Abstract

Spatially clustered synaptic inputs enable local dendritic computations important for learning, memory, and sensory processing. In the mammalian visual system, individual retinal ganglion cell axons form clustered terminal boutons containing multiple active zones onto relay cell dendrites in the dorsal lateral geniculate nucleus. This mature architecture arises through the addition of release sites, which strengthens selected afferents while weaker inputs are pruned. Following eye-opening, spontaneous activity and visual experience promote synaptic refinement and bouton clustering after binocular inputs have segregated. However, anatomical changes in release site addition and spatial patterning during earlier stages of eye-specific competition are not well understood. To investigate this, we examined the spatial organization of eye-specific active zones in wild-type mice and a mutant line with disrupted cholinergic retinal waves. Using volumetric super-resolution single-molecule localization microscopy and electron microscopy, we found that individual retinogeniculate boutons begin forming multiple nearby presynaptic active zones during the first postnatal week. Both eyes generate these ‘multi-active-zone’ (mAZ) inputs throughout refinement, but the dominant eye forms more numerous mAZ contacts, each with more active zones and larger vesicle pools. At the height of competition (postnatal day 4), the non-dominant-eye projection adds many single-active-zone synapses. Mutants with abnormal cholinergic retinal waves still form mAZ inputs but develop fewer synapses overall and show reduced synaptic clustering in projections from both eyes. Together, these findings reveal eye-specific differences in release site addition that correlate with axonal segregation outcomes during retinogeniculate refinement.

Introduction

A key mechanism for neuronal signal processing is the formation of spatially clustered synapses that facilitate local computations within individual dendrites (Rall, 1962; Mel, 1991; Poirazi and Mel, 2001). Through biophysical signal integration mechanisms, neighboring synaptic inputs play critical computational roles in learning, memory, and sensory processing underlying cognition and behavior (Mel et al., 2017; Kastellakis and Poirazi, 2019; Winnubst and Lohmann, 2012; Leighton and Lohmann, 2016). During circuit development, both spontaneous and sensory-driven neural activity regulate synaptic clustering, stabilizing some synapses and eliminating others to establish mature connectivity patterns (Winnubst and Lohmann, 2012; Leighton and Lohmann, 2016; Kirchner and Gjorgjieva, 2022).

The refinement of retinal inputs to the dorsal lateral geniculate nucleus (dLGN) of the thalamus is a model example of activity-dependent synaptic clustering (Liang and Chen, 2020). Over development, boutons within the terminal arbors of individual retinogeniculate axons cluster progressively (Bickford et al., 2010; Monavarfeshani et al., 2018; Hong et al., 2014; Mason, 1982), creating multiple synaptic contacts onto individual postsynaptic targets (Hamos et al., 1987; Hammer et al., 2015; Morgan et al., 2016). Concurrently, developing retinal ganglion cell (RGC) boutons add presynaptic release sites such that some mature retinogeniculate terminals contain several dozen active zones (AZs) (Morgan et al., 2016; Budisantoso et al., 2012). The formation of clustered boutons with multiple release sites subserves a critical ‘driver’ function of retinogeniculate input (Sherman and Guillery, 1998; Guillery and Sherman, 2002).

Retinogeniculate bouton clustering depends on visual experience, and dark-rearing after eye-opening reduces clustering within individual axon arbors (Hong et al., 2014). However, less is known about synaptic spatial relationships and activity-dependent release site refinement during eye-specific competition before eye-opening. During early axon ingrowth, individual RGCs extend sparse side branches in the incorrect eye-specific territory (Sretavan and Shatz, 1984; Sretavan and Shatz, 1986; Dhande et al., 2011). These transient branches form synapses that contain fewer presynaptic vesicles than those made by the same axon in the correct eye-specific domain (Campbell and Shatz, 1992). Functional recordings after eye-opening show that while many RGC inputs are pruned, the remaining inputs strengthen by adding release sites (Chen and Regehr, 2000). This leads to glutamate spillover and cross-talk between RGC inputs that increases postsynaptic relay neuron excitability (Hauser et al., 2014). Thus, early eye-specific differences in release site number or spacing may contribute to binocular input refinement prior to eye-opening.

In a previous study from our laboratory, we used volumetric stochastic optical reconstruction microscopy (STORM), anterograde tract tracing, and immunohistochemical labeling of synaptic proteins to show that dominant-eye synapses contain more total vesicles and have more vesicles near the AZ (putative docking) compared with non-dominant-eye synapses (Zhang et al., 2023). However, we did not examine whether eye-specific inputs differ in AZ number or AZ spatial proximity (clustering) during synaptic competition. To address this gap, we reanalyzed our published dataset, focusing on eye-specific AZ spatial relationships. We found that projections from each eye form a subset of retinogeniculate inputs in which several (2–4) AZs share a common cluster of presynaptic vesicles. We refer to these synapses as multi-active-zone (mAZ) inputs. All other retinogeniculate inputs contained one single active zone and its associated vesicle cluster, and we term these single-active-zone (sAZ) synapses.

During eye-specific synaptic competition, the dominant-eye projection formed more mAZ inputs, each with more AZs and a larger presynaptic vesicle pool compared to the non-dominant-eye projection. Similarly, the dominant eye had higher vesicle signal at sAZ inputs. At the peak of synaptic competition midway through the first postnatal week (postnatal day 4), the non-dominant-eye formed numerous sAZ inputs, equalizing the global synapse density between the two eyes. These eye-specific AZ patterns were disrupted in a mutant mouse line with abnormal stage II cholinergic retinal waves and retinogeniculate segregation defects.

Results

Retinogeniculate inputs form multiple active zones during eye-specific competition

To investigate active zone refinement during eye-specific segregation, we reanalyzed a volumetric super-resolution imaging dataset previously published by our laboratory (Zhang et al., 2023). We used volumetric STORM (Vatan et al., 2021) to image the dLGNs of wild-type (WT) mice at three postnatal ages (P2, P4, and P8) (Figure 1A). We labeled eye-specific inputs by monocular injection of Alexa Fluor-conjugated cholera toxin subunit B tracer (CTB) together with immunostaining for presynaptic Bassoon, postsynaptic Homer1, and presynaptic vesicular glutamate transporter 2 (VGluT2) proteins (Figure 1B). We collected separate image volumes (~45 K μm3 each) from three biological replicates at each age. To assess the impact of spontaneous retinal activity on synaptic development across the same time period, we performed identical experiments in β2-knockout (β2KO) mice lacking the beta 2 subunit of the nicotinic acetylcholine receptor, a mutation that disrupts spontaneous cholinergic retinal wave activity, eye-specific segregation, and retinogeniculate synapse development (Dhande et al., 2011; Zhang et al., 2023; Muir-Robinson et al., 2002; Xu et al., 2015; Xu et al., 2011; Xu et al., 2016; Rossi et al., 2001; Grubb et al., 2003; Sun et al., 2008; Stafford et al., 2009; Bansal et al., 2000; Burbridge et al., 2014; Figure 1A). Because eye-specific segregation is incomplete until ~P8, we limited our re-analysis to the future contralateral eye-specific region of the dLGN, which is reliably identified across all stages of postnatal development (Figure 1A, see also Materials and methods).

Retinogeniculate boutons form multiple active zones (mAZ) during eye-specific competition.

(A) Experimental design. CTB-Alexa 488 was injected into the right eye of wild-type and β2KO mice. One day after the treatment, tissue was collected from the left dorsal lateral geniculate nucleus (dLGN) at P2, P4, and P8. Red squares indicate the stochastic optical reconstruction microscopy (STORM) imaging regions that were analyzed. (B) Representative examples of individual single-active-zone (sAZ) and mAZ inputs, with corresponding active zone counts ranging from one to three. Upper panels show Z-projections of inputs and lower panels show the corresponding 3D volume. Arrowheads point to individual Bassoon clusters (active zones) paired with postsynaptic Homer1 labels within each input. All examples are from a WT P8 sample. (C) Electron micrographs of mAZ retinogeniculate inputs in a P8 SLC6A4Cre::ROSA26LSL-Matrix-dAPEX2 mouse. Darkly stained dAPEX2(+) mitochondria are present within ipsilaterally projecting retinal ganglion cell (RGC) terminals. Arrowheads point to electron-dense material at the postsynaptic density, apposed to individual active zones with clustered presynaptic synaptic vesicles.

By analyzing synapses in the contralateral dLGN from 18 mice across three ages and two genotypes (Supplementary file 1), STORM revealed two classes of retinogeniculate inputs distinguished by active zone (AZ) number (Figure 1B). We defined each retinogeniculate input as a single contiguous VGluT2 cluster together with all its associated presynaptic (Bassoon) and postsynaptic (Homer1) paired synaptic labels. Using this definition, inputs that had multiple (2–4) Bassoon AZs were classified as mAZ inputs, while those with a single Bassoon AZ were designated sAZ inputs (Figure 1B). Most mAZ inputs contained two AZs (~70–90%, varying with age, genotype, and eye-of-origin); smaller proportions contained three AZs (~10–20%) or four or more AZs (<5%) (Supplementary file 1).

Each mAZ input could be a single terminal bouton with several AZs, or a cluster of sAZ synapses within separate boutons (Bickford et al., 2010; Monavarfeshani et al., 2018; Hammer et al., 2015; Morgan et al., 2016; Hammer et al., 2014). To address this, we used electron microscopy (EM) to image retinogeniculate terminals in the dLGN at P8. We generated a transgenic line expressing mitochondrial matrix-targeted dimeric dAPEX2 reporter (Zhang et al., 2019) in ipsilaterally projecting RGCs (Koch et al., 2011; Su et al., 2024; Johnson et al., 2021), providing unambiguous mitochondrial labeling in ipsiRGC axons. EM images confirmed the presence of individual retinogeniculate boutons with multiple active zones, consistent with our STORM data (Figure 1C). Previous EM reconstructions of retinogeniculate inputs reported no evidence of RGC bouton convergence at the end of the first postnatal week (Monavarfeshani et al., 2018). Together, these results suggest that mAZ inputs in STORM images are single RGC terminals that house several closely spaced release sites. Hereafter, we use the word ‘synapse’ only to refer to each partnered active zone (Bassoon/Homer1 pairs); mAZ inputs contain several synapses, while each sAZ input is one synapse.

Changes in eye-specific input density during synaptic competition

In our previous analysis, we reported global eye-specific synapse densities that reflected the combined mAZ and sAZ inputs. Dominant-eye synapse density was greater than that of the non-dominant eye at P2 and P8, but eye-specific synapse densities were equivalent at P4 during the peak of synaptic competition (Zhang et al., 2023). To determine how these two input classes evolve during competition, we quantified the densities and percentages of mAZ and sAZ inputs in WT and β2KO mice (Figure 2A). Eye-of-origin for each retinogeniculate input was assigned by colocalizing CTB signal with VGluT2 (Figure 2B). Binocular CTB control injections showed that anterograde tracing labeled >97% of retinogeniculate synapses at P4 and P8, ensuring accurate eye-specific assignment (Zhang et al., 2023). Within the contralateral eye-specific region of the dLGN, CTB(+) VGluT2 clusters were classified as ‘dominant-eye’ inputs and CTB(−) VGluT2 clusters as ‘non-dominant-eye’ inputs (Figure 2B).

Changes in eye-specific input density during synaptic competition.

(A) Representative Z-projection images of multi-active-zone (mAZ) and single-active-zone (sAZ) inputs across ages and genotypes. Arrowheads point to individual Bassoon/Homer1 cluster pairs indicating release sites. (B) Representative CTB(+) dominant-eye (top panels) and CTB(−) non-dominant-eye (bottom panels) mAZ inputs in a WT P8 sample, showing synaptic (left panels), CTB (middle panels), and merged labels (right panels). Arrowheads point to individual Bassoon/Homer1 paired clusters. (C) Eye-specific mAZ (left) and sAZ (right) input density across development in WT (top panels) and β2KO mice (bottom panels). Black dots represent mean values from separate biological replicates and black lines connect eye-specific measurements within each replicate (N = 3 for each age and genotype). Error bars represent group means ± SEMs. Statistical significance between eye-specific measurements was assessed for each genotype using two-tailed paired T-tests with Benjamini–Hochberg false discovery rate (FDR) correction (α = 0.05) at each age. *p(adj) < 0.05.

After false discovery rate (FDR) correction for multiple comparisons within each age and genotype (see Quantification and statistical analysis; p(adj) values shown in all figures and Supplementary file 2), we found that the density of CTB(+) mAZ inputs tended to be higher than CTB(−) mAZ inputs in WT mice (Figure 2C, top left). Consistent with this trend, the mAZ input fraction (% of all inputs) was significantly higher for CTB(+) dominant-eye inputs at P4 and P8 (Figure 2—figure supplement 1, top). β2KO mice also developed differences in mAZ input density favoring the dominant-eye (Figure 2C, bottom left; Figure 2—figure supplement 1, bottom). For WT sAZ synapses, the dominant eye had a significantly higher synapse density at P2 [p(adj) = 0.026, Cohen’s d = 5.31] and trended higher at P8 [p(adj) = 0.072, Cohen’s d = 2.73]. At P4, however, sAZ synapse density was equivalent between the eyes (Figure 2C, top right; Supplementary files 2 and 3). This pattern resulted from non-dominant-eye sAZ synapse addition, which equalized the global input density between the eyes at P4 as we reported previously (5/95% confidence interval, −0.014 to 0.011 synapses/μm3). In β2KO mice at P4, the non-dominant-eye formed fewer sAZ synapses (Figure 2C, bottom right; see Supplementary files 2 and 3). Thus, β2KO mice with disrupted retinal activity maintain a higher mAZ input fraction in the dominant-eye projection, but sAZ synapse addition is reduced at the peak of competition.

mAZ and sAZ inputs from the dominant eye show increased vesicle pool size and vesicle proximity to the active zone

We previously reported a dominant-eye bias in VGluT2 volume when considering all retinogeniculate inputs (Zhang et al., 2023). Here, we assessed presynaptic maturation separately in sAZ and mAZ inputs by measuring their total VGluT2 volume. In WT mice, both mAZ (Figure 3A, left) and sAZ (Figure 3B, left) inputs showed significant eye-specific volume differences in the middle of eye-specific competition at P4. At this age in WT mice, the median VGluT2 cluster volume in dominant-eye mAZ inputs was ~3.55 ± 1.3 μm3 larger (mean ± SE) than that of non-dominant-eye inputs (Figure 3A, left). In contrast, β2KO mice showed a smaller ~1.9 ± 1.1 μm3 (mean ± SE) volume difference between median eye-specific mAZ inputs at the same age (Figure 3A, right panel). For sAZ synapses at P4, the magnitudes of eye-specific differences in median VGluT2 volume (mean ± SE) were ~2.1 ± 1.0 μm3 in WT (Figure 3B, left) and ~1.5 ± 1.1 in β2KO mice (Figure 3B, right). Thus, both mAZ and sAZ vesicle pool volumes are larger for the dominant eye, with the largest eye-specific differences seen for mAZ inputs in WT mice (see Supplementary file 3).

Dominant-eye inputs show larger vesicle pools that scale with active zone number.

Violin plots showing the distribution of VGluT2 cluster volume for (A) multi-active-zone (mAZ) and (B) single-active-zone (sAZ) inputs in WT (filled) and β2KO mice (striped) at each age. The width of each violin plot reflects the relative synapse proportions across the entire grouped dataset at each age (N = 3 biological replicates) and the maximum width was normalized across all groups. The black dots represent the median value of each biological replicate (N = 3), and the black horizontal lines represent the median value of all inputs grouped across replicates. Black lines connect measurements of CTB(+) and CTB(−) populations from the same biological replicate. Statistical significance was determined using a linear mixed model ANOVA with a post hoc Bonferroni correction, followed by Benjamini–Hochberg false discovery rate (FDR) correction (α = 0.05) for multiple comparisons at each age/genotype. Black asterisks indicate significant eye-specific differences at each age. *p(adj) < 0.05. (C) Eye-specific VGluT2 signal volume for all inputs separated by number of AZs in WT (left panel) and β2KO mice (right panel) at P4. (D) Average VGluT2 volume per AZ for all inputs separated by number of AZs in WT (left panel) and β2KO mice (right panel) at P4. In panels (C) and (D), error bars indicate group means ± SEMs (N = 3 biological replicates for each age and genotype). Black dots represent mean values from separate biological replicates and black lines connect eye-specific measurements within each replicate. Statistical significance between eye-specific measurements was assessed for each genotype using two-tailed paired T-tests with Benjamini–Hochberg FDR correction (α = 0.05): *p(adj) < 0.05.

In addition to total vesicle pool volume, we quantified the readily releasable pool by measuring VGluT2 volume within a 70-nm shell around each AZ, considering this a proxy for docked vesicles (Figure 3—figure supplement 1A–C; Zhang et al., 2023). In WT mice at P4, dominant-eye inputs showed greater vesicle volume per AZ than non-dominant inputs, in both mAZ and sAZ terminals (Figure 3—figure supplement 1B, left; Supplementary file 3). These eye-specific differences were absent in β2KO mice (Figure 3—figure supplement 1B, right; Supplementary file 3). However, when comparing mAZ and sAZ inputs from the same eye, vesicle volume per AZ was similar across all ages and genotypes (Figure 3—figure supplement 1A–C; Supplementary file 2). This confirms our previous finding that vesicle docking favors the dominant eye (Zhang et al., 2023) and shows that AZs formed by a single eye have similar docking levels in both their mAZ and sAZ terminals.

Vesicle pool size scales with active zone number

Because mAZ inputs showed greater total VGluT2 volume than sAZ synapses, yet exhibited comparable vesicle docking, the disparity could reflect a scaling effect of vesicle pool size with increased AZ number. To evaluate how presynaptic vesicle pool volume scales with AZ number, we compared the Bassoon cluster number to VGluT2 volume for every retinogeniculate input. In both WT (Figure 3—figure supplement 1D) and β2KO mice (Figure 3—figure supplement 1E), mAZ inputs contained an average of two to three Bassoon clusters (separate AZs) in the first postnatal week. In WT mice, CTB(+) dominant-eye mAZ inputs contained more AZs than CTB(−) non-dominant-eye mAZ inputs at P4 and P8 (Figure 3—figure supplement 1D). This maturation was delayed until P8 in β2KO mice (Figure 3—figure supplement 1E). For CTB(+) dominant-eye inputs in both genotypes at P4, vesicle pool volume correlated positively with AZ number (Figure 3C; Pearson correlation coefficients: WT [0.99] and β2KO [0.95]). A similar, but weaker correlation was observed for CTB(−) non-dominant-eye inputs (Pearson correlation coefficients: WT [0.97] and β2KO [0.90]). Dividing the total presynaptic VGluT2 volume by the AZ number revealed a consistent vesicle volume per AZ for both sAZ and mAZ inputs (Figure 3D; Figure 3—figure supplement 2; Supplementary file 3). Collectively, these results indicate that presynaptic vesicle pool volume scales with AZ number for each eye-specific input, while a dominant-eye bias persists throughout development.

Synapse clustering before eye-opening

Eye-specific competition is thought to involve stabilization of coactive, neighboring inputs from the same eye and elimination of out-of-sync inputs from the opposite eye (Assali et al., 2014; Fassier and Nicol, 2021; Penn et al., 1998). Because axon refinement depends on the relative strength of neurotransmission between competing inputs (Koch et al., 2011; Assali et al., 2017; Fredj et al., 2010; Hua et al., 2005; Rahman et al., 2020; Munz et al., 2014; Zhang et al., 1998; Matsumoto et al., 2024), mAZ inputs with multiple release sites could help stabilize nearby like-eye inputs (Rahman et al., 2020; Yasuda et al., 2021; Louail et al., 2020; Kutsarova et al., 2023).

To quantify synaptic clustering patterns, we measured the distance from every eye-specific sAZ synapse to all other sAZ and mAZ inputs within each image field. sAZ synapses were often found nearby other inputs from the same eye (Figure 4A). To define clustering, we searched volumetrically around each mAZ input and measured the fraction of sAZ synapses that were nearby at increasing distances (Figure 4—figure supplement 1A, B). We then compared the observed percentages with datasets in which sAZ synapse positions were randomly shuffled within each neuropil volume. The largest differences occurred within a 1- to 2-μm search distance (Figure 4—figure supplement 1A, B), and so we chose a 1.5-μm cutoff from the edge of each mAZ input to designate it ‘clustered’ if at least one neighboring sAZ synapse lay within this distance and ‘isolated’ if none did (Figure 4B). No significant clustering was detected when sAZ and mAZ inputs originated from opposite eyes (Figure 4—figure supplement 1C, D).

Eye-specific synapse clustering before eye-opening.

(A) Representative multi-active-zone (mAZ, left panels) and single-active-zone (sAZ, right panels) inputs in a WT P8 sample with nearby sAZ synapses (arrowheads) clustered within 1.5 μm (dashed yellow ring). Arrows point to the centered mAZ or sAZ inputs. (B) Ratio of clustered and isolated mAZ and sAZ inputs for CTB(+) (upper panels) and CTB(−) (lower panels) inputs in WT and β2KO mice at P4. (C) Comparison of the clustered input ratio between mAZ and sAZ inputs across different ages, genotypes, and eyes of origin. (D) Comparison of the average number of nearby sAZ synapses for clustered mAZ and sAZ inputs across different ages, genotypes, and eyes of origin. In panels B–D, black dots represent mean values from separate biological replicates and black lines connect measurements within each replicate (N = 3 for each age and genotype). Error bars represent group means ± SEMs. For each genotype, two-tailed paired T-tests with Benjamini–Hochberg false discovery rate (FDR) correction (α = 0.05) were used to test statistical significance between mAZ and sAZ inputs at each age. *p(adj) < 0.05.

At the peak of competition in WT mice (P4), more than 65% of both mAZ and sAZ inputs were clustered for both eyes, while this proportion fell to ~50% in β2KO mice (Figure 4B). In the WT dominant-eye projection, the fractions of clustered mAZ and sAZ inputs were similar at each age (Figure 4C, top left panel). For the non-dominant-eye projection, however, there were slightly more clustered mAZ inputs compared to clustered sAZ inputs at P4 (Figure 4C, bottom left panel), the age when this eye adds sAZ synapses (Figure 2C). β2KO mice showed no difference between mAZ and sAZ clustering at any age (Figure 4C, right panels). Additionally, in WT mice at P4 and P8, clustered mAZ inputs from both eyes had marginally more neighboring sAZ synapses than did clustered sAZ synapses (Figure 4D, left); this enrichment was absent in β2KO mice (Figure 4D, right). Thus, while most retinogeniculate synapses lie within 1.5 μm of another like-eye input, WT mice show a tendency toward forming more synapses near mAZ inputs during synaptic competition.

Clustered mAZ inputs in P4 WT mice were also closer together than isolated mAZ inputs. Clustered mAZ inputs formed by the dominant eye were ~32% closer to the nearest like-eye clustered mAZ input compared to isolated mAZ inputs (Figure 5A, left panel). In the non-dominant-eye projection, clustered mAZ inputs were ~55% closer together compared to isolated mAZ inputs (Figure 5B, left panel). Once segregation was complete at P8, distances between clustered and isolated mAZ inputs were more similar (Figure 5—figure supplement 1). In β2KO mice, distances between isolated and clustered mAZ inputs and their nearest clustered mAZ neighbor did not differ for either eye at P4 or P8 (Figure 5, right panels; Figure 5—figure supplement 1, right panels). These patterns are consistent with sAZ synapse addition when mAZ inputs are in close proximity during competition.

Clustered multi-active-zone (mAZ) inputs are closer than isolated inputs during competition.

Distance between clustered and isolated mAZ inputs and the closest like-eye clustered mAZ input, shown for (A) CTB(+) and (B) CTB(−) projections at P4 in WT and β2KO mice. Boxes indicate the 25–75% distribution of input measurements from N = 3 biological replicates, and whiskers extend to 1.5 times the interquartile range. Gray dots represent individual distance measurements for all mAZ inputs. Black and red dots represent mean values from separate biological replicates, and black lines connect measurements within each replicate (N = 3 for each age and genotype). Statistical significance was determined using a linear mixed model ANOVA with post hoc Bonferroni correction. For each genotype, p-values were corrected for multiple testing with Benjamini–Hochberg false discovery rate (FDR) correction (α = 0.05) at each age. Black asterisks indicate significant differences. *p(adj) < 0.05.

Finally, we tested whether sAZ vesicle pool volume varies with distance to mAZ inputs. We classified each sAZ as ‘near’ (≤1.5 μm) or ‘far’ (>1.5 μm) relative to the nearest like-eye mAZ input. Across ages and genotypes, vesicle pool volume was similar for near and far sAZ synapses in both eyes (Figure 5—figure supplement 2). Thus, vesicle pool size in sAZ synapses appears independent of their proximity to mAZ inputs.

Discussion

Spontaneous retinal activity guides eye-specific refinement by strengthening dominant eye inputs in the correct territory and pruning weaker inputs from the competing eye. Using volumetric super-resolution microscopy and eye-specific synaptic immunolabeling, we previously identified activity-dependent, eye-specific differences in presynaptic vesicle pool size and vesicle association with the active zone (AZ), both favoring the dominant eye during synaptic competition (Zhang et al., 2023). Here, we reanalyzed this dataset to measure synaptic spatial arrangements during normal development and how these are affected in a mutant model with abnormal eye-specific segregation.

Early input maturation and addition of release sites

Before eye-opening, RGC axons from both eyes formed terminals with either multiple active zones (mAZ inputs) or a single active zone (sAZ synapses). EM of dAPEX2-labeled ipsilateral RGC inputs revealed individual boutons with multiple active zones. Prior EM studies in mice showed no clustering from neighboring RGC boutons at postnatal day 8 (Bickford et al., 2010; Monavarfeshani et al., 2018), suggesting the mAZ inputs seen in STORM images are single boutons containing multiple release sites. During competition, the dominant eye projection generated more of these mAZ inputs, each with more active zones and larger vesicle pools than the non-dominant eye. We observed a linear relationship between presynaptic vesicle signal volume and active zone number, suggesting that release site addition and vesicle pool expansion may be linked during input maturation. Functional recordings indicate that release site addition is the primary driver of retinogeniculate input strengthening after eye-opening (Chen and Regehr, 2000). Our findings reveal that eye-specific differences in release site addition at individual terminals emerge at the earliest stages of binocular refinement.

Spatial interactions during competition

Ipsilateral RGC axons are delayed in entering the dLGN until after contralateral inputs have already innervated the nucleus (Godement et al., 1984). Despite their late arrival, these axons competed by forming numerous sAZ inputs, which equalized the overall input density between the eyes in the future contralateral eye domain at postnatal day 4 (P4). Most of these synapses formed nearby other like-eye neighbors and were enriched near mAZ inputs, supporting the idea of release site addition at strong synapses. Neighboring sAZ synapses could ultimately mature into mAZ inputs as release sites are added and vesicle pools expand.

Distance analysis showed that clustered mAZ boutons were closer together than isolated mAZ boutons, further supporting release site addition near strong neighboring inputs. It remains unknown, however, whether nearby mAZ inputs originate from the same RGC. Nearby mAZ inputs within individual RGC arbors could promote release site addition via intrinsic mechanisms that scale with release site number, such as increased presynaptic calcium entry or surface delivery of synaptogenic molecules via presynaptic release. Boutons with multiple release sites could also trigger non-cell-autonomous signaling mechanisms that stabilize neighboring inputs from the same eye (Rahman et al., 2020; Louail et al., 2020; Kutsarova et al., 2023). Release site addition increases extracellular glutamate concentration and promotes spillover onto adjacent synapses, which enhances the excitability of developing relay cells (Hauser et al., 2014) and could contribute to long-term synaptic plasticity evoked by high-frequency spiking in retinal wave bursts (Mooney et al., 1993; Ziburkus et al., 2009; Butts et al., 2007; Lee et al., 2014). Consequently, release site addition and local bouton clustering may act in tandem to stabilize coactive inputs.

Competitive refinement also involves synapse elimination and axonal retraction through punishment signals. Genetic deletions of VGluT2 or RIM1 proteins in ipsilaterally projecting RGCs decreased presynaptic vesicle release and prevented retraction of contralateral RGC axons from the ipsilateral territory (Koch et al., 2011; Assali et al., 2017). One downstream mediator of synaptic punishment is JAK2 kinase, which is phosphorylated in less active synapses (Yasuda et al., 2021). Similar to neurotransmission mutant phenotypes, disruption of JAK2 signaling prevents axon retraction (punishment) during competition (Yasuda et al., 2021). The spatial analyses we developed here will enable future mapping of input-specific punishment signals during synaptic competition. This includes phospho-JAK2 as well as molecular tags for glial pruning of weak inputs during eye-specific segregation (Chung et al., 2013; Stevens et al., 2007; Schafer et al., 2012).

Requirement for spontaneous retinal activity

β2KO mice showed significant defects in synapse addition during the first postnatal week. mAZs still formed with similar overall ratios as in WT controls (Figure 2—figure supplement 1; Supplementary file 3) and eye-specific differences in vesicle pool size still emerged. However, β2KO mice failed to form many sAZ synapses at the height of competition (P4), particularly in the late-arriving non-dominant (ipsilateral)-eye projection. The failure to add synapses could explain the observation that synaptic clustering was reduced and more inputs formed in isolation in the mutants compared to controls.

While our results highlight developmental changes in presynaptic release site addition and clustering, activity-dependent postsynaptic mechanisms also influence input refinement at later stages. Retinogeniculate synapses undergo postsynaptic strengthening and weakening through potentiation and depression mediated by AMPARs and NMDARs (Mooney et al., 1993; Ziburkus et al., 2009; Butts et al., 2007; Lee et al., 2014). After eye-specific segregation, spontaneous retinal activity is required for postsynaptic AMPAR insertion, synaptic strengthening, and elimination of weaker inputs (Hooks and Chen, 2006). Continued maintenance of segregation depends on calcium influx into relay neurons via L-type calcium channels, further implicating postsynaptic signaling in late-stage refinement (Dilger et al., 2015; Cork et al., 2001). Input targeting may also be guided by molecular cues to form non-random, eye-specific connections with postsynaptic targets. Ipsilaterally projecting RGCs have distinct gene expression profiles that specify axon guidance and may further support eye-specific synaptic targeting (Fernández-Nogales et al., 2022; Wang et al., 2016). Spontaneous retinal activity may permit axons to read out molecular regulators of synaptogenesis, as previously shown for RGC axon retraction (Nicol et al., 2007).

Release site addition as a general mechanism underlying synaptic competition

Early synaptic clustering during retinogeniculate development resembles other circuits where neural activity guides competitive synaptic and axonal remodeling. At the neuromuscular junction (NMJ), motor neuron terminals compete for control of a postsynaptic muscle fiber; a single motor axon input strengthens while competing axons are eliminated (Balice-Gordon et al., 1993; Gan and Lichtman, 1998; Wyatt and Balice-Gordon, 2003). Competition at the NMJ depends on inter-synaptic distance, with motor axons losing connections located near stronger competing synapses (Balice-Gordon et al., 1993; Gan and Lichtman, 1998). As winning terminals are enlarged, presynaptic release site addition maintains a consistent density of Bassoon clusters (~2–3/μm2) (Chen et al., 2012). Neighboring inputs with weaker synaptic transmission are selectively eliminated (Balice-Gordon and Lichtman, 1994; Buffelli et al., 2003). Competing motor axons differ in their release probabilities early in development (Kopp et al., 2000), suggesting that presynaptic efficacy triggers local ‘stabilization’ signals in winners and ‘punishment’ signals in losers (Sanes and Lichtman, 1999). Presynaptic agrin release stabilizes postsynaptic receptor clusters by counteracting the destabilizing, dispersal effects of acetylcholine release, emphasizing the importance of early presynaptic transmission in postsynaptic stabilization (Misgeld et al., 2005; Lin et al., 2005).

Similarly, in the developing cerebellum, Purkinje cells initially receive inputs from multiple climbing fibers (CFs) during the first postnatal week. Subsequently, one winning CF emerges and consolidates its synaptic inputs, while losing CFs are pruned (Hashimoto and Kano, 2013; Bosman and Konnerth, 2009). Here again, the winning input strengthens by adding presynaptic release sites, which increases multivesicular release and elevates glutamate concentration in the synaptic cleft (Hashimoto and Kano, 2003; Nitta et al., 2025; Wilson et al., 2019). Postsynaptic ultrastructural and molecular changes occur several days later as dominant CF inputs potentiate and losing CF inputs depress (Nitta et al., 2025; Bosman et al., 2008; Ohtsuki and Hirano, 2008) through spike-timing-dependent remodeling (Lorenzetto et al., 2009; Kawamura et al., 2013). Across these models, the earliest distinguishing feature between competing inputs is relative presynaptic transmission strength. Thus, release-site addition may be a conserved mechanism that biases synaptic refinement outcomes across developing neural circuits.

Materials and methods

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Genetic reagent (Mus musculus, male/female)	C57BL/6J; wild-type; WT	The Jackson Laboratory	RRID:IMSR_JAX:000664	Ages P2–P8
Genetic reagent (Mus musculus, male/female)	β2-nAChR−/−; CHRNB2 KO; β2KO	PMC4258148		Ages P2–P8
Genetic reagent (Mus musculus, male/female)	Tg(Slc6a4-cre)ET33Gsat/Mmucd; BAC-Cre Slc6a4-33	MMRRC	RRID:MMRRC_017260-UCD	Age P8
Genetic reagent (Mus musculus, male/female)	Gt(ROSA)26Sortm1.1(CAG-COX4I1/APX1*)Ddg/J; ROSA26LSL-Matrix-dAPEX2	The Jackson Laboratory	RRID:IMSR_JAX:032765	Age P8
Antibody	Donkey anti-Guinea pig IgG unconjugated	Jackson ImmunoResearch	Cat# 706-005-148; RRID:AB_2340443	(1:100)
Antibody	Donkey anti-Mouse IgG unconjugated	Jackson ImmunoResearch	Cat# 715-005-150; RRID:AB_2340758	(1:100)
Antibody	Donkey anti-Rabbit IgG unconjugated	Jackson ImmunoResearch	Cat# 711-005-152; RRID:AB_2340585	(1:100)
Antibody	Guinea pig polyclonal anti-VGluT2	Millipore Sigma	AB2251-I; RRID:AB_2665454	(1:100)
Antibody	Mouse monoclonal anti-Bassoon	Abcam	Ab82958; RRID:AB_1860018	(1:100)
Antibody	Rabbit polyclonal anti-Homer1	Synaptic Systems	Cat# 160 003; RRID:AB_887730	(1:100)
Sequence-based reagent	CHRNB2_F	PMC4258148	PCR primers	CAGGCGTTATCCACAAAGACAGA
Sequence-based reagent	CHRNB2_R	PMC4258148	PCR primers	TTGAGGGGAGCAGAACAGAATC
Sequence-based reagent	CHRNB2_mutant_R	PMC4258148	PCR primers	ACTTGGGTTTGGGCGTGTTGAG
Sequence-based reagent	SLC6A4_F	MMRRC	PCR primers	GGTCCTTGGCAGATGGGCAT
Sequence-based reagent	SLC6A4_R	MMRRC	PCR primers	CGGCAAACGGACAGAAGCATT
Sequence-based reagent	ROSA26LSL-Matrix-dAPEX2 _WT_F	The Jackson Laboratory	PCR primers	CTGGCTTCTGAGGACCG
Sequence-based reagent	ROSA26LSL-Matrix-dAPEX2 _WT_R	The Jackson Laboratory	PCR primers	AATCTGTGGGAAGTCTTGTCC
Sequence-based reagent	ROSA26LSL-Matrix-dAPEX2 _mutant_F	The Jackson Laboratory	PCR primers	CCATCAGCACCAGCGTGT
Sequence-based reagent	ROSA26LSL-Matrix-dAPEX2 _mutant_R	The Jackson Laboratory	PCR primers	GAACCCTTAGTGGGATCGGG
Peptide, recombinant protein	Catalase from bovine liver	Sigma-Aldrich	C1345
Peptide, recombinant protein	Normal donkey serum	Jackson ImmunoResearch	Cat# 017-000-121
Peptide, recombinant protein	Glucose oxidase	Sigma-Aldrich	G2133
Commercial assay or kit	EMbed 812 embedding kit with BDMA	Electron Microscopy Sciences	Cat# 14121
Commercial assay or kit	UltraBed Kit	Electron Microscopy Sciences	Cat# 14310
Chemical compound, drug	Alexa Fluor 405 NHS-ester	Thermo Fisher Scientific	Cat# A30000
Chemical compound, drug	Alexa Fluor 647 NHS-ester	Thermo Fisher Scientific	Cat# A20006
Chemical compound, drug	Atto 488 NHS-ester	ATTO-TEC GmbH	AD 488-31
Chemical compound, drug	Cacodylic acid- sodium cacodylate, trihydrate	Electron Microscopy Sciences	Cat# 12300
Chemical compound, drug	Calcium chloride	Electron Microscopy Sciences	Cat# 12340
Chemical compound, drug	Chloroform	Sigma-Aldrich	288306
Chemical compound, drug	Cy-3B mono NHS-ester	Cytiva	PA63101
Chemical compound, drug	Cysteamine	Sigma-Aldrich	30070
Chemical compound, drug	DY-749P1 NHS-ester	Dyomics GmbH	Cat# 749P1-01
Chemical compound, drug	Dulbecco’s phosphate buffered saline	Sigma-Aldrich	D8662
Chemical compound, drug	Ethanol	Pharmco	Cat# 111000200C1GL
Chemical compound, drug	FluoSpheres Infrared (715/755)	Invitrogen	Cat# F8799
Chemical compound, drug	FluoSpheres Orange (540/560)	Invitrogen	Cat# F8809
Chemical compound, drug	d-(+)-Glucose	Sigma-Aldrich	G7528
Chemical compound, drug	DAB (diaminobenzidine)	Sigma-Aldrich	RES2041D
Chemical compound, drug	Glutaraldehyde 70%, EM Grade	Electron Microscopy Sciences	Cat# 16360
Chemical compound, drug	Glycine	Sigma-Aldrich	G7126
Chemical compound, drug	Hydrogen peroxide, 30%	Thermo Fisher Scientific	Cat# BP2633500
Chemical compound, drug	l-Aspartic acid	Fisher Scientific	Cat# A13520
Chemical compound, drug	Lead nitrate	Electron Microscopy Sciences	Cat# 17900
Chemical compound, drug	Osmium tetroxide 4% aqueous solution	Electron Microscopy Sciences	Cat# 19140
Chemical compound, drug	Paraformaldehyde 16%, EM Grade	Electron Microscopy Sciences	Cat# 15710
Chemical compound, drug	Potassium ferricyanide	Electron Microscopy Sciences	Cat# 20150
Chemical compound, drug	Propylene oxide	Electron Microscopy Sciences	Cat# 20401
Chemical compound, drug	Sodium azide	Sigma-Aldrich	S2002
Chemical compound, drug	Sodium chloride	Sigma-Aldrich	S9888
Chemical compound, drug	Sodium hydroxide pellets	Sigma-Aldrich	567530
Chemical compound, drug	Thiocarbohydrazide	Electron Microscopy Sciences	Cat# 21900
Chemical compound, drug	Tris-base (Trizma-base)	Sigma-Aldrich	T8524
Chemical compound, drug	Triton X-100	Sigma-Aldrich	X100PC
Chemical compound, drug	Uranyl acetate	Electron Microscopy Sciences	Cat# 22400
Software, algorithm	3D-DAOSTORM analysis (single-molecule localization fitting code); version 2.1	PMC:PMC4243665		https://github.com/ZhuangLab/storm-analysis
Software, algorithm	Fiji (ImageJ)	PMC:PMC3855844		https://fiji.sc
Software, algorithm	MATLAB	MathWorks		https://mathworks.com
Software, algorithm	Python3	Python		https://www.python.org
Software, algorithm	Rstudio	Posit		https://posit.co/
Software, algorithm	SPSS	IBM		https://www.ibm.com/products/spss-statistics
Software, algorithm	STORM acquisition control code (packages include hal4000.py, steve.py, and dave.py); version V2019.06.28	Zhuang Laboratory, Harvard University		https://github.com/ZhuangLab/storm-control
Other	5 min epoxy in DevTube	Jenson Tools	Cat# 14250
Other	BEEM embedding capsules	Electron Microscopy Sciences	Cat# 70020-B
Other	Coverslip No. 1.5 (24 mm × 30 mm)	VWR	Cat# 48404-467
Other	Custom-built STORM microscope	PMC:PMC8637648		Information on our build is available from the Corresponding Author
Other	Gilder thin bar hexagonal mesh grids	Electron Microscopy Sciences	Cat# T200H-Cu
Other	Microscope slides	VWR	Cat# 16004-422

The raw imaging data in this paper were previously reported (Zhang et al., 2023). Materials and methods below are adapted from this work. All MATLAB and Python code used in the work is available on GitHub (https://github.com/SpeerLab/Aligned_data_analysis_SynapseClustering; copy archived at Zhang and Speer, 2025). Raw STORM images of the full data are available on the open-access Brain Imaging Library (Benninger et al., 2020). These images can be accessed here https://doi.org/10.35077/g.1164.

Animals

WT C57BL/6J mice (Stock Number 000664) and ROSA26LSL-Matrix-dAPEX2 mice (Stock Number 032765) used in this study are available from The Jackson Laboratory (Bar Harbor, Maine). SLC6A4Cre (cre recombinase expression under the serotonin transporter promoter), β2KO (genetic deletion of CHRNB2 encoding the β2 subunit of the nicotinic acetylcholine receptor), and ROSA26LSL-Matrix-dAPEX2 (cre-dependent expression of dimeric APEX2 targeted to the mitochondrial matrix) mice were generously gifted by Drs. Eric M. Ullian (University of California, San Francisco), Michael C. Crair (Yale School of Medicine), and Joshua H. Singer (University of Maryland), respectively. All experimental procedures were performed in accordance with an animal study protocol approved by the Institutional Animal Care and Use Committee (I