This work was supported by the National Natural Science Foundation of China with grant numbers of 21425519, 91853105 and 21621003.

1. Cell culture
<ol>
	<li>Prepare complete medium for U87 MG cells by adding fetal bovine serum (final concentration 10%) and penicillin-streptomycin (final concentration 1%) to the minimum essential medium (MEM). Use plastic cell culture dish for cells subculture.</li>
	<li>Passage cells 2 to 3 times a week.
	<ol>
		<li>Remove the culture medium and rinse the cell layer with Dulbecco&#39;s phosphate-buffered saline (D-PBS) 2~3 times when confluent (80%~90%).</li>
		<li>Add 1.0 to 2.0 mL of Trypsin-EDTA solution to the cell culture dish and observe cells under an inverted microscope until cells become round (3~5 min).</li>
		<li>Add 3.0 mL of prepared complete medium and disperse the cells by gently pipetting.</li>
		<li>Add cell suspension (1 mL) to new culture dish with fresh cell medium (3 mL) and resuspend the cells.</li>
	</ol>
	</li>
	<li>Maintain the cells at 37 &#176;C and 5% CO2 in a humidified atmosphere.</li>
</ol>
2. Microscope slide preparation
NOTE: U87 MG cells of third to tenth generation with high activity are used in SPT experiments.
<ol>
	<li>Sterilize 22 mm &#215; 22 mm coverslips already cleaned with Piranha solution by immersing in ethanol (99.9%).</li>
	<li>Use forceps to take out the cover slip from the ethanol solution (step 2.1) and sterilize by burning ethanol on the flame. Once all ethanol is burnt, place coverslips in a plastic cell culture dish (35 x 10 mm) filled with 2 mL of cell medium (no phenol red).</li>
	<li>Add 50 &#181;L of the cell suspension from step 1.2.3 on the coverslip and gently push the dish back and forth and left and right to evenly distribute the cells. Place it in a humidified atmosphere.</li>
	<li>When U87 MG cells on the coverslip reach 20%&#8211;40% confluency (~12 h), add 20 &#956;L of CTAB-AuNRs (138 pM) into the dish and disperse. Incubate in humidified atmosphere for 5 min.</li>
	<li>Add 100 &#956;L of the culture medium (no phenol red) from dish in step 2.4 into the groove of the grooved glass slide (Figure 1) which is precleaned with Piranha solution.</li>
	<li>Take the coverslip out from the dish and inverted on the top of the groove of the microscope glass slide (Figure 1). Seal with nail polish, let it dry and place it on the stage to perform SPT experiments.</li>
</ol>
3. Performing single particle tracking experiments with darkfield microscopy (Figure 1).
<ol>
	<li>Place a drop of oil on the oil-immersed darkfield condenser (NA 1.43-1.20) and turn the knob to make the condenser contact the glass slide.</li>
	<li>Put a drop of oil on the top of the cover glass and turn the focusing knob to make the 60x oil immersion objective (NA 0.7&#8211;1.25) touch the oil.</li>
	<li>Turn on the light source and slightly turn the focusing knob to focus the imaging plane. 
	NOTE: In the field of view, the background is black, the cells are bright, and the CTAB-AuNRs (aspect ratio~2:1, Figure 2) are small colored (red, yellow, or green) scattering spots.</li>
	<li>Capture sample scattering light by a color CMOS camera. Click &#8220;Camera icon&#8221; in the software to record and export TIFF format to save images.</li>
</ol>
4. Data acquisition
<ol>
	<li>Extract single long-term trajectory
	<ol>
		<li>Operate as described in Figure 3 to convert the time-series dark-field images from &#8220;RGB color&#8221; mode to &#8220;8 bit&#8221; mode. In the Image J click Image | Type | 8 bit. To adjust the contrast, click Image | Adjust | Brightness | Contrast.</li>
		<li>Select a target particle and cutoff the time-series backgrounds by boxing and deleting the background with &#34;Ctrl+X&#34;.</li>
		<li>Open the particle detection and particle Linking window by clicking the &#8220;Plugins | Particle Tracker Classic | Particle Tracker&#8221;.</li>
		<li>Set Radius to 6, Cutoff to 0 and Percentile to 0.01%. 
		NOTE: To detect the particle, adjust above three parameters with the assistance of Preview. Ensure that radius is slightly larger than the targeted particle and smaller than the smallest inter-particle separation. Percentile is the lower limit of intensity distribution that to be candidate particles.</li>
		<li>Set the Link Range to 5 and Displacement to 10. 
		NOTE: To link the particle between consecutive adjacent frames, adjust the above two parameters. Displacement is the maximum pixels that a particle can move between two succeeding frames, and Link Range is the number of consecutive frames to consider when determining the best corresponding match.</li>
		<li>Click &#8220;OK&#8221; to open the ParticleTracker Results window to see the results.</li>
		<li>Click &#8220;Visualize All Trajectories&#8221; to inspect the generated trajectories.</li>
		<li>Click &#8220;Relink Particles&#8221; menu at the top to re-link the detected particles with different link range and percentile parameters, if the software generated trajectory does not match the moving trajectory of the AuNR.</li>
		<li>Click &#8220;Save Full Report&#8221; to save results if the software generated trajectory and the moving trajectory of the AuNR are matched. 
		NOTE: An example of extracting single long-term trajectory with Image J is shown in Figure 4.</li>
	</ol>
	</li>
	<li>Extracting Multi-trajectories 
	NOTE: An example of extracting multi-trajectories with Fiji is shown in Figure 5. There are several stages, and each stage constitutes a step in the tracking process. The result of each step is displayed immediately, which allows the user to go back to readjust settings when the output is unsatisfactory.
	<ol>
		<li>Operate as described in Figure 3 to convert the time-series dark-field images from &#8220;RGB color&#8221; mode to &#8220;8 bit&#8221; mode. In the Image J pathway click &#8220;Image | Type | 8 bit&#8221;. To adjust their contrast, click &#8220;Image | Adjust | Brightness | Contrast&#8221;.</li>
		<li>Open the start panel by clicking &#8220;Plugins | Tracking | TrackMate&#8221;. Click &#8220;Next&#8221;.</li>
		<li>Choose the &#8220;LoG detector&#8221; from the drop-down menu and click &#8220;Next&#8221;.</li>
		<li>Adjust parameters of the detector configuration panel. Set Estimated blob diameter to 10, set Threshold to 0, and Select &#8220;Do Sub-pixel Localization&#8221;.</li>
		<li>Click &#8220;Next&#8221; to open initial spot filtering panel. 
		NOTE: Several parameters can be adjusted to further optimize the target points. In this example, no other parameters were adjusted.</li>
		<li>Click &#8220;Next&#8221; and select &#8220;HyperStack displayer&#8221; from the drop-down menu.</li>
		<li>Click &#8220;Next&#8221; to open Spot filtering panel. Set the Quality above 1.88, X above 38.86, Y above 56.54.</li>
		<li>Click &#8220;Next&#8221; and select &#8220;Simple LAP tracker&#8221; from the drop-down menu. Configure the simple LAP tracker by adjusting three parameters, that is, the Linking max distance = 15, Gap-closing max distance = 15 and Gap-closing max frame gap = 5. 
		NOTE: Linking-max distance is the maximum displacement of a point between two frames. Gap-closing max distance is the maximum displacement of two segments. Gap-closing max frame gap is the largest frame between two points to be bridged.</li>
		<li>Click &#8220;Next&#8221; to track. When finished, continue to click &#8220;Next&#8221;.</li>
		<li>Set filters on tracks, such as set Number of spots in track above 300.</li>
		<li>Keep clicking &#8220;Next&#8221; until the final save panel open. Select &#8220;Export tracks to XML file&#8221; from the drop-down menu, and then click &#8220;Execute&#8221; to save in csv format.</li>
	</ol>
	</li>
	<li>R/G values 
	NOTE: Scattering intensities of targeted AuNRs in the R and G channels are obtained from color darkfield images by using a code written in MATLAB (https://github.com/fenggeqd/JOVE-2020/tree/master/RGandPolarangle), and the extraction principle is presented in Figure 6.
	<ol>
		<li>Use the function of xycoordination.m to find the center pixel coordinate of the AuNR in each frame according to the x-y coordinate (extracted by ImageJ/Fiji).</li>
		<li>Use the function of RGextraction.m to delimit a 3 x 3 pixels matrix, extract the 9 scattering-intensity values of R or G channels, and calculate an average value (&#956;, defined as R or G). 
		NOTE: The 3 x 3 pixels matrix is centered on the pixel coordinates obtained from step 4.3.1.</li>
	</ol>
	</li>
	<li>Polar angle 
	NOTE: The polar angle is the angle between the longitude of the AuNR and the optical axis (as shown in Figure 1), which can reflect the spatial (Z axis) rotation dynamics of the AuNR.
	<ol>
		<li>Use the function of polarangle.m to calculate the polar angles (&#952;) by the dual-channel differential method22, <img alt="Equation 11" src="/files/ftp_upload/61603/61603equ10.jpg" />.</li>
	</ol>
	</li>
</ol>
5. Data analysis
NOTE: A systematic and robust data analysis framework is essential for the performance and efficiency of SPT analysis methods. The custom software written in MATLAB is used (https://github.com/fenggeqd/JOVE-2020/tree/master/Analysis_parameters). A graphing and analysis software (see Table of Materials) is used for drawing the plots.
<ol>
	<li>Analysis parameters
	<ol>
		<li>Use scripts of csv_data_extract_dis_vel_ss.m and csv_data_MSD.m to calculate dynamic parameters according to formulas shown in Table 1. 
		NOTE: These parameters are used to analyze the dynamics of AuNRs and consist of three parts. (1) Trajectory related parameters: displacement, step size, velocity, radius of gyration (Rg), and turning angle (Ta); (2) MSD parameters: diffusion coefficient (Dt) and abnormal diffusion exponent (&#945;); and (3) Rotation related parameters: polar angle and rotational lability (&#963;).</li>
	</ol>
	</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Measures</td>
			<td colspan="2">Definition</td>
			<td>Physical meaning</td>
		</tr>
		<tr>
			<td>Displacement</td>
			<td colspan="2"><img alt="Equation 1" src="/files/ftp_upload/61603/61603equ01.jpg" /></td>
			<td>Changes in position of objects</td>
		</tr>
		<tr>
			<td>Step size</td>
			<td colspan="2"><img alt="Equation 2" src="/files/ftp_upload/61603/61603equ02.jpg" /></td>
			<td>Distance between two adjacent points</td>
		</tr>
		<tr>
			<td>Velocity</td>
			<td colspan="2"><img alt="Equation 3" src="/files/ftp_upload/61603/61603equ03.jpg" /></td>
			<td>Speed of objects motion</td>
		</tr>
		<tr>
			<td>Rg</td>
			<td colspan="2"><img alt="Equation 4" src="/files/ftp_upload/61603/61603equ04.jpg" /></td>
			<td>Moving range of objects in a specific time interval</td>
		</tr>
		<tr>
			<td>Ta</td>
			<td colspan="2"><img alt="Equation 5" src="/files/ftp_upload/61603/61603equ05.jpg" /></td>
			<td>Motion direction of objects between two adjacent points</td>
		</tr>
		<tr>
			<td>MSD</td>
			<td colspan="2"><img alt="Equation 6" src="/files/ftp_upload/61603/61603equ06.jpg" /></td>
			<td>Average moving distance of objects in a specific time interval</td>
		</tr>
		<tr>
			<td>Dt</td>
			<td colspan="2"><img alt="Equation 7" src="/files/ftp_upload/61603/61603equ07.jpg" /></td>
			<td>Diffusion ability of objects</td>
		</tr>
		<tr>
			<td>&#945;</td>
			<td colspan="2"><img alt="Equation 8" src="/files/ftp_upload/61603/61603equ07.jpg" /></td>
			<td>Normal diffusion (&#945;~1)</td>
		</tr>
		<tr>
			<td>Polar angle</td>
			<td colspan="2"><img alt="Equation 9" src="/files/ftp_upload/61603/61603equ08.jpg" /></td>
			<td>3D orientation information of objects</td>
		</tr>
		<tr>
			<td>&#963;</td>
			<td colspan="2"><img alt="Equation 10" src="/files/ftp_upload/61603/61603equ09.jpg" /></td>
			<td>Degree of dispersion of the polar angle data set</td>
		</tr>
	</tbody>
</table>
Table 1: Three types of parameters used for analysis. These include trajectory related parameters (displacement, step size, velocity, Rg and Ta), MSD parameters (MSD, Dt and &#945;) and rotation related parameters (polar angle and rotational lability).
<ol start="2">
	<li>Visual analysis of trajectory 
	NOTE: Trajectory visualization can intuitively present the spatiotemporal heterogeneity of particle motion and the trajectory (coordinate) distribution of dynamic parameters, such as time-mapping trajectory, Rg- and Dt-mapping trajectory, and polar-angle mapping trajectory. Mapping trajectories were drawn using the graphing and analysis software.
	<ol>
		<li>Set x coordinate as X, y coordinate as Y, and time (Rg, Dt, polar angle) as Z.</li>
		<li>Click &#8220;Plot | Scatter plot | Color-mapping Plot&#8221;.</li>
		<li>Add the color bar.</li>
	</ol>
	</li>
	<li>MSD analysis 
	NOTE: Motion activity and motion mode of the particles can be obtained by MSD analysis23. The larger the Dt, the more active the diffusion motion of the particles. when &#945;~1, particles do normal diffusion motion, otherwise they perform abnormal diffusion motion.
	<ol>
		<li>MSD-&#964; figures
		<ol>
			<li>Set time interval (&#964;) as X, MSD data as Y.</li>
			<li>Click &#8220;Plot | Scatter plot&#8221;</li>
			<li>Fit data by clicking &#8220;Analyze | Fitting | Nonlinear curve fitting (Function: Allometricl)&#8221;.</li>
		</ol>
		</li>
		<li>MSD-&#964; double-logarithmic figure 
		NOTE: MSD-&#964; double-logarithmic figure, whose slope is &#945; and intercept is Dt, can intuitively present particle motion.
		<ol>
			<li>Set logarithmic time interval as X, and logarithmic MSD as Y.</li>
			<li>Click &#8220;Plot | Scatter plot&#8221;.</li>
			<li>Fit data by clicking &#8220;Analyze | Fitting | Linear curve fitting&#8221;.</li>
		</ol>
		</li>
		<li>D-&#964; figure (MSD/4t) 
		NOTE: D=MSD/4&#964;, is a function of time &#964; and anomaly factor &#945;, and the D-&#964; figure directly shows the change of diffusion coefficient with time. When D increases with time, &#945; is greater than 1 and particles do superdiffusion motion.
		<ol>
			<li>Set logarithmic time interval (&#964;) as X, and logarithmic D as Y.</li>
			<li>Click &#8220;Plot | Scatter plot&#8221;.</li>
			<li>Fit data by clicking &#8220;Analyze | Fitting | Nonlinear curve fitting (Function: &#8220;Allometricl&#8221;)&#8221;. 
			NOTE: The shorter the trajectories, the higher the inaccuracy of the diffusion estimates. In general, Dt and &#945; through MSD-&#964; analysis of long interval time (&#62; 30 &#964;) were obtained. However, simple and rough fitting will smooth out motion details. Hence, MSD-&#964; analysis of short interval time (&#60; 10 &#964;) should be performed to analyze the motion behavior of particles in a short time.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Statistical analysis
	<ol>
		<li>Multi-particles statistical analysis 
		NOTE: Multi-particles statistical analysis can reflect the motion state of particles in a spatial region, which indirectly indicates the spatial heterogeneity environment. For instance, if the histogram of Dt exhibits a large-scale distribution or multi-peaks distribution, it means that the motion activities of particles are heterogeneous.
		<ol>
			<li>Set dynamic parameters (such as Dt, Rg, max displacement) as Y.</li>
			<li>Click &#8220;Plot | Histogram&#8221;.</li>
			<li>Double click the histogram, and set division size or the number of divisions. Click &#8220;Apply&#8221;.</li>
		</ol>
		</li>
		<li>Single-particle statistical analysis 
		NOTE: The statistical analysis of single particles can show the motion behavior of individual particles, which also indirectly reflects the spatiotemporal heterogeneity of the surrounding environment.
		<ol>
			<li>Multiple frames
			<ol>
				<li>Calculate dynamic parameters (such as Ta, step size, polar angle) of all frames of single long trajectory, and copy to Origin table and set as Y.</li>
				<li>Click &#8220;Plot | Histogram&#8221;.</li>
				<li>Double click the histogram, and set division size or the number of divisions. Click &#8220;Apply&#8221;. 
				NOTE: If both Ta and step size exhibit a small value distribution, the particles do a small-step super-diffusion motion.</li>
			</ol>
			</li>
			<li>Moving windows
			<ol>
				<li>Calculate dynamic parameters (such as Rg, Dt) of all frames of single long trajectory through moving-window method (11 frames), and copy to Origin table and set as Y.</li>
				<li>Click &#8220;Plot | Histogram&#8221;.</li>
				<li>Double click the histogram, and set division size or the number of divisions. Click &#8220;Apply&#8221;.</li>
			</ol>
			</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Time-series analysis 
	NOTE: Statistical analysis can reveal the motion state of NPs, and time-series analysis can present the motion behavior as a supplement. Combining several time-series parameters, it can discriminate the motion behavior of NPs at the temporal and spatial levels.
	<ol>
		<li>Set time as X, time-series parameters as Y (such as displacement, polar angle and Rg).</li>
		<li>Click &#8220;Plot | Multi-pane diagram | Stacked plot | Line + Symbol&#8221;.</li>
	</ol>
	</li>
</ol>

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Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three+Dimensional+Orientational+Imaging+of+Nanoparticles+with+Darkfield+Microscopy.">Three Dimensional Orientational Imaging of Nanoparticles with Darkfield Microscopy.</a> Analytical Chemistry. 82, 5268-5274 (2010).</li><li>Ge, F., Xue, J., Wang, Z., Xiong, B., He, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time+observation+of+dynamic+heterogeneity+of+gold+nanorods+on+plasma+membrane+with+darkfield+microscopy.">Real-time observation of dynamic heterogeneity of gold nanorods on plasma membrane with darkfield microscopy.</a> Science China Chemistry. 62, 1072-1081 (2019).</li><li>Tinevez, J. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TrackMate:+An+open+and+extensible+platform+for+single-particle+tracking.">TrackMate: An open and extensible platform for single-particle tracking.</a> Methods. 115, 80-90 (2017).</li><li>Janczura, J., Weron, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ergodicity+testing+for+anomalous+diffusion:+small+sample+statistics.">Ergodicity testing for anomalous diffusion: small sample statistics.</a> The Journal of Chemical Physics. 142 (14), 144103 (2015).</li><li>Kim, D. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single+particle+tracking-based+reaction+progress+kinetic+analysis+reveals+a+series+of+molecular+mechanisms+of+cetuximab-induced+EGFR+processes+in+a+single+living+cell.">Single particle tracking-based reaction progress kinetic analysis reveals a series of molecular mechanisms of cetuximab-induced EGFR processes in a single living cell.</a> Chemical Science. 8 (7), 4823-4832 (2017).</li><li>Kurzthaler, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Probing+the+Spatiotemporal+Dynamics+of+Catalytic+Janus+Particles+with+Single-Particle+Tracking+and+Differential+Dynamic+Microscopy.">Probing the Spatiotemporal Dynamics of Catalytic Janus Particles with Single-Particle Tracking and Differential Dynamic Microscopy.</a> Physical Review Letters. 121 (7), 078001 (2018).</li><li>Lin, X., Pan, Q., He, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+situ+detection+of+protein+corona+on+single+particle+by+rotational+diffusivity.">In situ detection of protein corona on single particle by rotational diffusivity.</a> Nanoscale. 11 (39), 18367-18374 (2019).</li><li>Wei, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sub-diffraction-limit+localization+imaging+of+a+plasmonic+nanoparticle+pair+with+wavelength-resolved+dark-field+microscopy.">Sub-diffraction-limit localization imaging of a plasmonic nanoparticle pair with wavelength-resolved dark-field microscopy.</a> Nanoscale. 9 (25), 8747-8755 (2017).</li><li>Cheng, X., Dai, D., Xu, D., He, Y., Yeung, E. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Subdiffraction-limited+plasmonic+imaging+with+anisotropic+metal+nanoparticles.">Subdiffraction-limited plasmonic imaging with anisotropic metal nanoparticles.</a> Analytical Chemistry. 86 (5), 2303-2307 (2014).</li><li>Belyy, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PhotoGate+microscopy+to+track+single+molecules+in+crowded+environments.">PhotoGate microscopy to track single molecules in crowded environments.</a> Nature Communication. 8, 13978 (2017).</li></ol>

The authors have nothing to disclose.

The presented protocol is used to study the dynamics of AuNRs on cell membrane. The protocol consists of four parts, including microscopic imaging, data extraction, dynamic parameters calculation and data analysis methods, and each part is flexible and universal. Therefore, there are many possible future applications, for instance, studying movement of NP-linked membrane molecules on membrane, endocytosis dynamics of NP-labeled receptors, dynamic analysis of intracellular NPs and vesicle-coated NPs transportation along m...

The dynamics of nanoparticles (NPs) on the membrane is closely associated with the cellular uptake process, which is essential for the understanding of cell functions, viral or bacterial infections and the development of artificial nanomedical delivery systems1,2. Single particle tracking (SPT) technique is a robust tool for characterizing the heterogeneous behaviors of NPs3,4. In general, cell membrane is fluidic, which means that the components such as proteins and lipids can move laterally in the plasma membrane plane5,6,7. The spatiotemporal complexity of membrane organization and structure may lead to spatiotemporal heterogeneity of the interaction between NPs and membrane. Hence, direct visualization of the movement of NPs on the membrane requires both high spatial and temporal resolution.
Single particle tracking microscopy that monitors the localization of individual particles in living cells with a spatial resolution of tens of nanometers and a time resolution of milliseconds has been well developed to study the NPs or membrane molecules dynamics8,9. Fluorescence-based microscopic imaging techniques have become valuable tools for observing NPs/molecules in living cell environment9,10,11,12. For example, total internal reflection fluorescence microscopy, which images thin layers (~100 nm) of the sample at the substrate/solution interface with a high spatiotemporal resolution has been widely used in studies of membrane molecules dynamics13,14. However, the inherent disadvantages of single fluorophores, such as low intensity and rapid irreversible photobleaching reduce the accuracy and duration of tracking13. Therefore, non-fluorescent plasmonic NPs, which replace the fluorescent probes, have attracted more and more attention in long-term imaging studies due to their unique optical characteristics15. Based on the scattering signals of plasmonic NP probes, several kinds of optical microscopic imaging technologies have been used to study the mechanism of biological processes, such as dark-field microscopy (DFM)16, interferometric scattering (iSCAT) microscopy17 and differential interference contrast microscopy (DICM)18. In addition, the motion and rotation dynamic of AuNRs can be obtained using DFM and DICM18,19,20,21,22. Typically, in an SPT experiment, the motion of the object is recorded by the optical microscope, and then analyzed by SPT analysis methods3. The time-resolved trajectories and orientational angles generated by individual NPs are normally stochastic and heterogeneous, so it is necessary to present abundant dynamic information with various analysis methods.
Here, we provide an integrated protocol that monitors the dynamics of AuNRs on cell membrane using DFM, extracts the location and orientation of AuNRs with ImageJ and MATLAB and characterizes the diffusion of AuNRs with SPT analysis methods. As a demonstration, we show here how to use the SPT protocol to visualize dynamics of unmodified AuNRs (CTAB-AuNRs, synthesized by cetyltrimethylammonium ammonium bromide molecule as protective agent) on U87 MG cell membrane. It has been demonstrated that CTAB-AuNRs can adsorb proteins in biological environment, move on cell membrane and then enter cells2,20,22. U87 MG cell is the most common and most malignant tumor of the central nervous system, and its membrane receptors are abnormally expressed. The membrane receptors can interact with proteins on AuNRs, which influence the dynamics of AuNRs. Our protocol is generally applicable to other SPT experiments in the field of biology.

<table>
	<tbody>
		<tr>
			<td>CTAB coated gold nanorods(CTAB-AuNRs)</td>
			<td>Nanoseedz</td>
			<td>NR-40-650</td>
			<td>85 nm * 40 nm</td>
		</tr>
		<tr>
			<td>Color CMOS camera</td>
			<td>Olympus</td>
			<td>DP74</td>
			<td>Japan</td>
		</tr>
		<tr>
			<td>Coverslips</td>
			<td>Citoglas</td>
			<td>z10212222C</td>
			<td>22*22 mm</td>
		</tr>
		<tr>
			<td>Dark-field microscopy</td>
			<td>Nikon</td>
			<td>80i</td>
			<td>upright microscope</td>
		</tr>
		<tr>
			<td>Fetal bovine serum (FBS)</td>
			<td>Gibco</td>
			<td>10099141</td>
			<td></td>
		</tr>
		<tr>
			<td>Fiji</td>
			<td>National Institutes of Health</td>
			<td>2.0.0-rc-69/1.52 p</td>
			<td>a distribution of ImageJ</td>
		</tr>
		<tr>
			<td>Grooved glass slide</td>
			<td>Sail brand</td>
			<td>7103</td>
			<td>Single concave</td>
		</tr>
		<tr>
			<td>Image J</td>
			<td>National Institutes of Health</td>
			<td>1.52 j</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>R2019b</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB Code</td>
			<td></td>
			<td></td>
			<td>https://github.com/fenggeqd/JOVE-2020</td>
		</tr>
		<tr>
			<td>Minimum essential medium (MEM)</td>
			<td>Gibco</td>
			<td>10-010-CVR</td>
			<td>with phenol red</td>
		</tr>
		<tr>
			<td>Minimum essential medium (MEM)</td>
			<td>Gibco</td>
			<td>51200038</td>
			<td>no phenol red</td>
		</tr>
		<tr>
			<td>Origin</td>
			<td>OriginLab</td>
			<td>Origin Pro 2018C</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-streptomycin</td>
			<td>Gibco</td>
			<td>15140122</td>
			<td></td>
		</tr>
		<tr>
			<td>Plastic cell culture dishes</td>
			<td>Falcon</td>
			<td>353002</td>
			<td></td>
		</tr>
		<tr>
			<td>Plastic cell culture dishes</td>
			<td>Falcon</td>
			<td>353001</td>
			<td>35*10 mm</td>
		</tr>
		<tr>
			<td>U87 MG cell</td>
			<td>American Type Culture Collection</td>
			<td>ATCC HTB-14</td>
			<td>a human primary glioblastoma cell line</td>
		</tr>
	</tbody>
</table>

visualizing diffusional dynamics of gold nanorods on cell membrane using single nanoparticle darkfield microscopy

Analyzing the diffusional dynamics of nanoparticles on cell membrane plays a significant role in better understanding the cellular uptake process and provides a theoretical basis for the rational design of nano-medicine delivery. Single particle tracking (SPT) analysis could probe the position and orientation of individual nanoparticles on cell membrane, and reveal their translational and rotational states. Here, we show how to use traditional dark-field microscopy to monitor the dynamics of gold nanorods (AuNRs) on live cell membrane. We also show how to extract the location and orientation of AuNRs using ImageJ and MATLAB, and how to characterize the diffusive states of AuNRs. Statistical analysis of hundreds of particles show that single AuNRs perform Brownian motion on the surface of U87 MG cell membrane. However, individual long trajectory analysis shows that AuNRs have two distinctly different types of motion states on the membrane, namely long-range transport and limited-area confinement. Our SPT methods can be potentially used to study the surface or intracellular particle diffusion in different biological cells and can become a powerful tool for investigations of complex cellular mechanisms.

In the protocol, the unmodified 40 x 85 nm CTAB-AuNRs were used. As shown in Figure 2B, its longitudinal plasmonic maximum at is ~650 nm (red region) and transverse resonance is at 520 nm (green region). Previous literatures have revealed that the optical properties (such as LSPR intensity) of plasmonic AuNRs will change significantly with their diameter20,22. In Figure 2C, the scattering intensity from ...

Watch this Scientific Journal Video about Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy at JoVE.com

Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy

Here, we show the use of traditional dark-field microscopy to monitor the dynamics of gold nanorods (AuNRs) on cell membrane. The location and orientation of single AuNRs are detected using ImageJ and MATLAB, and the diffusive states of AuNRs are characterized by single particle tracking analysis.

Analyzing the diffusional dynamics of nanoparticles on cell membrane plays a significant role in better understanding the cellular uptake process and ...

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Universidad de Cartagena UDEC

Research

JoVE Journal

Biochemistry

4.3K Views.  Tsinghua University. Here, we show the use of traditional dark-field microscopy to monitor the dynamics of gold nanorods (AuNRs) on cell membrane. The location and orientation of single AuNRs are detected using ImageJ and MATLAB, and the diffusive states of AuNRs are characterized by single particle tracking analysis.

Video: Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring cells. Of interest, only few assays are capable of specifically probing such interactions directly in living cells. Here, we present an assay to measure the binding of proteins expressed at the surfaces of neighboring cells, at cell-cell contacts. This assay consists of two steps: mixing of cells expressing the proteins of interest fused to different fluorescent proteins, followed by fluorescence fluctuation spectroscopy measurements at cell-cell contacts using a confocal laser scanning microscope. We demonstrate the feasibility of this assay in a biologically relevant context by measuring the interactions of the amyloid precursor-like protein 1 (APLP1) across cell-cell junctions. We provide detailed protocols on the data acquisition using fluorescence-based techniques (scanning fluorescence cross-correlation spectroscopy, cross-correlation number and brightness analysis) and the required instrument calibrations. Further, we discuss critical steps in the data analysis and how to identify and correct external, spurious signal variations, such as those due to photobleaching or cell movement.
In general, the presented assay is applicable to any homo- or heterotypic protein-protein interaction at cell-cell contacts, between cells of the same or different types and can be implemented on a commercial confocal laser scanning microscope. An important requirement is the stability of the system, which needs to be sufficient to probe diffusive dynamics of the proteins of interest over several minutes.

This protocol describes a fluorescence fluctuation spectroscopy-based approach to investigate interactions among proteins mediating cell-cell interactions, i.e. proteins localized in cell junctions, directly in living cells. We provide detailed guidelines on instrument calibration, data acquisition and analysis, including corrections to possible artefact sources.

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring ...

Probing The Structure And Dynamics Of Nucleosomes Using Atomic Force Microscopy Imaging

Chromatin, which is a long chain of nucleosome subunits, is a dynamic system that allows for such critical processes as DNA replication and transcription to take place in eukaryotic cells. The dynamics of nucleosomes provides access to the DNA by replication and transcription machineries, and critically contributes to the molecular mechanisms underlying chromatin functions. Single-molecule studies such as atomic force microscopy (AFM) imaging have contributed significantly to our current understanding of the role of nucleosome structure and dynamics. The current protocol describes the steps enabling high-resolution AFM imaging techniques to study the structural and dynamic properties of nucleosomes. The protocol is illustrated by AFM data obtained for the centromere nucleosomes in which H3 histone is replaced with its counterpart centromere protein A (CENP-A). The protocol starts with the assembly of mono-nucleosomes using a continuous dilution method. The preparation of the mica substrate functionalized with aminopropyl silatrane (APS-mica) that is used for the nucleosome imaging is critical for the AFM visualization of nucleosomes described and the procedure to prepare the substrate is provided. Nucleosomes deposited on the APS-mica surface are first imaged using static AFM, which captures a snapshot of the nucleosome population. From analyses of these images, such parameters as the size of DNA wrapped around the nucleosomes can be measured and this process is also detailed. The time-lapse AFM imaging procedure in the liquid is described for the high-speed time-lapse AFM that can capture several frames of nucleosome dynamics per second. Finally, the analysis of nucleosome dynamics enabling the quantitative characterization of the dynamic processes is described and illustrated.

Here, we present a protocol to characterize nucleosome particles at the single-molecule level using static and time-lapse atomic force microscopy (AFM) imaging techniques. The surface functionalization method described allows for the capture of the structure and dynamics of nucleosomes in high-resolution at the nanoscale.

Chromatin, which is a long chain of nucleosome subunits, is a dynamic system that allows for such critical processes as DNA replication and transcription ...

Single Liposome Measurements for the Study of Proton-Pumping Membrane Enzymes Using Electrochemistry and Fluorescent Microscopy

Proton-pumping enzymes of electron transfer chains couple redox reactions to proton translocation across the membrane, creating a proton-motive force used for ATP production. The amphiphilic nature of membrane proteins requires particular attention to their handling, and reconstitution into the natural lipid environment is indispensable when studying membrane transport processes like proton translocation. Here, we detail a method that has been used for the investigation of the proton-pumping mechanism of membrane redox enzymes, taking cytochrome bo3 from Escherichia coli as an example. A combination of electrochemistry and fluorescence microscopy is used to control the redox state of the quinone pool and monitor pH changes in the lumen. Due to the spatial resolution of fluorescent microscopy, hundreds of liposomes can be measured simultaneously while the enzyme content can be scaled down to a single enzyme or transporter per liposome. The respective single enzyme analysis can reveal patterns in the enzyme functional dynamics that might be otherwise hidden by the behavior of the whole population. We include a description of a script for automated image analysis.

Here, we present a protocol to study the molecular mechanism of proton translocation across lipid membranes of single liposomes, using cytochrome bo3 as an example. Combining electrochemistry and fluorescence microscopy, pH changes in the lumen of single vesicles, containing single or multiple enzyme, can be detected and analyzed individually.

Proton-pumping enzymes of electron transfer chains couple redox reactions to proton translocation across the membrane, creating a proton-motive force used ...

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a fluorescent dye and are spotted by a photodetector following their separation. Single molecule fluorescence imaging can provide ultrasensitive protein detection with its ability for visualizing individual fluorescent molecules. However, the application of this powerful imaging method to electrophoresis assays is hampered by the lack of ways to characterize the homogeneity of fluorescent labeling of each protein species across the proteome. Here, we developed a method to evaluate the labeling homogeneity across the proteome based on a single molecule fluorescence imaging assay. In our measurement using a HeLa cell sample, the proportion of proteins labeled with at least one dye, which we termed &#8216;labeling occupancy&#8217; (LO), was determined to range from 50% to 90%, supporting the high potential of the application of single molecule imaging to sensitive and precise proteome analysis.

Here, we present a protocol to assess the labeling homogeneity for each protein species in a complex protein sample at the single molecule level.

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a ...

Evaluation of Protein&#8211;Protein Interactions using an On-Membrane Digestion Technique

Numerous intracellular proteins physically interact in accordance with their intracellular and extracellular circumstances. Indeed, cellular functions largely depend on intracellular protein&#8211;protein interactions. Therefore, research regarding these interactions is indispensable to facilitating the understanding of physiologic processes. Co-precipitation of associated proteins, followed by mass spectrometry (MS) analysis, enables the identification of novel protein interactions. In this study, we have provided details of the novel technique of immunoprecipitation-liquid chromatography (LC)-MS/MS analysis combined with on-membrane digestion for the analysis of protein&#8211;protein interactions. This technique is suitable for crude immunoprecipitants and can improve the throughput of proteomic analyses. Tagged recombinant proteins were precipitated using specific antibodies; next, immunoprecipitants blotted onto polyvinylidene difluoride membrane pieces were subjected to reductive alkylation. Following trypsinization, the digested protein residues were analyzed using LC-MS/MS. Using this technique, we were able to identify several candidate associated proteins. Thus, this method is convenient and useful for the characterization of novel protein&#8211;protein interactions.

Here, we present a protocol for an on-membrane digestion technique for the preparation of samples for mass spectrometry. This technique facilitates the convenient analysis of protein&#8211;protein interactions.

Numerous intracellular proteins physically interact in accordance with their intracellular and extracellular circumstances. Indeed, cellular functions ...

Visualizing Surface T-Cell Receptor Dynamics Four-Dimensionally Using Lattice Light-Sheet Microscopy

The signaling and function of a cell are dictated by the dynamic structures and interactions of its surface receptors. To truly understand the structure-function relationship of these receptors in situ, we need to visualize and track them on the live cell surface with enough spatiotemporal resolution. Here we show how to use recently developed Lattice Light-Sheet Microscopy (LLSM) to image T-cell receptors (TCRs) four-dimensionally (4D, space and time) at the live cell membrane. T cells are one of the main effector cells of the adaptive immune system, and here we used T cells as an example to show that the signaling and function of these cells are driven by the dynamics and interactions of the TCRs. LLSM allows for 4D imaging with unprecedented spatiotemporal resolution. This microscopy technique therefore can be generally applied to a wide array of surface or intracellular molecules of different cells in biology.

The goal of this protocol is to show how to use Lattice Light-Sheet Microscopy to four-dimensionally visualize surface receptor dynamics in live cells. Here T cell receptors on CD4+ primary T cells are shown.

The signaling and function of a cell are dictated by the dynamic structures and interactions of its surface receptors. To truly understand the ...

Production of Dynein and Kinesin Motor Ensembles on DNA Origami Nanostructures for Single Molecule Observation

Cytoskeletal motors are responsible for a wide variety of functions in eukaryotic cells, including mitosis, cargo transport, cellular motility, and others. Many of these functions require motors to operate in ensembles. Despite a wealth of knowledge about the mechanisms of individual cytoskeletal motors, comparatively less is known about the mechanisms and emergent behaviors of motor ensembles, examples of which include changes to ensemble processivity and velocity with changing motor number, location, and configuration. Structural DNA nanotechnology, and the specific technique of DNA origami, enables the molecular construction of well-defined architectures of motor ensembles. The shape of cargo structures as well as the type, number and placement of motors on the structure can all be controlled. Here, we provide detailed protocols for producing these ensembles and observing them using total internal reflection fluorescence microscopy. Although these techniques have been specifically applied for cytoskeletal motors, the methods are generalizable to other proteins that assemble in complexes to accomplish their tasks. Overall, the DNA origami method for creating well-defined ensembles of motor proteins provides a powerful tool for dissecting the mechanisms that lead to emergent motile behavior.

The goal of this protocol is to form ensembles of molecular motors on DNA origami nanostructures and observe the ensemble motility using total internal reflection fluorescence microscopy.

Cytoskeletal motors are responsible for a wide variety of functions in eukaryotic cells, including mitosis, cargo transport, cellular motility, and ...

A Model Membrane Platform for Reconstituting Mitochondrial Membrane Dynamics

Mitochondrial dynamics is essential for the organelle&#8217;s diverse functions and cellular responses. The crowded, spatially complex, mitochondrial membrane is a challenging environment to distinguish regulatory factors. Experimental control of protein and lipid components can help answer specific questions of regulation. Yet, quantitative manipulation of these factors is challenging in cellular assays. To investigate the molecular mechanism of mitochondria inner-membrane fusion, we introduced an in vitro reconstitution platform that mimics the lipid environment of the mitochondrial inner-membrane. Here we describe detailed steps for preparing lipid bilayers and reconstituting mitochondrial membrane proteins. The platform allowed analysis of intermediates in mitochondrial inner-membrane fusion, and the kinetics for individual transitions, in a quantitative manner. This protocol describes the fabrication of bilayers with asymmetric lipid composition and describes general considerations for reconstituting transmembrane proteins into a cushioned bilayer. The method may be applied to study other membrane systems.

Mitochondrial fusion is an important homeostatic reaction underlying mitochondrial dynamics. Described here is an in vitro reconstitution system to study mitochondrial inner-membrane fusion that can resolve membrane tethering, docking, hemifusion, and pore opening. The versatility of this approach in exploring cell membrane systems is discussed.

Mitochondrial dynamics is essential for the organelle&#8217;s diverse functions and cellular responses. The crowded, spatially complex, mitochondrial ...

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk population methods (Northern blot, PCR, and RNAseq) that measure mRNA levels lack the ability to provide information on cell-to-cell variation in responses. Precise single cell and allelic visualization and quantification is possible via single molecule RNA fluorescence in situ hybridization (smFISH). RNA-FISH is performed by hybridizing target RNAs with labeled oligonucleotide probes. These can be imaged in medium/high throughput modalities, and, through image analysis pipelines, provide quantitative data on both mature and nascent RNAs, all at the single cell level. The fixation, permeabilization, hybridization and imaging steps have been optimized in the lab over many years using the model system described herein, which results in successful and robust single cell analysis of smFISH labeling. The main goal with sample preparation and processing is to produce high quality images characterized by a high signal-to-noise ratio to reduce false positives and provide data that are more accurate. Here, we present a protocol describing the pipeline from sample preparation to data analysis in conjunction with suggestions and optimization steps to tailor to specific samples.&#160;

Single molecule RNA fluorescence in situ hybridization (smFISH) is a method to accurately quantify levels and localization of specific RNAs at the single cell level. Here, we report our validated lab protocols for wet-bench processing, imaging and image analysis for single cell quantification of specific RNAs.

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk ...

Simultaneous Visualization of the Dynamics of Crosslinked and Single Microtubules In Vitro by TIRF Microscopy

Microtubules are polymers of &#945;&#946;-tubulin heterodimers that organize into distinct structures in cells. Microtubule-based architectures and networks often contain subsets of microtubule arrays that differ in their dynamic properties. For example, in dividing cells, stable bundles of crosslinked microtubules coexist in close proximity to dynamic non-crosslinked microtubules. TIRF-microscopy-based in vitro reconstitution studies enable the simultaneous visualization of the dynamics of these different microtubule arrays. In this assay, an imaging chamber is assembled with surface-immobilized microtubules, which are either present as single filaments or organized into crosslinked&#160;bundles. Introduction of tubulin, nucleotides, and protein regulators allows direct visualization of associated proteins and of dynamic properties of single and crosslinked microtubules. Furthermore, changes that occur as dynamic single microtubules organize into bundles can be monitored in real-time. The method described here allows for a systematic evaluation of the activity and localization of individual proteins, as well as synergistic effects of protein regulators on two different microtubule subsets under identical experimental conditions, thereby providing mechanistic insights that are inaccessible by other methods.

Simultaneous Visualization of the Dynamics of Crosslinked and Single Microtubules In Vitro by TIRF Microscopy

Here, a TIRF microscopy-based in vitro reconstitution assay is presented to simultaneously quantify and compare the dynamics of two microtubule populations. A method is described to simultaneously view the collective activity of multiple microtubule-associated proteins on crosslinked microtubule bundles and single microtubules.

Microtubules are polymers of &#945;&#946;-tubulin heterodimers that organize into distinct structures in cells. Microtubule-based architectures and ...

Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy

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A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

Probing The Structure And Dynamics Of Nucleosomes Using Atomic Force Microscopy Imaging

Single Liposome Measurements for the Study of Proton-Pumping Membrane Enzymes Using Electrochemistry and Fluorescent Microscopy

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Evaluation of Protein–Protein Interactions using an On-Membrane Digestion Technique

Visualizing Surface T-Cell Receptor Dynamics Four-Dimensionally Using Lattice Light-Sheet Microscopy

Production of Dynein and Kinesin Motor Ensembles on DNA Origami Nanostructures for Single Molecule Observation

A Model Membrane Platform for Reconstituting Mitochondrial Membrane Dynamics

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Simultaneous Visualization of the Dynamics of Crosslinked and Single Microtubules In Vitro by TIRF Microscopy