Additional file 1:

Visualization of telomeres in live cells. (A) Cell cycle distribution of clonal cultures. Around 80% of the control parental cells are in G1 in these unsynchronized cultures. Clones expressing the green fluorescent protein (GFP)-TRF1 or GFP-TRF2 construct at low levels (GFP-TRF1 Clone 5 and GFP-TRF2 Clone 2) were chosen for use in the analyses in this work and were verified not to be different from those of the parental line in their cell cycle profile. (B) The intensity distribution of telomeres within a single representative nucleus of UMUC3 (upper right corner) shows a broad range. (C) One complete three-dimensional image stack of 17 z-sections was acquired every second (every third section shown). Green circles mark the telomeres tracked at that particular z-section. (D) Photobleaching graph of the image taking telomere intensity versus time shows a smooth reduction factor of two occurred during 200 seconds of imaging due to photobleaching. (E) Examples of nuclear trajectory images corrected for nuclear drift.

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Additional file 2:

Visualization of telomeres in UMUC3 mammalian cancer cells using OMX live imaging. The movie was recorded 10 frames/second. Around 40 to 60 telomeres were accurately located and tracked. The movie also shows a large variation of telomere intensities within a single nucleus.

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Additional file 3:

Visualization of telomeres in UMUC3 mammalian cancer cells using OMX live imaging, showing the large variability in telomere motion within a single nucleus. The movie was recorded 10 frames/second. Three UMUC3 nuclei were shown, the weaker expression cells were later chosen as clonal cell lines for minimal perturbation of telomeres during dynamic analysis.

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Additional file 4:

Visualization of telomeres in UMUC3 mammalian cancer cells using OMX live imaging, showing the large variability in telomere motion within a single nucleus. The movie is an enhanced-brightness picture of the bottom UMUC3 nucleus from Additional file 3. Visual inspection revealed heterogeneous telomeric motion within a single live cancer cell nucleus: some were moving rapidly, while others were moving at a slower speed.

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Additional file 5:

An example of visualization of telomere motion showing nuclear drift during image taking, using OMX live imaging. The movie was recorded 10 frames/second. Such nuclear drift was corrected for in the analyses.

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Additional file 6:

Six different ways of visualizing and quantifying telomere motions in live cells. (A) Kymographs (vertical axis is 10 frames/second), showing individual telomeres as tracks projected on to a two-dimensional image (shown) or as three-dimensional (3-D) (see Additional files 7 and 8). (B) A plot of individual telomere tracks showing the movement of each individual telomere as its projected position in the xy-plane (visualized as the horizontal plane) as a function of time T (vertical axis); this plot does not depict the changes with time in the position of the telomere in the z-axis, although the data were acquired. (C) A projection of the trajectory of each telomere showing its position in 3-D space as a function of time for 200 consecutive seconds; each green dot shows the distance path of the telomere traveled in 200 seconds. (D) For each telomere at time T, the end-to-end (E2E) distance in 3-D space the telomere has traveled from its original starting point position at time 0 (each line in the plot tracks the distance against time for an individual telomere, but the colors of the telomeres tracked are random). (E) The cumulative path distance traveled by a telomere between time 0 and time T 200 seconds. The line near the bottom of the x-axis indicates the distance a cell nucleus has drifted during imaging, which is corrected when quantifying telomere motion. (F) The average E2E distances for each telomere track. The E2E distances at all pairs of time points T seconds apart were averaged (see Additional file 9). Datasets using (C) to (E) are corrected for any nucleus drift during imaging.

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Additional file 7:

Three-dimensional visualization of telomere motion using kymographs. The movie was recorded 10 frames/second. Rotational movie of kymographs showing telomere motion in one representative UMUC3 cell nucleus. This allows 360 round inspection of the telomere motion in three-dimensional (3-D) space at any time point.

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Additional file 8:

Three-dimensional visualization of telomere motion using kymographs. The movie was recorded 10 frames/second. Telomere dynamics are visualized in kymographs as lines in 3-D space. Kymographs of three UMUC3 cell nuclei from Additional file 3 were shown.

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Additional file 9:

Quantifying telomere motions in live cells. Averaging of end-to-end (E2E) distances for quantitative measurement of telomere motion. The path of a moving particle (bottom left) is divided up into intervals of integer numbers of 1-second time points. For each 1-second increase in the interval considered, the number of intervals decreases by one; thus larger intervals have fewer samples and are more subject to stochastic variation. Lengths traveled during each interval are shown to the right of each figure. These lengths are averaged together (average length shown under the black line). When the average lengths from a particle undergoing unconstrained random diffusion are plotted, they scale with the square root of elapsed time. This procedure helps to reduce measurement noise, especially for short time differences. Telomeres tracked for N seconds are plotted for intervals up to N/2 seconds.

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Additional file 10:

Heterogeneous motions of telomeres. (A) An additional six other representative tracks of telomeres in UMUC3 nuclei, each line is an end-to-end (E2E) track of a single telomere. The top three panels show telomeres tracked using green fluorescent protein (GFP)-TRF1 and the bottom three panels show telomeres tracked using GFP-TRF2. These E2E measurements were used to calculate the effective diffusion coefficient D using Einstein's diffusion equation, E2E = √(6DT) (where T is the time in seconds). (B) Bootstrapping procedure to estimate errors in comparing histograms of D values. The procedure involves repeatedly and randomly splitting two populations of 200 telomeres each, and averaging the mean and standard deviation for the mode. Figure 2E was taken as a reference. One such split is shown in the right two panels. (C) Log-scale histogram showing that the skewed histogram for effective diffusion coefficient D values does not present a log-normal distribution.

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Additional file 11:

The wide range of heterogeneity of motion is telomeric specific. (A) Cumulative path distance measurements of telomeres and heterochromatin spots. TrackIt4D analysis of heterochromatin motion in three-dimensional space shows that the heterochromatin moves at much less motion compared with telomeres. D values for UMUC3 cell heterochromatin loci in our experimental settings are consistent with reports on heterochromatin regions in yeast, Drosophila and mammalian models (D values of 0.05–1.3 × 10^-4 mm²/second). (B) Overlap of D histograms for telomeres versus heterochromatin spots. The Komolgorov-Smirnov comparison value for these two histograms is 0.517 ± 0.029, which is statistically significant (N = around 400 telomeres, N = around 400 heterochromatin spots).

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Additional file 12:

Telomere movement is energy dependent. (A) Three-dimensional trajectory images of telomeres show dampening of telomere motion upon azide treatment. Medium containing 6 mM sodium azide was perfused into the live cell chamber for 30 minutes and images were acquired before, and after perfusion, and after azide wash out (upper three panels). This condition is similar to that used to show energy dependence of anaphase chromosome movement [29,49]. Washing away azide restores the telomere motion. These findings were corroborated by analyses of plottings of distance in space that at the accumulated telomere movements path as a function of time T seconds, telomeres had moved away from their original position at time 0 second after azide wash (lower panels and Figure 4A). (B) Nuclear areas/volumes were estimated by drawing the nuclear periphery contours with polygons in all optical sections and evaluation of the area by adding the polygons to estimate the area/volume of the nucleus. These polygon measurements of cell nuclei showed a <10% change in either area or volume of cells under the experimental perturbations due to azide treatment. Hence telomeres before, during and after the azide treatment had the same total space potentially available for sampling as the controls ruling out nucleus compaction. (C) Telomere histograms of control nuclei versus azide-treated cells (N = around 400 telomeres). Notably, the peak position mode for the histograms of D values did not change; rather, the faster moving telomere population was selectively diminished compared with the control.

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Additional file 13:

Telomere movements are related to telomere shortness and can be altered experimentally. (A) Telomere motion is lower in cell lines with longer telomeres. Track4D tracings are shown of telomere tracks in different cancer cell lines, whose mean telomere lengths (kilobases) are known to differ. Datasets of images were obtained using cells (from a single clone) expressing low levels of fluorescently tagged TRF1, as described for the UMUC3 cells, to track telomere dynamics. Mean telomere lengths by Southern blotting for the cell lines analyzed are: LOX, >30 kilobases; H1299, mean 12 kilobases; HeLa, mean 5 kilobases; UMUC3, 2–5 kilobases. (B) Quantitative analysis of datasets for the four cell lines for which representative plots were shown in (A) bold. N = around 200 telomeres for each cell line. (C) Track4D program allows picking up of an individual telomere from all tracked telomeres (indicated by the number at the side of the y-axis) in the nucleus. The intensity value of the particular telomere was then matched with its effective diffusion coefficient D. (D) Matching the group of telomeres having the highest and lowest 20% in intensity of the individual telomere with top and bottom 20% effective diffusion coefficient D in the same cell nucleus reveals that the majority of the fast moving telomeres are contributed by the shorter telomeres within the same nucleus (N = around 400 telomeres). (E-G) Experimentally increasing the mean bulk telomere length causes decreased telomere movements. The average telomere length was extended by expressing extra WT-hTER in the same cell background (UMUC3 cell line). (E) Cumulative distances paths traveled by telomeres in two representative UMUC3 cell nuclei with unperturbed (left panel, empty vector control) or lengthened (right panel) telomeres. (F) Telomere D value histograms of control (lentivector)-treated nuclei versus the WT-hTER overexpressing cells (N = around 400 telomeres). The Komolgorov-Smirnov (K-S) non-parametric comparison between parental UMUC3 cells and lentivector-treated cells scored 0.013 ± 0.031, which is significantly indifferent. However, the K-S comparison between cells expressing lentivector versus the WT-hTER histograms gave a score of 0.199 ± 0.027, which is significantly different (see Additional file 18). Hence telomere motion is significantly slower in cells in which the bulk telomere length was increased experimentally in the same cell background. (G) Intensity changes 1–2-fold in cell nuclei of UMUC3 cells expressing extra WT-hTER at a level 3–5-fold above endogenous levels (N = around 200 telomeres). By thus experimentally increasing average telomere length, these results also independently confirmed that the telomeric fluorescence signals are indeed telomeric, because their average intensity also increased; when telomeres in UMUC3 cells are extended with WT-hTER, the mean intensity value shifts to a higher value (right panel). This supported the assumption of using intensity values as a relative measure of telomere lengths.

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Additional file 14:

Telomere movement could be altered experimentally. (A) A schematic diagram of the mutant-template telomerase RNA MT-hTer expression lentivector (left panel), and the sequences of the 47A MT-hTer template and the telomeric repeats it causes to be incorporated (right panel) [38,50]. (B) Three-dimensional (3-D) kymographs of telomere motion projected on to a two-dimensional plane. 47A MT-hTer produces high motility and rates of movement of some telomeres, as seen by the group of wavy lines to the right of the kymograph for this representative cell nucleus. (C) 3-D trajectory images of telomeres showing greater motion in cells that had expressed 47A MT-hTer for 6 days. (D) Cumulative path distances traveled by telomeres under two different damaging conditions. 47A MT-hTer induces greater telomere motion, but methyl methanesulfonate (MMS), a general DNA damaging drug, does not. To test whether nuclear expansion or shrinkage is due to the 47A-expression or could have contributed to the changes in telomere motility, nuclear volumes were estimated by drawing the contour with polygons and evaluation of the area and adding the area of all optical sections. These measurements confirmed that any volume change was <5% upon 47A MT-hTer or MMS treatments to the cells. Hence, telomere motion is greatly increased when telomeres are uncapped by MT-hTer 47A, but not by general DNA damage (MMS). (E) Colocalization of telomeres with DNA damage response foci visualized by staining with an antibody against 53BP-1, a DNA damage protein (left three panels). In UMUC3 cells, introduction of a 47A MT-hTer construct induced a rapid DNA damage response and telomere uncapping response, including telomere dysfunction-induced foci, within 3 days. The 47A MT-hTer expressing cells showed more than 70% of DNA damage foci colocalization on telomeres, indicative of telomere-specific damage (telomere uncapping) (the bar plot in the right panel). Similar results were shown for colocalization of telomeres with other DNA damage proteins: ATM pS-1981 and γ-H2AX pS-139 upon 47A MT-hTer expression. In contrast, the 0.01% of MMS treatment for 1 hour induced DNA damage foci that did not colocalize with telomeres (left three panels shows the immunofluorescence pictures and the right panel shows the quantitative bar plot). (F) Histograms of telomere motion D values for control (empty lentivector-treated) nuclei versus 47A MT-hTer expressing cells (N = around 400 telomeres). Komolgorov-Smirnov non-parametric comparison between the two histograms was performed. The score was 0.269 ± 0.025, indicating a highly significant difference in the telomere motion between the controls versus the cells with uncapped telomeres.

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Additional file 15:

Sizes and diffusion coefficients of particles in the nucleus and of chromatin loci. (A) Copied from Gorisch SM, Lichter P, Rippe K. (2005): Dextran particles in different milieus. (B) Various other measurements of D in the nucleus.

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Additional file 16:

Justified assessment of quantifying telomere motions by effective diffusion coefficients. (A) Averaged mean-square-distance (MSD) versus time for around 800 telomeres shows that telomere motion imaged within 200 seconds does not reach the plateau indicative of constrained motion. The plot shows the MSD curves for the telomeres and demonstrates that the MSD value does not decrease (saturate the volume of nuclear three-dimensional (3-D) space available for movement) over a 200-second time interval of observation. (B) A 3-D trajectory image at any z-section and any time of a UMUC3 nucleus shown to estimate the distance of each telomere to the nuclear periphery (indicated by the red lines).

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Additional file 17:

Green fluorescent protein (GFP)-TRF1 labeled, parental UMUC3 clonal cells were imaged using OMX microscopy. Cells with similar intensity/AU under the same light exposure were chosen and their telomere motility (N = around 200) analyzed. The end-to-end (E2E) measurements of these telomeres were used to calculate their effective diffusion coefficients D (10^-4 μm²/second) using Einstein's diffusion equation, E2E = √(6DT) (where T is the time in seconds). Each telomere's intensity was plotted against its D value to give a scatter plot indicative of the relationship between telomere length and motility. The linear regression line shows a value of R² = 0.5917 in the graph.

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Additional file 18:

Table 1 – A summary of the Komolgorov-Smirnov non-parametric comparison between histograms discussed in the text. A Komolgorov-Smirnov (K-S) score three times the error (standard deviation) is considered significant. Red scores indicate statistically significant different histograms, while the orange score is at the borderline for statistically significant difference between the two histograms under comparison. The first value in the right column is the K-S value between histograms, and the second value after ± is the bootstrap value (error) for each histograms. Hence, the table shows that there is significant difference when comparing histograms of parental or vector control cells with either heterochromatin, azide-treated cells, WT-hTER-treated cells, or 47A MT-hTer-treated cells (red-colored scores and orange score). Bootstrap analysis for all histograms within error bars of < 0.05, hence comparisons between histograms are valid.

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