantage of allowing the recording of transmitter release from many distinguishable sites simultaneously. Furthermore, the optical plane of focus is easier to maintain or correct during long recording series than is the repositioning of an intracellular sharp microelectrode. 
In summary, a method for imaging of Ca2+ fluorescence is outlined which details the preparation of tissue, and the acquisition and analysis of data. We outline the use of several scripts for the analysis of such Ca2+ transients.

Focal Ca2+ Transient Detection in Smooth Muscle

Details methods for high-resolution Ca2+ imaging of smooth muscle within isolated organs, including: preparation of the tissue, image acquisition and data analysis.

Ca2+ imaging of smooth muscle provides insight into cellular mechanisms that may not result in changes of membrane potential, such as the release of Ca2+ ...

Biology

Calcium Imaging in Freely Behaving Caenorhabditis elegans with Well-Controlled, Nonlocalized Vibration

Nonlocalized mechanical forces, such as vibrations and acoustic waves, influence a wide variety of biological processes from development to homeostasis. Animals cope with these stimuli by modifying their behavior. Understanding the mechanisms underlying such behavioral modification requires quantification of neural activity during the behavior of interest. Here, we report a method for calcium imaging in freely behaving Caenorhabditis elegans with nonlocalized vibration of specific frequency, displacement, and duration. This method allows the production of well-controlled, nonlocalized vibration using an acoustic transducer and quantification of evoked calcium responses at single-cell resolution. As a proof of principle, the calcium response of a single interneuron, AVA, during the escape response of C. elegans to vibration is demonstrated. This system will facilitate understanding of neural mechanisms underlying behavioral responses to mechanical stimuli.

Calcium Imaging in Freely Behaving Caenorhabditis elegans with Well-Controlled, Nonlocalized Vibration

Reported here is a system for calcium imaging in freely behaving Caenorhabditis elegans with well-controlled, nonlocalized vibration. This system allows researchers to evoke nonlocalized vibrations with well-controlled properties at nano-scale displacement and to quantify calcium currents during responses of C. elegans to the vibrations.

Nonlocalized mechanical forces, such as vibrations and acoustic waves, influence a wide variety of biological processes from development to homeostasis. ...

Behavior

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

Two-photon Calcium Imaging in Neuronal Dendrites in Brain Slices

Calcium (Ca2+) imaging is a powerful tool to investigate the spatiotemporal dynamics of intracellular Ca2+ signals in neuronal dendrites. Ca2+ fluctuations can occur through a variety of membrane and intracellular mechanisms and play a crucial role in the induction of synaptic plasticity and regulation of dendritic excitability. Hence, the ability to record different types of Ca2+ signals in dendritic branches is valuable for groups studying how dendrites integrate information. The advent of two-photon microscopy has made such studies significantly easier by solving the problems inherent to imaging in live tissue, such as light scattering and photodamage. Moreover, through combination of conventional electrophysiological techniques with two-photon Ca2+ imaging, it is possible to investigate local Ca2+ fluctuations in neuronal dendrites in parallel with recordings of synaptic activity in soma. Here, we describe how to use this method to study the dynamics of local Ca2+ transients (CaTs) in dendrites of GABAergic inhibitory interneurons. The method can be also applied to studying dendritic Ca2+ signaling in different neuronal types in acute brain slices.

We present a method combining whole-cell patch-clamp recordings and two-photon imaging to record Ca2+ transients in neuronal dendrites in acute brain slices.

Calcium (Ca2+) imaging is a powerful tool to investigate the spatiotemporal dynamics of intracellular Ca2+ signals in neuronal dendrites. Ca2+ ...

Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans

It has become increasingly clear that neural circuit activity in behaving animals differs substantially from that seen in anesthetized or immobilized animals. Highly sensitive, genetically encoded fluorescent reporters of Ca2+ have revolutionized the recording of cell and synaptic activity using non-invasive optical approaches in behaving animals. When combined with genetic and optogenetic techniques, the molecular mechanisms that modulate cell and circuit activity during different behavior states can be identified.
Here we describe methods for ratiometric Ca2+ imaging of single neurons in freely behaving Caenorhabditis elegans worms. We demonstrate a simple mounting technique that gently overlays worms growing on a standard Nematode Growth Media (NGM) agar block with a glass coverslip, permitting animals to be recorded at high-resolution during unrestricted movement and behavior. With this technique, we use the sensitive Ca2+ reporter GCaMP5 to record changes in intracellular Ca2+ in the serotonergic Hermaphrodite Specific Neurons (HSNs) as they drive egg-laying behavior. By co-expressing mCherry, a Ca2+-insensitive fluorescent protein, we can track the position of the HSN within ~ 1 &#181;m and correct for fluctuations in fluorescence caused by changes in focus or movement. Simultaneous, infrared brightfield imaging allows for behavior recording and animal tracking using a motorized stage. By integrating these microscopic techniques and data streams, we can record Ca2+ activity in the C. elegans egg-laying circuit as it progresses between inactive and active behavior states over tens of minutes.

Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans

This protocol describes the use of genetically encoded Ca2+ reporters to record changes in neural activity in behaving Caenorhabditis elegans worms. &#8195;

It has become increasingly clear that neural circuit activity in behaving animals differs substantially from that seen in anesthetized or immobilized ...

Ex Vivo Imaging of Cell-specific Calcium Signaling at the Tripartite Synapse of the Mouse Diaphragm

The electrical activity of cells in tissues can be monitored by electrophysiological techniques, but these are usually limited to the analysis of individual cells. Since an increase of intracellular calcium (Ca2+) in the cytosol often occurs because of the electrical activity, or in response to a myriad of other stimuli, this process can be monitored by the imaging of cells loaded with fluorescent calcium-sensitive dyes.&#160; However, it is difficult to image this response in an individual cell type within whole tissue because these dyes are taken up by all cell types within the tissue. In contrast, genetically encoded calcium indicators (GECIs) can be expressed by an individual cell type and fluoresce in response to an increase of intracellular Ca2+, thus permitting the imaging of Ca2+ signaling in entire populations of individual cell types. Here, we apply the use of the GECIs GCaMP3/6 to the mouse neuromuscular junction, a tripartite synapse between motor neurons, skeletal muscle, and terminal/perisynaptic Schwann cells. We demonstrate the utility of this technique in classic ex vivo tissue preparations. Using an optical splitter, we perform dual-wavelength imaging of dynamic Ca2+ signals and a static label of the neuromuscular junction (NMJ) in an approach that could be easily adapted to monitor two cell-specific GECI or genetically encoded voltage indicators (GEVI) simultaneously. Finally, we discuss the routines used to capture spatial maps of fluorescence intensity. Together, these optical, transgenic, and analytic techniques can be employed to study the biological activity of distinct cell subpopulations at the NMJ in a wide variety of contexts.

Ex Vivo Imaging of Cell-specific Calcium Signaling at the Tripartite Synapse of the Mouse Diaphragm

Here we present a protocol to image calcium signaling in populations of individual cell types at the murine neuromuscular junction.

The electrical activity of cells in tissues can be monitored by electrophysiological techniques, but these are usually limited to the analysis of ...

In Vivo Calcium Imaging of Lateral-line Hair Cells in Larval Zebrafish

Sensory hair cells are mechanoreceptors found in the inner ear that are required for hearing and balance. Hair cells are activated in response to sensory stimuli that mechanically deflect apical protrusions called hair bundles. Deflection opens mechanotransduction (MET) channels in hair bundles, leading to an influx of cations, including calcium. This cation influx depolarizes the cell and opens voltage-gated calcium channels located basally at the hair-cell presynapse. In mammals, hair cells are encased in bone, and it is challenging to functionally assess these activities in vivo. In contrast, larval zebrafish are transparent and possess an externally located lateral-line organ that contains hair cells. These hair cells are functionally and structurally similar to mammalian hair cells and can be functionally assessed in vivo. This article outlines a technique that utilizes a genetically encoded calcium indicator (GECI), GCaMP6s, to measure stimulus-evoked calcium signals in zebrafish lateral-line hair cells. GCaMP6s can be used, along with confocal imaging, to measure in vivo calcium signals at the apex and base of lateral-line hair cells. These signals provide a real-time, quantifiable readout of both mechanosensation- and presynapse-dependent calcium activities within these hair cells. These calcium signals also provide important functional information regarding how hair cells detect and transmit sensory stimuli. Overall, this technique generates useful data about relative changes in calcium activity in vivo. It is less well-suited for quantification of the absolute magnitude of calcium changes. This in vivo technique is sensitive to motion artifacts. A reasonable amount of practice and skill are required for proper positioning, immobilization, and stimulation of larvae. Ultimately, when properly executed, the protocol outlined in this article provides a powerful way to collect valuable information about the activity of hair-cells in their natural, fully integrated states within a live animal.

The zebrafish is a model system that has many valuable features including optical clarity, rapid external development, and, of particular importance to the field of hearing and balance, externally located sensory hair cells. This article outlines how transgenic zebrafish can be used to assay both hair-cell mechanosensation and presynaptic function in toto.

Sensory hair cells are mechanoreceptors found in the inner ear that are required for hearing and balance. Hair cells are activated in response to sensory ...

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ imaging data is time consuming and subject to bias. Automated signal analysis algorithms based on region of interest (ROI) detection have been implemented for one-dimensional line scan measurements, but there is no current algorithm which integrates optimized identification and analysis of ROIs in two-dimensional image sequences. Here an algorithm for rapid acquisition and analysis of ROIs in image sequences is described. It utilizes ellipses fit to noise filtered signals in order to determine optimal ROI placement, and computes Ca2+ signal parameters of amplitude, duration and spatial spread. This algorithm was implemented as a freely available plugin for ImageJ (NIH) software. Together with analysis scripts written for the open source statistical processing software R, this approach provides a high-capacity pipeline for performing quick statistical analysis of experimental output. The authors suggest that use of this analysis protocol will lead to a more complete and unbiased characterization of physiologic Ca2+ signaling.

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.

Intracellular Ca2+ signals are commonly studied with fluorescent Ca2+ indicator dyes and microscopy techniques. However, quantitative analysis of Ca2+ ...

Brain Pericyte Calcium and Hemodynamic Imaging in Transgenic Mice In Vivo

Recent advances in protein biology and mouse genetics have made it possible to measure intracellular calcium fluctuations of brain cells in vivo and to correlate this with local hemodynamics. This protocol uses transgenic mice that have been prepared with a chronic cranial window and express the genetically encoded calcium indicator, RCaMP1.07, under the &#945;-smooth muscle actin promoter to specifically label mural cells, such as vascular smooth muscle cells and ensheathing pericytes. Steps are outlined on how to prepare a tail vein catheter for intravenous injection of fluorescent dyes to trace blood flow, as well as how to measure brain pericyte calcium and local blood vessel hemodynamics (diameter, red blood cell velocity, etc.) by two photon microscopy in vivo through the cranial window in ketamine/xylazine anesthetized mice. Finally, details are provided for the analysis of calcium fluctuations and blood flow movies via the image processing algorithms developed by Barrett et al. 2018, with an emphasis on how these processes can be adapted to other cellular imaging data.

Brain Pericyte Calcium and Hemodynamic Imaging in Transgenic Mice In Vivo

This protocol presents steps to acquire and analyze fluorescent calcium images from brain ensheathing pericytes and blood flow data from nearby blood vessels in anesthetized mice. These techniques are useful for studies of mural cell physiology and can be adapted to investigate calcium transients in any cell type.

Recent advances in protein biology and mouse genetics have made it possible to measure intracellular calcium fluctuations of brain cells in vivo and to ...

Imaging Local Ca2+ Signals in Cultured Mammalian Cells

Cytosolic Ca2+ ions regulate numerous aspects of cellular activity in almost all cell types, controlling processes as wide-ranging as gene transcription, electrical excitability and cell proliferation. The diversity and specificity of Ca2+ signaling derives from mechanisms by which Ca2+ signals are generated to act over different time and spatial scales, ranging from cell-wide oscillations and waves occurring over the periods of minutes to local transient Ca2+ microdomains (Ca2+ puffs) lasting milliseconds. Recent advances in electron multiplied CCD (EMCCD) cameras now allow for imaging of local Ca2+ signals with a 128 x 128 pixel spatial resolution at rates of &#62;500 frames sec-1 (fps). This approach is highly parallel and enables the simultaneous monitoring of hundreds of channels or puff sites in a single experiment. However, the vast amounts of data generated (ca. 1 Gb per min) render visual identification and analysis of local Ca2+ events impracticable. Here we describe and demonstrate the procedures for the acquisition, detection, and analysis of local IP3-mediated Ca2+ signals in intact mammalian cells loaded with Ca2+ indicators using both wide-field epi-fluorescence (WF) and total internal reflection fluorescence (TIRF) microscopy. Furthermore, we describe an algorithm developed within the open-source software environment Python that automates the identification and analysis of these local Ca2+ signals. The algorithm localizes sites of Ca2+ release with sub-pixel resolution; allows user review of data; and outputs time sequences of fluorescence ratio signals together with amplitude and kinetic data in an Excel-compatible table.

Imaging Local Ca2+ Signals in Cultured Mammalian Cells

Here we present techniques for imaging local IP3-mediated Ca2+ events using fluorescence microscopy in intact mammalian cells loaded with Ca2+ indicators together with an algorithm that automates identification and analysis of these events.

Cytosolic Ca2+ ions regulate numerous aspects of cellular activity in almost all cell types, controlling processes as wide-ranging as gene transcription, ...

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

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Chapters in this video

Focal Ca²⁺ Transient Detection in Smooth Muscle

Calcium Imaging in Freely Behaving Caenorhabditis elegans with Well-Controlled, Nonlocalized Vibration

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

Two-photon Calcium Imaging in Neuronal Dendrites in Brain Slices

Ratiometric Calcium Imaging of Individual Neurons in Behaving Caenorhabditis Elegans

Ex Vivo Imaging of Cell-specific Calcium Signaling at the Tripartite Synapse of the Mouse Diaphragm

In Vivo Calcium Imaging of Lateral-line Hair Cells in Larval Zebrafish

Automated Analysis of Dynamic Ca²⁺ Signals in Image Sequences

Brain Pericyte Calcium and Hemodynamic Imaging in Transgenic Mice In Vivo

Imaging Local Ca²⁺ Signals in Cultured Mammalian Cells