This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM124868 and by Duke University.

1. Setup Layout and Alignment
<ol>
	<li>Installation and collimation of the tracking excitation laser
	<ol>
		<li>Affix the laser to the optical table using a home-built mount. The mount is a simple aluminum plate with mounting holes for the laser head and the optical table. The laser should be firmly attached to a metal mount for stability and heat dissipation. For this work, use a 488 nm solid state laser for illumination (Figure 1), though the wavelength can be selected to suit a particular fluorophore or experiment. A critical factor is that the laser wavelength fit the 2D-EOD working range, which is determined primarily by the wave plate between the two deflectors (Figure 1: W2). The 488 nm laser can effectively excite the green or yellow fluorescent protein (GFP/YFP), which are common fluorescent tags in live cell experiments.</li>
		<li>Adjust the laser beam height and direction using a pair of dielectric mirrors (Figure 1: M). Make sure the laser beam is parallel to the optical table and adjust it to a suitable height. 
		NOTE: The height should be chosen based on the microscope platform. This height should be maintained for all subsequent mirrors used in this protocol, though it will not be explicitly stated.</li>
		<li>Collimate the beam with a pair of lenses (Figure 1: L1 and L2). The focal lengths of lenses should be carefully chosen to avoid the laser spot being clipped by the aperture of the 2D-EOD. Clipping is evident by a change in the laser beam shape after passing through the 2D-EOD. The quality of collimation here is very important because aberrations will be amplified by subsequent lenses and any divergence will deteriorate the performance of the 2D-EOD deflection. Ideally, collimate the beam by focusing the beam to a point at least 20 m away.</li>
	</ol>
	</li>
	<li>Place a pinhole (Figure 1: PH) of appropriate size at the focus of the first collimation lens (Figure 1: L1).
	<ol>
		<li>Choose an appropriately sized pinhole. The pinhole size can be chosen based on the following calculation: 
		<img alt="Equation 1" src="/files/ftp_upload/56711/56711eq1.jpg" /> 
		Where &#955; is the wavelength of laser, f is the focal length of the first lens, and D is the input beam diameter.</li>
		<li>Mount the pinhole on a 3D translation stage so that it can be placed precisely at the focus of the first collimation lens.</li>
		<li>Adjust the position of the pinhole. Place a laser power meter after the pinhole and adjust the pinhole position in XYZ to maximize the power meter readout. If a diffraction ring is observed, block it with an iris placed before the 2D-EOD.</li>
	</ol>
	</li>
	<li>Installation of Glan-Thompson polarizer
	<ol>
		<li>Put the Glan-Thompson polarizer (Figure 1: GP) after the second collimation lens (Figure 1: L2) to clean the polarization of the laser beam. Rotate the polarizer to find maximum transmission with the power meter.</li>
	</ol>
	</li>
	<li>Installation of EODs
	<ol>
		<li>Use 2 EODs (Figure 1: EOD1 &#38; EOD2) to deflect the laser in the X and Y directions. Maximize the laser transmission by adjusting the yaw, pitch, and height of each EOD.</li>
		<li>Align the two EODs with respect to each other using the alignment marker provided by the manufacturer on the side of each deflector.</li>
		<li>Apply a test pattern to the controller of the 2D-EOD to inspect the output of the EOD. This can either be the knight&#39;s tour (Figure 2) via the FPGA described below or a simple sine wave provided by a function generator. For the best performance, the deflection by each deflector should be parallel to either the X or Y direction of the piezo nanopositioner. This direction is dictated by the angular direction of the deflectors about the transmission axis. To rotate the deflection direction without moving the 2D-EOD, place a dove prism after the 2D-EOD to align the deflection axes to the piezo nanopositioner axes.</li>
	</ol>
	</li>
	<li>Installation of half-wave plate
	<ol>
		<li>Place a half-wave plate (Figure 1: W1) between the Glan-Thompson polarizer (Figure 1: GP) and the 2D-EOD to align the incoming laser beam polarization with the deflection axes of the 2D-EOD. Place a long focal lens (300 mm) after the 2D-EOD and place a sCMOS camera at the laser focus. Next, turn on the 2D-EOD to generate the knight&#39;s tour described below (Figure 2). Rotate the half-wave plate until an evenly square laser distribution is observed on the camera.</li>
	</ol>
	</li>
	<li>Installation of a beam expending lens pair and relay lens pair
	<ol>
		<li>Place a pair of lenses (Figure 1: L3 and L4) after the 2D-EOD to expand the beam to fill the back aperture of the objective lens (Figure 1: OL) and another pair of lenses (Figure 1: L5 and L6) to relay the focal plane to the microscope objective. Be sure to leave enough room between these two pairs of lenses as the TAG lens will be installed between them in a later step.</li>
	</ol>
	</li>
	<li>Install the dichroic filter, 10/90 beam splitter, focusing lens, APD, objective lens, and fluorescence emission filter to complete the detection pathway.
	<ol>
		<li>Install the dichroic filter (Figure 1: DC) to reflect the laser towards the objective lens. Align the laser to go straight through the center of the objective lens using two irises on the top and bottom of a long lens tube.</li>
		<li>Install a 10/90 beam splitter (Figure 1: BS) by which 10% of light is reflected for imaging by an sCMOS camera (described below) and 90% of passes through to the APD for tracking.</li>
		<li>Align the APD. Focus the laser onto a coverslip by the objective lens. Use the reflection from the coverslip to align all downstream elements by putting a coverslip at the objective focus and checking the intensity of the APD readout. After the beam splitter, install the APD focusing lens (Figure 1: L7) followed by the APD on a 3D translation stage. Position the lens such that the laser reflection from the objective goes through the center of the lens. The APD position can be optimized by adjusting the 3D position to maximize the intensity from the laser reflection on the coverslip. The APD position is at the optimal position when a move of the detector position in any direction decreases the intensity.</li>
		<li>Install a longpass fluorescence emission filter (Figure 1: F) before the beam splitter to remove the reflected and scattered light.</li>
	</ol>
	</li>
	<li>Installation of the TAG lens
	<ol>
		<li>Place the TAG lens between the two pairs of lenses (between Figure 1: L4 and L5) as mentioned before and shown in Figure 1. Adjust the yaw, pitch, height, and lateral position such that the beam passes perpendicularly through the center of TAG lens.</li>
	</ol>
	</li>
</ol>
2. Sample Preparation.
<ol>
	<li>Fixed particle preparation
	<ol>
		<li>Dilute 190 nm fluorescent beads to ~ 5 &#215; 108 beads/mL in PBS. Add 400 &#181;L beads solution onto a coverslip and mount on the sample holder of the piezoelectric stage (the volume of beads will depend on the sample holder; the diameter of the sample holder chamber used here is 18 mm). For the beads used here, PBS causes the particles to deposit on the coverslip.</li>
	</ol>
	</li>
	<li>Free moving particle preparation
	<ol>
		<li>Dilute 110 nm fluorescent beads to ~ 5 &#215; 108 beads/mL with DI water. Add 400 &#181;L beads solution onto a coverslip and mount onto the sample holder of the piezoelectric stage.</li>
	</ol>
	</li>
</ol>
3. Optimize Tracking Parameters
<ol>
	<li>Raster scan fixed particles
	<ol>
		<li>Put a fixed particle sample on the microscope.</li>
		<li>Turn on the laser, micropositioner controller, piezo nanopositioner controller, TAG lens controller, and EOD controller. Note that the order is not critical. Run the piezo nanopositioner in closed loop.</li>
		<li>Raster scan the sample in XYZ using a custom scanning software program (Figure S3, software available upon request from the authors), which drives the 2D-EOD in a knight&#39;s tour (Figure 2), collects counts from the APD, drives the piezo nanopositioner, and performs position calculation (Figure S2). The step size is 40 nm and the scanning range is 2 &#181;m.
		<ol>
			<li>To place the coverslip at the focus of the objective, remove the fluorescence emission filter and use the reflection of the laser from the coverslip to maximize the signal intensity as a function of the Z position of the micropositioner. After placing the sample at the focal plane, place back the fluorescence emission filter.</li>
			<li>Open the scanning program. Set the scanning range and step size by typing the number in the &#39;start&#39;, &#39;finish&#39;, and &#39;step&#39;. First set a large scanning range and step size to locate a particle (e.g., 10 x 10 &#181;m range with 200 nm step size). After finding the particle, shrink the scanning range and decrease step size (e.g., 2 x 2 &#181;m range with 100 nm step size). Click the &#39;scan&#39; button to perform a 3D raster scan to find the fluorescent focus.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>TAG lens settings (see also Figure 3)
	<ol>
		<li>Click on the TAG lens control software. Click &#39;connect&#39;, &#39;power on&#39; sequentially.</li>
		<li>To change the output phase of the trigger signals, it is necessary to change the output trigger mode. First change the mode from &#39;RGB&#39; to &#39;Multiplane&#39; and then change it back. Now the phase can be changed (Figure 3b).</li>
		<li>Set the output phase to be 0 &#176;, 90 &#176;, and 270 &#176;. While any three phases which cover the phase space can be used, these three are found to work well empirically (Figure 3c).</li>
		<li>Select the 68,500 Hz frequency setting.</li>
		<li>To find the optimal frequency it is often necessary to change the frequency search range. Click &#39;advanced&#39;, &#39;setting&#39;. Change the &#39;Max. Freq(Hz)&#39; of column 0 from 70,000 Hz to 71,500 Hz. This can be adjusted to whatever frequency range is appropriate. Click &#39;Save Calibration&#39;, &#39;Exit Calibration&#39; (Figure 3d).</li>
		<li>To make the new calibration effective, switch the resonance to another frequency (for example, 189,150 Hz) and then switched back to the 68,500 Hz frequency setting (Figure 3e).</li>
		<li>Change the amplitude gradually to 35%. Click &#39;Lock Resonance&#39;. After the frequency is locked, click &#39;Unlock Resonance&#39; (Figure 3e). The TAG lens is ready to use now.</li>
		<li>To calibrate, change the parameters that are used in the estimation to make the estimated position equal to the real position, as shown in Figure 4e, f. Input the scanning range of x, y, and z and then click the &#39;scan&#39; button to scan the sample in XYZ to move the particle through the moving laser spot. The position of the particle as it is scanned through the laser focus should agree with the estimated particle position determined by the position estimation loop (Figure S1). The relationship between the estimated position and real position (Figure 4d) can be extracted from estimated position images (Figure 4f). If the positions do not agree, adjust the values of laser position (ck) used in the position estimation loop (Figure S1).</li>
		<li>Align TAG lens
		<ol>
			<li>When the TAG lens controller is off, the raster scanned particle images (described below) taken before installing TAG lens and after installing TAG lens should look identical. Next, perform fluorescent particle Z stack scanning by moving the objective with the z nanopositioner to tune the position and angle of TAG lens (Figure 4). Tune the position of the TAG lens until there is no drift in the particle images in XY plane along the Z direction, which is achieved when there is no change in the XY position of the particle as a function of the Z position (Figure 4d). The tracking system is ready to use after alignment of the TAG lens.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Installation of sCMOS camera for particle monitoring
	<ol>
		<li>Install a sCMOS camera to visualize particles while they are being tracked. Install the sCMOS only after the all the components of the tracking system have been installed and optimized. Load a fixed fluorescent particle sample onto the microscope and run the tracking program to lock one particle in the objective focal volume. Then, install the 100 mm focal length lens (Figure 1: L8) and sCMOS. Adjust the sCMOS position so that the image of the particle is focused to the smallest and brightest spot.</li>
	</ol>
	</li>
</ol>
4. Real-time 3D Tracking of Freely Diffusing Nanoparticles
<ol>
	<li>Put the 110 nm free moving particle sample on the microscope.</li>
	<li>Turn on laser, micro-positioner controller, piezo nanopositioner controller, TAG lens controller, and EOD controller. Run the piezo nanopositioner in open loop. Set the TAG lens software according to step 3.2.</li>
	<li>Open and run the tracking software (available upon request from the authors). Set the position estimation parameters to their optimized values, which were found in Section 3.</li>
	<li>To place the coverslip at the focus of the objective, remove the fluorescence emission filter and use the reflection of the laser from the coverslip to maximize the signal intensity as a function of the Z position of the micropositioner. After finding the coverslip, increase the focus position 15 &#181;m to focus the laser away from the coverslip and into the solution. Place back the fluorescence emission filter.</li>
	<li>Set the integral control constants. The integral control constants can be set starting at a low value and slowly increasing until oscillations can be seen in the particle&#39;s position readouts. Once oscillations are observed, set the integral control constants to 80% of the value causing the oscillations. Typical values will be around 0.012 for the XY &#39;integral gain&#39; and 0.004 for the Z &#39;integral gain&#39;.</li>
	<li>Set the tracking thresholds in &#39;Track Threshold&#39; and &#39;Track Minimum&#39; and start the tracking experiment by clicking &#39;Search&#39; and &#39;Auto Track&#39;. The threshold for terminating tracking is set slightly higher than the background level and the threshold for triggering tracking is about two times higher than the background. Here, the threshold for triggering tracking is 3 kHz and the threshold for ending the trajectory is 1.5 kHz.</li>
</ol>

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The authors declare no competing financial interests.

Although many varieties of 3D single particle tracking methods have emerged in recent years, robust real-time tracking of high speed 3D diffusion at low photon count rates with a simple setup is still a challenge, which limits its application to important biological problems. The 3D-DyPLoT method described in this protocol addresses these challenges in a several ways. First, the excitation and detection pathways are simplified greatly compared to other implementations making alignment simple and robust. Secondly, the moving laser spot and position estimation algorithm provide precise position estimates for the feedback loop, making the feedback more stable. Thirdly, the effectively large detection range (1 x 1 x 4 &#181;m) of the dynamically moving laser spot allows for tracking of fast moving particles. To see why this large detection area is critical, it is important to consider the intrinsic response time of the piezoelectric nanopositioner. High speed piezoelectric stages have resonances on the order of 1 kHz, limiting the response time to be on the order of 1 ms. In 1 ms, a 100 nm nanoparticle with a diffusion coefficient of 4 &#181;m2/s, will diffuse on average 90 nm from the center of the diffraction-limited detection volume. This is only the average displacement and truly random motion will exhibit steps much larger than this, leading to the particle leaving the focal volume and ending the current trajectory. The situation is even worse for smaller particles, where increasingly larger thermal diffusive steps lead to loss of tracking. The effectively large detection area allows for the tracking system to overcome intrinsic piezoelectric stage lag and recover from large diffusive jumps in the particle position, increasing the overall robustness of the tracking mechanism. Finally, the large scan area allows the system to easily pick up new particles, allowing for consecutive trajectories to be rapidly acquired and large data sets compiled.
The user should keep in mind that there are some critical steps. First, the alignment of both the 2D-EOD and the TAG lens are critical. Both must be well aligned to obtain the optimum precision. Second, the parameters that are used in the position estimation in the fixed particle scanning step must be well calibrated (see Figure 4). The estimated particle position should match the particle position corresponding to the center of the laser scan range. Finally, the feedback integral constants (KI) should be tuned carefully, starting with a small value and then ramping up until oscillations are observed, then backing off to about 80% of that value.
There are a few caveats about 3D-DyPLoT to keep in mind depending on the desired application. While the optimized position estimation is designed for use with Brownian motion, it is also well suited towards directional motion. The algorithm can be directly applied to any type of motion since it is only the position uncertainty that is assumed to be Gaussian, not the real particle position. For cases where persistent linear motion is expected, an additional term can be added to the prediction term in the position estimation algorithm (see Equation 3 in the Supplementary Information).
The selection of the parameters for the 2D-EOD and TAG should be carefully considered when setting up the 3D-DyPLoT. Of particular importance is the bin time used for the knight&#39;s tour scanned by the 2D-EOD. In an ideal scenario, the knight&#39;s tour would be synchronized to the period of the TAG lens to ensure efficient sampling. However, it is critical to consider the response time of the 2D-EOD to step changes in voltage. For the unit used here, there is a 2-3 &#181;s response time after the voltage is applied before the desired spot is reached. At 20 &#181;s bin time, this is about 10-15% of the collection time. Reducing the time so that it matches the TAG lens period of ~ 14 &#181;s increases the fraction of the bin time, which is the lag time of the 2D-EOD. This leads to incorrect values of the laser position and incorrect position estimates.
Another factor for the experimenter to keep in mind is temporal resolution. While the data shown here are collected at a 100 kHz data rate, the ultimate temporal resolution is defined ultimately by which data are used for the position measurement. If the readout of the nanopositioner is used as the particle position (Figure 6), the temporal resolution will depend on the value of the integral control constant. For example, for the integral control shown in Figure 6a-d, the stage response is on the order of 1 ms, while for Figure 6e-h, it is on the order of 10 ms. If faster temporal resolution is desired, then the photon-by-photon result of the position estimation algorithm will correlate with the photon count rate. For example, a 10 kHz emission rate yields 100 &#181;s temporal resolution and a 100 kHz emission rate yields a 10 &#181;s temporal resolution. In addition, since the precision follows a <img alt="Equation 2" src="/files/ftp_upload/56711/56711eq2.jpg" /> relationship41, the spatial and temporal precision are coupled. As a result, the brightness of the probe determines both the spatial and temporal precision.
In this protocol we described the setup, layout, and alignment of a high speed real-time 3D single particle tracking method which utilizes a 2D-EOD and TAG lens to dynamically move a focused laser spot to achieve rapid particle position estimation. We then described sample preparation and parameter optimization methods. 3D-DyPLoT provides a robust and relatively simple method to lock-on to fast moving and lowly emitting diffusive particles. The simple optical layout allows it to be easily added onto the side-port of any existing microscope stand so that it might be combined with imaging modules. With this protocol, we hope that RT-3D-SPT can be more widely implemented by more investigators to address fast, three dimensional biological processes.

The emergence of advanced imaging techniques has opened a window to see ever more detailed structure of cellular phenomena, all the way down to the molecular level. Methods such as stochastic optical reconstruction microscopy (STORM)1,2,3, photo-activated localization microscopy (PALM)4,5,6,7, structured illumination microscopy (SIM)8,9,10,11, and stimulated emission depletion microscopy (STED)12,13,14 have gone far beyond the diffraction limit to deliver unprecedented detail into the structure and function of live cells. However, full understanding of how these systems behave requires dynamic information as well as structural information. The super-resolution methods listed above involve a trade-off between spatial resolution and temporal resolution, limiting the temporal precision with which dynamic processes can be probed. A method which provides both high spatial precision and temporal resolution is RT-3D-SPT15,16,17,18,19,20,21,22,23,24,25,26,27,28,29. Here, we draw a distinction between traditional 3D-SPT30 and RT-3D-SPT. Traditional 3D-SPT simply requires a time series of three-dimensional image data (which can be acquired either using a confocal microscope or an epifluorescence microscope given the right configuration). In traditional 3D-SPT, the coordinates of the particle are determined after data collection by locating the particle in each image stack and concatenating the locations in successive volumes to create a trajectory. For these methods, the ultimate temporal resolution is determined by the volumetric imaging rate. For confocal microscopes, this is easily on the scale of seconds to tens of seconds. For epifluorescence methods, wherein the optical path is manipulated so that the axial location information can be extracted, the temporal resolution is limited by the camera exposure or readout time. These epifluorescent methods are limited in the range over which axial information can be collected, though recent progress in Fourier plane phase masks design and adaptive optics is extending these ranges to 10 &#181;m or more31,32,33,34.
In contrast, RT-3D-SPT does not rely on acquiring a 3D image stack and finding particles after the fact. Instead, real-time location information is extracted via single point detectors and feedback is applied to effectively &#34;lock&#34; the particle in the focal volume of the objective lens through the use of a highspeed piezoelectric stage. This allows continuous measurement of the particle&#39;s position limited only by how many photons can be collected. Moreover, this method enables spectral interrogation of the particle as it moves over long ranges. RT-3D-SPT in effect works akin to a force-free optical trap for nanoscale objects, wherein the particle is continuously probed and measured in real-time without the need for large laser powers or optical forces. Given that RT-3D-SPT provides a means for continuous interrogation of fast diffusive objects (up to 20 &#181;m2/s)25,29 in three dimensions at low photon count rates20,29,35, it should provide a window into fast or transient biological processes such as intracellular cargo transport, ligand-receptor binding, and the extracellular dynamics of single virions. However, to this point, the application of RT-3D-SPT has been limited to the handful of groups working to advance this technology.
One barrier is the complexity of the optical layout required by RT-3D-SPT methods, which are varied. For most methods, the optical feedback is provided by a piezoelectric stage. As the particle makes small movements in X, Y, or Z, readouts from single point detectors are converted to error functions and fed at high-speed to a piezoelectric nanopositioner, which in turn moves the sample to counteract the particle&#39;s motion, effectively locking it in place relative to the objective lens. To measure small positional movements in X, Y, and Z, either multiple detectors (4 or 5 depending on the implementation)15,18,21 or multiple excitation spots (2 - 4, the lower of which can be applied if a lock-in amplifier is used to extract X and Y position using a rotating laser spot)25,28 are applied. The overlap of these multiple detection and emission spots make the systems difficult to align and maintain.
Herein, we present a high-speed target-locked 3D-SPT method with a simplified optical design called 3D-DyPLoT29. 3D-DyPLoT uses a 2D-EOD and a TAG lens36,37,38 to dynamically move a focused laser spot through the objective focal volume at a high rate (50 kHz XY, 70 kHz Z). Combining the laser focus position and the photon arrival time enables the particle&#39;s 3D position to be rapidly obtained even at low photon count rates. The 2D-EOD drives the laser focus in a knight&#39;s tour pattern39 with a square size of 1 x 1 &#181;m in the X-Y plane and the TAG lens moves the laser focus in axial direction with a range of 2 - 4 &#181;m. The 3D particle position is obtained with an optimized position estimation algorithm29,40 in 3D. The control of the 3D dynamically moving laser spot, photon counting from the avalanche photodiode (APD), real-time particle position calculation, piezoelectric stage feedback, and data recording are performed on a field programmable gate array (FPGA). In this protocol, we describe how to build a 3D-DyPLoT microscope step-by-step, including optical alignment, calibration with fixed particles, and finally free particle tracking. As a demonstration, 110 nm fluorescent beads were tracked continuously in water for minutes at a time.
The method described herein is an ideal choice for any application where it is desired to continuously monitor a fast-moving fluorescent probe at low light levels, including viruses, nanoparticles, and vesicles such as endosomes. In contrast to previous methods, there is only a single excitation and single detection pathway, making alignment and maintenance straightforward. Furthermore, the large detection area enables this microscope to easily pick up quickly diffusing particles, while the ability to track at low signal levels (down to 10 kHz) makes this method ideal for low-light applications29.

<table><tbody><tr><td>2D Electro-optic Deflector&amp;nbsp;</td><td>ConOptics</td><td>M310A</td><td>2 required</td></tr><tr><td>Power supply for EOD</td><td>ConOptics</td><td>412</td><td>Converts FPGA ouput to high voltage for EOD</td></tr><tr><td>TAG&amp;nbsp; Lens</td><td>TAG Optics</td><td>TAG 2.0</td><td>Used to deflect laser along axial direction</td></tr><tr><td>XY piezoelectric nanopositioner</td><td>MadCity Labs</td><td>Nano-PDQ275HS</td><td>Used for moving the sample to lock the particle in the objective focal volume in&amp;nbsp;</td></tr><tr><td>Z piezoelectric nanoposiitoner</td><td>MadCity Labs</td><td>Nano-OP65HS</td><td>Used to move the objective lens to follow the diffusing particle</td></tr><tr><td>Micropositioner</td><td>MadCity Labs</td><td>MicroDrive</td><td>Used to coarsely position sample and evaluate</td></tr><tr><td>Objective Lens</td><td>Zeiss</td><td>PlanApo</td><td>High numerical aperture required for best sensitivity. 100X, 1.49 NA, M27, Zeiss</td></tr><tr><td>sCMOS camera</td><td>PCO</td><td>pco.edge 4.2</td><td>Used to monitor the particle&#039;s position</td></tr><tr><td>APD</td><td>Excelitas</td><td>SPCM-ARQH-15</td><td>Lower dark counts beneficial</td></tr><tr><td>Field programmable gate array</td><td>National Instruments</td><td>NI-7852r</td><td></td></tr><tr><td>Software</td><td>National Instruments</td><td>LabVIEW</td><td></td></tr><tr><td>Tracking excitation laser</td><td>JDSU</td><td>FCD488-30</td><td></td></tr><tr><td>Lens</td><td>ThorLabs</td><td>AC254-150-A-ML</td><td>L1</td></tr><tr><td>Lens</td><td>ThorLabs</td><td>AC254-200-A-ML</td><td>L2</td></tr><tr><td>Pinhole</td><td>ThorLabs</td><td>P75S</td><td>PH</td></tr><tr><td>Glan-Thompson Polarizer</td><td>ThorLabs</td><td>GTH5-A</td><td>GT</td></tr><tr><td>Half-wave plate</td><td>ThorLabs</td><td>WPH05M-488</td><td>WP</td></tr><tr><td>Lens</td><td>ThorLabs</td><td>AC254-75-A-ML</td><td>L3</td></tr><tr><td>Lens</td><td>ThorLabs</td><td>AC254-250-A-ML</td><td>L4</td></tr><tr><td>Lens</td><td>ThorLabs</td><td>AC254-200-A-ML</td><td>L5</td></tr><tr><td>Lens</td><td>ThorLabs</td><td>AC254-200-A-ML</td><td>L6</td></tr><tr><td>Dichroic Mirror</td><td>Chroma</td><td>ZT405/488/561/640rpc</td><td>DC</td></tr><tr><td>Fluorescence Emission Filter</td><td>Chroma</td><td>D535/40m</td><td>F</td></tr><tr><td>10/90 beamsplitter</td><td>Chroma</td><td>21012</td><td>BS</td></tr><tr><td>PBS</td><td>Sigma</td><td>D8537</td><td></td></tr><tr><td>190 nm fluorescent nanoparticles</td><td>Bangs laboratories</td><td>FC02F/9942</td><td></td></tr><tr><td>110 nm fluorescent nanoparticles</td><td>Bangs laboratories</td><td>FC02F/10617</td><td></td></tr><tr><td>Coverslip</td><td>Fisher Scientific</td><td>12-545A</td><td></td></tr><tr><td>Powermeter</td><td>Thorlabs</td><td>PM100D</td><td></td></tr><tr><td>CMOS</td><td>Thorlabs</td><td>DCC1545M</td><td></td></tr><tr><td>Iris</td><td>Thorlabs</td><td>SM1D12D</td><td></td></tr><tr><td>Microscope</td><td>Mad City Labs</td><td>RM21</td><td></td></tr></tbody></table>

a protocol for real-time 3d single particle tracking

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although various RT-3D-SPT methods have been put forward in recent years, tracking high speed 3D diffusing particles at low photon count rates remains a challenge. Moreover, RT-3D-SPT setups are generally complex and difficult to implement, limiting their widespread application to biological problems. This protocol presents a RT-3D-SPT system named 3D Dynamic Photon Localization Tracking (3D-DyPLoT), which can track particles with high diffusive speed (up to 20 &#181;m2/s) at low photon count rates (down to 10 kHz). 3D-DyPLoT employs a 2D electro-optic deflector (2D-EOD) and a tunable acoustic gradient (TAG) lens to drive a single focused laser spot dynamically in 3D. Combined with an optimized position estimation algorithm, 3D-DyPLoT can lock onto single particles with high tracking speed and high localization precision. Owing to the single excitation and single detection path layout, 3D-DyPLoT is robust and easy to set up. This protocol discusses how to build 3D-DyPLoT step by step. First, the optical layout is described. Next, the system is calibrated and optimized by raster scanning a 190 nm fluorescent bead with the piezoelectric nanopositioner. Finally, to demonstrate real-time 3D tracking ability, 110 nm fluorescent beads are tracked in water.

Fixed particle scanning (Figure 4) and freely diffusing 110 nm fluorescent particle tracking (Figure 5) were performed following the protocol above. The particle scanning was performed by moving the piezoelectric nanopositioner and bin photons while simultaneously calculating the particle&#39;s estimated position at each point in the scan. The scanning image shows a square of even intensity (Figure 4a) and the estimated positions show a linear relationship with the particle&#39;s real position over a 1 &#215; 1 &#215; 2 &#181;m range in x, y, and z direction (Figure 4b-f).
To demonstrate real-time tracking, 110 nm fluorescent particles were tracked in water by 3D-DyPLoT (Figure 5a, b). The mean square displacement (MSD) analysis shows a typical linear behavior characteristic of Brownian motion (Figure 5c). MSD analysis of 30 trajectories showed a mean hydrodynamic diameter of 110 nm, in good agreement with the manufacturers specification for the size of the fluorescent nanoparticles being tracked (Figure 5d). In addition, Movie 1 shows the real-time piezoelectric nanopositioner readouts and synchronized sCMOS images for a 2 min long trajectory of a freely diffusing 110 nm fluorescent particle.
In addition to being able to track with high speed, slow moving particles can be localized with high precision. Figure 6a-d shows the application of the 3D-DyPLoT system to fixed particles using the same feedback parameters as used for high speed tracking, showing a precision of 17.6, 26.4, and 53.4 nm in X, Y, and Z, respectively with photon count rate of 105. Figure 6e-h shows the precision under feedback control reduced by a factor of 10, effectively swapping speed for precision and exhibiting a precision of 6.5, 8.3, and 10.5 nm in X, Y and Z, respectively.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56711/56711fig1.jpg" /> 
Figure 1. Schematic of 3D tracking system. The 2D-EOD (EOD1 &#38; EOD2) and the TAG lens (TAG) deflect the laser along the XY and Z directions, respectively. The APD (APD) is used to collect fluorescence photons, which are sent to the FPGA (FPGA). FPGA is used for the position calculation algorithm, photon counting, control of the 2D-EOD as well as control and readout of piezoelectric nanopositioner (NSxy and NZz). Other components labeled in the figure: mirrors (M); lenses (L#); pinhole (PH); Glan-Thompson polarizer (GP); half-wave plate (W1); dichroic filter (DC); objective lens (OL, 100X NA = 1.49); fluorescence emission filter (F); beam splitter (BS); XY micropositioner stage (MSxy); Z micropositioner stage (MSz). <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/56711fig1large.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/56711/56711fig2.jpg" /> 
Figure 2. Coordinates for the knight&#39;s tour implemented in 3D-DyPLoT. Axis 1 and axis 2 should be aligned along the X or Y axis by proper alignment of the 2D-EOD. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/56711fig2large.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/56711/56711fig3.jpg" /> 
Figure 3. TAG lens software settings. See section 4.2 TAG lens settings for more information. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/56711fig3large.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/56711/56711fig4.jpg" /> 
Figure 4. Particle scanning and position estimation. (a) Scanning image of 190 nm fluorescent beads with 2D-EOD driving the laser in a 1 &#215; 1 &#181;m square knight&#39;s tour pattern. Fluorescence intensity is denoted by color. Unit: kHz. (b) Estimation of xk, the particle&#39;s position relative to the center of the 2D-EOD scan in micrometers. The color in b, c, and d denotes the estimated position. Unit: &#181;m. (c) Estimation of yk in micrometers. (d) Estimation of zk, the particle&#39;s position relative to the axial center of the TAG lens scan in micrometers. (e) Estimated particle position yk as a function of the stage position averaged over the entire grid from (c). Note that the estimated particle position acquired from the position estimation algorithm (yk) agrees with the real position. (f) Estimated particle position zk as a function of the stage position averaged over the entire grid from (d). The estimated position shows a linear relationship with particle&#39;s real position over 1 &#215; 1 &#215; 2 &#181;m range in X, Y, and Z direction. Note that the estimated particle position acquired from the position estimation algorithm (zk) agrees with the real position. The estimated position shows a linear relationship with particle&#39;s real position over a 1 &#215; 1 &#215; 2 &#181;m range in X, Y, and Z direction. The white scale bars in (a-c) represent 500 nm and the black scale bar in (d) represents 2 &#181;m. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/56711fig4large.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/56711/56711fig5.jpg" /> 
Figure 5. Tracking 110 nm fluorescent particles in water with 3D-DyPLOT. (a) 3D trajectory of a freely diffusing 110 nm fluorescent nanoparticle in water. (b) Fluorescent intensity as a function of time for the trajectory in (a). (c) MSD of the trajectory in (a). The blue line is the measured MSD while the dotted red line is best fit line from linear regression. (d) MSD analysis of 30 trajectories, showing a mean hydrodynamic diameter of 110 nm, in good agreement with the size of the fluorescent nanoparticles being tracked. The diameter of the particles was calculated using the Stokes-Einstein relation. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/56711fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/56711/56711fig6.jpg" /> 
Figure 6. Precision of fixed particle tracking. (a) A fixed particle is tracked by 3D-DyPLoT at different count rates. The precision is (b) 17.6 nm in X, (c) 26.4 nm in Y, and (d) 53.4 nm in Z at an emission rate of 100 kHz. (e) When probing slower processes, the feedback control constant (KI) can be reduced by a factor of 10 to increase precision. Under this reduced feedback control, the precision is (f) 6.5 nm in X, (g) 8.3 nm in Y, and (h) 10.5 nm in Z at an emission rate of 100 kHz. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/56711fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure S1. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/fig_S1_position_estimation-01.pdf">Please click here to download this file.</a>
Figure S2. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/fig_s2_TAG_operation.pdf">Please click here to download this file.</a>
Figure S3. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/fig_s3_software.pdf">Please click here to download this file.</a>
Supplementary Information. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56711/170802_software_description.pdf">Please click here to download this file.</a>

Watch this Scientific Journal Video about A Protocol for Real-time 3D Single Particle Tracking at JoVE.com

A Protocol for Real-time 3D Single Particle Tracking

This protocol details the construction and operation of a real-time 3D single particle tracking microscope capable of tracking nanoscale fluorescent probes at high diffusive speeds and low photon count rates.

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although ...

a-protocol-for-real-time-3d-single-particle-tracking

Universidad San Sebastian

Research

JoVE Journal

Bioengineering

14.8K Views.  Duke University. This protocol details the construction and operation of a real-time 3D single particle tracking microscope capable of tracking nanoscale fluorescent probes at high diffusive speeds and low photon count rates.

Video: A Protocol for Real-time 3D Single Particle Tracking

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a relatively low cost. This manuscript describes an application of particle tracking velocimetry (PTV) to a bed-load experiment with lightweight sediment. The key characteristics of the investigated sediment transport conditions were the presence of a covered flow and of a fixed rough bed above which particles were released in limited number at the flume inlet. Under the applied flow conditions, the motion of the individual bed-load particles was intermittent, with alternating movement and stillness terms. The flow pattern was preliminarily characterized by acoustic measurements of vertical profiles of the stream-wise velocity. During process visualization, a large field of view was obtained using two action-cameras placed at different locations along the flume. The experimental protocol is described in terms of channel calibration, experiment realization, image pre-processing, automatic particle tracking, and post-processing of particle track data from the two cameras. The presented proof-of-concept results include probability distributions of the particle hop length and duration. The achievements of this work are compared to those of existing literature to demonstrate the validity of the protocol.

The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a ...

Environment

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

3D-PTV is a quantitative flow measurement technique that aims to track the Lagrangian paths of a set of particles in three dimensions using stereoscopic recording of image sequences. The basic components, features, constraints and optimization tips of a 3D-PTV topology consisting of a high-speed camera with a four-view splitter are described and discussed in this article. The technique is applied to the intermediate flow field (5 &#60;x/d &#60;25) of a circular jet at Re &#8776; 7,000. Lagrangian flow features and turbulence quantities in an Eulerian frame are estimated around ten diameters downstream of the jet origin and at various radial distances from the jet core. Lagrangian properties include trajectory, velocity and acceleration of selected particles as well as curvature of the flow path, which are obtained from the Frenet-Serret equation. Estimation of the 3D velocity and turbulence fields around the jet core axis at a cross-plane located at ten diameters downstream of the jet is compared with literature, and the power spectrum of the large-scale streamwise velocity motions is obtained at various radial distances from the jet core.

A three-dimensional particle tracking velocimetry (3D-PTV) system based on a high-speed camera with a four-view splitter is described here. The technique is applied to a jet flow from a circular pipe in the vicinity of ten diameters downstream at Reynolds number Re &#8776; 7,000.

3D-PTV is a quantitative flow measurement technique that aims to track the Lagrangian paths of a set of particles in three dimensions using stereoscopic ...

Engineering

Preparation and 3D Tracking of Catalytic Swimming Devices

We report a method to prepare catalytically active Janus colloids that "swim" in fluids and describe how to determine their 3D motion using fluorescence microscopy. One commonly deployed method for catalytically active colloids to produce enhanced motion is via an asymmetrical distribution of catalyst. Here this is achieved by spin coating a dispersed layer of fluorescent polymeric colloids onto a flat planar substrate, and then using directional platinum vapor deposition to half coat the exposed colloid surface, making a two faced "Janus" structure. The Janus colloids are then re-suspended from the planar substrate into an aqueous solution containing hydrogen peroxide. Hydrogen peroxide serves as a fuel for the platinum catalyst, which is decomposed into water and oxygen, but only on one side of the colloid. The asymmetry results in gradients that produce enhanced motion, or "swimming". A fluorescence microscope, together with a video camera is used to record the motion of individual colloids. The center of the fluorescent emission is found using image analysis to provide an x and y coordinate for each frame of the video. While keeping the microscope focal position fixed, the fluorescence emission from the colloid produces a characteristic concentric ring pattern which is subject to image analysis to determine the particles relative z position. In this way 3D trajectories for the swimming colloid are obtained, allowing swimming velocity to be accurately measured, and physical phenomena such as gravitaxis, which may bias the colloids motion to be detected.

A method to prepare catalytically active Janus colloids that can "swim" in fluids and determine their 3D trajectories is presented.

We report a method to prepare catalytically active Janus colloids that "swim" in fluids and describe how to determine their 3D motion using fluorescence ...

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and millisecond temporal resolution. These capabilities make single-molecule localization microscopy ideally suited to study molecular level biological functions in physiologically relevant environments. Here, we demonstrate an integrated protocol for both acquisition and processing/analysis of single-molecule tracking data to extract the different diffusive states a protein of interest may exhibit. This information can be used to quantify molecular complex formation in living cells. We provide a detailed description of a camera-based 3D single-molecule localization experiment, as well as the subsequent data processing steps that yield the trajectories of individual molecules. These trajectories are then analyzed using a numerical analysis framework to extract the prevalent diffusive states of the fluorescently labeled molecules and the relative abundance of these states. The analysis framework is based on stochastic simulations of intracellular Brownian diffusion trajectories that are spatially confined by an arbitrary cell geometry. Based on the simulated trajectories, raw single-molecule images are generated and analyzed in the same way as experimental images. In this way, experimental precision and accuracy limitations, which are difficult to calibrate experimentally, are explicitly incorporated into the analysis workflow. The diffusion coefficient and relative population fractions of the prevalent diffusive states are determined by fitting the distributions of experimental values using linear combinations of simulated distributions. We demonstrate the utility of our protocol by resolving the diffusive states of a protein that exhibits different diffusive states upon forming homo- and hetero-oligomeric complexes in the cytosol of a bacterial pathogen.

3D single-molecule localization microscopy is utilized to probe the spatial positions and motion trajectories of fluorescently labeled proteins in living bacterial cells. The experimental and data analysis protocol described herein determines the prevalent diffusive behaviors of cytosolic proteins based on pooled single-molecule trajectories.

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and ...

Biochemistry

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., &#946;2-adrenergic and &#947;-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., ...

3D Orbital Tracking in a Modified Two-photon Microscope: An Application to the Tracking of Intracellular Vesicles

The objective of this video protocol is to discuss how to perform and analyze a three-dimensional fluorescent orbital particle tracking experiment using a modified two-photon microscope1. As opposed to conventional approaches (raster scan or wide field based on a stack of frames), the 3D orbital tracking allows to localize and follow with a high spatial (10 nm accuracy) and temporal resolution (50 Hz frequency response) the 3D displacement of a moving fluorescent particle on length-scales of hundreds of microns2. The method is based on a feedback algorithm that controls the hardware of a two-photon laser scanning microscope in order to perform a circular orbit around the object to be tracked: the feedback mechanism will maintain the fluorescent object in the center by controlling the displacement of the scanning beam3-5. To demonstrate the advantages of this technique, we followed a fast moving organelle, the lysosome, within a living cell6,7. Cells were plated according to standard protocols, and stained using a commercially lysosome dye. We discuss briefly the hardware configuration and in more detail the control software, to perform a 3D orbital tracking experiment inside living cells. We discuss in detail the parameters required in order to control the scanning microscope and enable the motion of the beam in a closed orbit around the particle. We conclude by demonstrating how this method can be effectively used to track the fast motion of a labeled lysosome along microtubules in 3D within a live cell. Lysosomes can move with speeds in the range of 0.4-0.5 &#181;m/sec, typically displaying a directed motion along the microtubule network8.

In this video protocol we track - at high speed and in three dimensions - fluorescently labeled lysosomes within living cells, using the orbital tracking method in a modified two-photon microscope.

The objective of this video protocol is to discuss how to perform and analyze a three-dimensional fluorescent orbital particle tracking experiment using a ...

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature

We present a method for using temperature to tune the flow speeds of kinesin-driven, microtubule-based three-dimensional (3D) active fluids. This method allows for tuning the speeds in situ without the need to manufacture new samples to reach different desired speeds. Moreover, this method enables the dynamic control of speed. Cycling the temperature leads the fluids to flow fast and slow, periodically. This controllability is based on the Arrhenius characteristic of the kinesin-microtubule reaction, demonstrating a controlled mean flow speed range of 4&#8211;8 &#181;m/s. The presented method will open the door to the design of microfluidic devices where the flow rates in the channel are locally tunable without the need for a valve.

The goal of this protocol is to use temperature to control the flow speeds of three-dimensional active fluids. The advantage of this method not only allows for regulating flow speeds in situ but also enables dynamic control, such as periodically tuning flow speeds up and down.

We present a method for using temperature to tune the flow speeds of kinesin-driven, microtubule-based three-dimensional (3D) active fluids. This method ...

Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics

Short-lived or transient interactions of macromolecules at and with lipid membranes, an interface where a multitude of essential biological reactions take place, are inherently difficult to assess with standard biophysical methods. The introduction of mass-sensitive particle tracking (MSPT) constitutes an important step toward a thorough quantitative characterization of such processes. Technically, this was made possible through the advent of interferometric scattering microscopy (iSCAT)-based mass photometry (MP). When the background removal strategy is optimized to reveal the two-dimensional motion of membrane-associated particles, this technique allows the real-time analysis of both diffusion and molecular mass of unlabeled macromolecules on biological membranes. Here, a detailed protocol to perform and analyze mass-sensitive particle tracking of membrane-associated systems is described. Measurements performed on a commercial mass photometer achieve time resolution in the millisecond regime and, depending on the MP system, a mass detection limit down to 50 kDa. To showcase the potential of MSPT for the in-depth analysis of membrane-catalyzed macromolecule dynamics in general, results obtained for exemplary protein systems such as the native membrane interactor annexin V are presented.

This protocol describes an iSCAT-based image processing and single-particle tracking approach that enables the simultaneous investigation of the molecular mass and the diffusive behavior of macromolecules interacting with lipid membranes. Step-by-step instructions for sample preparation, mass-to-contrast conversion, movie acquisition, and post-processing are provided alongside directions to prevent potential pitfalls.

Short-lived or transient interactions of macromolecules at and with lipid membranes, an interface where a multitude of essential biological reactions take ...

3D Particle Tracking for Noninvasive In Vivo Analysis of Synaptic Microtubule Dynamics in Dendrites and Neuromuscular Junctions of Drosophila

Microtubules (MTs) play critical roles in neuronal development, but many questions remain about the molecular mechanisms of their regulation and function. Furthermore, despite progress in understanding postsynaptic MTs, much less is known about the contributions of presynaptic MTs to neuronal morphogenesis. In particular, studies of in vivo MT dynamics in Drosophila sensory dendrites yielded significant insights into polymer-level behavior. However, the technical and analytical challenges associated with live imaging of the fly neuromuscular junction (NMJ) have limited comparable studies of presynaptic MT dynamics. Moreover, while there are many highly effective software strategies for automated analysis of MT dynamics in vitro and ex vivo, in vivo data often necessitate significant operator input or entirely manual analysis due to inherently inferior signal-to-noise ratio in images and complex cellular morphology. &#160;To address this, this study optimized a new software platform for automated and unbiased in vivo particle detection. Multiparametric analysis of live time-lapse confocal images of EB1-GFP labeled MTs was performed in both dendrites and the NMJ of Drosophila larvae and found striking differences in MT behaviors. MT dynamics were furthermore analyzed following knockdown of the MT-associated protein (MAP) dTACC, a key regulator of Drosophila synapse development, and identified statistically significant changes in MT dynamics compared to wild type. These results demonstrate that this novel strategy for the automated multiparametric analysis of both pre- and postsynaptic MT dynamics at the polymer-level significantly reduces human-in-the-loop criteria. The study furthermore shows the utility of this method in detecting distinct MT behaviors upon dTACC-knockdown, indicating a possible future application for functional screens of factors that regulate MT dynamics in vivo. Future applications of this method may also focus on elucidating cell type and/or compartment-specific MT behaviors, and multicolor correlative imaging of EB1-GFP with other cellular and subcellular markers of interest.&#160;

This study presents a noninvasive intravital neuronal imaging strategy combined with a new software strategy to achieve automated, unbiased tracking and analysis of in vivo microtubule (MT) plus-end dynamics in the sensory dendrites and the neuromuscular junctions of Drosophila.

Microtubules (MTs) play critical roles in neuronal development, but many questions remain about the molecular mechanisms of their regulation and function. ...

Neuroscience

Protocol for Analysis and Consolidation of TrackMate Outputs for Measuring Two-Dimensional Cell Motility using Nuclear Tracking

Collective cellular migration plays a key role in many fundamental biological processes including development, wound healing, and cancer metastasis. To understand the regulation of cell motility, we must be able to measure it easily and consistently under different conditions. Here we describe a method for measuring and quantifying single-cell and bulk motility of HaCaT keratinocytes using a nuclear stain. This method includes a MATLAB script for analyzing TrackMate output files to calculate displacements, motility rates, and trajectory angles in single cells and in bulk for an imaging site. This motility analysis script allows for quick, straightforward, and scalable analysis of cell motility rates from TrackMate data and could be broadly used to identify and study the regulation of motility in epithelial cells. We also provide a MATLAB script for reorganizing microscopy videos collected on a microscope and converting them to TIF stacks, which can be analyzed using the ImageJ TrackMate plugin in bulk. Using this methodology to explore the roles of adherens junctions and actin cytoskeletal dynamics in regulating cell motility in HaCaT keratinocytes, we demonstrate evidence that Arp2/3 activity is required for the elevated motility seen after &#945;-catenin depletion in HaCaT keratinocytes.

This protocol for high-throughput measurement of cell motility in HaCaT keratinocytes describes methods for collecting and processing images of cell nuclei and performing particle tracking using the ImageJ plugin TrackMate.

Collective cellular migration plays a key role in many fundamental biological processes including development, wound healing, and cancer metastasis. To ...