Single molecule tracking of Ace1p in Saccharomyces
cerevisiae defines a characteristic residence time for
non-specific interactions of transcription factors with
chromatin

David A Ball, Gunjan D. Mehta, Ronit Salomon-Kent, Davide Mazza, Tatsuya Morisaki, Florian Mueller, James G. McNally, Tatiana S. Karpova

► To cite this version:

David A Ball, Gunjan D. Mehta, Ronit Salomon-Kent, Davide Mazza, Tatsuya Morisaki, et al.. Single molecule tracking of Ace1p in Saccharomyces cerevisiae defines a characteristic residence time for non-specific interactions of transcription factors with chromatin. Nucleic Acids Research, 2016, 44 (21), pp.e160. 10.1093/nar/gkw744. pasteur-01622680

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Single molecule tracking of Ace1p in *Saccharomyces cerevisiae* defines a characteristic residence time for non-specific interactions of transcription factors with chromatin

David A. Ball¹,†, Gunjan D. Mehta¹,†, Ronit Salomon-Kent¹, Davide Mazza²,
Tatsuya Morisaki³, Florian Mueller⁴, James G. McNally⁵ and Tatiana S. Karpova¹,*

¹CCR/LRBGE Optical Microscopy Core, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA, ²Istituto Scientifico Ospedale San Raffaele, Centro di Imaging Sperimentale e Università Vita-Salute San Raffaele, Milan 20132, Italy, ³Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA, ⁴Institut Pasteur, Computation Imaging and Modeling Unit, CNRS, URA 2582, Paris 75015, France and ⁵Institute for Soft Matter and Functional Materials, Helmholtz Center Berlin, Berlin 12489, Germany

Received June 13, 2016; Revised August 05, 2016; Accepted August 15, 2016

ABSTRACT

In vivo single molecule tracking has recently devel-
oped into a powerful technique for measuring and
understanding the transient interactions of transcrip-
tion factors (TF) with their chromatin response ele-
ments. However, this method still lacks a solid foun-
dation for distinguishing between specific and non-
specific interactions. To address this issue, we took
advantage of the power of molecular genetics of
yeast. Yeast TF Ace1p has only five specific sites
in the genome and thus serves as a benchmark to
distinguish specific from non-specific binding. Here,
we show that the estimated residence time of the
short-residence molecules is essentially the same for
Hht1p, Ace1p and Hsf1p, equaling 0.12–0.32 s. These
three DNA-binding proteins are very different in their
structure, function and intracellular concentration.
This suggests that (i) short-residence molecules are
bound to DNA non-specifically, and (ii) that non-
specific binding shares common characteristics be-
tween vastly different DNA-bound proteins and thus
may have a common underlying mechanism. We de-
velop new and robust procedure for evaluation of ad-
verse effects of labeling, and new quantitative anal-
ysis procedures that significantly improve residence
time measurements by accounting for fluorophore
blinking. Our results provide a framework for the reli-
able performance and analysis of single molecule TF
experiments in yeast.

INTRODUCTION

Binding of certain transcription factors (TF) to their tar-
get DNA sequences is highly dynamic, with residence time
on the scale of seconds, first observed in mammalian cells
by McNally *et al.* (1) and in yeast by Karpova *et al.* (2), re-
viewed in (3). The role that this rapid exchange plays in tran-
scription remains hotly debated. Is this noise, or is it tran-
scriptionally productive? Estimates of TF binding to spe-
cific sites may clarify the function of this fast exchange and
also provide a deeper understanding of the molecular mech-
anisms of transcription initiation. Morisaki *et al.* (4) applied
single molecule tracking (SMT) to two mammalian TFs,
p53 and glucocorticoid receptor, and demonstrated that TF
molecules that participate in transcription bind transiently
to polymerase foci. This important observation supports
the hypothesis that transient binding is transcriptionally
productive.

SMT provides direct information about molecular dy-
namics (5). Other microscopy methods, such as fluores-
cence recovery after photobleaching (FRAP) and fluores-
cence correlation spectroscopy (FCS), are based on popu-
lation analysis and mathematical modeling that adds addi-
tional difficulties in data analysis. In addition, FRAP and
FCS are difficult to implement on small specific targets such
as transcription factories or gene loci. Thus, the powerful
technique of SMT is the method of choice for studies of
transcription factor dynamics. However, this method still
lacks a solid foundation for distinguishing between spe-
cific and non-specific interactions of transcription factors
with chromatin. SMT provides a set of residence times for a
given protein, but then criteria have to be defined to decide
which residence times reflect specific or non-specific bind-

<footnote>*To whom correspondence should be addressed. Tel: +1 301 435 4611; Fax: +1 301 496 4951; Email: karpovat@mail.nih.gov</footnote>

<footnote>†These authors contributed equally to the paper as first authors.</footnote>
ing, where specific binding refers to binding of the transcription factor at specific response elements and non-specific binding refers to binding at all other DNA sites (6). Distinguishing between those two types of binding requires a benchmark for non-specific binding.

Development of this benchmark requires proteins that are conditionally incapable of DNA binding while preserving non-specific DNA binding. Such experiments are easier to perform in *Saccharomyces cerevisiae*, a unicellular eukaryotic model system. In this paper we took advantage of the well-studied yeast transcription factor Ace1p. With Ace1p we can distinguish specific from non-specific binding because Ace1p has only five specific sites in the genome (7); moreover, Ace1p may bind to these sites only upon associating with Cu²⁺ (8). Thus, in the absence of copper Ace1p should only bind to DNA non-specifically and therefore Ace1p provides a perfect benchmark for non-specific binding.

To track TF in yeast we had to overcome two specific technical problems. A first key technical problem is a lack of robust procedures to determine if the labeling strategies used to mark the transcription factor are innocuous and therefore to ensure that SMT estimates are correct. The current state of the art in SMT is to use organic dyes that enter cells and then bind covalently to genetically encoded HaloTag- or SNAP proteins (9,10). These bright and stable organic dyes enable SMT for extended periods (11) and were extensively used in mammalian cells. However, labeling of yeast cells is problematic due to the low retention of the dyes, and thus no SMT experiments were yet reported for yeast. Organic dyes have been introduced into yeast for the staining of cellular structures but not for SMT by either electroporation (12) or triple disruption of transporters (13,14). It was reported that triple disruption of ATP-binding cassette multidrug resistance (ABC-MDR) transporters encoded by *PDR5*, *SNQ2* and *YOR1* (15) substantially reduces the cell viability. Electroporation is also a harsh treatment that may lead to unintended physiological effects. Currently there are no tests sensitive enough to assay subtle effects of the labeling procedure on transcription. Therefore, it is important to develop such assays and to apply them to development of benign procedure for organic dye incorporation into yeast cells. The second technical problem is to the small size of the yeast nucleus, which is ~10-fold smaller in diameter than mammalian cells, and therefore the imaged area is ~100-fold smaller. The significantly smaller volume reduces the total number of easily separable tracks that can be obtained from a single yeast nucleus.

To weed out potential artifacts of different organic dye incorporation procedures we developed a biological control based on Ace1p. After treatment with CuSO₄, Ace1p undergoes a slow cycle of binding that peaks at 5–10 min after copper addition (2). Yeast strains carry 10–15X tandem copies of *CUP1* (16). This clustering of multiple Ace1p-3xGFP molecules in the tandem array amplifies the fluorescent signal and allows microscopic observation of TF binding. We tested whether different organic dye incorporation procedures perturb the Ace1p slow cycle in two different ways: first, by checking for binding of Ace1p to *CUP1* promoters in the absence of copper, and second by monitoring

the dynamics of the slow cycle of copper-induced TF binding. We found that electroporation procedure in yeast is detrimental to slow cycle, indicating that this commonly used procedure is problematic and cannot be applied to studies of yeast transcription. We overcame this problem by disrupting only one ABC-MDR transporter *PDR5*. We show here that disruption of this transporter allows organic dyes to be retained long enough to form covalent links to HaloTag. Importantly, we also show that cells of this single mutant (*pdr5Δ*) exhibit completely normal binding of Ace1p to the promoters of *CUP1*. To address the yeast nuclear size limitations, we optimized label density and track visualization in yeast cells. Finally, we developed a new computational correction procedure that can account for the reversible and irreversible photobleaching, which otherwise leads to an artifactual reduction in the length of some tracks and therefore to underestimation of residence time.

In this paper we have characterized non-specific binding of Ace1p and two other DNA-binding proteins, histone H3 (*HHT1*) and heat shock factor (*HSF1*). Estimates for non-specific residence times of these very different DNA-binding proteins are surprisingly similar. This result helps to define a characteristic residence time for non-specific binding, and also suggests that those non-specific interactions occur in very similar ways perhaps reflecting predominantly simple electrostatic interactions. In sum, our results provide a framework for the reliable performance and analysis of SMT of transcription factors in yeast.

# MATERIALS AND METHODS

## Strains and plasmids, media and growth conditions

*Saccharomyces cerevisiae* strains of this study listed in Supplementary Table S1 are derivatives of the haploid strains isogenic to S288C: BY4742, BY4741 from the *Saccharomyces* deletion project (Research Genetics/Invitrogen, Huntsville, AL, USA). Standard methods were used for yeast growth, yeast transformation and genomic DNA isolation (17) (See Supplementary data for details).

## Fluorescent labeling of HaloTag fusion proteins

A total of 5 mM stock solutions for HaloTag ligands were prepared in DMSO. The electroporation procedure for labeling of HaloTag proteins in yeast has been described in (12). A Genepulser (BioRad, Hercules, CA, USA) was used at 1000 V, 800 Ω, 25 µF settings for the electroporation. Cells were imaged either 5 min later or after 2 h recovery in CSM at 30°C. To label HaloTag fusion proteins (Hht1p or Ace1p or Hsf1p) in cell culture, strains were grown overnight in 3 ml CSM (or CSM+SC, as stated for appropriate experiments) to saturation in 14 ml polypropylene round-bottom tubes (Falcon, Mexico C.P., Cat. No.: 352059). Fifty microliters of this culture was inoculated into 3 ml of fresh medium and grown for another 4 h. Cells were pelleted and resuspended in the same tube in 1 ml of fresh medium with appropriate concentration of the HaloTag ligand. Cells were grown further for another 30 min at 30°C. Cells were washed twice with 3 ml of the growth medium and resuspended in 25 µl of CSM for imaging. We used lower concentration of HaloTag ligands for
SMT and higher concentration of HaloTag ligands for nuclear staining (Supplementary Table S3). To stain the nuclei, DAPI was added to the live culture at 25 µg/ml from 5 mg/ml stock in DMSO. For the successful DAPI staining of live yeast cells concentration of DMSO had to be maintained at 1%. TMR staining and DAPI staining were performed simultaneously for 30 min.

## Epifluorescence and HILO microscopy

Cells were concentrated and mounted for imaging as described in (2). In short, 5 µl of the concentrated yeast cell suspension was placed into LabTek II chamber (Nalge Nunc Intl., Rochester, NY, Cat. No.: 155379) and subsequently covered by a 10 mm × 10 mm CSM or CSM+SC agarose slab. For the induction of the CUP1 arrays, 100 µM CuSO₄ was added to agarose slabs. For epifluorescence microscopy, the DeltaVision Elite microscopy system (GE Healthcare Bio-Sciences, Pittsburgh, PA, USA) with 100X, 1.4 NA, oil-immersion objective (UPLSAPO100XO, Olympus Scientific Solutions, Waltham, MA, USA) was used. To observe the difference in staining efficiency between *pdr5Δ Hht1p-HaloTag* and *PDR5 Hht1p-HaloTag* strains, live cells were stained with 10–15 nM TMR and DAPI as described above; then washed twice with 0.1 M KPi buffer (pH 7) and mounted on the LabTek II chamber with KPi agarose pads for imaging. Single focal plane (SFP) images were acquired in TMR and DAPI channels, TMR images were deconvolved. Particles were counted from 83–105 nuclei. To visualize dynamics of Ace1p-3xGFP at the CUP1 array (Figure 4A, B and C), 11 z-sections with 0.4 µm step size were acquired with 4 min time lapse for 48 min total. These images were deconvolved and projected onto a single plane using SoftWoRx software (GE Healthcare Bio-Sciences, Pittsburgh, PA, USA). Cells containing visible arrays were counted from these projections. For SMT, SFP time lapse movies were acquired on a custom-built Highly Inclined Laminated Optical (HILO) sheet, (18)) imaging system (see Supplementary Methods for details). All the lasers were set at 2 mW output power, and images were acquired as fast as possible with constant laser exposure. Image size was cropped to 256 × 256 pixels, which resulted in a camera readout time of ~15 ms, corresponding to a frame rate of 66.7 Hz (frames per second), with total time acquisition of 4.5 s per 300 frames. To avoid loss of temporal resolution in two-color experiments of single molecules and saturated nuclei, the entire image sequence in the single molecule channel (647 nm) was collected first, after which the nuclear images were collected (561 nm). The 561 nm laser line was used to illuminate TMR, JF₅₄₉ and JF₅₈₅ whereas the 647 nm laser line was used to illuminate JF₆₃₅ and JF₆₄₆. In heat-shock experiments, cells were mounted onto pre-warmed (39°C) Lab-Tek II coverglass chambers and covered by pre-warmed (39°C) CSM+SC agarose slabs. The stage of the HILO microscope was preheated to 37°C using a stage-top incubation system (H501-NG, Okolab USA, Inc., Burlingame, CA, USA). For all samples, imaging from the same agarose pad was limited to the first 15 min to obtain consistent results and to record the molecular movements under active metabolic conditions.

## SMT analysis

For each experimental condition, we obtained ~250 tracks which were sufficient to generate a reasonably smooth cumulative histogram. We tracked the single molecules and analyzed the data with TrackRecord software (19) based on MATLAB (freely available at https://sourceforge.net/projects/single-molecule-tracking). TrackRecord identifies particle positions based on their peak intensities and fitting to a 2D Gaussian function. Tracks are generated using a nearest-neighbors approach that we prefer over other algorithms due to its use of parameters that are more readily interpretable by biologists and to its low computational demands. This software permits us to assay (i) the fraction of molecules that are freely diffusing and those that are immobilized due to binding (ii) to discern long-residence molecules within the fraction of immobile molecules (iii) to calculate residence times for long-residence and short-residence binding events. We introduced the following improvements to this software: (i) the ability to select multiple regions if interest (ROI) that allows tracking in only the nuclei of all cells located in the field of view; (ii) additional noise removal by Wiener and morphological top-hat filters to reduce speckle noise and uneven illumination, respectively; (iii) display and use of particle kymographs to simplify the manual correction of erroneous automatically generated tracks. We used kymographs to manually bridge the gaps generated by blinking of the ligands.

To extract binding times, the cumulative distribution of residence times ('survival plot') is fit with least-squares to both a one- and two-component exponential decay. Because of the chromatin motion, it is required to set a threshold on the allowable motion for a bound particle, called $r_{max}$. Tracking performed on chromatin-bound nucleosomal histone showed that 99% of single molecules of Hht1p had a frame-to-frame displacement of <0.22 µm at 66.7 Hz frame rate. It is also important to specify a minimum number of frames, $N_{min}$ that a particle must jump less than $r_{max}$ in order to remove slowly diffusing free molecules that contaminate the survival plot. The probability that a free molecule will be considered bound depends on its diffusion coefficient, $D$, the frame rate, $\Delta t$ and the values of $r_{max}$ and $N_{min}$ (5).

$$P(r_{max}, N_{min}) = \left(1 - e^{\frac{r_{max}^2}{4D\Delta t}}\right)^{N_{min}}$$

To achieve $P(r_{max}, N_{min}) < 0.01$ for free molecules with a diffusion coefficient of 0.5 µm²/s, and $r_{max} = 0.22$ µm, $N_{min}$ is set to 21. A statistical F-test between the two fits was used to determine whether the two-component exponential significantly improves the fitting.

## Simulations of photobleaching and blinking

Simulated images of single particles were generated using custom software developed in MATLAB. Three-dimensional positions of the particles were randomly generated from a uniform distribution within a sphere with the specified radius, and these positions were convolved with a Gaussian to estimate the point spread function at the particles depth, which could be adjusted with parameters for the objective N.A., refractive index of the imaging medium,
wavelength and pixel size of the camera. At each exposure ($t_{exp}$), there is the possibility that each molecule can either irreversibly photobleach or reversibly photoswitch to the dark state. Modeling of photobleaching is governed by a 2-component exponential decay, with the 2 rates ($k_{B1}$ and $k_{B2}$) and the proportionality ($F_{B1}$) between them adjustable. The time-dependent photobleaching curve is generated based on the user-supplied parameters, and at each time step, the number of molecules that should bleach is calculated. This number of randomly selected molecules is then switched off at this time point. Photoswitching is governed by power-law statistics (20) with an off rate constant ($m_{off}$) that is also adjustable. When a particle has reversibly gone dark, it will recover with a probability that is also modeled with power law with an adjustable rate constant ($m_{on}$). Each ‘on’ and ‘off’ period for every molecule is randomly selected from a power law distribution with the indicated exponents.

Molecules are allowed to move randomly within the volume with a mean squared displacement (MSD) dictated by the set diffusion coefficient, D: $MSD = 6D\Delta t$, where $\Delta t$ is the time between frames. Molecular motion is reflected when the volume boundary is encountered. The molecules also undergo binding and dissociation events, which are governed by user-supplied parameters. The time that each molecule spends diffusing before it binds is randomly selected from an exponential distribution with mean, $1/k_{bind}$, while the length of time that the molecule is bound is selected from one of two exponential distributions with means $1/k_{d1}$ or $1/k_{d2}$, to model both short and long binding events.

## RESULTS

### Optimization of HaloTag labeling of yeast proteins for single molecule tracking

Incorporation of TMR in cell culture of *pdr5Δ Hht1p-HaloTag*. The first challenge of SMT in yeast to overcome is inefficient organic dye incorporation. We optimized the labeling procedure by disrupting the gene encoding a transporter involved in resistance to xenobiotic compounds, *PDR5* (21). By disrupting just one of the three multidrug transporters we expected to obtain more efficient retention of the ligand in yeast cells with the least effect on physiology. We compared TMR staining efficiency in wild-type and *pdr5Δ* strains homozygous for *in situ* C-terminal fusion of HaloTag to *Hht1p* (histone H3). This protein is largely immobile because of its incorporation into the nucleosomes and thus it is suitable for prolonged observation of the same molecules in the same focal plane. We treated live cells of *pdr5Δ Hht1p-HaloTag* strain and *PDR5 Hht1p-HaloTag* strain with TMR and counter stained the nuclei with DAPI (as described in Materials and Methods). After staining with 10–15 nM of TMR we observed bright particles in the nuclei of *Hht1p-HaloTag* strains and these particles were co-localized with the nuclear DAPI staining. We could not observe any particles either in the absence of TMR or in the absence of *Hht1p-HaloTag* (Figure 1A). Thus, the particles correspond to individual molecules of *Hht1p-HaloTag* labeled with TMR. The average number of particles was 2.14 ± 0.21 in *pdr5Δ* strain and 0.13 ± 0.04 in *PDR5* strain (Fig-

<figure><img src="image_1.png" /></figure>

**Figure 1.** Disruption of *PDR5* increases efficiency of TMR staining of *Hht1p-HaloTag* cells. (A) Live cells of WT (YTK1479), WT *Hht1p-HaloTag* (YTK1490), *pdr5Δ Hht1p-HaloTag* (YTK1491) and *pdr5Δ* (YTK1492) were treated with 15 nM TMR/0 nM TMR and DAPI. Single focal plane (SFP) images were acquired on DeltaVision Elite microscope; TMR images were deconvolved. All images in the TMR channel are scaled equally. Yellow circles represent nuclei with TMR particles, whereas green circles represent nuclei without TMR particles. Scale bar = 2 µm. (B) Particles from the TMR channel were counted for 10 nM TMR/0 nM TMR. Center lines show the median values; box limits indicate the 25th and 75th percentiles, outliers are represented by dots; crosses represent sample means. N ranges from 83 to 105.

ure 1B). Thus, *pdr5Δ* disruption increases the efficiency of dye incorporation for the HaloTag by a factor of 20.

Tracking single molecules and correcting the tracks in small nuclei. A critical aspect for successful SMT is the labeling density. Excessive labeling of individual molecules interferes with the tracking algorithm and with manual observation of the tracks. Higher particle densities make it impossible to follow each particle as the tracks overlap and cross each other. In a mammalian cell nucleus, the limit for successful tracking is ~30 molecules per cell (4,19). The area of the yeast nucleus is ~100-fold smaller than that of a mammalian nucleus. To establish a limit of labeling for yeast, we performed tracking of *Hht1p-HaloTag* fusion stained with JF646, using a previously developed semi-automated tracking software (19). Briefly, the software takes images such as shown in Figure 2A, and filters out noise (Figure 2B) to identify single molecules. Tracking is performed within user-defined ROIs (Figure 2C and D) delineating the nucleus to confine analysis to molecules inside the nuclear compartment (see Materials and Methods for more details). Even at low labeling densities there is a possibility that two (or more) labeled molecules will be close together, and due to chromatin motion, even if both molecules are bound their tracks will likely cross. Reversible photoswitching of organic dyes (‘blinking’) may lead to artificial shortening of auto-detected tracks. It is therefore necessary to correct the mis-assignment that happened in the automatic tracking due to crossing tracks, and due to blinking. To improve manual quality control and thus to maximize correct detection of the tracks, we used kymographs, rather than explored the two spatial dimensions at each time-point. Kymographs dis-
<figure><img src="image_2.png" /><figcaption>**Figure 2.** Overview of the steps involved in kymograph-based tracking of single molecules of Htt1p-HaloTag labeled with JF646. Imaging is done on HILO microscope. (A) Projection of the intensity summed over all 300 frames of a representative movie to visualize all particles in the 647 nm channel. (B) Same projection as in (A) after noise reduction with the band-pass filtering module of the software. (C) Summed intensity projection of the same field-of-view as in (A) in the 561 nm channel to visualize nuclei. Red outlines indicate user-identified ROIs in which particle tracking is performed. (D) Overlay of 647 nm (red) and 561 nm (green) channels demonstrating particles residing in the nuclei (E and F) Kymographs XT, (E) Y-projections, and YT, (F) X-projection of all 300 frames from the movie shown in (B) after filtering. Both kymographs are used for manual correction of tracks because apparent track overlap in one dimension resulting from particles in different nuclei are typically well-separated in the other dimension. Colored arrows indicate the corresponding particles shown in the summed image displayed in (B). Examples of (G and H) correctly autodetected and (I and J) manually edited tracks shown as kymographs. (G) The software successfully identifies tracks that are continuous or contain gaps of a single frame. Red, green and blue spots indicate three separate tracks from this image. (H) The algorithm also identifies freely diffusing molecules, which are characterized by a bright spot in a single frame with dimmer (usually not autodetected) spots in the preceding and/or following frames. (I) If a track contains gaps of several frames, the user must manually join the autodetected track segments. (J) Over-crowding makes it challenging to follow a single particle, and so these types of tracks are typically thrown out. See text for our recommendations on when to merge track segments as in (I), or leave them separated as in (G).</figcaption></figure>

play only one of the spatial dimensions versus time, with the
second spatial dimension being maximum projected (Fig-
ure 2E and F). Kymographs allowed us to observe the en-
tire length of the tracks, and add/correct many track-points
at once, rather than adding/correcting the points in a track
one frame at a time.

Examples of auto-detected bound molecules and diffus-
ing molecules, and examples of the tracks that were man-
ually joined or rejected are presented in Figure 2G–J. The
decision to manually join tracks is made based on their x–y
coordinates, and on whether any residual fluorescence may
be seen in the frames between the identified tracks indicat-
ing track continuity. For example, in Figure 2G the blue, red

and green track segments are slightly displaced from one an-
other, and the intensity in between them is low. In contrast,
the five track segments (blue, red, green, magenta and cyan)
in Figure 2I are roughly at the same position, and the inten-
sity, although below the threshold that was set to locate par-
ticles, appears to show a single continuous track. Of course,
the threshold could be lowered to capture the interstitial re-
gions, however, this would also increase the number of false-
positive particle detections in the background signal. Man-
ual track correction is very labor-intensive, typically taking
an entire day for an experiment consisting of ~200 tracks
from ~10 movies. We found that using kymographs allowed
us to do the same number of corrections in half the time.
Even with the help of kymographs we have found that the
number of labeled molecules in a yeast nucleus should not
exceed three. Although this means that each cell does not
provide much data, the lack of statistics can be compensated
by simply observing more cells.

Development of the test for the physiological effects of or-
ganic dye incorporation

Assay for the physiological effects of labeling procedure. Al-
though neither staining in culture, facilitated by *pdr5Δ*,
nor fusions of the proteins to HaloTag have an impact on
growth rate or resistance to copper (Supplementary Fig-
ure S1A and B), disruption of the transporter may exhibit
subtle specific effects on transcription by introducing ionic
imbalance. To test for such effects, we designed an assay
based on the visualization of binding of the TA Ace1p to
the *CUP1* promoter. In unperturbed cells, Ace1p binds to
the promoter of the *CUP1* locus only in presence of cop-
per (8). After a treatment with 100 µM of CuSO₄, Ace1p
undergoes a slow cycle of binding that peaks at 5–10 min
after copper addition (2). We tested whether either *pdr5Δ*
or ligand staining induces binding of Ace1p to *CUP1* pro-
moters in the absence of copper, or impedes the slow cycle
of copper-induced TA binding by counting the fractions of
Ace1p-3xGFP cells exhibited green spots over time. No ef-
fect was observed for both, and this indicates that neither
*pdr5Δ* disruption nor TMR staining by itself result in a
measurable difference in Ace1p binding to *CUP1* promot-
ers (Figure 3A). By RT-PCR we observed similar dynam-
ics of *CUP1* transcription for wild-type Ace1p and Ace1p-
HaloTag; thus HaloTag fusion to Ace1p does not affect
Ace1p function (Supplementary Figure S1C).

Adverse effects of electroporation. Electroporation (EP) is an efficient method of introducing HaloTag ligands into the yeast cell (12). However, the effects of this procedure on transcription may be adverse. We tested TMR staining procedure by EP on the diploid carrying Ace1p-3xGFP. Logarithmically growing cell culture was split in three; one culture was stained with 1 µM TMR by EP (EP+TMR), another culture was treated with TMR in culture as described above (NoEP+TMR), and the third culture was left untreated (NoEP+NoTMR). We applied the Ace1p visualization assay to all three cultures. First, all three cultures were observed immediately after staining. No arrays were visible in controls, as expected, if no Cu²⁺ was added to the culture. However, in EP+TMR culture arrays were observed in 20% of cells, indicating that EP induces Ace1p binding to *CUP1*
<figure><img src="image_3.png" /></figure>

**Figure 3.** Disruption of *PDR5* does not alter the normal physiology of the cell in contrast to electroporation. (A) Dynamics of Ace1p-3xGFP occupancy on *CUP1* array were assayed by counting the number of cells with visible *CUP1* arrays observed over time with 4-min time intervals in *PDR5* Ace1p-3xGFP (YTK1479), and *pdr5Δ Ace1p-3xGFP* (YTK1404). N > 100. Error bars represent standard error of the proportion (s.e.p). Arrows point to visible arrays in the representative diploid nucleus of *PDR5* Ace1p-3xGFP. Scale bar = 2 µm. (B) *PDR5 Ace1p-3xGFP* (YTK1479) cells were observed on DeltaVision Elite microscope before and 5 min after electroporation (EP). Error bars represent the standard error of the mean (s.e.m.). Scale bar = 2 µm. (C) *PDR5 Ace1p-3xGFP* (YTK1479) were allowed to recover for 2 h before being treated with 100 µM CuSO₄ to observe dynamics of Ace1p binding to the *CUP1* promoters. Cells treated with TMR by electroporation (EP+TMR) were compared with Non-electroporated cells (No EP), treated and not treated with TMR. Error bars represent s.e.p. N ranges from 29 to 100.

promoters even in absence of Cu²⁺ (Figure 3B). EP further severely affected cell morphology, as observed microscopically (Figure 3B). Furthermore, in plate assays we found >50% reduction in the cell viability after electroporation (Supplementary Figure S2). When the cultures were allowed to recover for 2 h in CSM and then tested again, arrays were no longer visible in absence of Cu²⁺ in EP+TMR, as well as in controls. However, the response to Cu²⁺ induction was severely impaired in ER+TMR cells, while normal dynamics were observed in controls tested at the same time (Figure 3C). Thus, EP exhibits a long-term inhibitory effect on the dynamics of Cu²⁺-induced slow cycle of Ace1p binding. Therefore, electroporation is a harsh procedure, adversely affecting cell morphology, viability and TA binding, and it is not suitable for the staining of the HaloTag fusion proteins if physiological functions such as transcription initiation are to be investigated. Conversely, *pdr5Δ* disruption does not affect the transcription and the growth rate. In addition, *pdr5Δ* cells require a simpler procedure for staining the HaloTag proteins with the HaloTag ligands. Thus, *pdr5Δ* staining enhancement is preferable to electroporation for the labeling of HaloTag proteins in yeast.

<figure><img src="image_4.png" /></figure>

**Figure 4.** Development of correction procedures for the blinking in tracking data. (A and B) Extracted distributions of unbound, short- and long-lived binding events for simulations of molecules without blinking, and (A) without, or (B) with photobleaching. (C–F) Extracted distributions of unbound, short- and long-lived binding events for simulation with blinking and photobleaching where tracking is performed by filling in gaps with a maximum length of (C) 1 frame, (D) 4 frames, (E) 13 frames and (F) 37 frames. For all distributions, the average residence times of the short and long binding events are included next to the appropriate slice with the 95% confidence interval. The 1-frame and 4-frame gap setting data were adequately fit with a single-component exponential, while the remaining data sets required a two-component exponential based on the results of an F-test comparing the 2 fits. (G) Cumulative distribution of gap lengths for simulations (black, solid), Hht1p-HaloTag (blue), Ace1p-HaloTag (red) and Hsf1p-HaloTag (yellow). The dashed line at a fraction of 0.75 is the cut-off value used to determine the number of frames within a gap to fill for each tracked protein. (H) Comparison of gap length distributions for Hht1p-HaloTag in fixed (black) and live (red) cells. The longer gaps present in live cells are believed to be due to dissociation/re-binding events.

## Development of compensation procedure for the reversible photoswitching of HaloTag ligands

In order to obtain reliable estimates for the diffusion coefficient and residence times, we had to overcome artifacts that originate from photo-physical characteristics of the fluorescent dyes. Irreversible photobleaching shortens the visible tracks. Reversible photoswitching introduces gaps dividing a single track into several shorter ones. Thus, both types of photobleaching lead to underestimation of the real track lengths. The original TrackRecord software contained corrections for both photoswitching and photobleaching (19). Photobleaching was corrected by counting the number of detected particles in each frame of an experiment, and fit-
ting the time-course to a 2-component exponential. The ex-
perimental survival distribution was then divided by this
photobleaching curve to account for the fact that a disap-
pearing molecule could be due to either irreversible pho-
tobleaching or dissociation. Photoswitching (blinking) was
corrected by filling in gaps that appeared in otherwise con-
tinuous tracks. The allowed length of a gap was limited to a
single frame to avoid the possibility of a dissociation event
followed by the binding of a second molecule at the same
site. However, as kymographs improved visualization of the
tracks, we were able to measure the gap lengths more accu-
rately. When analyzing immobile molecules of H3 histone
in live cells we often found gaps of up to 10 frames where
a particle would disappear and then reappear in the same
spot. Thus, the maximum gap length could potentially be
increased to 10 frames (150 ms) for Hhtlp-HaloTag stained
with JF<sub>646</sub>. However, most TF are more mobile than H3 and
other dyes may have different blinking and photobleaching
characteristics.

We therefore used simulations to assess how the extracted
residence time depends on performance of the photobleach-
ing and blinking corrections. Parameters for the simulations
were chosen using previously observed experimental val-
ues, reproducing a population consisting of freely diffus-
ing, long-residence and short-residence particles. The par-
ticles were allowed to randomly bind with a rate of k<sub>B</sub> =
0.1 s<sup>-1</sup>, and randomly dissociate with one of two dissoci-
ation constants, k<sub>1</sub> = 0.6 s<sup>-1</sup> and k<sub>2</sub> = 0.1 s<sup>-1</sup>, corresponding
to residence times of 1.67 and 10 s, respectively, with 90%
of dissociation events occurring at k<sub>1</sub>. With these param-
eters we expect to have 82% (1 - k<sub>B</sub> / (0.9k<sub>1</sub> + 0.1k<sub>2</sub>)) of molecules
freely diffusing, 16% bound with the short residence time,
and 2% bound with the long residence time. For all simula-
tions of live cells, particles were set to move randomly in a
3D volume that approximated the size of a diploid yeast nu-
cleus, 4.2 µm<sup>3</sup> with a diffusion coefficient of D = 0.2 µm<sup>2</sup>/s.
200 particles were simulated in 200 cells (1 particle per cell).
We analyzed these simulated images with the same analysis
workflow as used for experimental data.

As a base-line measurement, a first set of simulations (S1)
was run with molecules that were always in their ‘on’ state,
i.e. exhibited no photobleaching or blinking. The fractions
of the three populations with the residence times extracted
through TrackRecord are shown in the pie-chart of Figure
4A, and closely match the expected values above.

For the second set of simulations (S2), we added photo-
bleaching (with an exponential rate constant of 0.9 s⁻¹),
but no blinking, and left all other parameters as for S1.
The distribution of residence times found for photobleach-
ing molecules after automated tracking, and photobleach-
ing correction is shown in Figure 4B, again giving a close
match to expected values. Therefore, the correction for pho-
tobleaching recapitulates the results from simulation with
no photobleaching.

Next, to characterize the blinking correction of Track-
Record, we conducted a third set of simulations (S3) with
photobleaching as above but adding fluorophore blink-
ing with a power law exponent of −1.5 (see Materials
and Methods, (20)). After analysis with TrackRecord, with
the single-frame gaps bridged, and without performing the

manual correction, we get, not surprisingly, a stark differ-
ence in the fitting results as shown in Figure 4C. In this
case, the survival distribution can be adequately fit with a
single-component exponential. We therefore examined the
possibility of improving the automated tracking by simply
increasing the allowable number of frames that a particle
can be absent.

To determine the proper limit for gap filling, in the fourth
set of simulations (S4) we used the same photobleaching
and blinking properties as in S3 but simulated immobile
molecules by setting the diffusion coefficient to 0. For im-
mobile molecules the gaps are generated only by blinking,
not by dissociation. The number of missing frames was de-
termined in each track by automatically identifying time-
points where the particle is not found. The cumulative dis-
tribution of gap lengths for the simulated fixed cells is shown
in the graph of Figure 4G (‘Simulation’). Of note is that only
20% of the gaps consisted of a single frame, while 47% were
4 frames or fewer, 78% were at most 8 frames and 90% are
13 frames or fewer.

We then determined the minimal number of frames to
bridge that would allow us to closely match the residence
time for diffusing molecules obtained in the simulations of
S1. We tracked the particles in the S3 simulation, setting
the gap parameter to 4, 8 or 13 frames. The extracted pa-
rameters for each case are shown in Figure 4D–F. Although
the parameters from S1 were never matched precisely, 8-
and 13-frame gap settings generated better matches to val-
ues obtained in the S1 than the original setting of 1 frame.
We therefore chose to use 8 frames, corresponding to the
75th percentile as the maximum gap setting for diffusing
molecules. Manual correction of the tracks returns a closer
match to the S1 parameters (data not shown), however, the
manual correction step added 10 h to the analysis in addi-
tion to the 2 h required for automated tracking.

Because the simulations indicated that we obtained the
smallest errors when we close at least ~75% of gaps, it was
important to determine the distribution of gap lengths in
real fixed cells. We first asked if fixation affects the distribu-
tion of gap lengths by checking the observed gaps in JF₆₄₆
in Hhtlp-HaloTag cells (Figure 4H). The gaps observed in
live cells were longer than those in fixed cells. The difference
in gap length could potentially be caused by changes in pho-
tophysics of the fluorophores due to environmental effects,
such as a change in pH, resulting from fixation. Tracks in
live cells could also exhibit molecule interchange (where one
molecule leaves a binding site, and another molecule binds
to the same site), which would not happen in fixed cells. The
gap length resulting from this interchange would depend
on the concentration of labeled molecules, with a higher con-
centration decreasing the amount of time the site remains
vacant. Because we label only a small number of molecules
in each cell, the gap length from this phenomenon would
likely be longer than from fluorophore blinking. We chose
to use the lower gap limit established from fixed molecules
to reduce the risk of falsely linking unrelated tracks, al-
though it may result in an under-estimation of residence
times.

We found that the distribution of gap lengths differed for
Hhtlp, Ace1p and Hsflp in fixed cells (Figure 4G, colored
curves), which could be due to slight changes in the fluo-
rophores' local environment because of where it is bound on the folded protein. The value corresponding to the 75th percentile of gap lengths in fixed cells was 4 frames for Hhtlp, 4 frames for Ace1p and 6 for Hsflp. However, to maintain consistent parameters when analyzing the different data sets, we chose to use the shortest gap value of 4 frames for all subsequent data analysis. Increasing the gap setting from 1 frame to 4 frames eliminated the need for manual corrections.

To verify that increasing the allowable gap-length improved the tracking, and resulting residence time analysis, we compared results using manually corrected tracks from 251 cells expressing Hhtlp-HaloTag to the automated 1-frame gap setting, and to the 4-frame gap setting. Using the 1-frame gap setting, we found 66% of the 72 000 total particles (0.5/cell/timepoint) were bound, while 25% had a short residence time of 0.25 ± 0.01 s, and 8% had a long residence time of 1.2 ± 0.07 s (Figure 5A). After manually correcting this data, the fraction of freely diffusing molecules dropped to 50%, and the fraction of short, and long binding events increased to 33% and 17%, respectively (Figure 5B). The extracted residence times did not change dramatically, and were 0.23 ± 0.006 s for short binding events, and 1.1 ± 0.03 s for long binding events. With the 4-frame gap setting, the extracted numbers are much closer to the manually corrected tracks than with the 1-frame setting (Figure 5C). With the longer gaps filled, we obtain 40% of freely diffusing molecules, and 40% short binding events with an average residence time of 0.27 ± 0.005 s, and 20% long binding events with a residence time average of 1.7 ± 0.03 s. Thus, the use of the 4-frame gap setting allows us to extract parameters that closely match those obtained with manual tracking without the need to spend many hours manually processing images and risk introducing operator bias.

## Single molecule tracking of DNA-binding proteins reveals similarities in characteristics of short-residence binding

TFs are capable of non-specific (and transient) binding (6). To evaluate transient binding and to estimate its parameters, we evaluated the dynamics of three different DNA-binding proteins: TF encoded by *ACE1* gene, heat shock transcription factor, encoded by *HSF1* and nucleosomal protein histone H3 encoded by *HHT1*. Homozygous diploids for *HHT1-HaloTag*, *HSF1-HaloTag* and *ACE1-HaloTag* were constructed in *pdr5Δ* background. The strains were treated with 0.005 nM and 0.05 nM *JF646*, respectively, to get the label density of 2–3 molecules per nucleus. To detect the nuclear boundaries, the nucleus was counterstained with *JF549* as described in Materials and Methods and Supplementary Table S3. Imaging for SMT was conducted for 600 frames at 66.7 Hz (9 s total). This fast frame rate was selected as only imaging as fast as possible allowed us to extract any tracks from the Ace1p-HaloTag cells. However, at such a fast frame rate, we expected to be limited to measuring maximum residence times of 1–2 s (~100 images) after which time the majority of fluorophores would be photobleached. Tracking was performed using TrackRecord with automatic linking closing of maximum 4 frames as discussed above. Bound track segments were identified as those that had at least 21 frames where

the frame-to-frame displacement did not exceed 220 nm, based on tracking performed on chromatin-bound nucleosomal histone. These parameters reduce the probability that a freely diffusing molecule will be classified as bound to <1% (see Materials and Methods).

Ace1p in absence of Cu²⁺ may be used as a benchmark for non-specific binding since under these experimental conditions Ace1p binds to DNA only non-specifically. Ace1p requires Cu²⁺ for the conformational change leading to specific binding (8). In absence of externally added Cu²⁺, no specific binding occurs, as confirmed by absence of visible arrays in of Ace1p-3xGFP cells (Figure 3C and (2)). In addition, Ace1p may bind specifically only to five sites in the whole genome (*CUP1*, *CRS5*, *FET3*, *FTR1* and *SOD1*) (7). Furthermore, we previously confirmed that Ace1p is capable of non-specific binding by FRAP (22). Thus, a predominant majority of Ace1p binding that we may observe in SMT experiments must be non-specific. For Ace1p-HaloTag, we performed automated tracking in 257 cells. Of the 60 300 total particles (0.4/cell/timepoint), 80% were freely diffusing, while 17% were bound with a short residence time comparable to the short residence time observed in Hhtlp-HaloTag (0.13 ± 0.003 s), and 3% were bound with a longer residence time of 1.3 ± 0.06 s (Figure 5D).

We compared Ace1p estimates for non-specific binding to those of the heat shock factor Hsflp. We performed tracking on Hsflp-HaloTag at physiological temperature and under heat shock conditions (HS). Search for Hsflp binding sites yielded ~1200 binding sites for Hsflp, called heat shock elements (HSEs), localized in promoters and in open reading frames (23). Those sites are of three types ('perfect', 'gapped' and 'stepped') that differ by sequence and by the binding affinity of Hsflp (24). Different HSEs may be present in the same promoter, and some of them may be constitutively occupied. For the promoter of HSF82 it has been shown that Hsflp may bind constitutively at least to some HSE (25). However, by whole-genome analysis, it has been shown that Hsflp binds to the great majority of the HSEs only after HS (26). Based on high number of HSEs and on HS induced binding of Hsflp to most of those HSE, we expected a dramatic difference in Hsflp mobility between HS and control conditions. We also expected to observe a fraction of constitutively bound molecules of Hsflp even in absence of HS.

Upon the induction of heat shock, Hsflp behaves more like Hhtlp, binding strongly to its target sequences. From the analysis of the residence times of 101 500 particles in 226 cells (0.75/cell/timepoint), we obtained 48% freely diffusing molecules, 38% bound with an average residence time of 0.32 ± 0.008 s, and 14% with a residence time averaging 2.2 ± 0.06 s (Figure 5E). Although this residence time appears to be longer than the long residence time observed in histones (1.7 ± 0.03 s, Figure 5C) further studies are required to determine if upon heat shock, the binding of Hsflp is indeed stronger than Hhtlp. In absence of HS we obtained data on 72 700 particles in 226 Hsflp-HaloTag cells (0.5/cell/timepoint). The residence time analysis of these particles revealed that 68% were freely diffusing, 25% showed short binding events with an average residence time of 0.21 ± 0.008 s, and 6% showed longer binding events with an average residence time of 1.1 ± 0.07 s (Figure 5F).
<figure><img src="image_5.png" /><figcaption>**Figure 5.** DNA binding is protein- and environment-specific, (A–C) Raw survival distribution (black dots), the best fitting 2-component exponential decay (red line), and extracted distributions of diffusing, short and long binding events (pie chart) in Hhtlp-HaloTag after (A) automated tracking filling in 1-frame gaps, (B) manual correction of the automated tracking in (A), and (C) automated tracking filling in gaps up to 4 frames. (D–F) Raw survival (black dots), best fit (red line), and distribution of binding events (pie chart) from automated tracking while filling in 4-frame gaps for (D) Ace1p-HaloTag, (E) Hsflp-HaloTag in heat shock conditions, and (F) Hsflp-HaloTag in absence of heat shock. For all distributions, the average residence times of the short and long binding events are included next to the appropriate slice with their 95% confidence interval. All distributions required a two-component exponential fit based on an F-test comparison between the one- and two-component fitting.</figcaption></figure>

Thus in absence of HS, the distribution of three fractions of molecules of Hsflp (not-bound and bound with short and long residence time) is similar to Ace1p (No Cu²⁺) and dramatically different from Hhtlp. These data indicate that in control cells Hsflp is predominantly bound non-specifically, similarly to Ace1p.

Surprisingly, the residence times for the short-residence molecules is very similar for all proteins/conditions tested. To ensure that the computation does not impose an artificial ceiling on residence time for the short-residence fraction we performed simulations for the sensitivity of curve fitting to residence time values up to 1 s (Supplementary Figure S3). Simulations demonstrate that the fitting algorithm is sen-
sitive to residence times of the 'short-residence' fraction at least 5 times higher than those detected in our experiments.

# DISCUSSION

## Biological significance of SMT estimates of residence time

SMT provides important biophysical characterization for the mammalian nuclear proteins. Notably SMT allowed direct measurement of the dwell time of the TF on transcriptional factories and therefore shed light on the long-standing question of the role for the dynamics of transcriptional complexes. However, important criteria are still not defined to accurately distinguish sequence-specific binding, non-specific binding and diffusion. The tracking algorithm is set to always generate the parameters for the three populations of the molecules: diffusing, bound with short residence time and bound with long residence time. How can we decide on the biological significance of short and long residence times? Do they correspond to specific and non-specific binding, or do they correspond to two different types of specific binding?

Yeast is a model eukaryotic organism that allows development of a simple benchmark for non-specific binding. DNA-binding TF may exhibit not only sequence-specific binding but also sequence-non-specific binding that may reflect a searching mechanism for the specific sites (6). The best control for the nonspecific TF binding would be not a freely diffusing protein, but a TF that binds specific sites conditionally, reflecting the ability of TF to bind both directly and indirectly by interacting with co-activators. Yeast TF Ace1p achieves the conformation suitable for specific binding only by binding Cu²⁺ (8). Thus, in absence of externally supplied Cu²⁺, the fraction of Ace1p in activated conformation must be low, as confirmed by lack of visual Ace1p-3xGFP binding to the CUP1 array. In addition, Ace1p may bind specifically only to five sites in the whole genome (CUP1, CRS5, FET3, FTR1 and SOD1) (7). Thus, a great majority of Ace1p binding that we may observe in SMT experiments must be non-specific. Previously, we demonstrated by FRAP that Ace1p is capable of nonspecific binding to nucleoplasmic targets, as opposed to GFP (22). Therefore, by quantifying the behavior of Ace1p we may obtain the baseline for the nonspecific binding. By SMT we observe 20 ± 0.9% of bound molecules of Ace1p; 86 ± 0.7% of those have a residence time of 0.13 ± 0.003 s. A small fraction (14 ± 0.7%) of Ace1p is bound with an average residence time of 1.3 ± 0.07 s.

To compare the non-specific residence time of Ace1p to other proteins we estimated residence times of the similar inducible TF–heat-shock factor, encoded in yeast by a single HSF1 gene. We tracked Hsflp-HaloTag with and without HS. Our data show that upon HS the estimated fraction of freely diffusing molecules decreases by a factor of 1.4 from 68% to 48%, and the average residence time of long binding events increases from 1.1 ± 0.07 s to 2.2 ± 0.06 s.

Our data lead to interesting conclusions: the estimated residence time of the short-residence molecules is essentially the same for Hhtlp, Ace1p and Hsflp (No HS), equalling 0.12–0.32 s. These three DNA-binding proteins are very different in their structure, function and intracellular concentration. This suggests that (i) short-residence molecules are

bound to DNA non-specifically, and (ii) that non-specific binding shares common characteristics between vastly different DNA-bound proteins and thus may have a common underlying mechanism.

## A fraction of long-residence H3 may represent dynamics of nucleosome assembly

We believe that the extracted population and residence time parameters from Ace1p represent characteristic values for proteins that only bind transiently to DNA, because very few long binding events, if any, are expected from Ace1p in the absence of Cu²⁺. As the time intervals adopted in this study were optimized for study of non-specific binding, we do not expect to extract the correct residence time for specific binding. It is known that the observed residence time strongly depends on the imaging conditions, particularly the imaging rate. This is likely due at least in part to the fact that fluorophores exhibit both reversible and irreversible photobleaching. Because a slower imaging rate allows the reversibly photobleached molecules to recover to their fluorescent state, longer intervals between images typically result in longer observed residence times (27–29). We believe that the residence time of H3 that we have extracted underestimates the true residence time. Because we expect histones to exhibit extremely stable binding lasting much longer than the extracted 1.7 s, this number represents the upper limit on the average observable residence time that can be obtained with the imaging conditions used due to photobleaching.

Regarding the fraction of bound molecules, we expected that as most of H3 is incorporated into the nucleosomes, the great majority of this protein would be stably associated with DNA. By SMT we observe that a sizeable fraction of H3 is bound, as expected (60 ± 0.9%). Unexpectedly, we observe not one but two fractions among those bound molecules. Our data agree with those reported for histone H2 in mammalian cells: fitting of the particle motion in SMT experiments required three components: freely (10%), moderately (19%) and slowly diffusing (71%) molecules (27). For H3 predictions may be derived from FRAP: by FRAP, 16% of H3 in mammalian cells diffuses freely, 16% exchanges with half-time of 130 min and 68% exchanges with half time of a few hours (30). Thus, 84% of the H3 population should be totally immobile for the time of observation of this study (9 s). It is unclear why we still observe two distinct populations of bound histone molecules while imaging cells at much shorter time intervals than (30). It is possible that (i) as the yeast cells have a very gene-dense genome, and (ii) the great majority of those genes undergoes dynamic nucleosomal repositioning (31), we are registering (i) and (ii) at the single molecule level as a fraction of molecules with higher exchange rate and lower residence time than those for H3 stably associated with nucleosomes. In addition, cells in logarithmically growing cultures typically assayed in yeast are actively dividing, thus chromatin replication may further contribute to nucleosomal dynamics (32).
Development of the test for the physiological effects of or-
ganic dye incorporation

Physiological tests are necessary to ensure that single
molecule labeling is benign to the transcription and thus
the produced estimates of residence time are accurate. Such
tests are difficult to develop even in such a simple model
organism as yeast, because they require monitoring of the
transcription initiation. We developed a biological test of
transcription initiation based on microscopic visualization
of the binding of a transcriptional activator Ace1p to CUP1
promoters. Due to high concentration of Ace1p binding
sites in the naturally occurring tandem array of CUP1
genes, induced binding of Ace1p-3xGFP to its target se-
quences may be monitored in real time. Dynamics of this
binding have been previously well characterized (2), and
therefore one may assay whether labeling perturbs this pro-
cess either by preventing or by over-stimulating Ace1p bind-
ing. Application of this test to staining procedures permit-
ted us to establish that the pdr5Δ-facilitated labeling proce-
dure developed in this study is benign and does not affect
the dynamics of TA binding to DNA. However, electropo-
ration, applied to labeling, leads to adverse effects on TA
binding. These results validate the test. We recommend this
test for evaluation of any manipulation of yeast cells for the
transcription studies.

Development of a new and robust labeling procedure based on
mutation of a single yeast transporter gene

SMT has not been yet successfully applied to yeast TFs due
to technical challenges, such as a small size of the nucleus
and imperfect labeling. We adapted SMT to yeast by de-
veloping a benign labeling procedure, and by improving the
tracking and quantification procedures.

*Development of benign organic dye incorporation procedure for HaloTag fusions in yeast.* SMT requires bright photo-storable labels such as HaloTag ligands. However, labeling of HaloTag fusion proteins in wild-type yeast cells is problematic because they pump out xenobiotic compounds very efficiently and hence the stable-state intracellular concentration of HaloTag ligands is very low. We explored the possibility of increasing intracellular ligand concentration by impairing the process of ligand export. Deletion of ABC-MDR transporter (*PDR5*) proved to be sufficient for successful labeling of HaloTag proteins. We demonstrated that *pdr5Δ*-enhanced staining procedure does not interfere with Ace1p binding and thus it is applicable to TF studies.

Alternatively, HaloTag ligands may be electroporated
into yeast cells (12). However, we demonstrated that electro-
poration interferes with normal physiology of Ace1p bind-
ing and thus is unsuitable for studies of transcription. Also,
intracellular SNAP-tag fusions were successfully labeled in
strains with triple deletions of the ABC-MDR transporters
PDR5, SNQ2 and YOR1, but this approach cannot be uni-
versally recommended as triple deletions strongly reduce
the cell's viability (13,14). Instead, we recommend *pdr5Δ*
enhanced staining procedure, which is benign and easy to
apply.

*Addressing blinking of the dyes in SMT analysis.* The lig-
ands we tested exhibit different rates of photobleaching

and blinking. By using computational simulations, we es-
tablished that the TrackRecord software performs a very
good automatic correction for photobleaching (19). How-
ever, blinking is not corrected adequately if the setting for
the maximal number of frames for the gap in the track gen-
erated by blinking is arbitrarily set to one frame. Although
the residence time is not that much affected, in simulations
we observed that the long binding events were completely
ignored, evidenced by the fact that the data analyzed with
the 1-frame gap setting was adequately fit with a single-
component exponential function. Thus, the correct param-
eters for blinking gap correction have to be determined from
real samples. By testing naturally immobile molecules of
histone H3 we observed that although the bleaching rate
changes for fixed molecules of the ligand-bound protein,
blinking rate remains the same. Therefore, one may extract
the blinking rate from fixed samples of the cells. It is worth
noting, that we observed slightly different blinking rates for
different HaloTag fusion proteins stained with the same lig-
and JF<sub>646</sub>. For the gap setting we used 4 frames for Hht1p,
Ace1p and Hsflp, as this accounted for ~75% of the gaps in
fixed cells. Thus, for each set of fusions, ligands and imag-
ing rates this parameter has to be established in preliminary
experiments.

New techniques developed in this study allowed us to in-
troduce SMT to studies of yeast transcription factor dy-
namics and to obtain important information for the char-
acteristics of non-specific binding.

SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online.

ACKNOWLEDGEMENTS

The authors would like to thank Dr Luke D. Lavis and
Dr Jonatan B. Grimm (Janelia Research Campus, Howard
Hughes Medical Institute, Ashburn, VA 20147, USA) for
JF HaloTag ligands. The authors acknowledge Euroscarf
for providing pUG6 plasmid.

FUNDING

Intramural Research Program of National Institutes of Health (NIH), National Cancer Institute (NCI) and Center for Cancer Research (CCR). Funding for open access charge: Intramural Research Program of National Institutes of Health (NIH).

*Conflict of interest statement.* None declared.

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