Utilizing the predictable binding kinetics of DNA-PAINT to denoise super-resolution images

Domain: physics
Published: 2026-02-05
Editorial outcome: major_revision

Abstract
DNA-Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT) is a versatile super-resolution technique that relies on the predictable binding kinetics between fluorescent imager strands and docking strands attached to target proteins. This makes DNA-PAINT particularly suitable for multiplexing and quantitative applications, but its performance is often limited by spurious signals from non-specific binding of imager strands. Here we describe a method to remove these non-specific binding events using a statistical test to distinguish between DNA-specific and non-specific interactions. To demonstrate the method, we imaged mosaic epithelial tissues in Drosophila melanogaster egg chambers and showed that >90% of non-specific and otherwise indistinguishable signal in the super-resolved images can be removed. This denoising improves the quality of DNA-PAINT super-resolved images and is essential for accurate measurements of spatial relationships and protein quantification.

Introduction
DNA-Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT) has emerged as a powerful and straightforward Single Molecule Localization Microscopy (SMLM) technique1–3. DNA-PAINT exploits the transient but repeatable binding between freely diffusing oligonucleotides attached to fluorophores (imager strands) and complementary oligonucleotides attached to proteins of interest (docking strands) to produce blinks of fluorescent light, which can then be localized to reveal the position of the target protein with sub-diffraction resolution1.

Since DNA-PAINT relies on DNA-binding kinetics to produce blinking events and not on the photo-physics of the fluorophores, a broader selection of fluorophores may be used, which simplifies multi-colour imaging1,4. In addition, since the fluorophores are not directly attached to the target proteins, but freely diffusing in the imaging solution, DNA-PAINT circumvents the problem of photobleaching, and under optimized imaging conditions that minimize docking strand photodamage can achieve higher labelling densities and resolution down to 5 nm5–7.

However, the presence of freely diffusing fluorophores during imaging can also produce non-specific localization events that appear as noise in the super-resolved images, which reduces image quality, obscures signal of interest and poses a substantial problem for any subsequent analysis steps8. An extreme example of this is the nucleus where the abundance of genomic DNA can lead to excessive numbers of non-specific hybridization events. Several methods have been developed to deal with this type of non-specific binding, which either rely on blocking e.g. with salmon sperm DNA9 or reducing the hybridization affinity by introducing left-handed DNA oligomers10. Besides the nucleus, non-specific binding is also present in other parts of the cell, where it is hypothesized to originate from electrostatic interactions between the imager strands and positively charged cellular molecules. Previous work focused on ways to reduce the frequency of non-specific binding events by engineering docking strands with multiple complementary motifs to the imager strands, enabling imaging with lower imager strand concentrations5,11. These optimized conditions can offer a sizeable, 5- to 10-fold, reduction in the non-specific binding frequency, but do not eliminate non-specific binding events and additional post-processing steps are required to exclude them, especially for applications that require multiplexing and/or higher resolution12. Previous approaches focused on empirical methods to detect non-specific interactions with very distinct characteristics e.g. non-repetitive binding or bursts in binding activity, but did not take full advantage of the predictable binding kinetics between the imager and docking strands12,13.

Here we develop a denoising algorithm that exploits the fact that the times separating real binding events between imager and docking strands closely fit an exponential distribution, whereas spurious time traces do not, and propose a filtering method based on a statistical test for exponentiality to distinguish between non-specific and specific DNA-binding events. Our method employs a two-step clustering process that makes it applicable not only to isolated clusters, but also to more crowded environments where target proteins may coalesce into larger structures.

We applied our method to Drosophila melanogaster egg chamber epithelial tissues, where we imaged the cell adhesion protein, E-cadherin, and showed that >90% of background signal from non-specific binding can be filtered out while retaining >98% of the specific signal. Our denoising approach improves the quality of DNA-PAINT images, but more importantly, can be combined with recent advances in measuring protein amounts and multiplexing to enable sub-cellular proteomics with single-molecule sensitivity and nanoscale resolution.

Results
Clustering of SMLM data
The steps needed to apply the denoising method are illustrated in Fig. 1. DNA-PAINT relies on the transient hybridization between a docking oligonucleotide and a freely diffusing imager oligonucleotide to produce blinking events. These events are subsequently localized to reveal the position of the docking strand with a precision primarily determined by the number of photons collected during binding. Due to the stochastic nature of this process, the times between hybridization events are expected to follow an exponential distribution. This characteristic exponential behaviour is independent of the number and/or spatial distribution of the docking strands, and binding events from a single or several docking strands can be grouped into clusters of any size or shape, as long as the blinks remain well separated in time2. Given that the docking strands are attached to proteins with unknown and complex spatial distributions, we reasoned that the localization precision offers an intuitive scale to cluster binding events, as localizations produced by a single isolated docking strand are expected to reside within a circular area with radius of approximately twice the localization precision, while more complex shapes can be described by a superposition of those localization-precision-sized clusters.

To develop the clustering method, we imaged the commercially available GATTA-PAINT nanoruler, which has DNA-binding sites arranged linearly in 3 marks, with a mark-to-mark spacing of 80 nm (Fig. 2a). We then clustered the localizations using the DBSCAN algorithm14, which has been widely used on SMLM data15. DBSCAN is a density-based algorithm that makes no assumptions about the number or shape of clusters but requires a search radius (ε) and a minimum number of localization (n) that are expected to reside within this search radius to form a cluster. Localizations that do not meet the above criteria are classified as noise. Given that a minimum of 5 binding events is required to run the statistical test, we set n = 5 and ε to the median localization precision. Under these conditions DBSCAN sometimes identified and correctly assigned localizations from a single mark to a single cluster, but in most cases because of the partial overlap between the localizations from neighbouring marks, DBSCAN merged localizations from two, and in certain cases all three neighbouring marks, into a single cluster (Fig. 2b).

To break these larger clusters into smaller, localization-precision-sized, clusters we applied a second clustering step based on k-means clustering16, which partitions the merged localizations into a pre-defined number of localization centroids. The number and start positions of the centroids were established by cross-correlating a 2D Gaussian template image with the reconstructed image of each cluster formed in the DBSCAN step (Supplementary Fig. 1) where the width of the 2D Gaussian template is equal to the localization precision. This second clustering step results in the expected number of 3 clusters per nano-ruler regardless of the initial DBSCAN clustering results (Fig. 2b). Importantly, this two-step clustering method works well for more complex structures and groups localizations into localization-precision-sized clusters in both low- and high- density areas. To demonstrate this, we imaged E-cadherin-GFP in the follicle epithelium of stage 7 Drosophila egg chambers (Fig. 2c)17. E-cadherin is a homophilic transmembrane adhesion protein that plays a key role in the formation of the adherens junctions at the top of the lateral borders between epithelial cells. Cis interactions between E-cadherin molecules in the same cell lead to E-cadherin clustering, while adhesive trans interactions with E-cadherin in the neighbouring cell result in the alignment of these clusters in adjacent cell membranes18. This behaviour is captured in DNA-PAINT images, where juxtaposed traces of E-cadherin can be resolved in the membranes on both sides of a cell-cell junction (Fig. 2d). Because of the high density of localizations in this region, DBSCAN tends to cluster localizations into few elongated clusters, that can span both cell membranes (Fig. 2e). The second clustering step breaks these large clusters into smaller localization-precision-sized clusters that are restricted to one cell membrane (Fig. 2e).

E-cadherin is also present in the cytosol e.g. in endocytic and exocytic vesicles, but at a lower concentration than the lateral membrane. Here, most localizations are clustered into localization-precision-sized clusters during the first DBSCAN clustering step, and as expected, the second clustering step only partitions the larger clusters (Fig. 2e).

Time trace construction
The next step uses the localizations in each cluster to construct a time trace of the binding events for that cluster. In DNA-PAINT, the binding time is determined by the homology between the imager and docking strand sequences and does not depend on the photophysics of the fluorophore attached to the imager strand. The duration of a binding event is inferred from the duration of a blinking event, with persistent blinks over consecutive frames counting as a single binding event and blinks separated by one or more frames as different binding events (Fig. 3a). Because of fluorophore photophysics, however, a fluorophore can undergo several blinking events during the time its imager strand is bound to the docking strand, especially when imaging further away from the coverslip, which often entails the use of higher excitation power densities19–21 than TIRF, for example. It is therefore important to account and correct for this photophysical blinking when constructing the time traces (Fig. 3a).

To assess the impact of photophysical blinking on time trace construction, we imaged the GATTA-PAINT nanoruler at a frame rate of 25 fps and constructed time traces using the localizations from individual marks, which, according to the manufacturer, are produced by binding events with an approximate range of ~0.5–1 s. If we only merge blinks that persist over consecutive camera frames, the cumulative distribution function (CDF) of the times between binding events poorly follows the expected exponential distribution (Fig. 3b). However, if we merge blinks that are separated by only a few dark frames, the RMSE error between the experimental CDF and an exponential model is reduced, indicating that the times between binding events follow the expected exponential distribution more closely (Fig. 3b). The similarity between the experimental CDF and the exponential model continues to improve as the time gap allowed between merged blinking events increases to ~15-frames (0.6 s) (Fig. 3c). This time coincides with the expected binding time, suggesting that to minimize the effect of photophysical blinking, it is important to merge blinks that are separated by up to approximately the expected binding time. Allowing for longer times between blinks does not offer any further improvements and in extreme cases with high binding frequencies may erroneously lead to merging of consecutive, independent binding events. We note that the binding time may also be affected by the local environment of the docking strands, as we have observed longer binding times with docking strands conjugated to the HaloTag, where the oligonucleotide lies in a pocket containing the Halo active site (Supplementary Fig. 2).

Test for exponentiality
Determining the optimal parameters for the statistical test. The last step in the denoising pipeline is to apply a null hypothesis test for exponentiality. We used the Anderson–Darling test to test for the null hypothesis that the times between binding events are drawn from an exponential distribution. The alternative hypothesis is that the times between binding events do not follow an exponential distribution. The significance level of the test indicates the probability of erroneously assigning localization clusters produced by real hybridization events between imager and docking strands as non-specific signal. Common choices for the significance level are 5% or 1% and to determine which one is more appropriate in this case, we used the GATTA-PAINT nanoruler. We selected regions with the characteristic 3 DNA-binding marks (Fig. 4a) and performed the 2-step clustering method described above to separate localizations into 3 clusters, analysing a total of 555 individual clusters. For each cluster, we linked the blinks based on the expected binding time between the imager and docking strands, allowing for a gap of up to 1 s, to obtain the times between binding events (Fig. 4b). We then applied the Anderson–Darling test to each individual cluster and plotted the fraction of non-rejected clusters as a function of significance level (Fig. 4c). Within the investigated range, the fraction of non-rejected clusters exhibited a near-monotonic decrease with increasing significance level, suggesting that a 1% significance level is more appropriate, as this level leads to the acceptance of more than 94% of the real clusters.

To determine the power of the test to correctly reject background clusters with non-exponential binding kinetics, we imaged follicle cells in Drosophila egg chambers expressing E-cadherin-GFP that had not been labelled with the docking strand-coupled GFP-nanobody but were otherwise prepared and imaged under standard conditions. In these samples, all the observed blinks are due to non-specific interactions between the fluorophore-coupled imager strand oligonucleotides and the sample (Fig. 4d). The binding time for these non-specific interactions is not known, but to maintain consistency in our analysis, we linked the non-specific blinks based on the expected binding time between the docking and imager strands to obtain the number of apparent binding events per cluster and the times between these apparent binding events. We then separated the clusters in groups based on the number of binding events per cluster, ran the Anderson–Darling test for each group and measured the fraction of rejected clusters. By plotting the fraction of rejected clusters versus the number of binding events per cluster, we observed that ~33% of the clusters with only 5 binding events were rejected, but the fraction of rejected clusters increased with increasing number of binding events per cluster and reached a plateau of >90% for clusters with ≥12 binding events, indicating that the power of the test increases with increasing sample size (binding events) up until this point and then remains constant (Fig. 4e). Since a large proportion of the background clusters contain few binding events, introducing a threshold on the minimum number of binding events per cluster provides an effective way to reject non-specific clusters that have too few blinks to apply the Anderson–Darling test effectively. In this way, clusters with fewer binding events than the threshold value, where the power of the test is low, will be directly rejected, while non-specific clusters with more binding events than the threshold value will be rejected because of the increasing power of the test. This cumulative effect is shown in (Fig. 4f) where the fraction of rejected clusters is plotted as a function of the minimum number of binding events per cluster. Here >95% of the non-specific clusters are rejected with a threshold of 6 binding events per cluster while the fraction of rejected clusters rises to >99% for 8 binding events per cluster.

The optimum value for the threshold of minimum binding events per cluster depends on the biological application. We suggest using a minimum of N = 12 binding events per cluster for quantitative methods such as qPAINT2, since this is the point at which the Anderson–Darling test becomes optimally effective. qPAINT counts the number of molecules present in a cluster by measuring the times separating consecutive binding events to estimate the mean time between binding events. The error on this estimate scales as 1/sqrt(N-1), where N is the number of binding events in the cluster. Thus, applications of qPAINT that collect enough data to reduce this error will usually have values of N that are higher than 12.

For other biological applications the optimum threshold value can be obtained from the super-resolved images themselves by comparing background regions with regions where most signal is specific. Here, setting the threshold involves a balance between increasing the efficiency of the method in rejecting non-specific clusters and minimizing the number of specific clusters that are rejected because their number of binding events fall below the threshold.

Demonstration of the denoising method. To demonstrate this approach, we imaged E-cadherin in follicle cells in stage 7 Drosophila egg chambers, but this time we induced mitotic recombination to generate clones of cells that lack E-cadherin-GFP next to cells that are homozygous for E-cadherin-GFP (Fig. 5a). The juxtaposition of E-cadherin-GFP expressing cells with non-expressing cells offers a straight-forward approach to evaluate the effectiveness of our method in removing non-specific localizations, as both the sample and the control undergo identical sample preparation steps and are imaged under identical conditions. In the super-resolved images, almost all clusters that lie in the adherens junctions along the borders between E-cadherin-GFP expressing cells represent specific interactions between the imager and docking strands, whereas the non-expressing clones are regions where non-specific interactions prevail. Note that borders between E-cadherin-GFP expressing cells appear as double lines, because the E-cadherin clusters in the two opposing cell membranes are resolved, whereas borders between E-cadherin-GFP expressing cells and non-expressing cells appear as a single line (Fig. 5a1-2). In the non-expressing region, the borders between cells are not visible. We then defined the clusters in these regions and analysed whether the temporal distribution of blinks in each cluster passed the Anderson–Darling test for exponentiality (Fig. 5b). Counting the clusters in each region gives the fraction of rejected clusters as a function of the minimum number of binding events as shown in Fig. 5c–e.

The E-cadherin-GFP non-expressing region is dominated by non-specific signal and largely behaves like the negative control sample. However, only ~89% of clusters are rejected with a threshold of 6 binding events compared to >95% in the negative control (Fig. 5c). These extra accepted clusters presumably correspond to signal from docking strands coupled to off-target nanobodies. The signal from these docking strands is indistinguishable from real signal and will follow the expected DNA-binding kinetics. The corresponding clusters will therefore not be rejected by the Anderson–Darling test.

The E-cadherin-GFP expressing region contains both specific and non-specific clusters. The rejection rate (r) in this mixed region will therefore decrease more slowly than in the non-expressing region (rb) as the minimum binding event threshold is increased, although they both start at approximately the same level (Fig. 5d). This can be explained by the prevalence of specific clusters in the expressing region. Non-junctional E-cadherin is found in endocytic and exocytic vesicles, and recycling endosomes, with each compartment carrying multiple molecules of E-cadherin. The specific clusters in this region will therefore contain many E-cadherin molecules and exhibit a higher number of binding events. These clusters are not eliminated by the test but are gradually rejected for failing to meet the threshold for the minimum number of binding events per cluster as the threshold increases. E-cadherin is highly enriched in the junctional region where both the concentration of specific clusters and the number of docking strands per cluster is expected to be the highest. Here rejection levels remain below 4% when the minimum number of binding events per cluster is 12 (Fig. 5e).

The background, non-specific clusters are presumably present at equivalent levels in both the expressing and non-expressing regions, but the E-cadherin specific clusters occur only in the expressing region. The percentage of clusters that are rejected should therefore be lower in the expressing region for low threshold values. As the threshold for the minimum number of binding events per cluster increases and the non-specific clusters are depleted from both regions, the rejection rate in the expressing region will become higher than the non-expressing region because some specific clusters are erroneously rejected from the remaining population. Thus, the optimum threshold value can be calculated by determining when the rejection rate in the non-expressing region (rb) equals the rejection rate in the expressing region (rs). This value can be determined graphically by normalizing the rejection rate in the expressing region to the one from the non-expressing region (rs/rb) and plotting it as a function of the minimum number of binding events per cluster (Fig. 5f). In this case we can set the threshold to the minimum number of binding events where the normalized rejection rate exceeds one, which in this case is 8 binding events. Under these conditions we reject >90% of the clusters in the non-expressing region, whereas <2% from the signal at the cell border is rejected. The denoised image is shown in Fig. 6a. It should be noted that in contrast to the cell-cell junctions, where E-Cadherin is enriched, identifying E-cadherin clusters in vesicles in the cytoplasm and filtering them out from the non-specific signal would be very difficult otherwise as they cannot be morphologically identified. Additionally, tubular structures, probably recycling endosomes, become sharper and more prominent22,23.

Denoising using parts of the image that lack specific signal. This method for setting the threshold of minimum number of binding events per cluster is not limited to mosaic tissues but is directly applicable to all images where a clear distinction between areas with non-specific signal and areas with specific signal can be established. To demonstrate this, we imaged TOM20 labelled mitochondria in U2OS cells (Fig. 6b). The characteristic shape of mitochondria facilitates the segmentation of the super-resolved images into areas with and without specific signal (Supplementary Fig. 3) and a denoised image is readily obtained with ~94% of the clusters in areas of the image not occupied by mitochondria rejected as shown in (Fig. 6b). We note that areas of the image with non-specific signal should be used with caution to estimate the background levels, since all signals in the areas outside the mitochondria are considered as noise, and this includes signal from off-target binding of docking strands. This signal from off-target docking strands is indistinguishable from specific signal and may result in the removal of excessive amounts of specific signal during the denoising process (Supplementary Fig. 3).

Denoising using a negative control. To avoid this problem and minimize the loss of specific signal, a negative control that lacks docking-strands can be used instead. To demonstrate this approach, we imaged microtubules in HeLa cells transfected with GFP-tagged α-tubulin (Fig. 6c, Supplementary Fig. 5a). Images of these cells acquired under standard DNA-PAINT imaging conditions without labelling the GFP with a docking strand serve as the negative control. They are used to denoise DNA-PAINT images of microtubules in separately prepared transfected cells that were processed under identical conditions except that GFP was labelled with a single domain antibody coupled to a docking strand (Supplementary Fig. 5b). Here, the denoising method removes >97% of the clusters in the negative control and ~42% of the clusters in the labelled sample image, most of which lie in areas between the microtubules (Fig. 6c, Supplementary Fig. 5).

Using a negative control sample to characterize the non-specific signal required for the denoising method is a more general method and can be applied in all cases but is more labour intensive and susceptible to experimental variations. Therefore, it is important to ensure that the negative control is prepared and imaged under standard experimental conditions.

Denoising samples with amorphous protein distributions. A key feature of our method is that it does not make any assumptions on the underlying distribution of the protein of interest, hence is directly applicable to proteins with amorphous distributions. To demonstrate this, we imaged the follicle cell epithelium in stage 7 Drosophila egg chambers in which clones of cells homozygous for Halo-tagged atypical protein kinase C (Halo-aPKC) lie adjacent to cells that do not express Halo-aPKC (Fig. 6d). aPKC is a cytosolic protein that is enriched at the apical side of epithelial cells, but it does not assume a characteristic shape. Here, the non-expressing region is obtained from a clone of Halo-aPKC non-expressing cells, similar to the approach taken with E-cadherin (Fig. 6d Inset, Supplementary Fig. 4). By clustering and linking blinks with a 2 sec gap (Supplementary Fig. 2), the denoising method eliminates ~96% of the clusters in this region with a threshold for the minimum number of binding events of 8. We note that non-specific clusters are indistinguishable from real aPKC clusters in the super-resolved images due to the amorphous distribution of aPKC. Denoising is therefore essential for obtaining accurate images of the spatial arrangement of aPKC (Fig. 6d).

Methods
Microscope setup
Data collection was carried out on a custom-built spinning disk microscope equipped with a spiral disc from Crest Optics as previously reported20. All imaging was carried out with a 100× 1.35NA silicone immersion objective lens (Olympus, UPLSAPO100XS). Fluorescence is collected with this objective lens and relayed to the primary image plane at the side port of the microscope base where the spiral disk is positioned. After passing through the spiral disk, fluorescence is separated from excitation laser light with a dichroic mirror (Chroma, ZT543rdc or ZT405/488/561/647rpc) and an additional bandpass filter (Chroma, ET560lp or ZET405/488/561/647). The primary image plane is relayed to an intermediate plane, using a 1X telescope, where an adjustable rectangular aperture (Owis, 27.160.1212) is positioned. This intermediate image plane is subsequently relayed to the camera sensor plane and passes through an additional bandpass filter (Chroma, 569/25×). A sCMOS (Hamamatsu, Orca Flash4.0V3) was used for image collection with an effective pixel size of ~98 nm. Microscope control and data collection was carried out on a standard Windows based PC running a custom microscope control program developed in the LabView environment and available on GitHub (https://github.com/Gurdon-Super-Res-Lab/Microscope-Control).

Drosophila melanogaster lines
Standard procedures were used for Drosophila maintenance and experimentation. DE-Cadherin images were taken using the GFP knock-in lines of Huang et al.17. DE-Cadherin::GFP clone images were obtained by recombining DE-Cadherin::GFP with FRT-G13 (w[*]; P{w[+mW.hs]=FRT(w[hs])}G13 BL1956), and then crossing to hs-FLP; FRT-G13, Ubi-mRFP.nls/CyO flies to obtain hs-FLP/+; FRT-G13, DE-Cadherin::GFP/FRT-G13, Ubi-mRFP.nls progeny. Clones were obtained by heat-shocking either pupal or adult stages for 3–4 days at 37°C for 2 h 2× per day. Flies were then fattened on yeast for 1–2 days at 25°C before dissection.

aPKC images were taken using the Halo-SNAP (HaSP)-aPKC CRISPR line from Erdmann et al.25. Halo-aPKC clone images were obtained by recombining HaSP-aPKC with FRT-G13 (w[*]; P{w[+mW.hs]=FRT(w[hs])}G13 BL1956), and then crossing to hs-FLP; FRT-G13, Ubi-mRFP.nls/CyO flies to obtain hs-FLP/+; FRT-G13, HaSP-aPKC/FRT-G13, Ubi-mRFP.nls progeny. Clones were obtained by heat-shocking either pupal or adult stages for 3–4 days at 37°C for 2 h 2× per day. Flies were then fattened on yeast for 1–2 days at 25°C before dissection.

Nup160 images were taken using the Nup160-Halo CRISPR line from Cheng et al.21. Freshly eclosed flies were fattened on yeast for 1–2 days at 25°C before dissection.

Drosophila sample preparation. Fattened ovaries were dissected in Schneider’s medium supplemented with insulin (7.5 µg/ml, Merck I9278). Ovarioles were dissected out of the surrounding muscle sheath and were fixed for 20 min at room temperature with prewarmed (38°C) fixative (4% methanol-free formaldehyde in 0.5× PBS). Samples were quenched by rinsing and then washing for 5 min in 50 mM ammonium chloride. They were then permeabilised in 0.2% Triton X-100 in PBS for 5 min, followed by 3× 5 min washes in PBS. Labelling was performed for 1 hour at 37°C with shaking in 0.5% bovine serum albumin (BSA), 1 µM DTT in PBS with either a 1:300 dilution of the GFP-nanobody conjugated to docking strand (Massive Photonics, Massive Tag-Q Anti-GFP Kit) for GFP labelling or 1 µM Halo-ligand conjugated to the P3 docking strand for Halo labelling3. After labelling, samples were washed 3× 5 min with PBS and then overnight with PBS + 0.1% Triton X-100. The next day, samples were washed 3× 20 min with PBS and mounted on 8-well chambered coverslips (Ibidi 80807) that had been treated with Cell-Tak (Corning 354240). Samples were imaged in 300 µl of imaging solution (PBS + 500 mM NaCl + 20 mM sodium sulphite + 1 mM Trolox) with 2 nM Imager 3 Cy3B (Massive Photonics) for GFP, or 2 nM P3 Cy3B imager strand for Halo.

Mitochondria sample
We utilized the Massive-PAINT 2-PLEX kit from Massive Photonics for mitochondria imaging in U2OS cells which includes all probes and reagents.

Cell culture, transfection, fixation and staining for microtubule sample
HeLa Kyoto cells were obtained from the Research Institute of Molecular Pathology (Vienna, Austria). Cells were cultured in media composed of DMEM GlutaMAX (Gibco, Thermo Fisher Scientific) supplemented with 10% foetal bovine serum (FBS), 1% penicillin-streptomycin and 1% L-Glutamine (all from Gibco, Thermo Fisher Scientific) at 37°C and 5% CO2 and were regularly tested for mycoplasma contamination.

Cells were plated onto 8-well glass bottom chambered coverslips (Ibidi, 80807) 24 h prior to transfection with a pEGFP-Tub plasmid (Clontech/Takara, 632349) to label α-tubulin. For each well, 0.25 µg DNA with 0.75 µL FUGENE HD Transfection reagent (Promega, E2311) in 125 µL Opti-MEM reduced serum medium (Gibco, Life Technologies) were used according to manufacturer’s instructions. Transfected cells were incubated in culture conditions as above for 36 h before fixation.

Cells were fixed and permeabilized simultaneously in 4% methanol-free formaldehyde (FA, Fisher Scientific) with 0.2% Triton in Cytoskeleton Buffer with Sucrose (CSB+S, 10 mM MES, 138 mM KCl, 3 mM MgCl2, 2 mM EGTA, supplemented with 4.5% sucrose w/v, pH 7.4) for 6 min at 37°C, following which they were fixed in 4% methanol-free formaldehyde in CSB+S for 14 min at 37°C and washed in PBS. Prior to staining, fixed cells were re-permeabilised in 1% Triton in PBS for 5 min at room temperature, blocked in Antibody incubation buffer (Massive Photonics) for 30 min, and incubated with 1:300 anti-GFP sdAB in the same Antibody incubation buffer for 45 min before being washed in Washing buffer (Massive Photonics).

SMLM data acquisition and analysis
All SMLM data was acquired on the microscope described above. For each acquisition, unprocessed camera frames were acquired as sixty 1000 frame blocks (cycles) and a camera exposure time of 200 ms (5 Hz). Drift correction was performed during imaging by measuring the XYZ position of the sample at a rate of ~0.5 Hz using a near IR transmitted light lamp and camera separate from the primary imaging path. Full details of this scheme have been reported previously21. Unprocessed camera frames were analysed using custom localization scripts developed in the MatLab environment and using the previously reported analysis/fitting package from Li and co-workers26. Additional XY drift correction was carried out in post processing using the previously reported redundant cross correlation algorithm from Wang and co-workers27.

Denoising analysis
Raw localizations were first clustered using the DBSCAN algorithm with search radius ε = the median localization precision, and a minimum number of localizations that are expected to reside within this search radius n = 5. Subsequently a super-resolved image was reconstructed for each DBSCAN cluster and cross-correlated with a 2D gaussian template with a sigma value set by the localization precision to determine the number and initial positions of centroids for a second k-means clustering step, which further partitions the localizations into localization precision-sized clusters. Typical processing times for a dataset with approximately two million (x, y) coordinates was 40 min for completion of both clustering steps on a standard PC running 64-bit Ubuntu with 32 GB RAM and an Intel i7 CPU at 3.2 GHz (example processing times and computer specifications are included with the example MATLAB scripts in the supplement). Localizations in each cluster separated by <2 s for Halo-tagged target and <1 s for all other tags were further linked to a single binding event to construct a time trace of the binding activity for each cluster and measure the times between the binding events. Finally, the distribution of the measured times between binding events for each cluster was tested against an exponential distribution with the Anderson–Darling test. Test results with a p-value < 0.01 were rejected as noise.

GATTA-PAINT nanoruler imaging
Data using the GATTA-PAINT nanoruler (GATTAquant HiRes 80R) were collected using the 70 µm spirals and 642 nm excitation with a power density of 5 kW/cm2. Camera frames were acquired in blocks of 1000 frames at a rate of 25 frames per second. In total 100,000 frames were collected for each image. Drift correction and localization analysis were performed as described for the SMLM data.

Docking strand depletion simulations
To estimate the effect of docking strand depletion on the performance of the denoising method, we ran simulations on 5000 clusters with 10 docking strands per cluster, assuming an average binding time of 1 s, an average time between binding events for a single docking strand of 1000 s, and a total acquisition time of 10,000 s (~1.3 h). We investigated a typical case of docking strand depletion with 10% of the docking strands depleted during imaging and an average lifetime of a docking strand of 80,000 s, and a more extreme case with ~60% of the docking strand depleted during imaging and an average lifetime of a docking strand of 10,000 s. Under the typical scenario the denoising method rejected ~1% of the clusters, while under the more extreme case the denoising method rejected 6% of the clusters.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled “Utilizing the predictable kinetic of DNA-PAINT to denoise super-resolution images” describes a process to ‘denoise’ DNA-PAINT data. DNA-PAINT uses ‘imagers’, fluorophores attached to diffusing oligonucleotides, to produce stochastic single molecule events. The predictable binding behavior of these small oligonucleotides has enabled the authors to describe a method to remove localization data suspected of originating from non-specific event detections. The authors have demonstrated the procedure on both synthetic DNA-origami as well as biological, Drosophila & cultured U2OS, samples. The manuscript could be further improved by addressing the following comments:\n1. In the second paragraph of the introduction the authors talk about circumventing the problem of photobleaching. Whilst this is true the authors should introduce the concept of docking strand ‘site-loss’ caused by photodamage. This process can lead to a decrease in localizations over time similar to photobleaching. (see: https://doi.org/10.3390/molecules23123165)\n2. Main Figure 1. The clustering step 1 and 2 boxes would benefit from additionally having “DBSCAN” and “k-means” mentioned within them. It would also be my preference to have greater detail in the figure legend.\n3. Main Figure 4a. Scale bar and/or define the expected size of the nanoruler in the legend.\n4. Main Figure 4d. The background/non-specific events you show here appears alarmingly high. Do you typically see higher levels of non-specific binding in your Drosophila samples in comparison to U2OS cells? Is 2 nM a typical imager concentration you would image with?\n5. Additional media files (a hundred frames or so) that show the raw ‘blinks’ from binding/non-specific events in your various samples would be appreciated.\n6. The authors should discuss the combination of imaging sparsely distributed super-resolution targets and how the effects of DNA-PAINT site-loss could affect their denoising approach.\n7. The comparison between raw and denoised super-resolution images in Figure 6 highlights a particular concern that the denoising procedure could remove specific events. In Figure 6b, showing the mitochondria, it is suggestive that potentially specific events have been removed from within the mitochondrial signal leaving a number of voids. Can the authors alleviate this concern?\n8. Have the authors tried fluorogenic DNA-PAINT (doi.org/10.1038/s41592-022-01464-9)? Perhaps they could comment on the compatibility of their approach with this modification of DNA-PAINT.\n9. I appreciate that whilst the approach is described throughout the main text, the manuscript would benefit with the addition of a concise methods section (probably with reference to Figure 1) on how the denoising analysis steps were taken and should be included either in the methods or as a supplementary.\n(Remarks on code availability)\nThe current linked repository only contains the readme and license. I assume it will be populated once the manuscript is published.\nUnfortunately, I was unable to test the code supplied with the initial upload due to a problem obtaining the necessary license to install the 'Add-On' ‘Statistics and Machine Learning Toolbox’ required to use dbscan in MATLAB.", "reviewer_2": "This manuscript presents a well-conceived and carefully executed method for denoising DNA-PAINT super-resolution microscopy data by utilizing the predictable binding kinetics of imager and docking strands. The manuscript is clearly written, the figures are informative, and the inclusion of example code is appreciated. I believe this work represents a valuable contribution to the SMLM and quantitative imaging community. Below are a few quries and suggestions intended to improve further the clarity, applicability, and reproducibility of the study, if addressed suitably.\n1) The two-step clustering method (DBSCAN followed by k-means) is effective, particularly in crowded regions. However, DBSCAN is sensitive to the choice of search radius ϵ and in this study it was set as the median localization precision. Could the authors clarify how robust the clustering outcome is to variations in ϵ ? Was any parameter sensitivity analysis performed to validate the choice of clustering parameters?\n2) The use of Gaussian template matching to determine initial centroids for k-means is interesting. Have the authors compared this strategy to other centroid initialization techniques or assessed clustering quality using established metrics such as the silhouette score?\n3) Is the dark-time threshold determined empirically for each sample type? Could the authors comment on the applicability of a fixed threshold in biologically heterogeneous environments where binding kinetics may vary locally?\n4) Have the authors compared the performance with other goodness-of-fit tests besides the Anderson-Darling test, such as Kolmogorov-Smirnov or Cramér-von Mises, especially in the context of small sample sizes?\n5) The authors propose a minimum binding event threshold (e.g., N ≥ 8 or 12) to reject low-confidence clusters. Would a probabilistic or likelihood-based approach offer a more flexible alternative to hard thresholding, particularly for detecting sparsely expressed proteins?\n(Remarks on code availability)\nThe method is implemented in MATLAB and demonstrates a reasonable runtime (~3 minutes in our machine for the attached dataset). Could the authors provide performance benchmarks for larger, experimentally acquired datasets? Is the current implementation scalable to 3D or high-throughput SMLM datasets, and if not, are there plans for optimization (e.g., GPU acceleration, parallelization, or batch processing)? Additionally, the provided code would benefit from more detailed in-line comments and documentation to improve clarity and facilitate adoption by other researchers. Could the authors consider enhancing the code readability and including function-level annotations and usage examples for standard datasets (for example the csv file created by ThunderSTORM) directly?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "1. In the second paragraph of the introduction the authors talk about circumventing the problem of photobleaching. Whilst this is true the authors should introduce the concept of docking strand ‘site-loss’ caused by photodamage. This process can lead to a decrease in localizations over time similar to photobleaching. (see: https://doi.org/10.3390/molecules23123165)\nResponse: We thank the reviewer for pointing this out. We have now modified the text and add the corresponding reference in the second paragraph to read: “DNA-PAINT circumvents the problem of photobleaching and under optimized imaging conditions that minimize docking strand photodamage can achieve higher labelling densities and resolution down to 5 nm”.\n2. Main Figure 1. The clustering step 1 and 2 boxes would benefit from additionally having “DBSCAN” and “k-means” mentioned within them. It would also be my preference to have greater detail in the figure legend.\nResponse: We agree with the Reviewer. The figure has been updated, and we have expanded the figure legend.\n3. Main Figure 4a. Scale bar and/or define the expected size of the nanoruler in the legend.\nResponse: We thank the reviewer for pointing this out. We have now added the expected size of the nanoruler to the legend.\n4. Main Figure 4d. The background/non-specific events you show here appears alarmingly high. Do you typically see higher levels of non-specific binding in your Drosophila samples in comparison to U2OS cells? Is 2 nM a typical imager concentration you would image with?\nResponse: We do not have experience with U2OS cells as we mostly work with Drosophila tissues. In our hands it is typical to observe these levels of non-specific binding across a variety of tissues including egg chambers, accessory glands, ejaculatory ducts, wing discs and embryos. Typical imager strand concentrations range between 1-3 nM, with 2 nM being most common.\n5. Additional media files (a hundred frames or so) that show the raw ‘blinks’ from binding/non-specific events in your various samples would be appreciated.\nResponse: We have added 100 frames showing the raw “blinks” when imaging the following samples in a figshare repository: E-cadherin (ecad.tif), a negative control E-cadherin sample (nc_ecad.tif), mitochondria sample (mito.tif) and a-PKC sample (apkc.tif).\n6. The authors should discuss the combination of imaging sparsely distributed super-resolution targets and how the effects of DNA-PAINT site-loss could affect their denoising approach.\nResponse: For a single docking strand, the denoising method will work if the docking strand is damaged after the number of binding events has passed the threshold, as this won’t affect the exponentiality in the distribution of the times between binding events. For clusters with several docking strands, the effect of docking strand loss on the denoising is more complex, as it depends on several parameters including the number of docking strands in a cluster, the depletion rate of docking strands and the total number of binding events. Blumhardt et al. (2018) observed that less than 10% of the docking strands are destroyed after more than an hour of DNA-PAINT imaging in the presence of PCD and PCA. To get an estimate on the performance of our denoising approach under these imaging conditions, we ran simulations on 5000 clusters with 10 docking strands per cluster, assuming average binding time of 1 sec, average time between binding events for a single docking strand of 1000 sec, total acquisition time of 10000 sec (~1.3 h) and average lifetime of a docking strand of 80000 sec. Under these conditions each cluster lost on average ~1 docking strand, i.e 10% of the docking strands were destroyed and the statistical test rejected ~1% of the clusters. Given that the significance level of the test is set at 1%, our simulation suggests that our denoising algorithm is insensitive to effects of docking strand depletion under standard imaging conditions. In a more extreme case, we reduced the average lifetime of a docking strand in the simulation by a factor of 8, so that ~60% of the docking strands are lost during imaging. Nevertheless, the statistical test only rejected ~ 6% of the clusters.\nTo emphasize these points we have now added the following text and the corresponding reference in the 4th paragraph in the Discussion section: “Also, care must be taken to minimize docking strand photodamage during imaging to ensure that the number of binding events per docking strand is maximized, as this improve the power of the statistical test and increase the levels of specific signal. Under typical imaging conditions with less than 10% loss in docking strands during imaging7, simulations suggest that the denoising algorithm is insensitive to docking strand depletion. However, excessive docking strand photodamage may cause the apparent distribution of the times between binding events to deviate from exponential, resulting in the rejection of specific clusters.”\n7. The comparison between raw and denoised super-resolution images in Figure 6 highlights a particular concern that the denoising procedure could remove specific events. In Figure 6b, showing the mitochondria, it is suggestive that potentially specific events have been removed from within the mitochondrial signal leaving a number of voids. Can the authors alleviate this concern?\nResponse: The denoising procedure compares the cluster rejection rates between regions within the mitochondria, dominated by specific signal, and regions outside the mitochondria with non-specific signal. We note that, in this case, all signals in the areas outside the mitochondria are considered as noise, including signal from off-target binding of docking strands. This signal from the off-target docking strands is indistinguishable from specific signal and may affect the levels of specific signal that are removed from regions within the mitochondria during the denoising process. This problem can be avoided by using a negative control sample that has not been exposed to docking strands but has been prepared and imaged under standard experimental conditions. The mitochondria sample was provided by a commercial supplier, and we relied on the company’s best practices to minimize off-target binding of docking strands as we did not have access to a negative control sample.\n8. Have the authors tried fluorogenic DNA-PAINT (doi.org/10.1038/s41592-022-01464-9)? Perhaps they could comment on the compatibility of their approach with this modification of DNA-PAINT.\nResponse: We have not tried fluorogenic DNA-PAINT because: (a) we are only aware of 2 fluorogenic imager strand sequences that have been reported to work, and this limits the scope for multiplexing. (b) the binding kinetics of fluorogenic DNA-PAINT probes complicate counting applications, such as qPAINT, because an unfolded fluorogenic probe can either refold or bind to the docking strand. However, with an appropriate statistical model to describe these more complex kinetics, the denoising method could be directly applied to fluorogenic-PAINT.\nWe also agree with the reviewer that it would be interesting and perhaps the focus of future work to investigate if the fluorogenic probes reduce noise levels because they may remain in their folded state when binding nonspecifically and therefore produce less fluorescence.\n9. I appreciate that whilst the approach is described throughout the main text, the manuscript would benefit with the addition of a concise methods section (probably with reference to Figure 1) on how the denoising analysis steps were taken and should be included either in the methods or as a supplementary.\nResponse: We have now added a concise method section, titled “Denoising analysis”, on the denoising analysis with additional information on processing times. We have also provided a more detailed annotation of the example code.\n10. The current linked repository only contains the readme and license. I assume it will be populated once the manuscript is published.\nResponse: Yes, absolutely. We apologize for not making the example code publicly available on GitHub yet. We have now uploaded the latest version on GitHub.\nReviewer 1 Code Availability\n1. Unfortunately, I was unable to test the code supplied with the initial upload due to a problem obtaining the necessary license to install the 'Add-On' ‘Statistics and Machine Learning Toolbox’ required to use dbscan in MATLAB.\nResponse: We are sorry that the required add-on was not available and hope that the reviewer was still able to read the analysis scripts and get a sense of how the example dataset would be analyzed and what to expect for the output. We appreciate that this is a limitation of using commercially available products like MATLAB. If it were possible to use a more accessible version of the DBSCAN function within MATLAB, we would be happy to do so. We have further annotated the analysis scripts with comments to help readers understand the details even if they’re unable to access the DBSCAN function in MATLAB.\nReviewer 2\n1. The two-step clustering method (DBSCAN followed by k-means) is effective, particularly in crowded regions. However, DBSCAN is sensitive to the choice of search radius ϵ and in this study it was set as the median localization precision. Could the authors clarify how robust the clustering outcome is to variations in ϵ ? Was any parameter sensitivity analysis performed to validate the choice of clustering parameters?\nResponse: We chose the localization precision as the search radius for the DBSCAN step to reflect the intrinsic uncertainty of the localization process. In the two-step clustering method, we have found that the final clustering outcome is mostly dependent on the size of the gaussian template used in the second k-means clustering step. This is because regardless of how big the clusters produced by the DBSCAN step are, they will eventually be partitioned into smaller clusters during the second k-means clustering step based on the size of the Gaussian template.\n2. The use of Gaussian template matching to determine initial centroids for k-means is interesting. Have the authors compared this strategy to other centroid initialization techniques or assessed clustering quality using established metrics such as the silhouette score?\nResponse: The Gaussian template offers a straight-forward way to determine both the number of clusters and their initial location. Given that the locations of the local maxima in the cross-correlation with the gaussian template indicate regions with a high density of localizations in the point cloud data, we reasoned that these locations would also provide a reasonable guess for the initial locations of the k-means centroids, and we did not investigate other initialization techniques. This method produced reasonable to strong clustering as >95% of the clusters exhibit silhouette scores >0.5 for the different sample cases presented in the manuscript, as shown in the figure below. We thank the reviewer for raising this concern regarding the clustering quality of the process which we also share. We have now added the mean silhouette scores for the clusters in Extended Figure 1 and have also modified the example code to output the mean silhouette value for the k-means clustering of each cluster produced by the first DBSCAN clustering step.\n3. Is the dark-time threshold determined empirically for each sample type? Could the authors comment on the applicability of a fixed threshold in biologically heterogeneous environments where binding kinetics may vary locally?\nResponse: The dark-time threshold is determined by the binding time between the imager and docking strands. Hence, one would expect that the major influence would be the sequences of the docking and imaging strands and the imaging buffer. In our hands, we have observed good agreement between the dark-time threshold (~1 sec) and the expected binding time based on the sequence when the docking strands are conjugated to antibodies, nanobodies and the SNAP-tag. The only exception we are aware of is the Halo-tag, which exhibits ~2x longer dark-time thresholds (~2s) than expected. This may be because the docking strand is located in a pocket in HALO, which favors rebinding of the imaging strand and reduces its apparent off-rate.\nSince the imager strand off-rate, and thus the dark-time threshold, are only influenced by the immediate environment of the docking strand, its sequence and the buffer, it seems highly unlikely that it will vary across the sample, however heterogeneous.\n4. Have the authors compared the performance with other goodness-of-fit tests besides the Anderson-Darling test, such as Kolmogorov-Smirnov or Cramér-von Mises, especially in the context of small sample sizes?\nResponse: We considered using the Kolmogorov-Smirnov test but, like the Cramér-von Mises test, it requires the null distribution to be specified and this requires a-priori knowledge of the average time between binding events for each cluster. Since this is not possible, and the average time between binding events must be estimated from the measured data, the result of the Kolmogorov-Smirnov test is not accurate. Instead, we considered the Lilliefors and the Anderson-Darling tests. We compared these two tests on E-cadherin negative control samples and found that the Anderson Darling test is more effective in rejecting non-specific signal, especially for small sample sizes.\n5. The authors propose a minimum binding event threshold (e.g., N ≥ 8 or 12) to reject low-confidence clusters. Would a probabilistic or likelihood-based approach offer a more flexible alternative to hard thresholding, particularly for detecting sparsely expressed proteins?\nResponse: We agree with the reviewer that the hard threshold of ≥12 binding events is geared towards applications that involve quantification e.g. qPAINT, because one needs this number of events to perform the downstream analysis accurately. Detecting sparsely-expressed proteins is challenging especially in situations where their distribution is not uniform across the image. In this case, one could consider segmenting the image into smaller regions and calculating a local threshold from the signal in each region. This approach can increase the numbers of non-rejected clusters with a low number of binding events as the local binding event threshold will not be affected by other regions with more abundant signal and higher numbers of binding events.\nReviewer 2 Code Availability\n1. Could the authors provide performance benchmarks for larger, experimentally acquired datasets?\nResponse: The slowest part of the analysis pipeline we present is the DBSCAN algorithm. On our computers, a typical PAINT dataset of approximately two million (x, y) positions takes around 40 minutes to run in full (both clustering steps). We have now added a Method’s section on the Denoising analysis and included the following text to highlight the required time for this step of the Denoising analysis:\n“Typical processing times for dataset with approximately two million (x, y) coordinates was 40 minutes for completion of both clustering steps on a standard PC running 64-bit Ubuntu with 32 GB RAM and an Intel i7 CPU at 3.2 GHz.”\nAdditionally, we have added an Excel file to the supplementary example code (Expected Processing Time.xlsx) that gives the specifications for the computer we use for data processing along with the number of (x, y) localizations and time for the analysis to run for six experimentally acquired datasets. We hope this helps readers understand the potential time cost of using this method.\n2. Is the current implementation scalable to 3D or high-throughput SMLM datasets, and if not, are there plans for optimization (e.g., GPU acceleration, parallelization, or batch processing)?\nResponse: Yes, this approach is scalable to 3D since the DBSCAN, k-means and correlation with the gaussian template can accommodate a third dimension. The main bottleneck for the analysis we present is the DBSCAN algorithm. Although using GPU or other acceleration or parallelization methods to speed up DBSCAN would be fantastic, it falls beyond our computer science capabilities, and we would welcome any input from the community in this direction. We typically ran several copies of MATLAB simultaneously per PC and ran several PCs in parallel anytime we wanted to process multiple datasets quickly.\n3. Additionally, the provided code would benefit from more detailed in-line comments and documentation to improve clarity and facilitate adoption by other researchers. Could the authors consider enhancing the code readability and including function-level annotations and usage examples for standard datasets (for example the csv file created by ThunderSTORM) directly?\nResponse: We agree that the example code would benefit from more annotations and in-line comments. We have reformatted the code to make it easier to read and added more annotations and comments to help the reader understand what is happening at each step. We hope that the updated level of annotation is sufficient for a typical MATLAB user. We have also added a section to the “ExampleCode.m” file that may be enabled (uncommented) and used to select and import data from a ThunderSTORM CSV file directly. We based the import on a CSV file generated by the latest (June 2025) ThunderSTORM plugin by running the ThunderSTORM analysis on the example dataset available with the publication (https://doi.org/10.1093/bioinformatics/btu202).\nReviewer 3\nThank you for working with one of the other reviewers as part of this excellent initiative to facilitate training in the peer review process. We appreciate the time you spent working on these reviews and we hope the process was beneficial for you!"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made some small changes/additions to their manuscript in an attempt to address the comments made by myself and the other reviewer/s. However, regarding points 4-7:\n4. The author’s initial response to this point, asking whether the background/non-specific events observed are typically higher in drosophila samples in comparison to their U2OS cells, was surprising: “We do not have experience with U2OS cells as we mostly work with Drosophila tissues”. Yet, from the text, lines 351-352 read “To demonstrate this, we imaged TOM20 labelled mitochondria in U2OS cells (Fig. 6b).”\n5. I thank the authors for providing the requested blink data via a Figshare repository, it is informative to see this data for the various samples imaged. The data provided did raise an additional query for the sample described as \"a negative control E-cadherin sample (nc_ecad.tif)\". Upon doing a simple maximum projection of the ~200 frames of the events captured one can see the E-cadherin structure, see image (showing both blink data and maximum projection for nc_ecad.tif and ecad.tif). Do I understand correctly that the negative control E-cadherin sample provided is an imager only experiment with supposedly no docking strands present? If so, how do the authors explain this appearance?\n6. Additional text has been added to the discussion regarding the effects of site-loss including a statement: ‘simulations suggest that the denoising algorithm is insensitive to docking strand depletion’. Arguably, the details of this/these simulation/s should now also be added to the methods/supplemental document.\n7. I appreciate that the authors say they have limited knowledge and experimental experience with cultured cells but they are demonstrating an approach to remove non-specific events and have chosen to image the mitochondria of U2OS cells. The concern is that other groups who attempt to adopt this may, similar to this demonstration, apply it to targets they are unfamiliar with or have limited control over, remove specific signal and (not the case here) over interpret their data when they attempt to quantify it. I would have expected the authors to go that extra distance, when provided the opportunity, to demonstrate that the removal of real sites with their approach is limited when conducted under optimal conditions using various sample types.", "reviewer_2": "Satisfied with the response and revision", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "4. The author’s initial response to this point, asking whether the background/non-specific events observed are typically higher in drosophila samples in comparison to their U2OS cells, was surprising: “We do not have experience with U2OS cells as we mostly work with Drosophila tissues”. Yet, from the text, lines 351-352 read “To demonstrate this, we imaged TOM20 labelled mitochondria in U2OS cells (Fig. 6b).”\nResponse: The U2OS data in our manuscript comes from a commercial sample purchased from Massive Photonics. We are not working with U2OS cells and therefore we cannot comment on typical levels of background/non-specific binding events for this type of sample. We used this sample to demonstrate how the denoising method can be applied by using areas of the image outside the mitochondria to determine the non-specific cluster rejection rates instead of a negative control sample, which in this case was not available to us. For this demonstration, we relied on Massive Photonics’s protocols to minimize non-specific binding events and off-target docking strand binding. We discuss the limitations of this approach and the implications it can have on the removal of specific signal given that the signal from off-target docking strands is indistinguishable from specific signal.\n5. I thank the authors for providing the requested blink data via a Figshare repository, it is informative to see this data for the various samples imaged. The data provided did raise an additional query for the sample described as \"a negative control E-cadherin sample (nc_ecad.tif)\". Upon doing a simple maximum projection of the ~200 frames of the events captured one can see the E-cadherin structure, see image (showing both blink data and maximum projection for nc_ecad.tif and ecad.tif). Do I understand correctly that the negative control E-cadherin sample provided is an imager only experiment with supposedly no docking strands present? If so, how do the authors explain this appearance?\nResponse: We are grateful to the referee for pointing out this mistake. The nc_ecad.tif data was incorrectly described as the negative control. These data are actually from a Drosophila egg chamber containing clones of cells expressing E-cadherin-GFP next to cells that do not express E-cadherin-GFP. While the non-expressing cells serve as the negative control, E-cadherin-GFP in the expressing cells gives specific signal, which is why the cell borders appear only in certain parts of the image as the reviewer pointed out. The correct description from the nc_ecad.tif data is “E-cadherin clone sample”. We have also uploaded data for a real negative control sample and clearly labelled it “negative_control.tif”.\n6. Additional text has been added to the discussion regarding the effects of site-loss including a statement: ‘simulations suggest that the denoising algorithm is insensitive to docking strand depletion’. Arguably, the details of this/these simulation/s should now also be added to the methods/supplemental document.\nResponse: We have now added a “Docking strand depletion simulations” section in the Methods section.\n7. I appreciate that the authors say they have limited knowledge and experimental experience with cultured cells but they are demonstrating an approach to remove non-specific events and have chosen to image the mitochondria of U2OS cells. The concern is that other groups who attempt to adopt this may, similar to this demonstration, apply it to targets they are unfamiliar with or have limited control over, remove specific signal and (not the case here) over interpret their data when they attempt to quantify it. I would have expected the authors to go that extra distance, when provided the opportunity, to demonstrate that the removal of real sites with their approach is limited when conducted under optimal conditions using various sample types.\nResponse: The amount of specific signal erroneously removed by the denoising method for a given significance level depends on the threshold of minimum number of binding events per cluster. The optimal value of this threshold is sample dependent and to minimize the erroneous removal of specific signal in various imaging applications we chose representative samples to demonstrate different approaches on how to set this threshold e.g. with or without a negative control sample, and illustrate the strengths and limitations in each case. These approaches and the corresponding discussion are applicable to all other sample types, beyond the ones used here as examples, and will enable other researchers to choose the best approach for their work and guide them for the correct interpretation of their data."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I have reviewed the additions to the manuscript and the example data. I welcome the corrections made to the example data and the inclusion of additional written methods for the work conducted.\nHowever, I still have a fundamental issue with this manuscript in that the denoising approach used can and appears to, in the limited data provided, remove a significant amount of ‘specific signal’. And so, when the authors say: “Denoising is therefore a crucial step that removes non-specific clusters for any downstream analysis and can be combined with existing quantitative methods to obtain more accurate measurements on spatial relationships and proteins quantities” I would have to argue that the denoising approach itself can also lead to inaccurate measurements on spatial relationships and protein quantities due to the removal of real sites and therefore the approach requires further validation and refinement."}, "author_response": "Response: We understand the reviewer’s concern. The Denoising method is based on a statistical test which is expected to become more efficient with increasing amounts of data, in this case the number of binding events per cluster. Therefore, setting the threshold of binding events per cluster involves striking a balance between an image still corrupted by noise and, as the reviewer pointed out, an image having excessive real signal removed. The proposed Denoising method determines this optimal threshold value with the help of an image, or areas of an image, that are expected to be void of any real signal and contain only noise. Experimentally this can be achieved by using either a negative control sample or areas of the same image that are expected to lack specific signal. In the previous version of the manuscript, we only showed examples in cultured cells of the latter using mitochondria specific and non-mitochondria specific areas of an image. This approach is blind to off-target binding of docking strands and as the reviewer pointed out, can lead to the removal of non-trivial amounts of specific signal. To rectify this, we now explicitly mention this limitation in the revised manuscript. Additionally, we now describe in more detail the negative control approach, which does not suffer from the problem of off-target docking strands, and demonstrate its application by imaging microtubules in HeLa cells."}]

Decision letter