Dynamic nanoscale architecture of synaptic vesicle fusion in mouse hippocampal neurons

Domain: physics
Published: 2025-12-13
Editorial outcome: major_revision

Abstract
Synaptic vesicle (SV) fusion is not only tightly coordinated but also happens at a millisecond timescale. Competing models for fusion initiation and propagation suggest tight docking and hemifusion of SVs or localized lipid rearrangements leading to tip-like membrane contacts. Yet, a direct nanoscale examination of the full SV fusion sequence has been lacking. Here, we establish a workflow for timed in situ cryo-electron tomography of optogenetically stimulated mouse neurons to capture the complete SV fusion sequence – from SV recruitment to fusion pore formation, opening and collapse – with near-native structural preservation. Notably, tethered SVs directly undergo fusion initiation via stalk formation, without preceding tight docking or SV flattening. The plasma membrane forms a minimal dimple during fusion initiation, contradicting preceding models that invoke strong membrane bending prior to fusion. In addition, we observe filaments linking fusing SVs to adjacent SVs, indicating a physical link between fusion and SV resupply.

Introduction
Neurotransmitter release is driven by the rapid fusion of synaptic vesicles (SVs) with the presynaptic plasma membrane, a process fundamental to neuronal communication. The presynaptic active zone (AZ) harbors release sites, where SVs are coupled to voltage-gated calcium channels and primed for fusion1,2. The synaptic fusion machinery, consisting of soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs), regulatory proteins like Munc13 and Munc18, and the calcium sensor synaptotagmin-1, catalyzes the fusion reaction, helping to overcome energy barriers imposed by repulsive forces during SV-AZ membrane apposition and lipid reorganization1,3,4. An SV has reached a primed state when trans-SNARE complexes have formed and closely interact with synaptotagmin-1 and complexin5,6. Calcium influx triggers membrane binding of synaptotagmin-1, initiating the vesicle fusion process within a fraction of a millisecond7–9.

While much is known about the molecular players of the fusion apparatus, the precise structural pathway by which SV and plasma membranes fuse remains largely unresolved. Advances in electron microscopy (EM) sample preparation techniques, combining optogenetic or electrical stimulation and high-pressure freezing, have provided snapshots of SV fusion at central synapses. Membrane pits thought to represent fusion events were observed several milliseconds after stimulation10, along with a decreased number of morphologically docked SVs10–13. However, the required dehydration, freeze-substitution and resin embedding of EM samples may introduce artifacts that impede the direct observation of native membrane dynamics14. In contrast, in situ cryo-electron tomography (cryo-ET) provides a near-native sample preservation, going along with a higher structurally interpretable resolution15–19. This led to differences in the assessment of SV-plasma membrane docking activity17. In addition, cryo-ET enabled the characterization of proteinaceous filamentous tethers that may contribute to the functional state of SVs15,20–23. Yet, adding millisecond (ms) temporal resolution has so far been challenging24. Although a small number of SV fusion intermediates have been captured using a spraying technique for synaptic stimulation recently22, a systematic characterization of SV fusion in situ is still lacking. Our current understanding of membrane dynamics during SV fusion is therefore essentially built on in vitro25–27 and in silico8,28–31 studies of membrane fusion events.

Several mechanistic models have been proposed to explain how SNARE zippering and synaptotagmin-1 membrane binding catalyze membrane contact and merger of SV and plasma membranes. Previous studies utilizing elastic continuum models suggested that SV fusion proceeds through energetically metastable hemifusion diaphragms-intermediates where the outer leaflets of the membranes merge while inner leaflets remain distinct32,33. According to simulations, this hemifusion state could be reached through a point-like contact formed between the two membranes via bending of their lipid bilayers32,33. Other models suggest that SV and plasma membranes can form large close-contact interfaces, with fusion nucleating at the edge of the contact zone through SNARE zippering3,25,26.

Alternative models, particularly from coarse-grained simulations, suggest that SV fusion can initiate between flat bilayers via highly localized lipid rearrangements. These include acyl chain splaying events, which create a small yet rapidly expanding hydrophobic core, seeding the formation of a fusion stalk34. A recent all-atom simulation of SV fusion showed that lipid chain splaying, resulting in a hydrophobic bridge and fusion pore formation, can be initiated by calcium-triggered rearrangements of the SNARE complex8. Despite the advances, it remains unclear which of these models accurately reflects the SV fusion process at central synapses, primarily because a direct visualization of the complete SV fusion process has thus far not been possible.

In this study, we develop a workflow for timed in situ cryo-ET using optogenetics. We utilize this workflow for a comprehensive reexamination of SV fusion under near-physiological conditions and within the native cellular environment. Our analyses reveal that stalk formation leads to the formation of asymmetric fusion pore without tight docking or hemifusion. Furthermore, we uncover structural evidence for mechanically coordinated SV resupply, suggesting a link between SV fusion and replenishment.

Results
Coupled optogenetic stimulation and cryofixation of mouse hippocampal neurons
To characterize the synaptic nanoscale architecture during and shortly after neurotransmitter release, we developed a workflow combining optogenetics and in situ cryo-ET (Fig. 1). For optogenetic stimulation, we expressed the channelrhodopsin-2 (ChR2) variant ChR2(E123T/T159C) in murine hippocampal neurons, which was shown to induce action potentials in a particularly fast and robust manner35,36. In addition to the established ChR2(E123T/T159C) version, which includes YFP for cellular localization (Fig. 1b), we used additional constructs harboring a Cerulean or mScarlet to avoid potential spectral overlap with fluorescent sensors in subsequent experiments (Supplementary Fig. 1a). We performed electrophysiological recordings of neurons infected with ChR2(E123T/T159C)-YFP or one of the two new constructs ChR2(E123T/T159C)-Cerulean and ChR2(E123T/T159C)-mScarlet to assess their efficacy to induce action potentials. Onset of a light pulse resulted reliably in an action potential with an average delay of 4.6-4.8 ms, a similar value as described for ChR2(E123T/T159C)-YFP in a similar experimental setting37 (Supplementary Fig. 1b), and a robust response to light stimuli up to a frequency of 40 Hz for all three tested constructs (Supplementary Fig. 1c and 1d).

Neurons cultured on EM grids (for details of our cell culture setup, see Supplementary Fig. 2) were plunge frozen at DIV16-18 using a modified Vitrobot Mark IV (Thermo Fisher Scientific) plunge freezer equipped with an LED connected to optical fibers inside and below the chamber (Fig. 1b and Supplementary Fig. 3a, b). Optogenetic stimulation (2 pulses at 10 Hz) was performed at ~37°C and an elevated extracellular calcium concentration of 4 mM to increase the vesicular release probability. The first stimulus was applied within the chamber at a maximum of 100 ms before vitrification for efficient fluorescent labeling of stimulated synapses (see Supplementary Fig. 4c). The second stimulus, which induced the fusion events morphometrically characterized in this study, was applied while the sample grid traveled towards the cooled ethane. This second stimulus started approximately 7 ms before vitrification, inducing an action potential 2–5 ms before the grid was dipped into cooled ethane (additional cooling time of the sample grid to 0°C < 1 ms38). The exact timing of the LED pulses and freezing were monitored using a high-speed camera (Supplementary Fig. 3c). Considering that most action potentials were induced 3–6 ms after light onset in our electrophysiological experiments (see Source Data) and that the delay between action potential generation at the presynapse and synchronous neurotransmitter release is typically 1 ms or shorter39, our setup was well suited for cryofixing neurons shortly before, during, and directly after neurotransmitter release.

Confirmation of neurotransmitter release using the glutamate sensor iGluSnFR3 in cryofixed neurons
After plunge freezing, we aimed to validate successful stimulation using a fluorescent biosensor for synaptic activity. For this purpose, we first characterized the kinetics and fluorescence intensity changes of the calcium sensor, SynGCaMP6f40, and different variants of the glutamate sensor, iGluSnFR, via live imaging of neurons cultured on coverslips (Fig. 2a and Supplementary Fig. 4a). Of all tested constructs, the fluorescent glutamate sensor iGluSnFR3.v857.GPI containing a GPI anchor for postsynaptic enrichment (41, from now iGluSnFR3) yielded the best-fitting properties with a maximum fluorescence intensity of 0.4 ± 0.03 ΔF/F0, (Supplementary Fig. 4b), an increase to half-maximum of τ50% = 22.6 ± 3 ms (Supplementary Fig. 4d), and an increase to maximal intensity of τmax = 64.7 ± 6.2 ms (Supplementary Fig. 4c). To examine the cellular localization of iGluSnFR3 signals, we performed a post hoc immunofluorescence staining of synaptic proteins on samples used for live imaging (Supplementary Fig. 4f). With this correlation, we could verify that action potential-induced iGluSnFR3 signals overlap primarily with the postsynaptic marker Homer1.

Assuming that the glutamate-bound, highly-fluorescent conformation of iGluSnFR3 can be preserved under cryogenic conditions, we acquired cryo-confocal stacks of optogenetically stimulated and plunge frozen neurons expressing iGluSnFR3 (Fig. 1c). We compared fluorescence intensities in neurites of unstimulated neurons, optogenetically stimulated neurons, and optogenetically stimulated and tetrodotoxin-treated (TTX, pharmacologically blocks sodium channels required for action potential induction) neurons of four independent cultures (two for TTX, Fig. 2b). In all four cultures, of which two were infected with ChR2(E123T/T159C)-YFP, one with ChR2(E123T/T159C)-mScarlet, and one culture with both, the mean fluorescence intensity of the stimulated samples was consistently higher than of the unstimulated samples (for details, see Source Data), indicating that our setup combining optogenetic stimulation and plunge freezing works reliably.

We therefore pooled the measurements of the individual cultures and calculated fluorescence intensity histograms for all three conditions (Fig. 2c). In the stimulated samples, the mean fluorescence intensity of 36.3 ± 1.3 AU was significantly higher than in the two control conditions (unstimulated: 30.0 ± 1.1 AU, TTX-treated: 31.0 ± 1.5 AU, Kruskal-Wallis-test p < 0.001, KWS = 15.12, Fig. 2d). Likewise, the fraction of pixels with a high fluorescence intensity (>70 AU), likely reflecting glutamate-bound iGluSnFR3, was significantly increased after stimulation (20.6 ± 0.9% vs. 13.0 ± 0.9% and 11.9 ± 1.2% without stimulation and after TTX treatment, respectively, Kruskal-Wallis-test p < 0.001, KWS = 38.16, Fig. 2e). Of note, the difference in mean fluorescence intensities of optogenetically stimulated and control conditions was less pronounced under cryogenic conditions than during live imaging, likely because low-expressing neurons were not selected during live imaging and non-responding neurons were excluded from the data analysis (no measurable response in 14%, weak response of ΔF/F0 < 0.1 in 23% of recordings for iGluSnFR3.v857.GPI, see Supplementary Methods and Source Data), whereas cryo-confocal stacks were acquired from regions selected blindly.

To test if high iGluSnFR3 fluorescence intensity can be used as a marker for SV fusion events, we performed correlative cryo-confocal microscopy and cryo-EM (cryo-CLEM) on a stimulated EM grid (Fig. 2f and Supplementary Fig. 5). As visible in the overview of four grid meshes, the overall morphology of neurons was preserved in our on-grid cell culture system. For tilt series acquisition, we selected regions containing a high density of neurites but no cell somata (yellow box in the middle panel of Fig. 2f). Correlating fluorescence and transmission electron microscopy (TEM), we observed the highest fluorescence intensity around large boutons likely resembling synapses (yellow arrowheads in box in Supplementary Fig. 5). The correlation of iGluSnFR3 fluorescence and tomogram slice (Fig. 2f’) revealed that the highest fluorescence intensity was visible at the synaptic cleft between a presynapse and a postsynaptic bouton. At the presynaptic AZ membrane, a putative forming fusion pore was observed (Fig. 2g).

In situ cryo-ET of SV fusion intermediates
Having confirmed that neurons cultured on EM grids were optogenetically stimulated before being subsequently plunge frozen, we acquired cryo-ET data from those grids to morphometrically and biophysically characterize SV fusion (Fig. 1d, e). Although we could show that the iGluSnFR3 fluorescence signal is per se suited to select regions of interest for cryo-ET, we acquired tilt series without correlation of each position to avoid ice contaminations, which form during prolonged cryo-confocal microscopy, and to avoid the thinning of our samples via focused ion beam (FIB)-milling, going along with a lower sample throughput. We therefore acquired tilt series from regions with good cell and ice quality after confirming successful stimulation via cryo-fluorescence microscopy at only a few positions of the grids. From the acquired tilt series, we reconstructed 312 tomograms from stimulated and 95 tomograms from control (TTX-treated) samples (see Supplementary Video 1 for an exemplary tomogram of a stimulated synapse). We screened each tomogram manually for synapses with a visible, cross-sectioned AZ. This resulted in 75 synapses in the stimulated and 28 synapses in the TTX-treated samples that were used for further analysis. Each AZ was then examined in more detail for membrane rearrangements that may be attributed to SV fusion (see Supplementary Fig. 6 for examples of excluded structures).

Based on all observed events, and in accordance with previously described SV fusion intermediates3,21,22, we defined seven categories: invagination of the AZ membrane (1), stalk formation (2), closed fusion pore (3), open fusion pore (4), dilating fusion pore (5), collapsing fusion pore (6), and small bumps (7) (Fig. 3a, b, Supplementary Fig. 8, see Supplementary Fig. 7 for morphometric criteria of each category). In addition to exemplary tomogram slices (Fig. 3b), we visualized 3D volumes of each category using UCSF ChimeraX42 (Fig. 3c). We further performed subtomogram averaging (StA) of selected fusion events from most categories using Dynamo43 (Supplementary Fig. 8a) and applied C61 symmetry (Supplementary Fig. 8b) to visualize the general membrane shape and bending. We were not able to generate a StA of open fusion pores because this category was particularly heterogeneous with open pore widths ranging from 2 to 18 nm.

SVs of category 1 (n = 10 SVs in stimulated synapses) were spherical, and in a distance of 6.4 ± 0.4 nm to the AZ membrane (Fig. 3a), the AZ membrane below the center of the SV was slightly invaginated (2.6 ± 0.3 nm; Supplementary Fig. 8c and Source Data). In category 2 (stalk formation, n = 22), SVs were droplet-shaped with an evagination at the SV bottom, while the AZ membrane was almost flat or slightly invaginated. At closed fusion pores belonging to category 3 (n = 6), the SV and AZ membrane had already fused, resulting in a continuous membrane, as confirmed by grayscale intensity measurements along the stalk/fusion pore neck (Supplementary Fig. 7 and Source Data). In comparison to category 2, closed fusion pores were slightly taller, the invagination of the AZ membrane was more pronounced. Open fusion pores (category 4, n = 7) were smaller than closed fusion pores, the width of the open pores was between 2.3 and 18.3 nm. Based on our definition, open pores contained outward (positive) and inward (negative) membrane curvature at the pore neck, whereas dilating pores (category 5, n = 8) only showed inward curvature at the junction of SV and cell membrane, as illustrated in Supplementary Fig. 7. The side walls of dilating pores were vertical or angular. Compared to dilating pores, collapsing fusion pores (category 6, n = 12) were lower (dilating pore: 48.4 ± 2.6 nm vs. collapsing pore: 29.6 ± 2.1 nm) and wider (dilating pore: 37.4 ± 1.1 nm vs. collapsing pore: 50.4 ± 2.3 nm). Interestingly, the top membrane of collapsing fusion pores appeared thickened in relation to the surrounding AZ membrane by a factor of 1.5 (Fig. 3a and Supplementary Fig. 8c). Small bumps (category 7, n = 21) were again lower (7.7 ± 0.4 nm) than collapsing fusion pores and varied in size and shape.

Overall, optogenetically stimulated synapses showed a significantly different abundance of membrane rearrangements compared to TTX-treated control synapses (Fisher’s exact test, p = 0.007). We observed higher fractions of events in categories 1–4 and 6, with the most striking difference in category 2 vs. no membrane rearrangements (corrected Fisher’s exact test, q = 0.035), in stimulated neurons (Fig. 3d), whereas the fraction of dilating pores (5) and small bumps (7) was alike under both conditions. Closed and open fusion pores were only present in the stimulated sample without TTX treatment. Therefore, we defined membrane rearrangements of categories 2–6 as ongoing SV fusion and cell membrane invaginations below tethered, round SVs (category 1) as events likely preceding SV fusion. Based on this definition, we observed ongoing SV fusion in 52% (39/75 synapses), bumps in 14.7% (11/75), and no membrane rearrangements in 33.3% (25/75) of all stimulated synapses. In the TTX-treated group, the distribution between these three conditions was significantly different (Fisher’s exact test, p < 0.001): we found fusion events in 10.7% (3/28 synapses), bumps in 17.9% (5/28), and no membrane rearrangements in 71.4% (20/28) of all synapses (Fig. 3e).

Since some synapses contained more than one fusion event, we further counted each fusion event individually (Fig. 3f). Of all fusion events observed in the stimulated samples (N = 55), the majority was stalk formation (40%), followed by collapsing fusion pores (21.8%). Closed (10.9%), open (12.7%), and dilating (14.5%) fusion pores were less prevalent. Presuming that more transient and volatile conditions are stochastically less likely to be captured during plunge freezing, our observed numbers of events per fusion state may serve as a morphological readout for their speed. Based on this assumption, we generated a Markov state model to elucidate the kinetics of transitions between our defined states (Supplementary Fig. 9a). According to this model, fusing SVs may remain in state 2 (stalk formation) for comparatively longer, likely because energy barriers need to be overcome when the membranes of SV and AZ are approached and perturbed3. Alternatively or in addition, some of the formed stalks may not lead to SV fusion but instead get stuck or disassemble again44,45. We therefore made the transition from state 1 to state 2 reversible.

Initiation of SV fusion via stalk formation
In previous studies, not only stalk formation but alternatively also (tight) docking has been suggested as prefusion state3,26. During tight docking, the SV approaches the AZ until the membranes are in direct and broad contact; the lipids of the SV and AZ membranes are supposed to intermix until they reach a hemifusion (diaphragm) state. In our examples of stalk formation, the SVs were droplet-shaped, and the average distance between SV and membrane was 4 ± 0.3 nm (smallest measured distance: 2.3 nm). This space between SV and AZ membrane appeared partially blurry in some of our examples (Supplementary Fig. 8c), which has previously been attributed to starting lipid intermixing22. In contrast, we observed one morphologically tightly docked SV at an AZ (Supplementary Fig. 8d), as well as one tightly docked SV (Supplementary Fig. 8e) and a putative hemifusion diaphragm (Supplementary Fig. 8f), both not in an AZ. To test whether SVs could still undergo tight docking in the early phase of SV fusion despite the high prevalence of stalk formation, we generated additional Markov state models assuming tight docking preceding or following stalk formation (Supplementary Fig. 9b, c, respectively). In both cases, mean waiting times exceeded the predefined total duration of 2 ms, indicating that these transitions are comparatively very unlikely. Together, our observations indicate that a transition from tethering to stalk and fusion pore formation without (tight) docking is the predominant fusion mechanism.

Furthermore, a slight invagination of the AZ membrane (state 1) was present below 6.5% of all tethered SVs within a distance of 4–8 nm from the AZ membrane (10/155 SVs). We observed these invaginations at stimulated synapses with and without additional fusion events. To test whether an invagination of the AZ membrane is beneficial for SV fusion initiation and may thus precede stalk formation, we generated a coarse-grained simulation of an SV approaching the AZ membrane (Fig. 3g). In this model, proteins (e.g., resembling synaptotagmin-1) actively induce membrane curvature as soon as the SV has reached a distance of 6 nm to the AZ and also interact with SNAREs. We thereby incorporated size and distance measurements of our morphometric analyses (Figs. 3a, 4c–g and Supplementary Fig. 11). With this model, we tested the effects of different concentrations of membrane curvature-inducing proteins on SV approximation and SNARE complex formation. Since previous studies reported on different copy numbers of synaptotagmin-1 per fusing SV, ranging from below 1046, approx. 1547 up to more than 2048, and also other curvature-inducing proteins may be present close to the fusion site, we tested for 0, 10, 20, 30, and 40 copy numbers of actively membrane curvature-inducing proteins. Interestingly, higher copy numbers of these proteins did not facilitate fusion but rather impeded the recruitment of the SV, likely because SNARE complexes could not be formed efficiently anymore (Fig. 3h and Supplementary Fig. 10). Instead, 0 or 10 copies resulted in a fast SV approximation (in our model, SV fusion was not enabled). This means that although a slight invagination of the AZ membrane, as observed by us and in a previous study22, may precede SV fusion, it is unlikely induced by proteins like synaptotagmin-1 but rather a consequence of SNARE zippering7,9.

Depletion of membrane-proximal SVs in stimulated synapses
In addition to ongoing SV fusion events, we analyzed the distribution of tethered SVs within a distance of 24 nm from the AZ membrane (Fig. 4a, b). In previous EM studies, changes in the abundance of membrane-near SVs have been used as confirmation for successful action potential induction and subsequent release10–13,37,49. To test whether we could reproduce these findings with our workflow, we first compared all stimulated synapses (n = 515 SVs from 54 synapses) to TTX-treated synapses (n = 162 SVs, 19 synapses), whereby synapses with only bumps (state 7) were not included. While the total number of SVs within a distance of 24 nm was not significantly different between the two groups (Mann-Whitney test p = 0.477, U = 456, Fig. 4c), we counted on average 1.4 ± 0.6 fewer SVs per AZ within a maximum distance of 6 nm (Mann-Whitney test p = 0.035, U = 352.5, proximal SVs, Fig. 4d, e) in stimulated synapses. Between 6 and 12 nm (intermediate SVs), we observed slightly more SVs in stimulated synapses (Supplementary Fig. 11c). A comparable redistribution of SVs was reported using “zap-and-freeze” and freeze substitution, however, the effects were more drastic (loss of approx. 40% of all “docked” SVs due to SV fusion and putative undocking)10.

In addition, we analyzed SV distributions of stimulated synapses with ongoing SV fusion (n = 29) and without membrane rearrangements (n = 25) individually. In the membrane-proximal SV pool (0–6 nm distance), we observed 0.6 ± 0.4 fewer SVs per AZ in stimulated synapses containing at least one fusion event (Fig. 4f). Consequently, also the subgroup of stimulated synapses without observed ongoing SV fusion had on average less membrane-proximal SVs than TTX-treated synapses (counted difference 1.1 ± 0.7 SVs, Supplementary Fig. 11b, e). It is likely that this subgroup of stimulated synapses without membrane rearrangements consists of synapses post fusion (neurotransmitter release has already taken place) and synapses without neurotransmitter release (non-releasing synapses). Assuming that the distribution of membrane-proximal SVs in post fusion synapses is comparable to synapses with ongoing fusion, as reported recently for synapses up to 11 ms after action potential induction10, whereas non-releasing synapses assumingly resemble TTX-treated synapses10,50, we calculated the theoretical fraction of non-releasing synapses (see Supplementary Methods): Within the group of synapses without membrane rearrangements, the fraction of non-releasing synapses would be 17%. Of all stimulated synapses, the fraction of non-releasing synapses would be 8%, resulting in a theoretical synaptic release probability of 92% with our workflow. The release probability of excitatory hippocampal synapses at a calcium concentration of 4 mM and near-physiological temperature was reported to be ~85%51, as also confirmed by our iGluSnFR3 response rate for electrically stimulated neurons of 86% (Source Data). Taking into account that action potentials could be induced in 80-90% of optogenetically stimulated neurons (Supplementary Fig. 1c), the expected synaptic release probability for our setup is 68–77% and thus even lower. In other words, we observed on average fewer membrane-proximal SVs in the subgroup of stimulated synapses without ongoing fusion than expected. Kusick and colleagues10 came to the same conclusion and attributed the low SV number to transient SV undocking during or shortly after fusion.

Previous work has shown that not only the distribution of SVs, but also the number of tethers dynamically changes during synaptic activity, whereby the formation of three or more tethers connecting SV and AZ membrane was suggested to be a morphological correlate of priming and a prerequisite for SV fusion21,22. Although our tomograms of synapses were comparatively thick, going along with a potentially worse signal-to-noise ratio than FIB-milled samples or purified synaptosomes, we were able to quantify tethers in our stimulated samples. Overall, we manually quantified tethers at 114 SVs from 18 synapses with ongoing fusion and 145 SVs from 15 synapses without membrane rearrangements. In both groups, we observed a linear correlation of tether number and distance between SV and AZ membrane (Supplementary Fig. 11h), whereby membrane-proximal SVs had the highest average number of tethers (4.6 ± 0.1 and 4.4 ± 0.2 for synapses without and with fusion event, respectively) and distal SVs the lowest (Fig. 4g). The number of tethers below SVs with slight membrane invagination was 4.2 ± 0.3 and did thus not differ from the other SVs within the same distance to the cell membrane. Of note, we observed short, vertical tethers predominantly below SVs (Fig. 4h and Supplementary Fig. 12) and longer, angular and curved tethers predominantly at the sides of membrane-proximal SVs (Supplementary Fig. 12). For size estimation, we positioned atomic models of different exocytic proteins/complexes next to tethered SVs (Fig. 4j and Supplementary Fig. 12). Based on their size, SNARE proteins may be involved in the formation of the short tethers observed here, as suggested previously15,22,23. However, not only assembled SNARE complexes together with synaptotagmin-1 and complexin (PDB: 5W5D6), which would indicate a primed state of the SV, are likely candidates. Syntaxin-1 and Munc18 (PDB: 4JEU52) or syntaxin-1, synaptobrevin-2 and Munc18 (PDB: 7UDB53), both representing states preceding SV priming, would fit equally well. Size-wise, Munc13 (PDB: 7T7V or 7T7X54) could be involved in the formation of longer angled tethers, however, we cannot rule out that Munc13 is also part of short vertical tethers. Of note, we also observed filaments connected to SV fusion intermediates, e.g., around the space between SV and AZ membrane during stalk formation (Supplementary Fig. 8c). Whether these filaments resemble parts of the SV fusion machinery, e.g., assembled SNARE complexes, need to be investigated further.

Multivesicular release and release site refilling
In 23% of all stimulated synapses with ongoing SV fusion (9/39 synapses), we observed multiple fusion events (Fig. 5a, c), whereby most of these synapses contained two (Fig. 5d). Likewise, we observed multiple small bumps per synapse in 55% of synapses containing bumps (6/11 synapses, Fig. 5b, e). At the synapses with multivesicular release (MVR), the individual fusion events did not preferentially fall into the same category.

Particularly during fast and sustained neurotransmitter release, release sites need to be refilled with SVs. In our synapses, we observed that SVs were not only connected to each other via pleomorphic interconnectors, but also partially connected to fusion intermediates of different categories (Fig. 5f, also see refs. 21, 22). Considering that filamentous connections between membrane-near SVs were shown to persist or even increased during SV fusion21,22, it is likely that also the linkers between SVs and fusion events are stable and/or strengthened during action potential-induced calcium influx. This way, fusing SVs may directly recruit new SVs for very fast release site refilling. We indeed observed filamentous interconnectors at 62% of fusion events (26/42). To test whether these interconnectors are involved in the recruitment of nearby SVs during fusion, we performed a nearest neighbor analysis. We first measured distances between fusion events and nearest neighbor SVs, as well as distances between membrane-proximal SVs (max. distance of 6 nm to the AZ membrane) and their respective nearest neighbor SVs in the groups of stimulated synapses without membrane rearrangements and stimulated synapses with ongoing fusion (Fig. 5g–i). We compared average distances for membrane-proximal SVs and fusion events per synapse (Fig. 5g), resulting in a nonsignificant trend towards greater nearest neighbor distances in synapses with ongoing fusion. The direct synapse-wise comparison of distances between SVs and between fusion events and SVs (Fig. 5h) revealed no difference between the two groups. We further compared nearest neighbor distances in membrane-proximal SVs and fusion events individually, revealing significantly lower nearest neighbor distances for membrane-proximal SVs in synapses without membrane rearrangements than in synapses with ongoing fusion (Kruskal-Wallis test, p < 0.001, KWS = 14.55). We attribute this difference primarily to the overall lower number of membrane-near SVs in the group of stimulated synapses with ongoing fusion. Interestingly, fusion events without and with interconnectors formed two subpopulations, whereby fusion events without interconnectors were associated with significantly larger nearest neighbor distances (Mann-Whitney test, p < 0.001, U = 26.5). Moreover, we noticed that fusion events with interconnectors were more prevalent in earlier fusion states, whereas fusion events without interconnectors were primarily associated with collapsing fusion pores (Fig. 5j, two-way ANOVA, p < 0.001, F = 20.14 for interconnected vs. not interconnected fusion events). Together, these findings point towards a role of filamentous interconnectors in direct and immediate release site refilling, particularly in the early phase of SV fusion.

Methods
Mass culture of mouse primary hippocampal neurons
Astrocyte feeder culture. Astrocytic and neuronal mass cultures were prepared from P0-P2 C57/BL6/N mice of either sex. To prepare astrocyte feeder layers, mice were decapitated, and cortices were isolated in cold HBSS-HEPES. The tissue was digested in 0,05% trypsin-EDTA for 15–20 min at 37°C followed by manual trituration. The isolated astrocytes were transferred to T75 flasks and cultured for two weeks in Dulbecco’s modified Eagle medium supplemented with 10% fetal calf serum (FCS), 100 U/ml penicillin and 100 µg/ml streptomycin (DMEM) at 37°C and 5% CO2. Confluent astrocytes were trypsinated (0,05% trypsin-EDTA) and seeded on collagen/poly-D-lysine coated coverslips (for RT experiments) or coated wells (for cryo experiments and banker cultures) in a density of 7.5 × 104 cells/well (six-well plates). The astrocytes were cultured for an additional week before FUDR (8.1 mM 5-fluoro-2-deoxyuridine and 20.4 mM uridine in DMEM) was added to arrest glia proliferation.

Neuronal culture on coverslips. For live imaging, RT confocal imaging and electrophysiological recordings, primary hippocampal neurons were used as co-cultures with astrocytes or separated from astrocyte feeder layers as banker cultures. Since we did not find differences in the responsiveness of neurons in the two different culture systems, we pooled data from mass cultures and banker cultures. To isolate neurons, hippocampi were dissected in cold HBSS-HEPES and digested using 20 U/ml papain for 45 min at 37°C, followed by manual trituration. For co-cultures, isolated neurons were seeded directly on 1-2 weeks old astrocyte feeder layers in a density of 3 × 104 − 5 × 104 neurons/well. For banker cultures, neurons were seeded on coverslips coated with collagen/poly-D-lysine and ornithine (5 × 104 neurons/well), and the coverslips were transferred to well plates containing astrocyte feeder layers after neurons were allowed to adhere to the coverslips for 1 h. Neurons were cultured for 14–18 days at 37°C in neurobasal-A medium containing 2% B-27, 1% Glutamax, 100 U/ml penicillin and 100 µg/ml streptomycin (NBA), lentiviruses and AAVs were added on DIV 2-3.

Neuronal culture on EM grids. For culturing neurons on EM grids, mesh pedestals were 3D printed (see Source File 1 for a printing scheme for 6-well and 12-well plates) and placed on top of astrocyte feeder layers. DMEM medium was replaced by NBA medium. Quantifoil R3.5/1 AU holey carbon grids (400 mesh, 200 mesh and 200 mesh finder grids) were cleaned with chloroform and acetone, followed by glow-discharging. Directly afterwards, the grids were placed on droplets of collagen/poly-D-lysine coating solution and incubated for 20 min under UV light and 1-2 h at 37°C. The grids were washed in PBS overnight and then transferred to the well plates containing the astrocyte feeder layers and pedestals, 3 grids per well. Primary hippocampal neurons were seeded in a density of 1.5 × 105 - 2 × 105 cells/well, viruses were applied on DIV 2-3.

Live imaging of biosensors
Live imaging of neurons expressing the glutamate sensors iGluSnFR, iGluSnFR3-PDGFR, or iGluSnFR3-GPI was performed at elevated temperature (~32-34°C) using an inverted microscope (Olympus IX51) with a custom-built in-line heating system and a 60x water immersion objective with a heated collar (Warner Instruments). Cells were perfused with high-calcium extracellular solution containing the following: 140 mM NaCl, 2.4 mM KCl, 10 mM HEPES, 10 mM glucose, 4 mM CaCl2, and 1 mM MgCl2 (300 mOsm; pH 7.4). 6 µM NBQX, 30 µM bicuculline, and 10 μM AP-5 were added to block neuronal network activity of the mass culture. Action potentials (2 ms depolarization) were induced using a field stimulation chamber (Warner Instruments), Multiclamp 700B amplifier, and an Axon Digidata 1550B digitizer controlled by Clampex 10 software (all Molecular Devices). To assess the suitability of the biosensors for our cryo-ET workflow, two action potentials with an inter-stimulus interval of 25 ms were induced, which corresponds to the minimal time between the first and second stimulus of the optogenetic plunge freezer (limitation due to the bottom of the incubation chamber of the Vitrobot). Samples were illuminated by a 490 nm LED system (CoolLED) with an exposure time of 10 ms, images were captured with an Andor iXon Life 897 camera using Andor Solis v4.32 (Oxford instruments) at a frame rate of 40 fps. The imaging was performed blind.

Optogenetic stimulation and vitrification
For optogenetic stimulation of neurons, a plunge freezer (Vitrobot Mark IV, Thermo Fisher Scientific) was equipped with a high-intensity LED (Schott LLS3, wavelength 470 nm), from which one PMMA optical fiber with PVC insulation (inner diameter 3 mm) reached inside and one below the chamber of the Vitrobot. The endpoints of the optical fibers were installed with a distance of max. 5 mm to the path of the plunge frozen EM grids and illuminated the grids entirely. The LED was controlled by an infrared sensor that detected the downward motion of the tweezer holding the EM grid. The grid was illuminated twice for 5 ms: the first light pulse was applied within the chamber, max. 100 ms before freezing, and the second light pulse was started approximately 7 ms before the sample reached the liquid ethane. The correct timing and illumination were confirmed using the super-slow-motion mode of a Samsung Galaxy S20 camera with a frame rate of 960 fps.

At DIV 16-18, each EM grid containing neurons was briefly washed in a high-calcium solution containing 140 mM NaCl, 2.4 mM KCl, 10 mM HEPES, 10 mM glucose, 4 mM CaCl2, 1 mM MgCl2, 3 μM NBQX, and 30 μM bicuculline (~300 mOsm; pH 7.4) pre-warmed to 37°C and directly transferred to the plunge freezer. 4 µl of high-calcium solution supplemented with 10 nm BSA-gold (Aurion, OD ~2) were applied on the grid prior to blotting for 12–16 s (backside blotting, blot force 10) at 37°C and a relative humidity of 80%. The grids were plunge-frozen in liquid ethane and stored in liquid nitrogen until further use. For TTX-treatment, grids were incubated in high-calcium solution containing 1 µM TTX for 1-2 min prior to freezing.

Cryo-confocal microscopy
Data acquisition. After plunge freezing, EM grids were clipped into autogrids and transferred to a TCS SP8 cryo-confocal microscope equipped with a 50x CLEM cryo-objective, NA 0.9 (Leica Microsystems). Overviews of each grid were acquired in brightfield and fluorescence mode; the reflective mode was used on a subset of grids to estimate the ice thickness. The iGluSnFR fluorescence signal was used to confirm overall successful grid stimulation. For the comparison of iGluSnFR fluorescence intensities without and with stimulation, only the fluorescence signal of the fluorophore attached to ChR2 (YFP or mScarlet) was used to select regions of interest for subsequent cryo-confocal microscopy to avoid bias. Cryo-confocal stacks (z-steps 0.5 µm, pixel size 0.11 µm) were acquired with optimized filter settings of the HyD detector to minimize YFP or mScarlet crosstalk with the GFP signal. From grids intended for correlative confocal and electron microscopy, only a few confocal stacks were acquired, and the overall acquisition time per grid was limited to 30 min to avoid strong ice contaminations.

Analysis of cryo-confocal microscopy and correlation with cryo-electron tomography. Fluorescence intensities in cryo-confocal stacks of unstimulated, stimulated, and TTX-treated plunge-frozen neurons were compared using fiji software v1.5464. Maximum intensity z-projections of each confocal stack were generated, and 300×300 pixel regions of interest (ROIs) containing individual neurites were extracted. The mean fluorescence intensity (Fig. 2d) was measured per ROI and averaged per confocal stack. To compare the distribution of fluorescence intensities (Fig. 2c), fluorescence intensity histograms were generated for each ROI. A threshold of 15 was applied for background subtraction, and the pixel counts per intensity were normalized to the total pixel number per ROI. These relative intensity histograms of ROIs were averaged per confocal stack. The threshold of 70 for high-intensity pixels was visually assessed in stimulated samples. The fraction of pixels > 70 (Fig. 2e) was calculated per ROI and averaged per confocal stack. The correlation of fluorescence and TEM (Fig. 2f) was performed manually using the navigator of the Leica lasx software and fiji.

Cryo-electron tomography
Data acquisition. Cryo-ET data collection of optogenetically stimulated neurons cultured on EM grids was performed on a Titan Krios G3i electron microscope (Thermo Fisher Scientific) equipped with a K3 direct electron detector with BioQuantum energy filter (Gatan) and operated at 300 kV. Tilt series were typically acquired with 10 frames per tilt at a magnification of 15,000x and a pixel size of 3.2 Å in superresolution mode using SerialEM65 and PACE-tomo66. Tilt angles ranged from −50° to +50° and 2° angular increment in a dose-symmetric67 tilt-scheme. The defocus values ranged from -3 to -6 µm and the total electron dose was 106–125 e-/Å².

Tomogram reconstruction. Tomograms used for analysis were reconstructed semi-automatically using the tomoBEAR68 pipeline: Aligned frames were motion-corrected using MotionCor269. The tilt series alignment was performed by DynamoTSA43 and manually refined using 10 nm gold fiducial markers in IMOD70,71. For each projection, defocus values were measured by Gctf72, and CTF correction was performed using the IMOD command ctfphaseflip73. Four-times binned 3D reconstructions (final pixel size 12.28 Å) from CTF-corrected, aligned stacks were obtained by weighted back projection in IMOD.

In total, 312 tilt series of stimulated neuronal samples and 95 tilt series of TTX-treated neuronal samples were reconstructed with tomoBEAR and manually screened for synapses, which were only recognizable after reconstruction. We visually identified synapses as presynaptic boutons filled with SVs and a synaptic AZ, a synaptic cleft of 10–30 nm width, and a postsynaptic bouton with visible postsynaptic density. Based on these criteria, we identified 75 synapses in the stimulated samples and 28 synapses in the TTX-treated sample that were used for further analysis. For cryo-ET analyses of stimulated synapses, we did not analyze each freezing/grid individually but pooled synapses of three grids/two freezings because we did not note any significant differences in numbers of SVs or putative fusion events between them.

Segmentation and analysis of cryo-electron tomography data
Contrast enhancement. The contrast of reconstructed tomograms was enhanced to improve the quality of automated and manual segmentation and for the visual quantification of filamentous tethers. We used IsoNet74 for “missing wedge” correction and denoising: A content-representative fraction of our CTF-corrected 4-times binned tomograms was used to train an IsoNet model for 30 iterations of “missing wedge” restoration coupled with denoising by progressive introduction of Gaussian noise at iterations 10, 15, 20, 25 with the respective noise levels of 0.05, 0.1, 0.15, 0.2. The trained IsoNet model was then applied to restore the “missing wedge” and enhance contrast in all CTF-corrected 4-times binned tomograms.

Segmentation. Automated segmentations of synapses (Figs. 1 and 2g) were performed with MemBrain v275 on CTF-corrected, 4-times binned IsoNet-restored tomograms. Membranes (intracellular organelles and plasma membranes) were segmented automatically with MemBrain v2 and corrected manually using Amira (Thermo Fisher). The segmentations were re-colored and aligned with tomogram slices using ChimeraX v1.8. Manual segmentations of AZs (Fig. 4a) were made with IMOD v4.11 using 4-times binned, IsoNet-corrected tomograms.

Morphometric characterization of membrane rearrangements. All synapses were screened for membrane rearrangements potentially resembling fusion events. From these ROIs, 4-times binned subtomograms with a box size of 200x200x200 pixels (pixel size 12.28 Å) were generated using dynamo_catalogue76. As a quality control and to avoid bias, the original tomograms were screened, and ROIs were preselected by the first author. The second author double-checked all positions independently and generated the subtomograms. Initial ROIs were excluded if the putative fusion event was not located at an AZ with recognizable postsynaptic density, if a halo resembling a clathrin coat around the pore was visible, or if the resolution of the tomogram was poor (see Supplementary Fig. 6 for examples of excluded ROIs). The subtomograms were denoised and corrected for missing wedge effects using IsoNet. These denoised subtomograms were used for the classification of putative fusion states. In addition, we double-checked the correct classification for a subset of ROIs using the original (undenoised) dataset. Based on our observations, we defined 7 categories of membrane rearrangements at the AZ (opposing the postsynaptic density), also see Supplementary Fig. 7 for morphometric characteristics of each category.

Centerslices of putative fusion events were exported from IMOD as tiff files. Morphometric measurements (Fig. 3a and Supplementary Fig. 7) were performed in fiji: the horizontal diameter of SVs with membrane invagination, stalk formation, closed, and open fusion pores (indicated as measurement a in Supplementary Fig. 7) was measured at the widest region of the respective SV/fusion event between the outer borders of the lipid bilayers. The width of dilating and collapsing fusion pores, as well as bumps, was measured between the two positions of the AZ membrane where inward curvature was observed (Supplementary Fig. 7, measurement a in bottom row). For the height of stalks, closed, open, dilated, and collapsing fusion pores, only the distance between the AZ membrane outer (upper) border and the outer border of the fusion event membrane at the highest position was measured, whereby invaginations of the AZ membrane (stalk formation, closed fusion pore) were interpolated (Supplementary Fig. 7, measurement b). For the distance of tethered SVs and SVs during stalk formation (Supplementary Fig. 7, measurement c), the space between lipid bilayers of SV and membrane was measured, whereby membrane- and evaginations were interpolated (Supplementary Fig. 7, measurement c). The height of membrane invaginations (state 1; Supplementary Fig. 7, measurement d) was measured between the highest point of the invagination and the outer border of the AZ membrane. To discriminate between state 1 and state 2, the roundness of SVs was assessed by fitting a circle to the outer border of the SV. Distances between the circle and outer borders of the SV at the highest (Supplementary Fig. 7, measurement e1) and lowest (e2) position were measured. If the ratio of e2 × e1−1 was > 1.3, the SV shape was defined as a droplet (state 2). To discriminate between state 2 and state 3, the continuity of SV and AZ membrane was measured via grayscale intensity measurements along a vertical line scan spanning over 3 nm (horizontally) from the inner leaflet of the SV/fusion pore to the bottom border of the AZ membrane (Supplementary Fig. 7, measurement f). A “gap” (as indicator for missing continuity) between the SV and AZ membrane was defined as follows: mean grayscale intensity values and standard deviations (STD) were calculated for the line scan. Pixels with values below mean-STD were identified. If regions of at least 7 pixels, corresponding to 1 nm, with values below mean-STD were identified in the line scan, also local grayscale intensity maxima above and below this minimum were identified. For the minimum and both maxima, 7 pixels (1 nm) were averaged to account for noise, and the average of the two maxima regions was calculated. If the ratio of minimum-region × maxima-region−1 was < 0.6, the fusion event was defined as stalk formation containing a “gap” between SV and AZ membrane. If no minimum was detectable, we set minimum-region × maxima-region−1 = 1. This was the case for 4 out of 6 fusion events in category 3. The height of the neck of the closed fusion pore was measured between the inner leaflet of the bottom membrane of the fusion event and the bottom membrane of the AZ, meaning the region in which the side walls of the fusion pore were in direct contact (Supplementary Fig. 7, measurement g). To discriminate between state 3 and state 4, we measured if the pore neck of the fusion event was closed or open (Supplementary Fig. 7, measurement h). Analogous to measurement f, we measured if the pore neck was continuous (state 3) or contained a “gap” (state 4). Grayscale intensity values were thereby measured as horizontal line scans between outer borders of the pore neck at the narrowest position. Again, a gap was defined as minimum-region × maxima-region−1 < 0.6. We did not detect a minimum in any of the fusion events of category 3. The pore width of the open fusion pore was defined as the space between inner lipid bilayers of the pore walls at the narrowest position of the neck (Supplementary Fig. 7, measurement i). To discriminate between state 4 and state 5, we measured if outward curvature was present (j1, state 4) or not (j2, state 5). The angle j1 was measured between the vertical membrane of the pore neck and a line fitted to the bottom of the fusion pore head. To discriminate between state 5 and state 6, the thickness of membranes was measured at the top of the pore (Supplementary Fig. 7, measurement k1) and next to the pore base (k2). If the top of the fusion pore was thickened (k1 × k2−1 > 1.3), it was defined as collapsing pore (state 6). The height ((Supplementary Fig. 7, measurement b) was used as a criterion to discriminate between state 6 and state 7, whereby collapsing fusion pores were defined as events with b > 10 nm.

Quantification of fusion events, multivesicular release and bumps. Based on our definition of membrane rearrangements, we quantified numbers of observations per category. We first quantified synapses containing at least one of these events (Fig. 3d). If synapses contained more than one event, only the event closest to category 4 (as center) was used to define the overall state. Since stimulated synapses contained more events of categories 2–6 than TTX-treated synapses, we defined these states as “ongoing SV fusion”. Based on this definition, we had three groups of synapses: synapses with ongoing fusion, synapses with bump(s), and synapses without membrane rearrangements (Fig. 3d, e). Synapses containing at least two fusion events (categories 2-6) were counted as MVR synapses (Fig. 5c, d). In addition, we counted each fusion event individually (Fig. 3f).

Quantification of SV distances, tethers and interconnectors. The quantification of distances and filamentous tethers connecting SVs and the AZ membrane (Fig. 4d–g), as well as distances between SVs/fusion events and neighboring SVs (Fig. 5g–j) was performed in IMOD v4.11 using IsoNet-corrected tomograms. To make sure that the observed filaments were not a denoising artifact, we double-checked our annotations in a subset of ROIs using the original (un-denoised) dataset. For distance measurements, we first labeled all membrane-near SVs of each synapse above the AZ up to a distance of 24 nm at lower zoom and then measured their exact distances at higher zoom using the IMOD measuring tool. The distance between the SV and AZ membrane was measured at the center slice of the SV and defined as the space between the lipid bilayers of the SV and the membrane. We analyzed distributions of membrane-near SVs per synapse, whereby synapses containing no or only one SV were excluded. For this, we binned distances in 2 nm steps. Based on this distribution, previous reports10,54 and our observation that membrane invaginations were visible below SVs with an average distance of 6.4 nm, we further defined three subpools of SVs: membrane-proximal SVs with a max. distance of 6 nm, intermediate SVs with a distance of 6–12 nm, and distal SVs with a distance of 12–24 nm.

Only synapses with very good structural resolution (high signal-to-noise ratio, clearly visible membrane bilayers, etc.) were used for tether analysis. Tethers were defined as vertical or angular filamentous connections between the SV and AZ membrane. We quantified tethers per SV manually, whereby we went back and forth in the z direction several times and at different zooms. The slicer window was additionally used to rotate SVs around the x and y axis. Only if a filament was visible on multiple z slices, it was counted. To reduce bias, synapses of both groups (non-releasing vs. releasing synapses) were analyzed in parallel.

For the analysis of distances to neighboring SVs, minimal distances between fusion events/ membrane-proximal SVs and nearby SVs were measured in x, y and z using the IMOD measuring tool. These distances were not necessarily measured from the center slice of the SV. If connecting filaments (interconnectors) between fusion events and neighboring SVs were observed in several consecutive slices and/or via x/y rotation using the slicer window of IMOD, these fusion events were counted as interconnected (Fig. 5i, j).

Simulation
The coarse-grained model of the SV and the AZ includes particle-based representations of bilayer membranes, curvature-inducing proteins, and bead and spring models of tether and SNARE proteins. For the membranes, we used the membrane model developed by Sadeghi et al.77. This two-particle-per-thickness coarse-grained model is parameterized to mimic the mechanics of a fluid membrane with specified bending rigidity, has tunable in-plane viscosity, and is coupled with a hydrodynamics model that reproduces the out-of-plane kinetics of membranes in contact with solvents of prescribed viscosity78,79.

To parameterize the membrane model, we used reported values of the elastic response of SVs to indentation forces in atomic force microscopy (AFM) measurements80. We used these values in conjunction with a theoretical model, initially developed for the AFM indentation of influenza virus envelopes81, that relates the overall stiffness (or spring constant) of a spherical vesicle to the bending rigidity of its membrane. We found a mean membrane bending rigidity of 0.8 ⨯ 10−19 J, which is well within the range of values obtained for lipid bilayers82.

We modeled the SV in the initial state as a sphere with the outer diameter of 42.5 nm, to reflect the mean values obtained from tomograms (Fig. 3a), and added a harmonic volume-preserving potential to its outer leaflet particles. This potential acts against any changes in the enclosed volume, while allowing for otherwise arbitrary deformations. The plasma membrane underneath the vesicle is modeled as a planar square membrane patch of 180 nm side length. The simulation box is coupled in-plane to a stochastic barostat that controls the lateral pressure components around zero.

We modeled the curvature-inducing proteins in the AZ (Fig. 3g) via a force field masking mechanism that allows for tagged particles to locally modify the interparticle interactions using Monte Carlo moves, while letting these particles freely diffuse within the membrane. The modified force field reflects a preferred signed curvature (upward/downward) around these particles83. We used reported values of membrane curvatures upon binding cyclic peptides derived from synaptotagmin-1 C2B domain to assign preferred local curvatures to these particles84.

The tether proteins (Fig. 3g) are included as fixed-length elements initially formed between particles on the vesicle to anchor particles on the plasma membrane. The position of these tethers are decided randomly at the initial state. We incorporated a harmonic angle-bending potential that, when activated, exerts a torque to rotate the tethers about their anchor point on the plasma membrane, in effect pulling the SV toward the AZ.

We included chain-like representations of two sets of SNARE proteins, namely v-SNARE on the SV and t-SNARES in the AZ. The sizes of the chains roughly match the overall structure of SNARE proteins synaptobrevin, syntaxin, and SNAP-25 in the zippered complex. We included selective short-range attractive pairwise interactions between beads that form v-SNARE and t-SNARE chains such that they prefer to match one-to-one in the correct zippered configuration. Spatial exclusion, modeled via soft harmonic repulsions, energetically prohibits other conformations. We chose the copy number of SNARE proteins based on the reported proteomics data for SVs47, and calibrated the strength of attractive interactions to match the data on zipping/unzipping forces measured with magnetic tweezers85.

To test the effect of curvature-inducing proteins in the AZ, we developed models with 0, 10, 20, and 30 copies of these proteins. For each model, we started 5 simulation replicas, each with different random distributions of tether, SNARE, and membrane-curving proteins. We used anisotropic Brownian dynamics with a timestep of 0.1 ns to simulate the motion of all the particles in the system, and assigned a cytosolic viscosity of 2.21 cP86 to calculate particle mobilities in our hydrodynamic coupling method79.

At the start of each simulation, the system is allowed to relax for 5 μs, with the torque on the anchor points of tether proteins disabled, effectively having the SV floating at a constant distance with the AZ (Supplementary Fig. 10a). Afterwards, the tethers are activated, pulling the vesicle toward the AZ. The gap between the two membranes in their closest approach is continuously monitored. When the gap falls below 6 nm, we initiate the force field masking mechanism for curvature-inducing proteins, which results in curvature being developed in the AZ membrane. Each simulation thus continues for another 100 μs to follow the docking dynamics (Supplementary Fig. 10).

Trajectories are obtained by sampling the positions of all the particles at 100 ns intervals. All the subsequent trajectory post-processing, data analysis, and plotting is done through Python scripts, using Numpy87 and Matplotlib88 software packages. The 3D visualization is done via the software package Visual Molecular Dynamics (VMD)89.

Statistics and data representation
Fluorescence microscopy data were analyzed using fiji v1.54 (fluorescence intensity means and histograms) and Python, cryo-ET data were quantified using IMOD and fiji. GraphPad Prism v8 was used for calculations of mean, standard error of the mean (sem), median, 95% confidence interval (CI) of the median, normality and statistical tests. Datasets were tested for Gaussian distribution using D’Agostino-Pearson, Shapiro-Wilk and Anderson-Darling tests. If datasets were normally distributed according to these tests, two-tailed unpaired t-tests (to compare two groups) were performed. Otherwise, two-tailed Mann-Whitney tests (two groups) or Kruskal-Wallis tests (more than two groups) and Dunn’s post hoc tests were performed, corrections for multiple comparisons were included. For paired, two-tailed comparison, a Wilcoxon test of pairs was performed. Two-tailed Fisher’s exact tests for 2×2 tables were performed in GraphPad Prism, followed by Benjamini-Hochberg correction for multiple comparisons. For Fisher’s exact tests of more complex tables, R v4.5 was used. P-values were defined as follows: * p < 0.05, ** p < 0.01, *** p < 0.005. Graphs were generated using the python packages Seaborn90 and Matplotlib88 and optimized for visualization using Affinity Designer 2. Schematic illustrations were generated with Affinity Designer v2 (Fig. 1b, c and Supplementary Fig. 7) and Microsoft 3D builder (Supplementary Fig. 3a). Isosurfaces of bandpass-filtered tomograms and segmentations were visualized using ChimeraX, whereby the “hide dust” function was used. Only for subtomogram averages, the erase tool was used to remove artifacts that were not in direct contact with the particle and introduced/amplified by C61 symmetry. If not stated differently, data are represented as mean ± sem. In violin plots and bar graphs, the center line depicts the median, the upper and lower lines/borders of the box display the 25% and 75% percentiles. Whiskers in box plots indicate the 10–90% percentile range in all graphs except Supplementary Fig. 10 (here min. to max.). XY plots show lines connecting means, and the semitransparent areas indicate the sem.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors set up a time-resolved system to observe synaptic vesicles in situ. Optogenetics stimulation of action potential is\nachieved milliseconds before vitrification, and verified by monitoring fluorescence of a glutamate sensor. This is a nice set\nup, potentially useful to other applications. The authors collect cryo-tomograms of synapses and perform a thorough\nmorphological analysis to derive fusion states, arrangement of tethers, etc. This analysis contributes to our understanding of\nsynapses, for instance discriminating between current models for fusion. I have several points that require clarification.\n1. The rationale for the double light pulse is unclear to the non-expert\n2. When comparing fluorescence at 34C and in cryo, a less pronounced effect of stimulation is reported. This is attributed to\nthe fact that bright neurons were selected in warm conditions, while in cryo the choice of cells to image was made blindly. In\naddition, CLEM was not used to target synapses for tomography. So we have to assume that a certain percentage of\nsynapses that contribute to the cryo-ET dataset are not stimulated. Is it possible to infer what this percentage might be by\ncomparison with the ration of bright/total neurons in warm conditions? How could this affect the analysis/conclusions?\n3. Fig 3 a-d represents possibly the most important conclusion from this study, where the authors derive a temporal\nsequence of events within the milliseconds between stimulation and neurotransmitter release. Are those differences seen in\npanel d statistically significant? Dilated fusion pores are the same in stimulated and control cells: this is not mentioned in the\ntext, but it requires mention and an explanation.\n4. Related to point 3, the authors claim no evidence for tight docking. What if it was a very short-lived intermediate? Can the\nauthors calculate on how long lived any intermediate must be to have any probability to be detected in 1/60 tomograms\ngiven their experimental set up?\n5. Regarding the subtomogram averaging around the fusion point for the various states. I suppose C61 symmetry was\nchosen to average around the membrane-to-vesicle axis. (Why C61?) I couldn’t understand from the methods whether this\nangle was determined by connecting the fusion point with the vesicle centres or if it was assumed to run perpendicular to the\naxis of view? (The latter case being potentially a significant approximation)\n6. How are ‘tethers’ identified? Is it based on manual detection of densities such as that shown in 4j? Was this done in\ntomograms that were CTF corrected? Were they denoised? It seems to me this is by far the weakest part of the analysis.\nThere is no evidence the density in 4j is more real than any of the other dark ‘blobs’ visible in the background. I would\nsuggest removing any attempts to fit tethers and other molecules. I find this weakens the paper due to the obviously\nspeculative nature, rather than adding to it.\n7. The finding that clusters of vesicles form, and that some vesicles are seen attached to actin filaments: this is potentially\ninteresting. But can the author calculate whether either of these two events is seen with a more than random frequency,\ngiven the number of vesicles and of actin filaments in the volumes analysed? If not, I’m afraid this part of the analysis is too\nspeculative.", "reviewer_2": "Reviewer Report for ESCI-2024-00928: \"Dynamic nanoscale architecture of synaptic vesicle fusion in mouse hippocampal\nneurons\"\nOverall Summary:\nThis study presents a technically advanced integration of optogenetic stimulation, rapid cryofixation, and cryo-electron\ntomography (cryo-ET) to capture synaptic vesicle (SV) fusion intermediates in cultured mouse hippocampal neurons. The\nauthors report observing synaptic activity using cryo-preserved iGluSnFR3 fluorescence and categorize diverse fusion\nintermediates, highlighting \"stalk formation\" as a predominant prefusion state. Key findings challenge the requirement for\ntight docking/hemifusion, demonstrate proximal SV depletion post-stimulation, and note filaments potentially linking fusion to\nvesicle resupply.\nMajor Strengths:\n1. Methodological Integration: The combined optogenetics/cryo-freezing/iGluSnFR3 validation is a significant technical\nachievement, enabling millisecond-resolution snapshots of fusion dynamics.\n2. Scale of Analysis: The dataset of 75 stimulated synapses provides substantial morphological information on fusion\nintermediates under near-native conditions.\n3. Insight into Fusion Mechanism: The high frequency of \"stalk formation\" intermediates aligns with in silico models of local\nlipid splaying and challenges hemifusion-diaphragm models.\nMajor Concerns: Novelty of Workflow vs. Biological Insight:\n1. The core strength lies in the biological findings enabled by the technique (stalk prevalence). Framing the workflow itself\nas the primary novelty requires greater distinction from existing rapid-freezing cryo-ET approaches (e.g., Radecke et al.\nEMBO Rep 2023; Fernandez-Busnadiego et al. JCB 2010, 2013; Papantoniou et al. Sci Adv 2023; or similar method such\nas ‘zap-and-freeze’ Kusick et al. Nat. Neurosci. 2020). Claims that this specific workflow yields \"more near-native\" states\nleading specifically to \"more stalk formation\" require significantly stronger support. Quantitative comparison of intermediate\nfrequency (especially stalks) with datasets from prior cryo-ET studies using chemical stimulation or synaptosomes is absent.\nIs the observed stalk prevalence genuinely due to enhanced preservation, or influenced by differences in stimulation\nparadigm (optogenetic vs. chemical/electric), neuronal model (cultured neurons vs. synaptosomes), or analysis criteria?\nRigorous comparison is crucial to attribute novelty to the workflow.\n2. Subjectivity in Fusion Intermediates Classification: While Supplemental Table 1 provides valuable definitions for the 7\nfusion states, the reliance on qualitative descriptors (e.g., \"droplet-shaped,\" \"slightly invaginated,\" \"minimal dimple\")\nintroduces subjectivity, especially at state boundaries (e.g., State 1 \"invagination\" vs. State 2 \"stalk\"). The manuscript does\nnot detail objective, quantitative metrics applied during classification (e.g., curvature measurements, distance/bilayer\ncontinuity thresholds derived from subtomogram features) or procedures to minimize bias (e.g., blinded classification).\nWithout quantitative, unbiased classification rules applied consistently, the statistical significance of the reported\ndistributions (Fig. 3d-f) and subsequent interpretations is diminished.\n3. Interpretation of State Distribution as Temporal Progression: A minor thought related to the previous point. Could the\nrelative abundance of states (Fig. 3f) reflects the duration of metastable intermediates? Which then implies state 2 (stalk)\npresents a higher kinetic barrier. However, several critical caveats challenge this interpretation:\n  Strictly Frozen Moment: The data captures vesicles frozen at various states all within a narrow temporal window (2-5 ms\npost-stimulation). This represents only one specific \"slice\" in time after stimulation onset. It does not capture the full temporal\nevolution of individual vesicles from initial contact to collapse (which would require serial capture across multiple\ntimepoints).\n  Assumption of Linear Progression: Interpreting the distribution as reflecting progression times assumes all vesicles traverse\nthe defined states in sequence. This model remains hypothetical.\n  Potential Sampling Bias: The observed distribution is also influenced by capture efficiency (volatility, detectability) and\nstochastic variation inherent in snapshots.\n  Unknown Kinetics at Other Timepoints: A very different distribution might be observed at slightly earlier (e.g., 1 ms) or later\n(e.g., 10 ms) timepoints post-stimulation. For instance, vesicles destined for rapid fusion might bypass several intermediates\nentirely or be underrepresented in the 2-5 ms window.\nSpecific Points Requiring Clarification / Enhancement:\n1. The authors repeatedly emphasize that their method is “time-resolved”. However, they didn’t utilize their instruments,\nwhich indeed has the capability to do time-lapsed freezing, to generate measurements at different time points of the\noptogenetics stimulation.\n2. The description in line 269-271 is not accurate. If fraction of category 7 for stimulated and TTX-treated groups are alike,\nwhat about stage 5?\n3. On page 11, second paragraph, the copy number range of curvature-inducing proteins shall be addressed before\nmentioning 0 or 10 copies. Otherwise this is very confusing.\n4. The description on line 333 is too vague.\n5. On page 13, second paragraph, the authors seem to define a criterion and soon afterwards use it to validate their\nworkflow? (line 365-367)\nConclusion and Recommendations: This study presents a valuable methodological advance and rich cryo-ET dataset\ncapturing SV fusion intermediates in a near-physiological context. The key biological finding is the high prevalence of \"stalk\nformation\" as the major fusion initiation pathway under these spatiotemporal conditions. However, the novelty of the\nworkflow relative to extant methods needs clearer delineation, the objectivity of fusion state classification requires\nstrengthening, and the interpretation of intermediate abundance as a direct proxy for kinetic dwell times must be significantly\nmoderated.\nRecommendation: Major Revision"}, "author_response": "NCOMMS-25-33984 Point-by-point response\nPoint-by-point response\nWe sincerely thank both reviewers for their valuable feedback, which helped us to further\nimprove the quality, clarity, and comprehensibility of our manuscript. Our revised manuscript\nnow contains quantitative criteria for the classification of vesicle fusion states (Fig. 3 and Suppl.\nFig. 7) and a nearest neighbor analysis for fusion events and vesicles (Fig. 5g-j). We generated\nMarkov State Models for the progression of vesicle fusion with and without tight docking (Suppl.\nFig. 9). We extended our discussion: we now highlight technical advances of our workflow\ncompared to existing workflows and added a paragraph about the limitations of our study.\nReviewer #1 (Remarks to the Author):\n1. The rationale for the double light pulse is unclear to the non-expert\nWe added explanations to the results section, lines 119-121.\n2. When comparing fluorescence at 34C and in cryo, a less pronounced effect of stimulation\nis reported. This is attributed to the fact that bright neurons were selected in warm conditions,\nwhile in cryo the choice of cells to image was made blindly. In addition, CLEM was not used to\ntarget synapses for tomography. So we have to assume that a certain percentage of synapses\nthat contribute to the cryo-ET dataset are not stimulated. Is it possible to infer what this\npercentage might be by comparison with the ration of bright/total neurons in warm conditions?\nHow could this affect the analysis/conclusions?\nWe calculated which fractions of recordings were excluded during data analysis due to\nchanges in fluorescence intensity lower than 10%. For the glutamate sensor\niGluSnFR3.v857.GPI, which was used for cryo-confocal microscopy, we excluded 15/66\nrecordings (22.7%) because of ΔF/F < 0.1. In 5 of these recordings, we were still able to fit\n0\nrise and decay functions, meaning that we were not able to identify changes in fluorescence\nintensity in 13.6% of all recordings with this glutamate sensor. We chose the threshold of 0.1\nbecause the primary aim of the recordings was the characterization of on- and off-kinetics. We\nnow report the exclusion rate in the results section (lines 168-171), Supplementary methods\nand Source table 15.\nA direct comparison of these live imaging results with our cryo-ET experiments is challenging\nbecause of the different experimental setups: for live imaging, we applied electrical field\nstimulation. Although this method allowed us to directly induce bouton depolarization, the\nelectrical field is typically not homogeneous throughout the imaging chamber but stronger near\nthe electrodes and weaker in the center, where most recordings are performed due to the\noptics of the setup. This means that although the depolarization is more direct, we cannot\nguarantee that action potentials were reliably induced in all performed recordings. On the other\nhand, we were able to induce single action potentials in 90-100% of synapses using\noptogenetics. When applying two light pulses (40Hz frequency) ~80% of synapses responded\nto both pulses (Suppl. Fig. 1d). In these cells, we were not able to optically measure\nneurotransmitter release due to the overlapping wavelengths of ChR2 activation and\niGluSnFR3 excitation. Consequently, we do not have a readout for fractions of induced action\npotentials in the live imaging data, whereas we do not have a readout for fractions of releasing\nneurons following action potential induction.\nHowever, if we assume a synaptic release probability of ~85%, as recently reported for\nhippocampal neurons using optical measurements (Dürst et al. Nat Com 2022, DOI:\n10.1038/s41467-022-33565-6), it is likely that in nearly all neurons action potentials were\ninduced in our live imaging setup. Considering that optogenetic stimulation led to action\npotentials in 80-90% of all neurons, we may expect fusion events in 68-76.5% of all synapses\nin our cryo-ET samples. Indeed, the fraction of synapses without membrane rearrangements\nin the group of stimulated synapses was 33.3%, which closely matches this calculation. Yet,\nthe number of synapses with ongoing fusion (according to our definition) was lower than 68-\n76.5%, indicating that we either captured a fraction of ~20% synapses postfusion or that our\noptogenetic cryo-ET workflow led to a lower synaptic release probability, e.g. due to the\nculturing system on grids.\nIn the previous version of our manuscript, we utilized the synaptic release probability of ~85%\nto calculate whether the distribution of membrane-proximal SVs in the fraction of stimulated\nsynapses without ongoing fusion resembles more likely non-releasing or postfusion synapses.\nWe added additional information about our live imaging experiments to this section (lines 328-\n332).\n3. Fig 3 a-d represents possibly the most important conclusion from this study, where the\nauthors derive a temporal sequence of events within the milliseconds between stimulation and\nneurotransmitter release. Are those differences seen in panel d statistically significant? Dilated\nfusion pores are the same in stimulated and control cells: this is not mentioned in the text, but\nit requires mention and an explanation.\nWe performed statistical tests for panels 3d and 3e and now mention them in the respective\nfigure legend and results section (lines 232-236 and 243-244). All results can be found in\nSource Table 4. We mention that numbers of dilated fusion pores are comparable in the results\n(line 234) and comment on this observation in the discussion (lines 523-531).\n4. Related to point 3, the authors claim no evidence for tight docking. What if it was a very\nshort-lived intermediate? Can the authors calculate on how long lived any intermediate must\nbe to have any probability to be detected in 1/60 tomograms given their experimental set up?\nDue to the limited temporal readout of our setup, we were not able to calculate exactly how\nlong-lived an intermediate must be to be detected. However, to estimate if a (very short-lived)\ntight docking step could be included in the fusion sequence suggested by us, we\ngenerated Markov state models without and with tight docking (Suppl. Fig. 9). These models\nprovide relative waiting times for transitions between states. According to these models, a tight\ndocking step prior to or following stalk formation is very unlikely, as described in the results\nsection (lines 269-274).\n5. Regarding the subtomogram averaging around the fusion point for the various states. I\nsuppose C61 symmetry was chosen to average around the membrane-to-vesicle axis. (Why\nC61?) I couldn’t understand from the methods whether this angle was determined by\nconnecting the fusion point with the vesicle centres or if it was assumed to run perpendicular\nto the axis of view? (The latter case being potentially a significant approximation)\nWe revised our Supplementary method section and added explanations for the symmetry axis\nand the C61 symmetry.\n6. How are ‘tethers’ identified? Is it based on manual detection of densities such as that shown\nin 4j? Was this done in tomograms that were CTF corrected? Were they denoised? It seems\nto me this is by far the weakest part of the analysis. There is no evidence the density in 4j is\nmore real than any of the other dark ‘blobs’ visible in the background. I would suggest removing\nany attempts to fit tethers and other molecules. I find this weakens the paper due to the\nobviously speculative nature, rather than adding to it.\nWe performed the tether (between SV and active zone membrane) and interconnector\n(between SVs) analysis in tomograms that were CTF-corrected by phase flipping using IMOD,\nfollowed by denoising and “missing wedge” correction using IsoNet. We now describe these\npost-processing steps in more detail in the Methods section “Contrast enhancement” (lines\n685-693). To ensure consistency of the visually identified features before and after IsoNet-\ncorrection, we double-checked our results in a fraction (at least 20%) of tomograms again in\nCTF-corrected but undenoised (non-IsoNet-corrected) datasets. Although the analysis of\ntethers is prone to bias, it is still one of the most common cell biological electron microscopy\nmeasurements to detect subtle changes in the organization of SV pools. Automated\nquantification tools for tether/interconnector analysis (e.g., “Pyto package”, used here:\nPapantoniou et al. Sci Adv 2023 (DOI: 10.1126/sciadv.adf6222) or here: Radecke et al. EMBO\nRep 2023 (DOI: 10.15252/embr.202255719)) and manual analyses (e.g., Michanski et al.\nEMBO Rep 2023 (DOI: 10.15252/embr.202256702)) are likewise accepted in the field. The\nmanual analysis of tethers in our study was performed by the first author, who is an expert in\nelectron microscopy image analysis with ~10 years of hands-on experience. Generally, the\ntether analysis was only performed in the tomograms with the best signal-to-noise ratio (in total\n34 synapses of stimulated neurons). To distinguish tethers from noise (“dark blobs”), several\nconsecutive tomogram slices were screened: only structures that appeared in several slices\nwere counted as tethers. Additionally, the slicer window of IMOD was used to rotate the SV in\nx and y direction and to generate averaged views. To reduce bias, the analyzer used\nsubtomograms (box size 200x200x200 pixels) and analyzed synapses of both groups (non-\nreleasing vs. releasing synapses) in parallel.\nWe agree with the reviewer that our data do not provide the necessary structural information\nto unambiguously fit atomic models to isosurfaces of tethers. We revised the respective Suppl.\nFig. (now Suppl. Fig. 12) and now only position atomic models next to tomogram slices. We\nfurther show more examples of tethered SVs.\nWe added a “limitations of our study” section to the discussion. We comment on the subjective\nnature of tether analysis there (lines 531-538).\n7. The finding that clusters of vesicles form, and that some vesicles are seen attached to actin\nfilaments: this is potentially interesting. But can the author calculate whether either of these\ntwo events is seen with a more than random frequency, given the number of vesicles and of\nactin filaments in the volumes analysed? If not, I’m afraid this part of the analysis is too\nspeculative.\nWe performed analyses of nearest neighbor distances between fusion events and close-by\nSVs. For comparison, we also quantified nearest neighbor distances for membrane-proximal\nSVs. The results are now shown in Fig. 5g-j. We further analyzed presynaptic cytoskeletal\nfilaments in more detail. Since we did not observe significant differences in the organization of\npresynaptic filaments between releasing and non-releasing synapses, we now show the\nrespective tomogram slices and a quantification of filament distances to the active zone in\nSuppl. Fig. 13. Although not significantly different, we believe that the identification of\nconnections between putative actin filaments and SVs is of high enough interest for the\nneuroscience community to keep it as part of this manuscript.\nReviewer #2 (Remarks to the Author):\n1. The core strength lies in the biological findings enabled by the technique (stalk prevalence).\nFraming the workflow itself as the primary novelty requires greater distinction from existing\nrapid-freezing cryo-ET approaches (e.g., Radecke et al. EMBO Rep 2023; Fernandez-\nBusnadiego et al. JCB 2010, 2013; Papantoniou et al. Sci Adv 2023; or similar method such\nas ‘zap-and-freeze’ Kusick et al. Nat. Neurosci. 2020).\nClaims that this specific workflow yields \"more near-native\" states leading specifically to \"more\nstalk formation\" require significantly stronger support. Quantitative comparison of intermediate\nfrequency (especially stalks) with datasets from prior cryo-ET studies using chemical\nstimulation or synaptosomes is absent.\nIs the observed stalk prevalence genuinely due to enhanced preservation, or influenced by\ndifferences in stimulation paradigm (optogenetic vs. chemical/electric), neuronal model\n(cultured neurons vs. synaptosomes), or analysis criteria? Rigorous comparison is crucial to\nattribute novelty to the workflow.\nWe performed a thorough comparison of several studies combining stimulation and electron\nmicroscopy. We provide the resulting table here with our point-by-point response and added\nthe most important points to our discussion (lines 426-452).\nSeveral factors influenced why we observed a higher prevalence of stalk formation compared\nto previous studies. Most strikingly, the overall number of observed fusion events in our study\nis higher than in any other cryo-ET study thus far. Beyond that, the timing in our optogenetic\nworkflow was ideally suited to capture SV fusion events, whereas earlier studies used chemical\nstimulation applied for minutes (Fernández-Busnadiego et al. JCB 2010 (DOI:\n10.1083/jcb.200908082)) or a spraying technique in which earliest time points were reported\nas ~7 ms post-stimulation (Radecke et al. EMBO Rep 2023 (DOI:\n10.15252/embr.202255719)). Although the temporal resolution (meaning variance in action\npotential induction) in electrically stimulated samples is typically even lower compared to\noptogenetics, recent studies combining electrical stimulation and electron microscopy failed to\ncapture stalk formation. The reason here is that the thereby used freeze substitution (e.g.\nKusick et al. Nat Neurosci 2020 (DOI: 10.1038/s41593-020-00716-1)) leads to a lower spatial\nresolution. On the one hand, the space between SV stalk and active zone membrane of less\nthan 5 nm is very likely masked in TEM projections from 40 nm thick sections of resin-\nembedded samples. On the other hand, freeze-substituted samples typically contain heavy\nmetal stains, which are incorporated into cellular structures for contrast enhancement.\nCryo-ET studies Room temperature EM studies\nPublication This study Fernández- Radecke et al. Glynn et al. Cell Watanabe et al. Kusick et al. Nat\nBusnadiego et al. EMBO Reports Rep Methods Nature 2013 Neurosci 2020\nJCB 2010 2023 2025\nSample Mouse Rat Rat synaptosomes Mouse brain Mouse Mouse\nhippocampal cerebrocortical tissue (cortex or hippocampal hippocampal\nneurons cultured synaptosomes hippocampus) neurons cultured on neurons cultured\non EM grids sapphire glass on sapphire glass\n(banker culture) disks (astrocyte co- disks (astrocyte\nculture) co-culture)\nFreezing device Plunge freezing Plunge freezing Plunge freezing High-pressure High-pressure High-pressure\n(Vitrobot) (device not (home‐built plunge freezer (Leica freezer (Leica HPM freezer (Leica EM\nmentioned) freezer) EM ICE) 100) ICE)\nFreezing 37°C Not mentioned 23–25°C Room 34°C 37°C\ntemperature temperature\nFreezing buffer, 4 mM Ca2+ and 1 mM MgCl , 1.3 mM CaCl Combinations of 4 mM Ca2+ and 1.2 mM CaCl and\n2 2 2\nkey components 1 mM Mg2+ (to calcium not (~physiological phosphate buffer/ 1 mM Mg2+ (to 3.8 mM MgCl\n2\nincrease release mentioned; conc.) and 1 mM PBS and increase release (both\nprobability); MgCl in buffer; cryoprotectants, probability); ~physiological\n2\n30 mM KCl for including conc.); additional\n3 µM NBQX and chemical 1 mM CaCl and Dectran, sucrose, 3 µM NBQX and exp. with elevated\n2\n30 µM bicuculline depolarization 52 mM KCl in ethylene glycol 30 µM bicuculline CaCl\n2\nto block network spraying solution\nactivity 3 µM NBQX and\n30 µM bicuculline\nStimulation Optogenetic, 5 ms Chemical, 30 mM Chemical, 52 mM No stimulation Optogenetic, 10 ms Electrical,\nlight pulse to KCl for 1 min KCl sprayed on light pulse to induce 10 V∙cm−1 applied\\induce one action grid during one action potential for 1 ms across a\npotential plunging 6 mm space\nFusion initiation Via action Via direct Via direct spontaneous Via action potential Likely via action\npotential membrane membrane generation potential\ngeneration depolarization and depolarization and (mediated by generation (direct\n(mediated by opening of VGCCs opening of VGCCs sodium influx) membrane\nsodium influx) depolarization\npossible, too)\nTemporal Vitrification on None, vesicle Vitrification 7 ms none Vitrification Vitrification\nresolution average 7-8 ms fusion not timed or 35 ms after between 6 and earliest 5 ms after\nafter light onset, 1- and network spraying, stated 12 ms after action action potential\n5 ms after action activity not temporal potential generation generation, ± 1 ms\npotential blocked within resolution “few\ngeneration (mainly 1 min stimulation milliseconds or\n2-3 ms) even sub-\nmilliseconds”\nConfirmation of Direct, glutamate none Direct, via none none Indirect, via FM 1-\nstimulation sensor for cryo- diffusion marker 43FX uptake\nconfocal assay live before\nmicroscopy freezing\nSample None None None Focused ion Freeze substitution using glutaraldehyde\ntreatment post- beam milling (lift- and osmium tetroxide, resin embedding,\nfreezing out) sectioning (40-70 nm for 2D, 200 nm for\ntomography)\nMicroscope CM300 (Philips) Tecnai F20, Titan Krios G4 Zeiss 902, Phillips CM 120,\nand T30 Polara operated at operated at Megaview III operated at 80 kV\n(FEI), operated at 200 kV, field 300 kV, Selectris camera; and on the ×93,000\n300 kV, field emission gun, Energy Filter, Tecnai F20, 4k setting, AMT XR80\nemission gun, 2k × 2k CCD Falcon 4i direct CCD camera (FEI camera\n2k × 2k charge- camera (Gatan); electron detector Eagle) for\ncoupled device and Titan Krios, camera tomography\ncamera (Gatan), K2 direct electron\npost-GIF energy detector (Gatan)\nfilter (Gatan) without energy\nfilter\nPixel/voxel size 0.68 (CM300) and 0.75 or 1.2 nm 0.31 or 0.2 nm Not mentioned Not mentioned\n0.66 nm (Polara); (Tecnai) and\nbinned twice 0.6 nm (Krios)\nduring\nreconstruction,\nfinal voxel size\n2.72 and 2.64 nm\nN analyzed Untreated 7 Control 9 56 tomograms Unstimulated 193 Unstimulated 274\ntomograms/ tomograms; tomograms; containing 107 micrographs, micrographs,\nsynapses/ 30 mM KCl 6 stimulated 9 synapses stimulated 193 stimulated 315\nmicrographs tomograms tomograms (6 late, acquired, micrographs; micrographs (at\n3 early) additional least 2 samples);\n43 synapses additional serial\nfor tomography sections\nDefinition and  Vesicles with Early‐stage Fusion events in Fusing vesicles: Definition exocytic\nnumbers of open pore events: 7% of synapses “omega figures” pits: smooth\nfusion events n = 2  Vesicle and curvature at AZ in\n Patches of AZ plasma Observed fusion Random 2D an otherwise\nof high, membrane states ranging sections: fusion pits straight membrane\nconcave slightly bent from initial SV in 19% of (greater than\ncurvature n = 2 toward each tethering, micrographs from 10 nm), not\n(full-collapse other, no stalk/fusion pore stimulated samples, mirrored by\nfusion events) contact n = 8 formation, fusion 1% in unstimulated postsynapse\n Contact pore opening and\nbetween collapse Random 2D\nvesicle and sections: exocytic\nplasma pits in 18% of\nmembrane micrographs from\nbilayer n = 3 stimulated\n Point contact n samples, 2% in\n= 5 unstimulated;\nLate‐fusion stage Serial sections:\nevents: exocytic pits in\n35% of stimulated\n Small SV\nsynapses, 3% in\nfusion pore\nunstimulated\nopening n = 3\n Wide fusion\npore opening n\n= 1\n Small bump\nn = 11\nObservation of Yes Not mentioned Yes One example, No (technically No (technically\nstalk formation either stalk or challenging in challenging in\n(as defined in our closed fusion >40 nm sections) >40 nm sections)\nstudy) pore\nObservation of Yes, n = 1 No No Not mentioned Yes Yes\ntight docking\n(membranes in\ndirect contact)\nAdditional electron tomography studies using optogenetics for synapse stimulation in acute brain slices (Borges-Merjane 2020\nhttps://www.sciencedirect.com/science/article/pii/S0896627319310888?via%3Dihub, no fusion events) and Imig 2020: Omega profiles described,\nvarious size (mossy fiber)\n2. Subjectivity in Fusion Intermediates Classification: While Supplemental Table 1 provides\nvaluable definitions for the 7 fusion states, the reliance on qualitative descriptors (e.g., \"droplet-\nshaped,\" \"slightly invaginated,\" \"minimal dimple\") introduces subjectivity, especially at state\nboundaries (e.g., State 1 \"invagination\" vs. State 2 \"stalk\"). The manuscript does not detail\nobjective, quantitative metrics applied during classification (e.g., curvature measurements,\ndistance/bilayer continuity thresholds derived from subtomogram features) or procedures to\nminimize bias (e.g., blinded classification). Without quantitative, unbiased classification rules\napplied consistently, the statistical significance of the reported distributions (Fig. 3d-f) and\nsubsequent interpretations is diminished.\nWe thank the reviewer for raising this important point. We defined quantitative classification\nrules for all described states and now describe them in Suppl. Fig. 7 via schematic illustrations\nof all measurements. We extended our method section and now also explain the\nmeasurements and calculations here in more detail (lines 716-766). We updated Fig. 3 and\nadded missing values to Fig. 3a. We now summarize all measurements in source table 16.\n3. Interpretation of State Distribution as Temporal Progression: A minor thought related to the\nprevious point. Could the relative abundance of states (Fig. 3f) reflects the duration of\nmetastable intermediates? Which then implies state 2 (stalk) presents a higher kinetic barrier.\nHowever, several critical caveats challenge this interpretation:\nStrictly Frozen Moment: The data captures vesicles frozen at various states all within a narrow\ntemporal window (2-5 ms post-stimulation). This represents only one specific \"slice\" in time\nafter stimulation onset. It does not capture the full temporal evolution of individual vesicles from\ninitial contact to collapse (which would require serial capture across multiple timepoints).\nAssumption of Linear Progression: Interpreting the distribution as reflecting progression times\nassumes all vesicles traverse the defined states in sequence. This model remains hypothetical.\nPotential Sampling Bias: The observed distribution is also influenced by capture efficiency\n(volatility, detectability) and stochastic variation inherent in snapshots.\nUnknown Kinetics at Other Timepoints: A very different distribution might be observed at\nslightly earlier (1 ms) or later (10 ms) timepoints post-stimulation. For instance,\nvesicles destined for rapid fusion might bypass several intermediates entirely or be\nunderrepresented in the 2-5 ms window.\nWe agree with the reviewer that not only the transition kinetics between states but also other\nfactors likely influence the numbers of observed fusion events per state. As suggested by the\nreviewer, acquiring a time series would help supporting our assumption of linear progression\nbased on the shapes of the fusion intermediates. This would require a huge additional effort in\ndata acquisition, that we like to pursue in future studies. We fully agree with the reviewer that\nthe temporal progression suggested by the order of the structures shown in Figure 3 is not\ntested experimentally. While we feel that the order of structures as displayed are the most\nlogical one, given what we know when vesicles undergo collapse during fusion, the assumed\norder is not strictly relevant for our conclusions. Important is that we observed at the time point\nof membrane arrest several different structural intermediates that are absent in unstimulated\nsynapses, and this argues that in the few ms after release the structural changes of the\ncollapsing vesicle are highly dynamic. To provide some feasibility check for the order, we\nnevertheless generated Markov State Models reflecting relative transition times between\nstates (in response to reviewer 1), which are based on numbers of observed events. We make\nclear that these models are simplified and do not account for a putative sampling bias or\nseveral reversible steps during fusion. We discuss this point within the “limitations of our study”\nsection in the discussion (lines 538-552).\nSpecific Points Requiring Clarification / Enhancement:\n1. The authors repeatedly emphasize that their method is “time-resolved”. However, they didn’t\nutilize their instruments, which indeed has the capability to do time-lapsed freezing, to generate\nmeasurements at different time points of the optogenetics stimulation.\nWe changed “time-resolved” to “timed”, where appropriate. We comment in the discussion that\nour workflow has the potential for millisecond temporal resolution (lines 419-424) but that we\nstudied a single time point/ time span (lines 541-543).\n2. The description in line 269-271 is not accurate. If fraction of category 7 for stimulated and\nTTX-treated groups are alike, what about stage 5?\nWe revised the respective results section (line 236) and comment on the discrepancy in the\ndiscussion (lines 523-531).\n3. On page 11, second paragraph, the copy number range of curvature-inducing proteins shall\nbe addressed before mentioning 0 or 10 copies. Otherwise this is very confusing.\nDone (lines 287-291).\n4. The description on line 333 is too vague.\nWe specified this sentence (lines 311-312).\n5. On page 13, second paragraph, the authors seem to define a criterion and soon afterwards\nuse it to validate their workflow? (line 365-367)\nWe clarified that the criteria (distributions of SVs under different conditions) are based on\nprevious studies (lines 323-324).\n10"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors responded satisfactorily to almost all my comments.\nI would like to challenge them on point 7: if the difference in filaments is not significant, then their presence next to vesicles if\njust as likely to be by chance than not. I therefore do not think this observation should be reported, or at least not in the\ncurrent form.\nIf reported, the authors need to be very explicit about the fact that it is impossible to know whether any specific interactions\nare present and that further analysis is needed.", "reviewer_2": "The authors have made substantial revision to the manuscript, \"Dynamic nanoscale architecture of synaptic vesicle fusion in\nmouse hippocampal neurons.\" In this version, the authors have made reasonable alterations to address some of the more\ndifficult questions raised by the reviewers. The current manuscript provides crutial contribution to the field, and matches the\nstandard of publication in Nature Communications. I recommend the publication of this manuscript."}, "author_response": "NCOMMS-25-33984A Point-by-point response\nPoint-by-point response\nReviewer #1 (Remarks to the Author):\nThe authors responded satisfactorily to almost all my comments.\nI would like to challenge them on point 7: if the difference in filaments is not significant, then\ntheir presence next to vesicles if just as likely to be by chance than not. I therefore do not think\nthis observation should be reported, or at least not in the current form.\nIf reported, the authors need to be very explicit about the fact that it is impossible to know\nwhether any specific interactions are present and that further analysis is needed.\nWe thank the reviewer for the positive feedback.\nWe agree that this point needs further investigation. Following the reviewer’s recommendation,\nwe removed the supplementary figure and the corresponding Results section.\nReviewer #2 (Remarks to the Author):\nThe authors have made substantial revision to the manuscript, \"Dynamic nanoscale\narchitecture of synaptic vesicle fusion in mouse hippocampal neurons.\" In this version, the\nauthors have made reasonable alterations to address some of the more difficult questions\nraised by the reviewers. The current manuscript provides crucial contribution to the field, and\nmatches the standard of publication in Nature Communications. I recommend the publication\nof this manuscript.\nWe thank the reviewer for the positive feedback."}]

Decision letter