MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells

Domain: chemistry
Published: 2025-12-21
Editorial outcome: major_revision

Abstract
The cardiac ryanodine receptor (RyR2) constitutes the molecular basis of the process of calcium-induced calcium release where activation of RyR2s can be locally regenerative. Here, we present purely optical data of RyR2 distribution with sub-molecular resolution by applying 3D MINFLUX microscopy. Using single-domain antibodies and DNA-PAINT we determine the location of individual RyR2 subunits with high precision (~3 nm) and resolve the 3D orientations of RyR2s in-situ. We measured labeling efficiencies of ~50%, implying RyR2 tetramer detection probability approaching 95%. In HEK293 cells, RyR2 expression was dense, with some clusters containing several hundred RyR2s. Ventricular myocytes from mice contained large clusters containing many tens of close-packed RyR2s, resolving apparent discrepancies between electron microscopy and previous super-resolution microscopy data. The methodology developed here reveals the full 3D morphological complexity of RyR2 channels and is applicable to other multi-subunit complexes in a variety of cell types.

Introduction
The large (~2.3 MDa) cardiac type 2 ryanodine receptor (RyR2) protein acts as a calcium channel and controls Ca2+ release from intracellular stores. It is now well-established that the organization of RyR2s into adjacent groups of channels or clusters is an important modulator of Ca2+ release in cardiac ventricular myocytes (VMs), and thus cardiac contractile function. Using conventional super-resolution imaging we and others have shown that RyR2s are organized into clusters of widely varying sizes (with an average size of ~10 RyR2s1,2), and that these form the molecular basis of Ca2+ release1,3. The 3D arrangement of RyR2 tetramers within clusters has been shown using 3D electron microscopy to be highly variable4,5, exhibiting both adjoining and oblique interactions6 that may affect the gating of interacting RyR2s.

Optical super-resolution imaging of RyR2s has become an important technique to characterize the malleable relationship between subcellular RyR2 distribution at the nanoscale and local, as well as cell wide Ca2+ release. For example, RyR2 dispersion, a nanoscale RyR2 rearrangement that includes fragmentation of RyR2 groupings into more numerous, smaller clusters, without any change in overall channel number, was first observed in myocytes from failing hearts using dSTORM super-resolution imaging7. Similarly, nanoscale ryanodine receptor configurations were correlated to calcium sparks in cardiomyocytes using live cell photoactivated localization microscopy (PALM)8. Similarly, functional Ca2+ signals where correlated using in situ fixed myocytes imaged with DNA-PAINT9. Despite the strong impact that optical super-resolution imaging of RyR2s in ventricular myocytes has had to date, several critical issues remain. While the importance of the relative orientation of RyR2s has been suggested by previous EM studies4,5, non-equivocal determination of RyR2 orientations using optical super-resolution imaging has remained elusive. Similarly, there are some apparent discrepancies between data from optical super-resolution imaging of RyR2s and 3D EM studies. While EM studies suggest a fairly close packing of RyR2s in larger clusters4,5, RyR2 clusters imaged with optical super-resolution methods seem more sparsely filled with receptors2.

Recently, more advanced optical super-resolution imaging approaches have become available, that have demonstrated truly molecular resolution approaching the single digit nanometer scale in biological samples10,11. MINFLUX microscopy pairs that with full, near-isotropic 3D resolution using a single objective12. We therefore examined if MINFLUX 3D imaging of RyR2s would allow some of the issues mentioned above to be overcome.

Our results demonstrate that MINFLUX 3D imaging of RyR2s combined with a small marker approach can determine RyR2 3D orientations in intact cells. We show, to the best of our knowledge, the first fully isotropic 3D light microscopy data of RyR2s in HEK293 cells and mouse ventricular myocytes where localization precision is better than 3 nm in all directions. We demonstrate how to optimize labeling and detection in MINFLUX 3D imaging and achieve 50% effective labeling efficiency for GFP tagged subunits, implying a homo-tetramer RyR2-GFP detection probability approaching 95%. The high density of RyR2 observed in large clusters in mouse myocytes resolves the apparent discrepancy between super-resolution and electron tomography data as we show that densities of RyR2s in large clusters are comparable between modalities if we assume a subunit detection efficiency of ~50%. Given increasing evidence that the neighbor arrangement of RyR2s on a scale of ~10 nm affects the coordination of RyR2 openings and cluster excitability13, MINFLUX 3D based imaging of RyR2s provides an important assay for mechanistic studies of pathophysiological changes in the control of calcium release in cardiac myocytes.

Results
3D MINFLUX imaging of RyR2s in HEK293 cells
An important consequence of using MINFLUX microscopy for the 3D imaging of RyR2s becomes apparent in 3D views of the labeling distribution in HEK293 cells expressing RyR2s (the RyR2-GFP fusion protein RyR2 D4365-GFP as previously characterized14), as shown in Fig. 1. To visualize RyR2s, using MINFLUX imaging, the cells were stained with single-domain antibodies (sdABs or nanobodies) against GFP that harbor DNA-PAINT docking strands to enable MINFLUX DNA-PAINT imaging15. To visualize locations of RyR2 subunits with high precision, repeated raw MINFLUX localizations from the same DNA-PAINT imager (identified by the same MINFLUX trace ID – parameter TID15) were combined12,15. Each location obtained in this way originated from a single imager attaching to a DNA-PAINT docking strand on an sdAB. This results in localizations with a median precision of better than 2 nm in all three-dimensions (see, Supplementary Fig. 1). Notably, the resulting 3D MINFLUX data has nearly isotropic resolution in all three dimensions, which has previously generally required more complex 4Pi microscopy with opposing objectives16.

As expected, the distribution pattern of RyR2 labeling reflects the tubular ER morphology (Fig. 1A, x-y view) in some areas, and its highly three-dimensional architecture is apparent in y-z views (Fig. 1B). We compared the appearance of the RyR2 MINFLUX data with data obtained with conventional DNA-PAINT wide-field super-resolution imaging with axial resolution improved by biplane imaging17. When compared at the slightly lower lateral spatial resolution available in wide-field DNA-PAINT, lateral x-y views look broadly similar in both modalities once the MINFLUX data is laterally slightly blurred, albeit with a very notable improvement in z-resolution in the 3D MINFLUX data (Supplementary Figs. 2 and 3). The importance of 3D information is emphasized by inspection of a small image region that in an x-y view shows a group of adjacent RyR2s (Fig. 1C). In a y-z view it becomes clear that the data has about equal extent in the axial direction (z, Fig. 1D) emphasizing the complex 3D architecture of the ER. The high 3D precision in the data is critical when determining cluster groupings of adjacent RyR2s as we show below.

In situ measurement of effective labeling efficiency and detection probability with MINFLUX DNA-PAINT
In preliminary experiments with MINFLUX dSTORM photoswitching, we had noticed relatively sparse apparent labeling which was greatly improved by use of MINFLUX DNA-PAINT. We attribute this not to a better chemical labeling efficiency but more likely, in our hands, to a higher effective photo-detection probability of chemically bound markers with DNA-PAINT18. We implemented a quantitative in situ effective labeling efficiency assay to confirm our qualitative observations as in MINFLUX DNA-PAINT care need to be taken to fully collect localizations from the available marker distribution18. This aims at maximizing the marker detection probability P detect which can be conveniently monitored using the labeling of Nup96 proteins within the nuclear pore complex18 (as recently established as a robust assay to measure effective labeling efficiency in conventional super-resolution imaging19). We therefore co-cultured U2OS-Nup96-mEGFP cells with HEK293 cells stably expressing RyR2 D4365-GFP and then labeled these co-cultures with anti-GFP DNA-PAINT sdABs. We proceeded to record MINFLUX 3D images of Nup96-mEGFP labeling in U2OS-Nup96-mEGFP cells alongside labeling of RyR2-GFP in adjacent regions of interest (ROIs) in nearby HEK293 cells so that MINFLUX scanning multiplexed between these two ROIs, acquiring a dataset containing U2OS-Nup96-mEGFP 3D distribution adjacent to data of 3D staining of RyR2 D4365-GFP (Fig. 2A, compare also Fig. 1). To estimate effective GFP labeling efficiency from the NPC 3D data we fitted a double ring template into imaged NPC structures to account for variations in 3D orientations of NPCs within the nuclear envelope (Fig. 2B). We used the detected and 3D aligned NPC localizations to measure the number of labeled segments (up to 16 in total with 8 on the cytoplasmic side and another 8 on the nucleoplasmic side). The obtained histograms were compared to the expected distributions for different labeling efficiencies to obtain an estimate of the effective labeling efficiency p LE. Further details of the template fitting procedure and the associated analysis are illustrated in Supplementary Fig. 4. For the NPC data shown in Fig. 2B this approach yielded p LE = 60.1% ± 0.2% (Fig. 2C). In addition, we analyzed the geometry of NPCs and obtained the expected ring spacing of ~50 nm (Supplementary Fig. 5). Consistent with the comparison between MINFLUX and SMLM biplane data of RyRs (Supplementary Fig. 3) MINFLUX based NPC analysis benefited from the high axial resolution in 3D MINFLUX imaging and provided more precise geometry parameter estimates as compared to widefield SMLM data (Supplementary Fig. 5C–E).

On average, in such combined RyR2 and U2OS-Nup96-mEGFP datasets, the effective GFP labeling efficiency p LE was 51.2% ± 2.3% (N=10 MINFLUX data sets from n=3 biological replicates, Fig. 2D). This confirmed that imaging durations were sufficient to fully capture the information from anti-GFP sdABs in the sample thus maximizing the marker detection probability P detect18. This required extensive imaging durations (from 5 to 11 h) to ensure capturing all chemically labeled locations in the sample and helped ensure that information from labeled RyR2 D4365-GFP was also fully captured, as the marker detection probability P detect should be about equal regardless of the specific target as it mainly depends on the DNA-PAINT attachment. In addition, the assay was used to measure the number of visits per site18 which was 1.18 ± 0.09 (N=10 MINFLUX data sets from n=3 biological replicates, Fig. 2D inset) confirming that virtually no overcounting of the DNA-PAINT localizations is taking place which is useful for the interpretation of the observed labeling densities of RyR2s derived from the number of localizations per area or volume.

Resolving RyR2 tetramers and RyR2 orientation in 3D
Having confirmed that we achieve substantial marker detection, we aimed to resolve the individual subunits making up the RyR2 homo-tetramer given the superior resolution available with MINFLUX 3D imaging. Among the marker systems available to visualize RyR2s with fluorescence super-resolution microscopy, the size of some marker combinations may defeat the purpose of achieving truly molecular resolution20. Labeling with primary and secondary antibody (AB) labeling systems while aiming for molecular imaging resolution is complicated by the considerable size of IgG molecules (7–12 nm, Fig. 3A). By contrast, the use of single-domain antibodies (sdABs) in conjunction with the relatively compact fluorescent proteins (FPs) should encounter smaller linkage errors, on the order of ~3–4 nm (Fig. 3A). Insertion of GFP after residue D4365 has been previously performed and the resulting RyR2 D4365-GFP has been shown to be functionally essentially indistinguishable from wild type RyR214. The locations of the 4 inserted GFPs in the RyR2 homotetramer were previously localized by single particle cryoEM and found to be arranged on the corners of a square with ~16 nm side length that is oriented at nearly 45 degree to the square outline of the tetramer itself14 (Fig. 3B). Imaging HEK293 cells stably expressing RyR2 D4365-GFP stained with an sdAB against GFP we could localize Atto-655 conjugated DNA-PAINT imagers transiently binding to these sdABs carrying complementary docking strands, with high precision in 3D, as shown in Figs. 1 & 2. Given the relatively small size of sdABs we aimed to localize individual subunits of a RyR2 tetramer.

We hypothesized that small groups of adjacent subunit localizations that we observed in MINFLUX datasets of RyR2 D4365-GFP labeled with anti-GFP sdABs may represent adjacent subunits of the same RyR2 tetramer. Figure 3C shows an overview of a region of RyR2 D4365-GFP labeling (color coded for the z coordinate) indicating a small region of interest that is shown magnified in Fig. 3D. Three areas are shown, magnified further, in Fig. 3E and slightly rotated in 3D when necessary, to display 4 (Fig. 3Ei) or 3 (Fig. 3Eii & iii) punctate foci of staining in the plane of rendering. These arrangements are suggestive of corresponding to adjacent subunits of a single RyR2. Candidate positions, given the geometrical arrangement of GFP residues in RyR2 D4365-GFP, are indicated in Fig. 3Fi-iii, with the individual RyR2 tetramers oriented in 3D as indicated in the axes shown in Fig. 3Ei-iii. This data shows that, when at least 3 subunits of a tetramer are labeled and detected this allows the 3D orientation of an RyR2 to be estimated with good precision within a cell. This is a unique ability of MINFLUX 3D imaging and demonstrates the potential to fully resolve the placement of multi-subunit complexes, such as RyR2, within cells and tissues.

Due to the relatively dense labeling detected in the MINFLUX DNA-PAINT datasets with optimized DNA-PAINT acquisition (Fig. 2) automatic detection of candidates for such directly labeled adjacent subunits proved difficult. Often, it was not possible to unequivocally assign subunit positions to individual candidate receptor positions in densely stained regions. In principle, such an automated assignment would enable estimating the effective labeling efficiency in situ from the fractions of receptors with 1 to maximally 4 labeled subunits and provide an alternative to the use of NPC based calibration.

RyR2 clustering in HEK293 cells
Our estimate of effective GFP labeling efficiency of ~50% would imply a high likelihood to detect an RyR2 of ~95% (i.e. with at least one GFP containing subunit labeled), if EGFP accessibility in RyR2 D4365-GFP is similar to that in U2OS-Nup96-mEGFP, see also the discussion. Prior to RyR2 clustering analysis we combined closely adjacent localizations (as these may have originated from the same sdAB due to repeated visits to a single DNA-PAINT docking strand, although the mean site-visit numbers should be close to 1, see also Fig. 2D) by DBSCAN clustering and combining such groups into a single event (using a distance ε of 1 nm). We proceeded to analyze the three-dimensional distribution of the resulting subunit locations with the aim to achieve improved insights into the underlying RyR2 distribution.

To determine groupings of RyR2s that may be at a distance to effectively cross-talk via their released Ca2+, we carried out clustering analysis to identify functionally coupled groupings of RyR2. Using a cutoff distance for the fire-diffuse-fire activation21 of neighboring receptors via the mechanism of CICR by an open RyR2 (typically chosen around 100 nm based on Ca2+ diffusion modelling3,22), DBSCAN clustering with ε of 100 nm provides a mechanistically motivated clustering approach to determine functionally coupled RyR2 clusters. As shown in the distribution of RyR2 in a HEK293 cell staining in Fig. 4A, with this signaling distance several clusters are very large spanning several microns. Here the three largest clusters contain >68% of all RyR2 labeling, each containing several thousand subunit locations. The high 3D resolution reveals the three-dimensional complexity of the architecture of these large clusters as shown in Fig. 4B–D which detail the large cluster in the bottom left corner of Fig. 4A. In x-z and y-z views one can clearly see an ER loop that spans >100 nm in the axial direction (see also Supplementary Movie 1 for animated 3D views). On average, clusters contain 30.9 ± 5.2 subunits (N=10 MINFLUX datasets from n=3 biological replicates); with a correction that uses U2OS-Nup96-mEGFP effective labeling efficiency as indicative of the RyR2 effective labeling efficiency (see discussion), clusters contain on average 58.8 ± 8.2 subunits (Fig. 4E). As illustrated in Fig. 4A most RyR2s are in large clusters, so that the three largest clusters together contain 34.8% of the total RyR2s on average (Fig. 4F, N=10 MINFLUX datasets from n=3 biological replicates). When corrected subunit numbers are converted to the number of RyR2s (by diving by 4), clusters contain a mean of 14.7 ± 2.1 RyR2s (Fig. 4G). This number seems moderate when noticing the very large clusters and can be explained as the mean cluster size is greatly affected by the presence of many small clusters, as seen in typical cluster size histograms (Supplementary Fig. 6). From a calcium signaling point of view the remarkably large functional groupings dominate, compatible with the idea that local spontaneous release from individual RyR2s can quickly travel over micron distances through the cell by spreading between adjacent receptors via the process of calcium-induced calcium release23.

MINFLUX imaging of RyR2s in ventricular myocytes
Using the recently characterized PA-RFP RyR2 knock-in mouse line24 we also carried out MINFLUX imaging of RyR2s in isolated ventricular myocytes. Myocytes from PA-RFP RyR2 knock-in mice express RyR2 T1365-PATagRFP, an RyR2 with RFP insertion at the equivalent site to the previously structurally characterized RyR2 T1366-GFP25. For initial confocal imaging and to localize RyR2 clusters near the surface sarcolemma (Fig. 5A, B) we first stained with sdABs conjugated to Alexa 647. For molecular resolution 3D MINFLUX imaging, PA-RFP RyR2 mouse myocytes were instead labeled with anti-tagRFP sdABs that harbor DNA-PAINT docking strands as a small marker system. Using MINFLUX DNA-PAINT imaging, regions equivalent to those appearing blurred in confocal images (Fig. 5B) were clearly resolved into punctate groups of localizations (Fig. 5C). Using the subunit assignment approach, described above, followed by DBSCAN clustering with ε of 100 nm to capture functionally coupled clusters of RyR2s, the underlying RyR2 clusters were clearly revealed. Many of the prominent large clusters extend over distances >100 nm and contain several hundred subunits as shown by the color coding in Fig. 5C. The mean cluster size is, as in HEK293 cells above, strongly affected by the presence of many smaller clusters (which outnumber the prominent but fewer extended clusters) resulting in a mean cluster size of 9.0 ± 0.5 SUs or 16.5 ± 0.9 SUs when only looking at clusters with more than one SU (N=22 MINFLUX datasets from n=5 cell isolations, Fig. 5D). Isolated individual localizations are pointed out in Fig. 5E. Nevertheless, the majority of RyR2s reside in large clusters, where a cluster is classified as large if it contains at least 50 SUs, with on average 72.4% ± 2.3% of all RyR2s residing in large clusters (N=22 MINFLUX data sets from n=5 cell isolations, Fig. 5F).

The locations of the TagRFP insertion at T1365 map to a square with side length of ~20 nm that is approximately aligned with the four corners of the intact RyR2 tetramer25 (see also Supplementary Fig. 7). In a detailed view of an RyR2 cluster shown in Fig. 5G, several subunit localizations are highlighted that are compatible with the position of individual tetramers as indicated in the receptor overlays. In several locations there are 3 labeled subunits; in two places all 4 subunits are labeled suggesting that MINFLUX can resolve the subunits of an RYR2 tetramer and thus directly show its 3D orientation within the cell. Converting the number of subunits per cluster to RyR2 numbers (by dividing by 4) result in a mean RyR2 cluster size of 2.3 ± 0.1 RyR2s or 4.1 ± 0.2 RyR2s when including only clusters which contain more than one SU (Fig. 5Hi, N=22 MINFLUX datasets from n=5 cell isolations). These mean cluster sizes seem comparatively small when compared to the prominent large clusters that dominate the visual appearance (see Fig. 5C). This is largely explained by the fact that there are many more small clusters than large clusters, in fact large clusters (>50 SUs, i.e. containing >12.5 RyR2s) make up less than 5% of all clusters when counted on a per cluster basis (4.7 ± 0.3%, N=22 MINFLUX datasets from n=5 cell isolations, Fig. 5Hii) even though nearly three quarter of all RyR2s reside in such large clusters (Fig. 5F).

While the majority of the RyR2 clusters recorded near the coverslip reside in peripheral couplings with the surface sarcolemma, occasionally clusters extending into the myocyte are seen that are likely in internal couplings with transverse tubules that invaginate at the surface sarcolemma (Fig. 5Ii, ii). The clusters in peripheral couplings, while close to the surface sarcolemma, exhibit significant 3D structure that deviates from a purely flat 2D plane arrangement. These peripheral RyR2 clusters are often seen to subtly curve away from the coverslip surface which becomes apparent with the high axial precision available in MINFLUX 3D datasets (see Supplementary Fig. 8, see also Supplementary Movie 2 for animated 3D views).

Relationship of MINFLUX data to previous EM tomography data
It is possible that the effective labeling of the tagRFP residues in RyR2 T1365-PATagRFP is less than complete although we currently do not have access to an assay to measure it directly. As a proxy, we carried out a qualitative comparison between MINFLUX and EM tomography data of RyR2 clusters. We retraced EM tomography data from two previous studies4,26 and used the obtained RyR2 cluster morphologies as the starting point to represent close to 100% detection efficiency of RyR2s (with the caveat that EM tomography may achieve this only for larger RyR2 clusters, small groups of RyR2s may be less sensitively detected). The traced RyR2 cluster layouts were used to generate corresponding tagRFP locations (see also Supplementary Fig. 7) and then simulate a subunit detection efficiency of 50%. Subunit locations randomly selected according to this procedure (blue dots in Fig. 6A) were used to mimic MINFLUX subunit localizations by also adding 3D localization errors with a standard deviation of ~3 nm. The resulting simulated subunit clusters (Fig. 6Bi, ii) qualitatively resemble the larger clusters in our MINFLUX 3D RyR2 data recorded in myocytes (Fig. 6Biii, iv). This suggests an effective labeling efficiency approaching 50% is compatible with the MINFLUX DNA-PAINT data recorded in RyR2 T1365-PATagRFP myocytes, broadly similar to that obtained for MINFLUX imaging of U2OS-Nup96-mEGFP with anti-GFP sdABs above.

To address the question if the observed non-complete labeling is purely a property of the sdAB labeling system used here, we carried out a comparison of labeling approaches using widefield DNA-PAINT super-resolution imaging. DNA-PAINT imaging previously achieved the highest resolution among conventional optical super-resolution microscopy methods. In previous studies, RyR2 positions were estimated from the location of punctate DNA-PAINT labeling in 2D rendered super-resolution images. We produced such images using (1) DNA-PAINT data of sdAB labeled PA-RFP RyR2 mouse myocytes or (2) DNA-PAINT data of primary/secondary AB labeled RyR2s in mouse ventricular myocytes. When these images were analyzed using a blob finding algorithm, similar as done previously2, the nearest neighbor distance distributions of blobs (previously identified as RyR2 positions) were comparable (Supplementary Fig. 9) with a median nearest neighbor distance between puncta of 36.8 versus 41.5 nm (ABs vs sdABs, Supplementary Fig. 9A, B) that was not significantly different (p=0.31). This suggests that the sdABs used in our MINFLUX imaging experiments have per se a broadly comparable RyR2 labeling efficiency as other antibody labeling systems used previously. The amplification available in primary-secondary AB labeling (due to several docking sites per secondary AB and possible multiple binding) should have a negligible effect on puncta distances, since due to the lower DNA-PAINT resolution (typically ~24 nm Fourier Ring Correlation (FRC) resolution) these should all be merged into the same punctum. We also rendered MINFLUX myocyte data into 2D super-resolution images and analyzed these with the same blob finding algorithm. In these MINFLUX 2D super-resolution images the median nearest neighbor distance between puncta was reduced to 21.8 nm (Supplementary Fig. 9C, significantly smaller than distances from widefield DNA-PAINT imaging both with ABs and sdABs, p<0.001, respectively). This reduced mean distance was in turn comparable to the NN distance distribution generated from EM tomography data and simulating a subunit detection efficiency of 50% which resulted in a mean NN distance of 19.4 nm (Supplementary Fig. 9D). Both the mean and the shape of this simulated distribution resembled that of the distribution obtained from MINFLUX DNA-PAINT data.

Methods
For experiments carried out at the University of Oslo, all animal protocols were performed in accordance with the Norwegian Animal Welfare Act and NIH Guidelines (NIH publication No. 85-23, revised 2011) and were approved by the Norwegian Food Safety Authority (permit number 8951). For experiments carried out at the University of Bern, all animal protocols were performed in accordance with the Swiss Animal Welfare Act and were approved by the Amt für Veterinärwesen of the Canton of Bern (permit number 35279). Housing and breeding were carried out in a departmental animal facility with controlled environmental conditions (22°C, 40% relative humidity, 12 h light/dark cycle), with mice having free access to laboratory chow and water. In this study we used adult male mice (N=5, 2–12 months old).

Preparation of HEK-293 cells stably expressing RyR2 D4365-GFP for MINFLUX imaging
HEK293 cells stably expressing inducible RyR2 D4365-GFP created in the laboratory of Dr. Wayne S. R. Chen14 and maintained in the laboratory of Pete Jones, University of Otago, were grown as described further below. Genotyping for HEK-293 was carried out by Pete Jones at the University of Otago, and its GFP-specific expression pattern following induction was verified in the laboratory of Dr. Christian Soeller. They were grown at 37°C with 5% CO 2 in Dulbecco’s Modified Eagle’s Medium – high glucose (Sigma), supplemented with 5% fetal bovine serum (Gibco) and 1% Antibiotic-Antimycotic (Gibco).

HEK293 RyR2 D4365-GFP cells were seeded on precleaned coverslips treated with 0.1% Poly-l-lysine for 10 min. After 1–2 days, the expression of RyR2 D4365-GFP was induced by 1 μg/ml doxycycline. 24 h after the induction, cells were fixed with 4% PFA in PEM buffer43 (80 mM PIPES, 5 mM EGTA, 2 mM MgCl 2, pH 6.8) with 4% sucrose at RT for 10 min before being washed 3x with PBS. Coverslips with fixed cells were mounted on custom made slide chambers made from acrylic44. Cells were permeabilized for 10 min in PBS containing 0.1% Triton and then subsequently blocked using MP antibody incubation solution (Massive Photonics, München) for 1 h at RT.

For labeling of GFP residues on RyR2 D4365-GFP for MINFLUX, cells were incubated with a 1:200 dilution of TAG-X2 anti-GFP (sdAB, clones 1H1 & 1B2, batch N° 3302002, Massive Photonics, München) harboring a conventional docking strand (MP DS3, undisclosed sequence, Massive Photonics, München) for 1 h at RT in MP antibody incubation solution. Samples were washed in DNA-PAINT buffer (PBS containing an additional 500 mM NaCl) 3x. These were imaged with a nominal 1 nM concentration of Imager 3 diluted in DNA-PAINT buffer.

For comparison widefield DNA-PAINT imaging in 3D biplane mode HEK293 RyR2 D4365-GFP cells were labeled with TAG-X2 anti-GFP sdAb harboring a fast docking strand (MP FAST DS3, undisclosed docking sequence, Massive Photonics, München) and imaged using 0.5 nM Imager F3 ATTO 655 diluted in DNA-PAINT buffer with a camera integration time of 25–50 ms.

Preparation of co-cultures of HEK-293 cells stably expressing RyR2 D4365-GFP with U2OS-Nup96-mEGFP cells
HEK293 cells stably expressing inducible RyR2 D4365-GFP 14 and U-2 OS-Nup96-mEGFP (Cytion, clone 195, # 300174) were cultured at 37°C with 5% CO 2. U-2 OS-Nup96-mEGFP were not further authenticated after purchase, except for verification of the expected GFP expression pattern at the nuclear pore. HEK293 cells were maintained in Dulbecco’s Modified Eagle’s Medium – high glucose, GlutaMAXTM Supplement, pyruvate (Sigma), and U-2 OS cells were cultured in McCoy’s 5A medium (Fisher Scientific). Media were supplemented with 10% fetal bovine serum (Gibco) and 1% Antibiotic-Antimycotic (Gibco). For co-culturing, 4×104 HEK293 cells were seeded on pre-cleaned coverslips. After 2 days, the medium was refreshed and RyR2 D4365-GFP expression was induced by the addition of 1 μg/ml doxycycline. 24 h after the induction, 1.2×105 U-2 OS were added in McCoy’s 5A medium. Co-cultured cells were fixed one day later and labeled in the same manner described above for single culture HEK293 MINFLUX experiments.

Cardiac myocyte isolation from PA-RFP RyR2 mice
Genetically modified PA-RFP RyR2 mice on the C57BL/6/6N background that harbor PA-TagRFP inserted after T1365, within exon 31 of the mRyR2 gene on chromosome 13 as described8, were housed at the University of Bern and used to provide isolated myocytes. Single cardiomyocytes were isolated as previously described45. Briefly, the adult male mice were first anesthetized by intraperitoneal injection of pentobarbital (150 mg/kg of body weight). After the disappearance of the tail pinch reflex, the heart was rapidly excised, cannulated, and retrogradely perfused on a Langendorff system at 37°C. Isolation buffer (Ca2+-free modified Tyrode solution) containing 140 mM NaCl, 5.4 mM KCl, 1.1 mM MgCl 2, 1 mM NaHPO 4, 10 mM HEPES and 10 mM D-glucose (pH adjusted to 7.4 using NaOH) was first used to flush the heart of blood. Once fluids ran clear the perfusion was switched to an isolation buffer which included collagenase type II (160 U/mL, Worthington Biochemical Corporation, LS004177) and protease type XIV (0.21 U/mL, Sigma, P5147) for ~15 min. After digestion the heart was cut down into isolation buffer and the ventricles were diced into small chunks. The cell suspension was then filtered through a filter mesh (pluriStrainer PET 100 µm), the cardiomyocytes were sedimented and the Ca2+ concentration gradually increased to 0.25 mM. Chamber slides with an attached coverslip were pre-treated with laminin (Gibco, 23017015) diluted in PBS 3:200. The chambers were left at room temperature overnight and subsequently washed with Ca2+-free Tyrode’s solution on the day of the isolation. Cell solution was pipetted onto these prepared chambers and allowed to settle for 2 h at 37°C. Subsequently, cells were fixed using 4 % PFA diluted from stock (Electron Microscopy Sciences, 15714) for 10 min at room temperature. The samples were washed with PBS for 10 min and then stored in a PBS solution containing 0.5% BSA and 0.1% sodium azide until later use.

Labeling of myocytes from PA-RFP RyR2 mice for MINFLUX and widefield DNA-PAINT imaging
Fixed cardiomyocytes were permeabilized with 0.1% Triton X-100 for 10 min and then blocked with MP antibody incubation solution for 1 h. Cells were then incubated with Massive-Tag-Q anti-TagFP single-domain antibody (Clone 1H7, Massive-Photonics, München) harboring strand DS3 for 2 h at room temperature diluted 1:200 in MP antibody incubation solution. Samples were washed >3 times in DNA-PAINT buffer. For confocal imaging the cells were incubated with FluoTag® X2 anti-TagFP (#N0502, Clone 1H7, NanoTag Biotechnologies, Göttingen) diluted 1:50 in incubation solution made from PBS containing 2% BSA, 2% NGS, 0.05% Triton X-100 and 0.05% NaN 3 for 2 h at RT.

For comparison DNA-PAINT experiments, involving primary and secondary antibodies, after the samples were permeabilized the cells were blocked in PBS containing 1% BSA for 1 h at RT. Cell were then incubated with a ryanodine receptor monoclonal antibody (C3-33) (MA3-916, Lot N° XC345381, Invitrogen) overnight at 4°C, 1:200 dilution in an incubation solution. Samples were washed 3 times in PBS before being incubated with custom conjugated oligonucleotides as described below to AffiniPure goat anti-mouse IgG (H+L) secondary antibodies (Jackson ImmunoResearch, further details below) for 2 h at RT, 1:200 in incubation solution. Finally, samples were washed 3x in DNA-PAINT buffer.

Custom Oligonucleotide-antibody conjugation
Docking strands were purchased from Integrated DNA Technologies (Coralville, Iowa, United States) with 5’ azide and 3’ Cyanine 3 dye modifications. Imagers were purchased from Eurofins Genomics (Ebersberg, Germany) with 3’ ATTO 655 modification. All oligos were HPLC purified and shipped lyophilized. Oligos were reconstituted in PBS at 100 µM and stored at -20°C. The conjugation of oligo to antibody followed a click-chemistry protocol. Briefly, AffiniPure goat anti-Mouse IgG (H+L) secondary antibodies (115-005-003, Jackson ImmunoResearch) were incubated with DBCO-Sulfo-NHS ester (CLK-A124-10, Jena Bioscience) for 45 min. The reaction was stopped with 80 mM Tris-HCl for 10 min. The antibody-DBCO mixture was filtered using 7k MWCO Zeba Spin Desalting columns (Thermo Scientific) and the resulting antibody-DBCO was incubated with docking strand D2 (5’ [AzideN]TTTTAGGTAAATTTTGATTGTGAGGAAG[Cy3], IDT), overnight at 4°C. Finally, the conjugation mixture was purified using Amicon Ultra-0.5 (UFC5100, Merck) and measured using an Implen N60 touch spectrophotometer (Fisher Scientific) to confirm >1 oligo conjugation per antibody.

Sample mounting for MINFLUX microscopy
For active sample-stabilization during MINFLUX experiments, 150 nm colloid gold beads (BBI Solutions, EM.GC150) were used, as fiducial markers46. Gold beads were added undiluted to the samples prior to imaging and allowed to settle for 5–10 min. Excess fiducials were removed with several washes in DNA-PAINT buffer. Once loose fiducials were removed, the chambers were filled with DNA-PAINT buffer containing nominally 1–2 nM Imager 3 ATTO 655 and made airtight with a glass coverslip placed over the chamber and sealed with Pinkysil (Barnes Products, Moorebank).

MINFLUX 3D imaging
Samples were imaged on an Abberior Instruments MINFLUX microscope that was equipped with a 642 nm excitation laser. It could be run in confocal and MINFLUX imaging modes using a SLM-based beam shaping module and an EOD-based scanner for precision scanning (used in MINFLUX mode), similar as previously described47,48. Emitted fluorescence signals were measured using two photon-counting avalanche photodiodes and suitable fluorescence filters (650–750 nm). In addition to MINFLUX modes, the microscope allows epifluorescence and 488 nm and 640 nm excitation confocal imaging acquisition. This was sometimes used to identify structures based on Alexa Fluor 647 nm labeling (when using sdABs conjugated to Alexa Fluor 647). To enable measurements with molecular precision, the Abberior MINFLUX microscope is equipped with a reflection-based stabilization unit, based on a 980 nm laser. It features real-time feedback that dynamically adjusts a 3-axis nano-positioning stage to fix the sample position with respect to the objective lens with standard deviations typically below 1 nm over minutes and up to hours under normal laboratory conditions47 (if mechanical agitation of the microscope by external sources are kept at a reasonably low level). To compensate for changes in environmental conditions (especially temperature) that might manifest as residual system drift, a beam line monitoring mode was activated (in addition to the simultaneously operating real-time feedback stabilization) by selecting several in-focus gold nanoparticles whose position is tracked over time. The particle-averaged tracking information from these nanoparticles is used to measure and correct drift by subtracting the apparent nanoparticle trajectory from the localization coordinates. Beam line monitoring can be maintained over hours and keeps drift induced position errors typically below 1.5 nm. The current implementation of beam line monitoring requires nanoparticles to be in focus so that the MINFLUX imaging volume in practice need to be close to the coverslip interface during operation as most gold nanoparticles attach on the coverslip. The GFP signal from cultured cells were used to select suitable MINFLUX scan regions. For experiments involving myocytes the PMT signal was used to select cells that had adhered strongly to the coverslip, regions with good attachment appeared darker. Fast confocal scans, of potential regions of interest, were then taken using the 642 nm laser to excite the ATTO 655 imager. Binding events appeared stochastically as bright, long lasting, diffraction limited flashes confirming that the sample had been appropriately labeled with DNA-PAINT markers before acquiring MINFLUX data for several hours. The default 3D Imaging sequence provided by Abberior was used to determine the localizations of the fluorescent molecules.

DNA-PAINT widefield imaging
Data was acquired using a modified Nikon Eclipse Ti-E inverted microscope (Nikon, Tokyo) with a fixed 60× 1.49 NA APO oil immersion TIRF objective (Nikon, Tokyo)49. Images were captured with an Andor Zyla 4.2 sCMOS camera (Andor, Belfast) generally utilizing a 100 ms integration time, resorting to 25–50 ms when using fast DNA-PAINT imagers (with HEK293 RyR2 D4365-GFP cells). Focus was controlled using a piezo objective scanner (P-725, Physik Instrument, Karlsruhe). For widefield fluorescence, a tuneable LED-light source (CoolLED, Andover) was used to aid cell selection and confirm proximity of 200 nm red fluorescent beads (fiducials) within the imaging region. These fluorescent markers were later used to correct for any lateral drift experienced over the course of data acquisition. Active z-stabilization was maintained whilst imaging through the use of an auxiliary camera monitoring transmitted light at a non-interfering wavelength from the super-resolution channel2,49. For comparison widefield DNA-PAINT imaging of HEK293 cells stably expressing RyR2-GFP, the 3D biplane configuration of the microscope was used. A continuous wave 647 nm diode laser (Omikron LuxX, Rodgau) was used to excite ATTO 655 imagers for single molecule measurements. Single molecule data was recorded using the open-source python software package called PyME50 (Python Microscopy Environment, https://github.com/python-microscopy/python-microscopy). The imagers for widefield imaging of cardiomyocytes, either ‘Imager 3’ (Massive Photonics, undisclosed sequence) or A2 (5’ TACAATCAAA [ATTO 655], Eurofins), were diluted to 0.6 nM in DNA-PAINT buffer depending on whether the sdAB or the primary/secondary antibody system was used.

3D DNA-PAINT biplane widefield imaging
For 3D biplane imaging (used with HEK293 RyR2 D4365-GFP cells and U2OS-Nup96-mEGFP cells) a splitter device positioned between the camera and microscope side port (Cairn, Australia), fitted with a 50/50 beamsplitter dichroic and a weak lens (f=4 m) placed in one of the paths, was employed. Prior to conducting biplane experiments a z-stack of ~200 images were taken of a field of view containing dark-red fluorescent 200 nm beads (Thermo Fisher FluoSpheres F8807) using a step size of 50 nm. Beads were excited using an LED light source (p4000 CoolLED, Andover) emitting light nominally at 635 nm towards a Cy5 dichroic (Semrock, FF650) and the resulting fluorescence propagated through an appropriate emission filter (Semrock, LP02-647RU) towards the splitter device and camera. The z-stack was then used to obtain the point spread function (PSF) of the system through the open source super-resolution microscopy and analysis software PyME and saved as a .tif file. Additionally, a vector map of the shifted position, between bead locations as viewed through channel 1 compared to channel 2, was acquired to digitally align the two channels in x and y with sub-pixel precision in PyME. This required a dense field of 200 nm dark-red fluorescent beads to be recorded, at a focal position approximately halfway between the two channels, as the sample was gradually moved in x and y directions until a completed map of the imaging field of view was constructed. Following these calibration steps biplane acquisition with real-time localization analysis was carried out under control of PyME.

Analysis of 3D MINFLUX data
Raw MINFLUX localizations were exported from Abberior Instruments Imspector software47 in .npy format and imported into PYME Visualize50 for further processing using custom developed open source plugins for MINFLUX data loading and processing. Raw MINFLUX localizations were processed by combining localizations to represent different aspects of the underlying structures, including docking strand locations and subunit locations. Docking strand locations were obtained from raw MINFLUX localizations to obtain locations that each represent a docking strand location during repeated localization that are part of a single MINFLUX trace. To obtain unique subunit locations we used an approach similar to the procedure used previously to reassign filtered MINFLUX localizations to individual molecules12. These subunit locations were obtained by combining docking strand locations that arose from marker molecules that reappear due to repeated imager binding. Subunit locations were subjected to cluster analysis by DBSCAN clustering with the aim to characterize the underlying RyR2 clustering properties. Further details on the procedure to obtain the locations and clusters mentioned above are described in the supplementary methods. Data was visualized in two ways: (1) 3D views were obtained by OpenGL based rendering of MINFLUX localizations with coloring typically applied for a selected event property such as z-elevation, cluster size, cluster ID using the Point sprite rendering mode of PYME visualize50 which results in a Gaussian rendering in 3D. Alternatively, (2) images were rendered using the Gaussian Rendering function of PYME visualize50 generally with a Gaussian sigma of 4 nm.

Estimates of effective labeling efficiency from MINFLUX 3D NPC data
To estimate effective labeling efficiency we built on the approach established by Thevathasan et al.19. As in their method, we first detected NPCs in the super-resolution data and then went on to fit and align a template to the data, performed rotational alignment, determined how many segments were labeled (given the 8-fold symmetry of NPCs) and the resulting histogram was finally fit to a probabilistic model. Given the high 3D resolution in MINFLUX 3D imaging with approximately isotropic resolution we extended the analysis by additionally implementing maximum-likelihood fitting of a 3D model31 to precisely determine the 3D orientation of each NPC and separately evaluated labeling of nucleoplasmic and cytoplasmic segments of NPCs. Details of the full procedure are described in the supplementary methods and illustrated in Supplementary Fig. 4. Briefly, MINFLUX 3D data of U2OS-Nup96-mEGFP labeled with anti-GFP sdABs modified with DNA-PAINT docking strands was rendered to a 2D image and locations of NPCs were detected by a semi-manual approach in Fiji51 and stored as regions-of-interest (ROIs). ROIs were read into the PYME visualize50 SMLM data visualizer to generate a mask and label localizations belonging to each NPC with a unique ID. For each NPC the associated set of localizations were fit to a 3D double ring template using an algorithm similar to a previous approach31 as detailed in the supplementary methods. The best-fit with a global maximizer yielded estimates of NPC geometry (diameter and vertical spacing) as well as the center and orientation of each NPC with high precision. This revealed distinct changes in orientation of NPCs across the field of view as illustrated in Supplementary Fig. 4C & D. Following rotational alignment, the number of labeled segments on the cytoplasmic and nucleoplasmic sides were determined for each NPC. The data from all NPCs in the dataset evaluated in this way were pooled into a cumulative histogram and fitted to a probabilistic model that predicts the relative number of 0 to 16 labeled segments as a function of a fit parameter p LE denoting the labeling efficiency. The complete fitting procedure was implemented in the form of plugins for PYME visualize. The best fit parameter p LE together with its uncertainty Δp LE are then provided as an estimate of effective labeling efficiency. Site visit counts were determined from the average number of localizations per segment and dividing this number by two as there are two target sites per segment. Comparisons using widefield SMLM biplane data of U2OS-Nup96-mEGFP labeled with anti-GFP sdABs modified with DNA-PAINT docking strands were analyzed in the same way.

Blob analysis of widefield DNA-PAINT and MINFLUX DNA-PAINT data
Single molecule localization DNA-PAINT data were rendered using PYME Visualize50 into TIFF images with a pixel size of 5 nm by jittered triangulation52. A PyME object finding module (PYME.localization.ofind3d) was used to detect puncta within these images using a threshold of 1.0 and blur size 2.0. The 2D coordinates of the detected puncta were used to determine their nearest neighbor (NN) distances. Similarly, MINFLUX 3D datasets were rendered using PYME Visualize50 into TIFF images with a pixel size of 3 nm by Gaussian rendering. Rendering was performed from docking strand locations to benefit from the increased spatial resolution in MINFLUX data. Again, the PyME object finding module (PYME.localization.ofind3d) was used to detect puncta within these images using a threshold of 1.0 and blur size 2.0. The 2D coordinates of the detected puncta were used to determine their nearest neighbor (NN) distances and these were displayed in histogram form.

Data display and statistical comparisons
Boxplots show the median as centerline, box limits are upper and lower quartiles, whiskers show 1.5x interquartile range and all data points are shown. In addition, the mean is shown as an open circle. Statistics are reported as mean ± standard error of the mean unless noted otherwise. For individual fits of labeling efficiencies we report best fit value ± fit error.

For comparing nearest neighbor puncta distances, we conducted statistical comparisons implemented in the Python programming language within a Jupyter notebook environment. First, a one-way ANOVA test was performed using the scipy.stats module to assess the null hypothesis that the means of the three groups tested were equal. The result suggested that at least one of the groups exhibited a significantly different mean compared to the others. Pairwise differences were further investigated using the statsmodels.api library utilizing the pairwise_tukeyhsd function. This function implements the Tukey’s Honestly Significant Difference (Tukey HSD) test and compares all possible pairs of group means to identify statistically significant differences. The p-values were then recorded.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors used 3D MINFLUX to understand the distribution and orientation of RyR2 in cardiac myocytes. They employed single-domain antibodies (sdABs) to label RyR2 subunits with a precision of 3 nm in xyz.\nHere are my major concerns:\n1. In the abstract, via this statement,\n\"In practice, this capability is currently limited by a modest effective labeling efficiency (~10% subunit detection efficiency resulting in ~35% RyR2 labeling efficiency), which we measure in-situ using a novel procedure enabled by the true molecular resolution of MINFLUX microscopy.\"\nThe authors imply that the true molecular resolution of MINFLUX in some way helps them address the problem of labeling efficiency.\nIn light of the statement below from Gwosch et al. 2020 (which the authors cite), \"With lens-based fluorescence microscopy having finally reached true nanometer resolution, it is important to bear in mind that fluorescence microscopes map nothing but the fluorophores; once the microscopes have fulfilled this task, they have accomplished their mission.\"\nI would like to remind the authors that true molecular resolution doesn't solve the problem of labeling efficiency, and they should revise the sentence not to imply this implicitly.\n2. Given the expertise of the authors in SMLM methods, and to really address the molecular resolution claim of MINFLUX, the author should do a comparison with any standard SMLM method and prove that indeed MINFLUX provides superior data.\n3. A couple of recent papers have raised concerns regarding the consistency of MINFLUX data:\nPrakash and Curd (2023) show that 2D and 3D MINFLUX data provide inconsistent quantitative data when the imaging modalities change.\nhttps://www.nature.com/articles/s41592-022-01694-x\nPrakash (2022) shows that MINFLUX data is less consistent and undersampled when compared to other standard SMLM datasets on nuclear pores samples\nhttps://royalsocietypublishing.org/doi/full/10.1098/rsta.2020.0145\nIn light of both papers, a comparison with other SMLM methods is highly requested to gain confidence in the claims made in this paper.\n4. In Figure 1, how do the authors establish the ground truth? Providing SMLM datasets with similarly tight distributions as in Figure 4 should be very convincing, especially when MINFLUX suffers from undersampling.\n5. The FOV in Figure 1 is rather small, and I would like to see the full HEK293 cell. I realize MINFLUX doesn't provide large FOVs, so SMLM imaging would indeed be helpful here.\nOverall, I request further SMLM datasets or other methods with similar resolution (like SIMFLUX) before I gain confidence in the MINFLUX data presented in the paper, which looks highly undersampled.", "reviewer_2": "MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells\nReview Sept 23, 2023\nIn this work, the authors employed 3D MINFLUX microscopy to map the subunit structure of RyR2 homotetramers and their 3D orientation. The work exploited the use of single domain antibodies against FP-labelled domains of the RyR2 tetramer to provide 3D super-resolved localization data related to the RyR2 clustering, orientation, and structure. Coupling this with insights from complementary simulations, EM reference data, and DNA-PAINT experiments, the authors present a compelling workflow and approach that illustrates using this well-explored system how 3D MINFLUX could be applied to other similar molecular complexes and architectures. The work is well conducted and there are extensive reference experiments, simulations, and complementary data sets that are compelling as to the validity of this approach. The methods and materials are nicely described and the approaches appear to be quite robust and should be readily accessible to the community. There are a few questions / comments / updates that would make help strengthen this nice contribution\n1. There are some nomenclature inconsistencies…\na. P. 3 – in the figure capture, the DBSCAN clustering uses “eps=7 nm” but later in the text on p4, “eps” is referred by its Greek letter\nb. On at least one occasion DBSCAN isn’t capitalized in the text – p. 7\n2. On p. 8, the authors refer to FRC resolution – but the term : Fourier ring correlation – isn’t defined in the text. It is however defined in the Supplementary materials\n3. The formatting of the literature references is not consistent (some are in title case, others are in sentence case)\n4. There is quite a bit of good information in the Supplementary Materials and it would be perhaps useful to see if more of the approaches could be include in the main body of the work. I was particularly interested in the details of the simulation work because it is key to interpreting and correlating with the experimental data and it would have been good if some details were included in the main text (or at least more explicit references to the Supplementary Materials section)\n5. The authors provided a very important insight / perspective on P.10 when they emphasized the fact that the localization data is really for the label location on the protein, with an accounting for potential linkage effects. Moreover much of the interpretation relies heavily on a priori insights into the structure of the complex of interest, which in this case was provided by EM data. It would be interesting to expand this to a system that is perhaps less well organized or that exhibit a more complex and heterogeneous organization (i.e. mixture of known monomeric/dimeric/oligomeric species). It would be interesting to postulate some of the challenges that lie in those studies.\n6. On P3 of the supplermentary materials, the authors refer to “90 rotation” – I believe this should read “90 degree rotation”"}, "author_response": "We thank the reviewers for their insightful comments and helpful suggestions for improving our manuscript. The comments on detection efficiency prompted us to adapt our methodology to utilise DNA-PAINT instead of the originally used photoswitching (dSTORM) approach with MINFLUX. In doing so, it became clear that our previous data was indeed undersampled. We developed an assay to confirm the detection efficiency in situ and at the same time ensure that increased densities of localisations do reflect actual extra detected target sites (or more precisely, extra detected markers on target sites) rather than “repeat site visits” that could in principle result from using DNA-PAINT.\nWe thus repeated all our experiments in HEK293 cells and isolated myocytes with MINFLUX DNA-PAINT and present this new data for review. The main figures have all been updated to reflect this new, much higher quality data and we believe this addresses previous concerns. The difference in appearance is striking and quite apparent when inspecting the new data sets and in our view fully justified the redoing of all experiments. We thank the reviewers for bringing up the point of possible undersampling as the new data reveals the intricacies of RyR2 distribution that previously could not be resolved (which results from both spatial resolution and proper sampling).\nCrucial for being able to redo these experiments was the delivery and installation of a MINFLUX microscope at our local facility and the ability to spend extensive time and effort on these experiments locally rather than travelling to a remote demo site as had been done for the initial data. As a consequence, completing this revised study has taken considerable time which was extended by some early hardware failures that we needed to rectify in close collaboration with the manufacturer.\nTo fully optimise the conditions we have introduced a refined and robust labelling efficiency assay and with that conducted to the best of our knowledge the first MINFLUX study with a focus on explicitly maximising the effective labeling efficiency, particularly the component that we term the “photo-detection probability” or “marker detection probability”. This reflects our observation that with dSTORM photoswitching and MINFLUX, at least in our hands, the dyes bleached very quickly (relative to the long scan times) while MINFLUX was searching for blink events to image so that many of the markers were never “sampled” by MINFLUX before the localisation rate declined. This could be avoided by judicious use of DNA-PAINT with MINFLUX where the assay is used to ensure that DNA-PAINT imaging has progressed to the point that all marker positions have been “harvested”. Site-loss in this case also helped ensure that markers were visited by successful imager localisations on average only once thus avoiding overcounting. Sub-maximal photo-detection probability may indeed have hampered some previous MINFLUX studies.\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for their response to my previous comments. The manuscript has undergone substantial revision, including the installation of a local MINFLUX microscope. The authors have also employed DNA-PAINT to improve detection probability.\nAs the authors rightly point out, it is important to distinguish between labeling efficiency and detection probability. My understanding is that DNA-PAINT helps with more detections but does not necessarily improve labeling efficiency unless a RESI type approach is used. If authors agree, this distinction should be made clearer in the text, else I am happy to hear their explanation.\nA key aspect of scientific reporting is not only to highlight what works but also to communicate what does not. Much of the current work in the SMLM field remains two-dimensional. Therefore, it would be valuable for the community if the authors could explicitly state that in 2D, the MINFLUX data is broadly comparable to conventional SMLM. Unless I missed it in the main manuscript, I suggest adding this sentence either in the abstract or results section which authors mention in their cover letter.\n“In x–y, the data appears broadly similar between both modalities once the MINFLUX data is laterally slightly blurred.”\nComing to 3D MINFLUX, in the cover letter, the authors state:\n“As we have previously emphasized, the true improvement we observe with MINFLUX imaging is in the axial resolution, as clearly illustrated in SI Fig. 3D & E.”\nIf this is indeed the case, I agree that it represents an advancement as previous studies have reported inconsistencies in 3D MINFLUX estimates, particularly when comparing 1-color vs. 2-color imaging modes\n(https://www.nature.com/articles/s41592-022-01694-x).\nGiven that the revised manuscript now focuses on 3D MINFLUX, I encourage the authors to present quantitative estimates of the axial separation between the nucleoplasmic and cytoplasmic rings of the nuclear pore complexes. For this, the authors may consider using publicly available tools such as:\n1. The software from Jonas Ries (https://www.nature.com/articles/s41592-022-01676-z)\n2. Alistair Curd’s PERPL (https://pubs.acs.org/doi/full/10.1021/acs.nanolett.0c03332)\n3. Other tools from Bernd Rieger/Sjoerd Stallinga, or John Danial (ASAP, Nature Methods)\nThese tools are user-friendly and allow for modeling of single or multiple pores within a field-of-view, providing quantitative assessments. If the above is not feasible, the authors could select 15–20 well-resolved pores, perform line fitting, and report the axial spacing statistics.\nI strongly recommend including a comparison with 3D SMLM datasets to contextualize the improvement provided by MINFLUX.\nIn summary, I am not requesting additional lab experiments, but a quantitative comparison of 3D SMLM and MINFLUX that would serve as a minimal yet necessary benchmark to demonstrate that 3D MINFLUX results are indeed better and also consistent with current standards in the field.", "reviewer_2": "This is a substantially revised resubmission of the original manuscript. The authors have done an excellent job at addressing the key concerns of both reviewers and the work has been significantly improved. The authors' careful reconsideration and re-examination was well conducted and indeed presents a cautionary lesson for those applying these and related techniques..\nAfter this adjustment, the work is well conduced and will be well received by the community."}, "author_response": "We thank both reviewers for their insightful comments and helpful suggestions for improving our manuscript. [...]"}, {"round": 3, "reviewer_comments": {"reviewer_1": "I would like to commend the authors for their revisions and for the successful implementation of a commercial MINFLUX microscope along with the associated data analysis pipeline.\nThe manuscript now presents a coherent dataset. However, I would appreciate further clarification regarding the following:\n1. While I appreciate the consistency shown between SMLM and MINFLUX results, the manuscript does not fully resolve the long-standing discrepancy between ~60 nm interlayer spacing reported in previous EM and SMLM studies versus the 50 nm value supported here. Notably, recent work by the Susan Cox lab (Rosten et al., 2025: https://www.biorxiv.org/content/10.1101/2025.08.06.668903v1.full.pdf) confirms the 60 nm interlayer distance between the cytoplasmic and nucleoplasmic rings, consistent with von Appen et al. and at odds with the findings reported in the current study.\nCould the authors clarify from where this discrepancy arises? An explicit discussion would be valuable.\n2. The following paragraph from the manuscript lacks specific information and could be interpreted as generic content:\n\"Imaging of specific proteins that are part of the NPC has become both an important assay to quantify labeling efficiency…\"\nI recommend rewriting this paragraph with concrete quantitative results. It would also be appropriate to cite Gwosch et al. (2020) here and their originally reported NPC measurements.\n3. Given that many of the clustering results rely on a 100 nm cutoff and that comparable SMLM data already report similar patterns, it remains unclear why MINFLUX is necessary? Could the authors clarify what unique insights MINFLUX enables here that SMLM cannot, beyond improvements in some precision numbers?\n4. If the methodological advances are primarily incremental, I encourage the authors to articulate more clearly what new biological insights emerge from this work. For instance, while the subunit orientation of RyR2 tetramers is described, the functional implications of these structural findings are not evident. As someone less familiar with this specific biological system, the novelty and broader biological significance are not immediately clear from the current presentation.\n5. I also think abstract, introduction, discussion can be further synced and polished. Presently, it is very difficult to read.\nI thank the authors for their efforts and look forward to their responses."}, "author_response": "We thank the reviewer for their constructive assessment and address their additional remarks below. [...]"}]

Decision letter