Introduction

The brain is a complex network of neurons, and understanding their wiring strategies provides crucial insights into their roles in neuronal computation. Release sites in the presynaptic terminal of Drosophila neurons are decorated with electron-dense projections composed of active-zone (AZ) scaffolds that are essential for efficient synaptic transmission (Akbergenova et al., 2018; Fouquet et al., 2009; Kittel et al., 2006; Matkovic et al., 2013; Newman et al., 2022; Paul et al., 2015; Wagh et al., 2006). These dense projections serve as a synapse marker enabling the whole-brain connectomes, comprehensive mapping of synaptic connections at the electron microscopic level (Dorkenwald et al., 2024; Scheffer et al., 2020; Schlegel et al., 2024; Takemura et al., 2024; Zheng et al., 2018). While connectome-based approaches catalyzed the exploration of cell type distinctions, circuit motifs, and neurotransmitters, their application to comparisons across different conditions is still challenging due to the limited throughput of data acquisition and dense reconstruction. Since the synaptic structure and connectivity reflect substantial variability of development and experiences (Fernández et al., 2008; Gilestro et al., 2009; Kremer et al., 2010; Sachse et al., 2007; Schlegel et al., 2024; Turrel et al., 2022; Zhang et al., 2018), cross-individual comparisons are necessary. Fluorescence labeling offers high throughputs and contents, especially when comparing synaptic organizations across cell-types (Mosca and Luo, 2014) or brain regions (Gao et al., 2019). Nevertheless, characterizing endogenous synaptic proteins in specific cells using fluorescence-labeling remains challenging in the central nervous system (CNS). This has driven growing interest in designing cell-type-specific fluorescence-tagging strategies for endogenous synaptic protein (Chen et al., 2014).

The circuit of Drosophila mushroom bodies (MBs) plays a central role in olfactory associative learning (Davis, 2023). The major intrinsic MB neuron, known as Kenyon cells (KCs) respond to odor stimulation and synapse onto the spatially segregated dendrites of MB output neurons (MBONs) that divide the MB into compartments. Distinct types of dopaminergic neurons (DANs) synapse onto specific compartments and thereby modulate KC-MBON synapses in the corresponding compartments (Aso et al., 2014). Presynaptic calcium levels of these DANs undergo sub-compartmental GABAergic modulation and inform memory specificity, postulating the distinct synapse structures at individual release sites (Yamagata et al., 2021). Two types of giant interneurons, the anterior paired lateral neuron (APL) and the dorsal paired medial (DPM) neuron extensively ramify across the MB lobes and provide recurrent modulations to KC synapses (Haynes et al., 2015; Keene et al., 2006, 2004; Lin et al., 2014; Liu and Davis, 2009; Pitman et al., 2011; Waddell et al., 2000; Yu et al., 2005). Considering their comprehensive projections throughout the MB lobes, the modulation of local circuits requires intracellular synaptic tuning. These studies together underscore the importance to spatially distinguish individual synapses within a cell. However, the high AZ density in the MB lobes precluded conventional approaches of fluorescent microscopy from profiling synaptic structures of specific neurons (Scheffer et al., 2020).

Here, we present a spatial analysis of individual AZs within the MB circuit at the single-cell resolution, achieved by cell-type-specific visualization of the endogenous active zone protein Bruchpilot (Brp) using the CRISPR/Cas9-mediated split-GFP tagging system. Drosophila ELKS/CAST/ERC family member Brp plays a central role in molecular assemblies at AZs by accumulating calcium channels and synaptic vesicles (Fouquet et al., 2009; Hallermann et al., 2010; Kittel et al., 2006; Matkovic et al., 2013; Wagh et al., 2006). Therefore, Brp enrichment serve as a structural proxy suited for estimating synapse function, such as release probability at single AZs (Akbergenova et al., 2018; Newman et al., 2022). We developed a high-throughput quantification pipeline to systematically profile Brp clusters of individual AZs in different MB-innervating neurons. Characterizing the distinct parameters and localization of Brp clusters revealed AZ distribution stereotypy across individuals and significant synaptic heterogeneity within single neurons. These cell-type-specific synapse profiles suggest that AZs are spatially organized at multiple scales, ranging from interindividual stereotypy to neighboring synapses in the same cells.

Results

Establishing an experimental system for cell-type-specific profiling of active zone structures

As the molecular assembly of synapses is reported to be sensitive to the dosage of Brp, we visualized endogenous Brp instead of transgenic expression of tagged Brp (Huang et al., 2020). To label endogenous Brp specifically in designated cell types, we employed the split-GFP tagging system (Kamiyama et al., 2016; Kondo et al., 2020). Using CRISPR/Cas9, we inserted the GFP₁₁ fragment (the eleventh β-strand of the super-folder GFP) just prior to the stop codon of brp gene. The GFP_1-10 fragment is expressed in cells of interest by the GAL4/UAS system. The self-assembly of the split-GFP fragments is therefore directed only in GAL4-expressing cells and visualizes endogenous Brp through reconstituted GFP fluorescence (Figure 1A).

Figure 1

As a proof of principle, we first validated the localization and plasticity of split-GFP tagged Brp (Figure 1B). To verify tagged Brp localization in an individual neuron, we directed GFP reconstitution and expressed a plasma membrane marker in the serotonergic DPM neuron using VT64246-GAL4 (Figure 1C). Confocal microscopy revealed Brp::reconstituted GFP (Brp::rGFP) signals only to the MB lobes, aligning with DPM terminals (Waddell et al., 2000). These signals were distributed as discrete clusters within plasma membrane varicosities resembling presynaptic boutons (Figure 1C), suggesting that Brp::rGFP clusters labeled individual AZs in the DPM neuron. AZ localization of Brp::rGFP was further validated by co-localization with anti-Brp immunostaining in the calyx and the voltage-gated calcium channel subunit Cacophony (Cac) in the PAM dopaminergic neurons (Figure 1D-E) (Kawasaki et al., 2004; Kittel et al., 2006). We found that GFP₁₁ tagging did not affect brp mRNA levels, but slightly decreased protein expression without altering overall expression pattern in the brain (Figure 1 – figure supplement 1-2). Brp::rGFP successfully visualized previously reported light-induced AZ plasticity in the photoreceptors (Sugie et al., 2015). Constant light exposure reduced the numbers of Brp::rGFP clusters in the R8 terminals in the medulla, compared to constant darkness (DD) (Figure 1F). This was further substantiated by increased Brp::rGFP cluster intensities upon knocking-down rab3 expression (Figure 1 – figure supplement 3), recapitulating Rab3-dependent regulation of Brp allocation in the presynaptic terminals of the motor neurons (Graf et al., 2009).

To systematically profile Brp::rGFP clusters, we developed an image processing pipeline (Figure 1A). In brief, image deconvolution was applied to reduce out-of-focus light and enhance signal sharpness (Figure 1 – figure supplement 4). Individual Brp::rGFP clusters were then segmented using the 3D suite plugin in Fiji (Ollion et al., 2013), generating the 3D region-of-interests (3D ROIs) that encircle each cluster. Brp::rGFP signal intensity, volume and location of individual clusters were measured using the 3D ROIs. To optimize the segmentation, we systematically varied the parameters and calculated the F-score to measure detection accuracy against manually defined ground truth (see Materials and Methods). The F-score is near 1.0 in segmenting Brp::rGFP clusters within DPM and APL neurons and achieved > 0.90 in KCs with certain combinations of segmentation parameters (Figure 1 – figure supplement 5).

To confirm that Brp clusters represent AZs, we quantified the numbers of Brp::rGFP clusters in DPM and APL neurons specifically and compared them to the connectome data. We found that cluster counts closely matched the number of AZs annotated by the hemibrain connectome (Figure 1C) (Scheffer et al., 2020). This result validated our method that accurately isolated Brp::rGFP clusters corresponding to single AZs.

AZ profiles within KCs are compartmentally heterogeneous

Brp accumulation at individual AZs is known to be heterogeneous even within a single motor neuron (Akbergenova et al., 2018; Ehmann et al., 2014; Gratz et al., 2019; Paul et al., 2015). To study such AZ heterogeneity in CNS neurons, we quantified the signal intensity of individual Brp::rGFP clusters in MB lobes. Using KC subtype-specific drivers to direct split-GFP reconstitution in γ KC (MB009B-GAL4), α’/β’ KCs (MB370B-GAL4) and α/β KCs (MB008B-GAL4). we found that within-cluster Brp::rGFP intensities are highly diverse in all KC subtypes (Figure 2A).

Figure 2

To visualize spatial patterns of Brp::rGFP intensity in each KC subtype, we reconstructed 3D distribution of Brp::rGFP clusters and color-coded them by signal intensities (Figure 2B). We found that clusters showed noticeable intensity differences between compartments in all subtypes. Even adjacent compartments such as γ1 and γ2, β’2 and β’1, α2 and α3, showed drastic differences (Figure 2B). Importantly, these compartmental patterns were stable across individuals (Figure 2C) (Wu et al., 2025). This result suggests that AZs in different compartments have distinct structures. Since individual KCs of each subtype synapse onto all compartments (Scheffer et al., 2020; Schlegel et al., 2023; Takemura et al., 2024; Zheng et al., 2018), this Brp compartmental heterogeneity is likely formed intracellularly.

Intracellular heterogeneity of Brp concentration at individual AZs and cell-type dependent diversity

Super-resolution microscopy has shown that the Brp molecular density at individual AZs is dynamically adjusted in motor neurons (Ghelani et al., 2023; Mrestani et al., 2021). We hypothesized that the amount of Brp does not necessarily correlate with AZ cluster size. To verify this, we calculated the Brp concentration by quantifying both the volume (number of voxels within a 3D ROI) and the Brp::rGFP intensity of each cluster. We found that clusters with similar sizes can exhibit vastly different signal intensities in the DPM neuron, suggesting the heterogeneity of Brp concentration at individual AZs (Figure 3A).

Figure 3

Correlation analysis between the Brp::rGFP intensity and cluster volume revealed a substantial variability among cell types (Figure 3B-E). There were even significant differences among KC subtypes. These results suggest cell-type dependent variability of the Brp concentration of individual clusters.

Stereotypy of intracellular distribution of AZs

To study the spatial distribution of Brp clusters within single neurons, we focused on DANs. MB504B-GAL4 labels four types of single PPL1 DANs projecting to distinct compartments (Vogt et al., 2014). Using this driver, we analyzed Brp::rGFP distribution in each compartment and found it uneven in terminals of both PPL1-α2α’2 and α3 (Figure 4A-B). To better characterize cluster distribution, we calculated the “AZ density” defined as the number of Brp::rGFP clusters surrounding a particular cluster within a specified radius (see Figure 1A and Materials and Methods for detail). By color-coding Brp::rGFP clusters according to their AZ density, we found that AZs in PPL1-α3 were more localized to the core of the α3 compartment. This pattern was consistent across individuals (Figure 4C-D), suggesting a stereotyped AZ distribution within PPL1-α3 neurons.

Figure 4

We extended the analysis to two other single pairs of neurons innervating the MBs, the DPM and APL neurons. Visualizing AZ density revealed a stereotyped and compartment specific distribution in the DPM neuron (Figure 5A). Specifically, the AZ density was constantly high in the α’/β’ lobes across all individuals, supporting the known functional importance of DPM branches in the α’/β’ lobes (Keene et al., 2006). In contrast, this compartmental pattern was less pronounced in the APL neuron. Notably, the ratio of AZ density between α3 and α’3 is strikingly different among individuals (Figure 5B). Taken together, these data suggest that the individual variability in AZ distribution depends on the cell type.

Figure 5

Regulation of spatial configuration of AZs

Characteristics of Brp clusters and AZ distribution in KCs revealed spatial organization at the level of compartments. Recent studies further showed intra-compartmental variability of synaptic plasticity upon associative learning, prompting us to analyze sub-compartmental organization of AZs (Bilz et al., 2020; Davidson et al., 2023). We thus examined potential local patterns among AZs in proximity by calculating the correlation between nearest neighbor clusters in terms of their Brp::rGFP intensities (Figure 6A-B). This analysis revealed strong correlations in KCs and APL but not in DPM (Figure 6C).

Figure 6

We further analyzed the sub-compartmental AZ patterns by examining the spatial distribution of Brp::rGFP intensity. To reduce the high frequency variability of Brp::rGFP intensities (Figure 2B), we calculated the “local intensity” for each cluster by applying mean filtering (the average Brp::rGFP intensity of all surrounding clusters within a specified radius r, see Materials and Methods for detail). By visualizing the local intensity in γ KCs, we identified AZ “hot spots”, sub-compartment-sized small groups of AZs with high local intensities, on top of the compartmental differences (Figure 6D). Since Brp molecules are clustered more in AZ-dense boutons of a single motor neuron (Paul et al., 2015), we hypothesized that these hot spots correspond to regions with higher AZ densities. Indeed, we found that hot spots appear in high AZ densities regions in γ KCs (Figure 6D). Consistently, correlation between the local intensity and the AZ density was high in all KC subtypes. In contrast, for DPM and APL neurons, the local intensity of Brp::rGFP was not associated with AZ density (Figure 6E-H). Taken together, these results suggest previously unidentified sub-compartmental synaptic configuration organized across individual KCs.

Associative learning re-organizes sub-compartmental synaptic configuration

Sub-compartmental synaptic configuration may undergo experience-dependent changes, such as through associative learning (Turrel et al., 2022; Zhang et al., 2018). To examine such structural plasticity in local AZ configurations (Figure 6D, F-H), we visualized Brp::rGFP specifically in KCs using R13F02-GAL4 (Figure 7A). This induction did not alter MB morphology and short-term memory compared to wild-type (Figure 7 – figure supplement 1-2). We presented flies with odor (4-methylcyclohexanol) with a concomitant (paired) or shifted (unpaired) electric shock (Fig. 7B). Since both paired and unpaired groups were exposed to the same odor and electric shock, the difference between these groups purely represents the effect of association. As single odor conditioning induced memory decaying gradually over one day (Figure 7B), we measured the correlation between the AZ density and local intensity of individual Brp::rGFP clusters (Figure 6F-H) at different time points after conditioning (from 3 min to 1 day). Strikingly, we found that associative learning induced a transient modification to the KC synaptic configurations in a compartment-specific manner (Figure 7C). This learning-induced structural plasticity was specific at 90 min after conditioning and disappeared within one day (Figure 7D), consistent with previous studies reporting short-lived AZ remodeling in the MBs by conditioning (Turrel et al., 2022; Zhang et al., 2018). These results showcase the advantage of split-GFP system in reporting the dynamics of local synapse subsets with high throughputs and contents. Altogether, we suggest that associative learning induces transient memory traces of local structural plasticity in AZ configuration.

Figure 7

Discussion

By leveraging the CRISPR/Cas9 genome editing of the brp locus and split-GFP technique, we systematically profiled presynaptic structures of MB neurons in a cell-type-specific manner. This approach enabled high-throughput analysis of multiple brain samples and detailed characterizations, revealing previously inaccessible structural features of AZs in CNS neurons. Cell-type specific labeling of other endogenous synaptic proteins, such as vesicle-associated proteins, may provide complementary insights to AZ profiling and allow molecular reconstruction of synapses.

Single-cell level analysis of AZ profiles revealed cell-type-specific stereotypy in spatial regulation (Figure 4-6). We found that AZ density in the DPM neurons is consistently high in the α’/β’ lobes across individuals (Figure 5). This AZ stereotypy of DPM explains the functional significance of the branches in the α’/β’ lobes. Overexpression of a Down syndrome cell adhesion molecule (DScam) variant in the DPM neurons was shown to disrupt their innervation except in the α’/β’ lobe, while memory formation remained unaffected (Keene et al., 2006). Similarly, our analysis identified the intracellular stereotypy in AZ distribution of the PPL1-α3 DAN within the α3 compartment (Figure 4). This sub-compartmental organization of the single DAN and layered projections of α/β KCs may underlie the differential dopaminergic modulation of KC subtypes in associative learning (Perisse et al., 2013). While such stereotypy is less pronounced in APL, we found strong correlations between Brp::rGFP intensities of neighboring AZs in contrast to DPM (Figure 6D). This suggests that AZs in APL neurons are organized more locally, allowing them to modulate microcircuits composed of KCs, DANs and MBONs (Amin et al., 2020). Collectively, the spatial scale of AZ organizations depends on cell types, reflecting the function of individual MB neurons.

We observed that Brp::rGFP intensity correlates strongly with the AZ size in KC subtypes, in contrast to DPM and APL neurons (Figure 3). Previous studies using localization microscopy showed that the molecular density of synaptic proteins at AZs can be dynamically adjusted in motor neurons, potentially modulating the synaptic vesicle release (Ghelani et al., 2023; Mrestani et al., 2021; Sachidanandan et al., 2023). The variable Brp concentrations (i.e. the ratio of the Brp::rGFP intensity to the AZ size) in DPM and APL neurons may represent AZs with distinct Brp densities and therefore different functional modes (Figure 3) (Mrestani et al., 2021). Notably, a recent study successfully predicted the neurotransmitter type of neurons using the ultrastructure of dense projections as one of the parameters (Eckstein et al., 2024), suggesting neurotransmitter-specific AZ structures. Interestingly, both the DPM and APL neurons are reported to release multiple neurotransmitters (Davie et al., 2018; Haynes et al., 2015; Lee et al., 2011; Liu and Davis, 2009; Wu et al., 2013; Zeng et al., 2023). Distinct Brp concentrations at AZs might thus support particular release modes or neurotransmitter types.

In KCs, the spatial analysis of local AZ configuration revealed strong correlations between Brp::rGFP intensities at neighboring AZs as well as AZ “hot spots”, regions with high Brp localization and AZ densities (Figure 6). These results suggest previously unidentified, highly localized sub-compartmental AZ structures, which are likely organized across individual KCs. Presynaptic terminals of a single KC are found to be highly heterogeneous in terms of their plasticity upon dopamine modulation and associative learning (Bilz et al., 2020; Davidson et al., 2023). A recent study found the sub-compartmentally heterogeneous activities of dopamine terminals explain memory specificity (Yamagata et al., 2021). Such locally distinct dopamine input may be a source of the AZ hot spot formation in KC terminals. This model is consistent with the localization of two opposing dopamine receptors Dop1R1 and Dop2R to AZs (Hiramatsu et al., 2024).

Furthermore, we showed that the local configuration undergoes structural plasticity upon associative learning that involves presynaptic dopaminergic modulation (Aso et al., 2012, 2010; Aso and Rubin, 2016; Burke et al., 2012; Liu et al., 2012; Yamagata et al., 2015) (Figure 7). Since cAMP signaling plays a crucial role in the AZ structural plasticity especially during associative memory formation (Baltruschat et al., 2021; Sachidanandan et al., 2023; Wu et al., 2025), it may underlie learning-induced plasticity in sub-compartmental AZ configuration (Turrel et al., 2022; Zhang et al., 2018).

Materials and Methods Animals

Flies were maintained on standard cornmeal food at 25 °C under a 12:12 h light-dark cycle for all experiments. All flies used for experiments are 3-7 days old adult males. Flies were transferred to fresh food vials after hatching and flipped every two days before experiments. The GAL4-UAS system was used to express transgenes, and balancers were removed for animals used in all experiments. Fly strains and resources used in this study are as follow: MB008B-GAL4 (BDSC 68291) (Aso et al., 2014), MB009B-GAL4 (BDSC 68292) (Aso et al., 2014); MB370B-GAL4 (BDSC 68319) (Aso et al., 2014), MB504B-GAL4 (BDSC 68329) (Aso et al., 2014), VT64246-GAL4 (VDRC 204311) (Tirian and Dickson, 2017), GH146-GAL4 (BDSC 30026) (Stocker et al., 1997), R58E02-GAL4 (BDSC 41347) (Pfeiffer et al., 2008), R13F02-GAL4 (BDSC 48571) (Pfeiffer et al., 2008), R86E01-GAL4 (BDSC 45914) (Jenett et al., 2012), Amon-GAL4 (BDSC 30554) (Rhea et al., 2010) , Rh6-GAL4 (BDSC 7464, by Dr. Claude Desplan), UAS-CD4::tdTomato (BDSC 35841, by Dr. Yhu Nung Jan & Chun Han), UAS-Rab3-RNAi (BDSC 34655) (Perkins et al., 2015), brp::GFP₁₁ (This study and (Wu et al., 2025)), UAS-GFP_1-10 (Kondo et al., 2020).

Generation of brp::GFP₁₁

CRISPR/Cas9-mediated homologous recombination was used to insert GFP₁₁ right before the stop codon of brp. The donor vector contained a left homology arm of 747 bp, the GFP11 sequence (Kondo et al., 2020), a floxed 3xP3-RFP marker and a right homology arm of 741 bp. A guide RNA sequence (TCGCAAAGCCACAGATACAC) targeting the brp locus was cloned into pBFv-U6.2 (Kondo and Ueda, 2013) to generate a gRNA expression vector. The donor and gRNA vectors were co-injected into fertilized eggs carrying the nos-Cas9 transgene (Kondo and Ueda, 2013). Successful transformants were identified by eye-specific RFP fluorescence. Flies were then crossed to a CyO-Cre balancer to remove the RFP marker. Primers used to clone homology arms are as followed (lowercase letters indicate homologous recombination regions with the doner vector): left arm forward: gcttgatatcgaattcAAGGACATCGAGGAAAAGGAGAAGAAG; left arm reverse: agttgggggcgtaggGAAAAAGCTCTTCAAGAAGCCAGCTGGTCC; right arm forward: tagtataggaacttcTCGCAAAGCCACAGATACACACATCTTGG; right arm reverse: cgggctgcaggaattcATCGTCGATAATTGTAAGTGCATGCTG

qRT-PCR experiment

Procedures are as previously described (Saito et al., 2025). For each sample, RNA extract from ∼20 heads was purified using TRIzol LS Reagent (Invitrogen) and Direct-zol RNA Microprep kit (ZYMO RESEARCH), following the provided protocol. The concentration of extracted RNA samples was then measured using the NanoDrop Spectrophotometer (Thermo Fisher Scientific) and adjusted to a similar level using diluted water. Reverse transcription was performed using ReverTra Ace qPCR RT Master Mix (TOYOBO, FSQ-301). qRT-PCR was performed using iTaq Universal SYBR Green Supermix (BIO-RAD) and CFX96 Touch Real-Time PCR Detection System (BIO-RAD). No template controls were also prepared and measured alongside. Three primer pairs for brp were designed and used in the experiment but only one pair that had Ct<30 for all samples was used. Primers for brp, forward: AGCTCAAGGACCACATGGACATC; reverse: GCGCCATATCCACCTGGTTGTC (Signma-Aldrich). Primers for Ubiquitin-5E, forward: TCTTCACTTGGTCCTGCGTC; reverse: ATGGCTCGACCTCCAAAGTG (eurofins Genomics). Primers for αTubulin84B, forward: GATCGTGTCCTCGATTACCGC; reverse: GGGAAGTGAATACGTGGGTAGG (eurofins Genomics).

Light exposure experiment

Procedures are as previously described (Sugie et al., 2015). In brief, animals hatched within one day were transferred to constant darkness (DD, 0 LUX) or constant light (LL, 2000-4000 LUX by LED panels) environment at 25 °C and kept for 2-3 days. Animals were then dissected and Brp::rGFP cluster number in individual R8 photoreceptor terminals in the medulla was quantified.

Associative conditioning

For experiments in Figure 7C-D, adult flies at 3-7 days after eclosion were kept in a 25 °C incubator before and after conditioning. Approximately 50 flies were put in a training tube that is set inside the climate box for 2 minutes before the conditioning. Temperature in the climate box was set at 24 °C and the relative humidity was set to 70%. For the paired group, 2% 4-methycyclohexanol (4-MCH, Sigma-Aldrich, diluted in paraffin oil, Sigma-Aldrich) was presented in a cup with the diameter of 5 mm, together with 12 pulses of electric shocks at 90 V for 1 minute inside the training tube. The unpaired group received the same 1-minute electric shock first, followed by 1-minute rest, and then the same 1-minute 4-MCH presentation. Flies were dissected at 3 min, 20 min, 90 min, 270 min and 1 day after conditioning. Experiments were performed multiple times on different days for a single group.

For measuring the memory decay curve of single odor conditioning, 2% 4-MCH and pure paraffin oil were used for the training. One odor was presented with electric shocks for 1 minute, followed by 1 minute rest, then another odor was presented without electric shocks. For each reciprocal, preference was tested between 2% 4-MCH and paraffin oil. For the differential odor conditioning and short-term memory performance test, 10% 4-MCH and 10% 3-OCT were used for the conditioning, following the same training protocol. Short-term memory performance was tested immediately after the conditioning.

Performance index was calculated as #(𝐶𝑆−) − #(𝐶𝑆+)⁄#(𝐶𝑆−) + #(𝐶𝑆+).

Sample preparation

Animals used in experiments involving comparisons between control and experimental groups were dissected on the same day. Data were collected from multiple batches of experiments performed on different days. Flies were anesthetized by ice and placed on ice before dissection. The dissection was performed in cold PBS to collect the brain without the ventral nerve cord. The brains were subsequently fixed by 2% paraformaldehyde for 1 h and then washed 3×20 min by 0.1% PBT (0.1% Triton X-100 in PBS) in a PCR tube, typically 5-6 brains in one tube. Samples were mounted on microscope slides using mounting medium SeeDB2S (Ke et al., 2016). For nc82 immunostaining, fixed brains were washed 3×20 min with 0.1% PBT and incubated in 3% normal goat serum (NGS; Sigma-Aldrich; G9023)-0.1% PBT blocking solution for 1 hour at room temperature. The nc82 antibody (DSHB) solution was diluted 1:20 in the blocking solution. Samples were incubated in antibody solutions at 4°C for 48 hours for both primary and secondary antibodies.

Confocal imaging

The image acquisition was performed using an Olympus FV1200 confocal microscope platform equipped with both PMT and GaAsP high sensitivity detectors and a 60×/1.42 NA oil immersion objective (PLAPON60XO, Olympus). The Kalman filter was turned on to 2× averaging while scanning. For imaging Brp::rGFP clusters in MB-projecting neurons, scanning speed was set at 2∼4 µs/pixel with a voxel size of lateral 0.079 × 0.079 × axial 0.370 µm. For experimental point spread function (PSF) imaging, SeeDB2S immersed beads were scanned in a setting that is 60×/1.42 NA oil immersion objective; 473 nm laser power: 2.0%; 559 nm laser power: 1.5%; voxel size: 0.079 µm × 0.079 µm × 0.370 µm; scanning speed: 4 µs/pixel, to produce multiple bead images.

PSF acquisition and image deconvolution

Sub-resolution fluorescent beads (Tetraspeck Microspheres 0.1 µm, Thermo Fisher Scientific, T7279) were imaged to generate the PSF. The bead solution was diluted with distilled water and sonicated several times to eliminate aggregation. PSF images acquired were processed using Amira software (Thermo Fisher Scientific) with the Extract Point Spread Function module. The PSFs extracted from each image were averaged to create a single PSF for later image deconvolution (Resizing voxel size: 0.079 µm × 0.079 µm × 0.370 µm; 32 pixels × 32 pixels × 21 slices of image size). Image deconvolution was performed by using the Richardson-Lucy iterative non-blind algorithm in a Fiji plugin DeconvolutionLab2 (Sage et al., 2017), or by CLIJ2 GPU-based Richardson-Lucy deconvolution (Haase et al., 2020). Images were deconvolved 20 times iteratively to improve image quality.

Brp clusters segmentation and data analysis

For Brp::rGFP cluster detection, the image stack size was adjusted to double the XY plate pixel number using Fiji to increase the detection precision. The images were subsequently processed using the “3D maxima finder” and the “3D spot segmentation” function of the “3D suite” package in Fiji. 3D maxima were identified for each cluster and used as the starting point for pixel clustering (Ollion et al., 2013). An intensity threshold (threshold) was first set to reduce noise. Pixels having values below the threshold would become NaN (not a number). The “noise tolerance” was then set in the 3D maxima finder to adjust its sensitivity. To be noticed, threshold is only applied to images processed by the 3D maxima finder to generates the “peak” image stack. 3D spot segmentation function uses both the “peak” image and the deconvolved Brp::rGFP image to generate “3D ROIs”. 3D ROIs were then applied on the raw images to quantify the volume (total voxel number in ROI) and Brp::rGFP intensity (total pixel value in ROI) of individual clusters using the “3D ROI manager”. For processing most images, both threshold and noise tolerance were set to 200∼400, where the segmentation was optimal based on the F-score benchmarking. “Local thresholding method” in 3D spot segmentation was set to “Gaussian fit”, and “SD value” was set to 2.

The above procedures may include a tiling and stitching step for images and data because of the limited computer power. Typically, an image was tiled into 25 sub-stacks with overlapped margins. After being processed separately, the data acquired from all tiled sub-stacks was stitched back with a deletion of overlapping data points to generate the complete detection result. Nearest neighbors were identified for each particle and spatial distances in between (NND) were calculated using Python. For each sample, mean NND was calculated separately. For a particular Brp::rGFP cluster, the number of surrounding clusters within r (r = 3 × mean NND) spherically including itself is referred as the “AZ density”. The “local intensity” of a particular Brp::rGFP cluster is the average of Brp::rGFP intensity of all clusters surrounding within r (r = 3 × mean NND) including itself. The plot function of Python was used to plot the color-coded 3D reconstructions. A sample code for analyzing NND, AZ density and local intensity is provided with a demonstration data sheet (Supplementary file 1 and analysis code).

Detection optimization

The 3D detection parameters were optimized by matching the 3D maxima detection results to the ground truths. Ground truths were defined by annotating AZs manually, according to the size, position and signal pattern of Brp clusters in the images. The F-score was used to assess detection precision. F-score is the harmonic mean of a system’s precision and recall value, calculated by the following formula: 2 × [(Precision × Recall) / (Precision + Recall)]. The precision is the number of true positives divided by the total number of identified positives, including incorrect identification. The recall is the number of true positives divided by the total number of true data points defined as the ground truth. F-scores shown in Figure S5 were automatically calculated using a customized Fiji Marco that aligned 3D maxima with ground truth. Different combinations of noise threshold and noise tolerance were tested for the test images cropped from raw images.

Statistical analysis

Statistical analysis was performed using GraphPad Prism version 8, 9 and 10 or Python. For statistical comparisons, Kruskal-Wallis test with original False Discovery Rate method of Benjamini and Hochberg correction was applied unless otherwise specified. The desired false discovery rate was set to 0.05. Pearson’s correlation coefficient (R) was calculated using Python.

Figure supplements

Figure 1 – figure supplement 1

Figure 1 – figure supplement 2

Figure 1 – figure supplement 3

Figure 1 – figure supplement 4

Figure 1 – figure supplement 5

Figure 7 – figure supplement 1

Figure 7 – figure supplement 2

Acknowledgements

We thank all lab members of Tanimoto Lab at Tohoku University for valuable discussions. We thank Dr. Yuki Tsukada at Keio University for crucial advice on Fiji-based image analysis. We thank Vienna Drosophila Resource Center and Bloomington Drosophila Stock Center for transgenic flies.

Additional information

Data and materials availability

Further information and requests on resources and reagents should be directed to and will be fulfilled by the corresponding author, Dr. Hiromu Tanimoto (hiromut@m.tohoku.ac.jp). All data and codes needed to evaluate the conclusions in the paper are present in the paper.

Author contributions

Conceptualization: HW, HT Methodology: HW, YM, NY, SE, SK Investigation: HW, YM, SE Visualization: HW, SE Resources: SK Supervision: HW, NY, HT Writing—original draft: HY, HT Writing—review & editing: HW, SE, HT

Funding

MEXT | Japan Science and Technology Agency (JST) (JPMJSP2114)

• Hongyang Wu

Tohoku University (東北大学) (Frontier Research in Duo)

• Hiromu Tanimoto

Ministry of Education, Culture, Sports, Science and Technology (MEXT) (20H00519)

• Hiromu Tanimoto

Ministry of Education, Culture, Sports, Science and Technology (MEXT) (22H05481)

• Hiromu Tanimoto

Ministry of Education, Culture, Sports, Science and Technology (MEXT) (22KK0106)

• Hiromu Tanimoto

Additional files

Supplementary file 1. Python code used for nearest neighbor, local intensity and AZ density analysis.

Supplementary file 2. Example data of a DPM neuron generated by the 3D spot segmentation, before being processed by the analysis code.

Supplementary file 3. Numerical data for figures.