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Accurately determining single molecule trajectories of molecular motion on surfaces
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Image of FIG. 1.
FIG. 1.

(a) Schematic of the setup. A 532 nm laser was used for excitation. Fluorescence was collected by the objective and sent to an APD. A dichroic beam splitter and filter removed the 532 nm excitation light. To form an image, the sample was scanned and the fluorescence intensity stored using a surface probe controller with a photon counting board interfaced to a computer. (b) Chemical structure of the TRITC-tagged nanocar.

Image of FIG. 2.
FIG. 2.

(a)–(d) are images of nanocars acquired at times 0, 51, 179, and 204 s, respectively. This series shows the typical behaviors and challenges of molecule identification and tracking. The red arrow points to a stationary, nonblinking molecule. The circle highlights three molecules that move relative to each other. The boxed molecule shows both movement and blinking. Diffusion analysis for the boxed molecule and the molecule to which the arrow is pointing is shown below.

Image of FIG. 3.
FIG. 3.

An illustration of search radius variation. Initially (top) no associations were made; then as the search radius increased (middle) an increasing number of associations became possible. This continued until the search areas started to overlap (bottom), which complicated association.

Image of FIG. 4.
FIG. 4.

Two molecules were identified in one frame and a search radius was drawn around them. The molecules could then move into an overlapping region in the next frame. In cases (a) and (b), but not (c), it was possible to uniquely determine which molecules are associated with the previous molecule (shown in gray). These cases were generalized for an arbitrary number of molecules.

Image of FIG. 5.
FIG. 5.

Fluorescence images for high (a) and low (b) density polystyrene beads. Beads that were identified but did not meet the cutoff values have a yellow circle around them, while beads that were retained for further analysis have red circles. Beads too close to the edge were excluded from identification. The association efficiency vs search radius plots for the corresponding image sequences are shown in (c) and (d), respectively. Also included are simulations (light, solid lines) for beads with similar intensity, size, and density per image.

Image of FIG. 6.
FIG. 6.

Association efficiency for a time series of fluorescence images collected for TRITC (a) and TRITC tagged nanocars (b). The peak of the association efficiency for the nanocars was at a search radius of 5.5 pixels (vertical dotted line), which was significantly greater than the peak for TRITC. This effect was observed even though only 3 out of 12 nanocars were moving in the corresponding image. Both samples were of comparable density.

Image of FIG. 7.
FIG. 7.

(a) Trajectory of a moving nanocar from the data in Fig. 2. Inset: A stationary nanocar from the same data set on the same scale. The moving molecule corresponds to the boxed molecule, while the stationary molecule is the molecule to which the arrow is pointing in Fig. 2. The centroid of the stationary molecule stayed within the error bars of the entire trajectory, while the moving molecule clearly moved a greater distance than the error. (b) Squared displacement SD vs time. A linear fit yielded single molecule diffusion constants of and for the moving (solid) and stationary (dashed) molecules, respectively.

Image of FIG. 8.
FIG. 8.

Distributions of the of the diffusion constant . (a) Experimental distribution of diffusion constants for polystyrene beads, the fluorescent dye TRITC, and TRITC labeled nanocars. The bimodal distribution of the nanocars reflects the presence of moving and nonmoving molecules. (b) Simulated data for the three samples (see text for a full discussion of simulation parameters). The high S/N ratio of the beads allowed their positions to be accurately determined and was responsible for the low values of . The immobile TRITC molecules had higher values due only to noise, which introduced errors in locating the centroid positions. The simulated nanocars showed a bimodal distribution when assuming that of the nanocars were translating distances larger than the error of the centroid position.


Generic image for table
Table I.

Average single molecule diffusion constants . The cutoff between the stationary and moving nanocars was determined from Fig. 8 to be . Those with a lower were included in the average of the stationary nanocars, while those with a higher were included in the average of the moving nanocars. The value in parenthesis for TRITC corresponds to the simulation run with the lower S/N ratio.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Accurately determining single molecule trajectories of molecular motion on surfaces