Introduction

The endocrine or hormonal systems in animals play a pivotal role in regulating development and a multitude of physiological processes including growth, metabolism, and reproduction (Edgar, 2006; Nässel and Zandawala, 2019; Aref et al., 2024). In addition, peptide hormones can target neuronal circuits to modulate diverse behaviors ranging from feeding and locomotion to courtship and aggression (Bargmann, 2012; Marder, 2012; Taghert and Nitabach, 2012; Kim et al., 2017; Schoofs et al., 2017; Nässel and Zandawala, 2022). Peptide hormones also enable organisms to adapt to changing external environments and internal states by permitting communication between the nervous system and peripheral tissues (Friedman and Halaas, 1998; Murphy and Bloom, 2006). This inter-organ signaling is crucial in orchestrating the functions of different tissues to attain homeostasis (Droujinine and Perrimon, 2016; Castillo-Armengol et al., 2019; Koyama et al., 2025). Given its importance, it is not surprising that disrupted endocrine signaling can result in several disorders including obesity, diabetes, hypertension, infertility and growth defects amongst others (Golden et al., 2009; Castillo-Armengol et al., 2019). Understanding the regulation of endocrine signaling can thus provide insights into the prevention or treatment of endocrine-related disorders.

Although peptide hormones can be produced by several tissues, the nervous system represents a major source of hormones. In vertebrates, the hypothalamus and pituitary gland contain neurosecretory cells (NSC) that are the source of several neuropeptides/hormones (Meister, 1993). These hormones, the tissues producing them, and their target tissues are categorized into different axes which regulate distinct functions. For example, the hypothalamic-pituitary-adrenal (HPA) axis utilizes corticotropin-releasing hormone (CRH), adrenocorticotropic hormone, and cortisol to primarily regulate the stress response (Herman et al., 2016). The hypothalamic-pituitary-thyroid (HPT) axis controls growth and metabolism whereas the hypothalamic-pituitary-gonad (HPG) axis regulates reproductive processes, both of which utilize several different hormones (Brent, 2012; Plant, 2015). Interestingly, there is also interaction between these systems. For instance, stress-regulating CRH can act on the HPG axis to reduce the production of sex hormones and suppress gonadal function (Ferin, 1999; Maeda and Tsukamura, 2006). Hence, these axes are interconnected, which underscores the regulatory complexity of the vertebrate endocrine system.

In contrast to vertebrates, the Drosophila brain contains a small number of NSC in the pars intercerebralis, pars lateralis, and subesophageal zone (SEZ). These NSC project their axons towards the corpora cardiaca (CC) and corpora allata (CA), a set of endocrine glands closely associated with the aorta and anterior gut (Figure 1A). Despite the large evolutionary timescale separating vertebrates and insects, there are similarities between their neuroendocrine systems (Nässel and Zandawala, 2020). These systems share significant similarities in structure, signaling pathways, and cell fate determinants during development (Hartenstein, 2006; Tessmar-Raible, 2007). Hence, the pars intercerebralis and CC are analogs of the vertebrate hypothalamus and pituitary, respectively. Strikingly, there is also conservation in some of the neuropeptides utilized by these systems. For instance, CRH is homologous to diuretic hormone 44 (DH44), and both hormones regulate stress responses (Furuya et al., 1995; Zandawala et al., 2018a; Nässel and Zandawala, 2019). Similarly, homologs of other hypothalamic neuropeptides such as prolactin-releasing peptide, neuromedin U, and gonadotropin-releasing hormone that regulate hormone release are also expressed in the Drosophila neuroendocrine system (Melcher et al., 2006; Kapan et al., 2012; Terhzaz et al., 2012; Zandawala et al., 2018b; Yanez-Guerra et al., 2020). Therefore, Drosophila with its smaller neuroendocrine system is an attractive model to unravel evolutionary conserved pathways which regulate peptide hormone signaling. While previous studies have characterized the connectomes of neuroendocrine centers in the larvae of Drosophila and Platynereis dumerilii (Williams et al., 2017; Huckesfeld et al., 2021), the neuroendocrine connectome of an adult animal is lacking. Given the expansion and transformation of the nervous system during metamorphosis, it remains to be seen which input pathways to NSC are conserved across animal development. Importantly, there are stark differences in physiology and behavior of larval and adult Drosophila. Therefore, the larval neuroendocrine system, which is mainly concerned with growth and development, is not entirely suitable to understand adult physiology and behavior.

Figure 1.

Table 1.

To address this gap, we leveraged connectomics to characterize the first synaptic connectome of an adult neurosecretory network in an invertebrate. We deciphered all the major neuronal inputs to Drosophila NSC and focused on direct and indirect sensory input pathways to NSC. We also utilized single-cell transcriptomic analyses to explore putative paracrine interconnectivity between NSC classes, as well as endocrine inter-organ pathways. Our analyses shed light on the broader principles governing hormonal regulation and their impact on organismal physiology and behavior.

Results

Identification of 10 cell classes comprising the neuroendocrine network in the Drosophila brain

To characterize the synaptic connectome of the adult Drosophila neuroendocrine network, we first identified all endocrine or NSC in the brain which are a major source of circulating hormones. These endocrine cells can be broadly classified into lateral, medial, and subesophageal zone NSC (l-NSC, m-NSC, and SEZ-NSC, respectively) based on their location in the brain. Their axons exit the brain via a pair of nerves (nervii corpora cardiaca, NCC), and depending on the NSC cell type, innervate the CC, CA, hypocerebral ganglion, crop, aorta, or the anterior midgut (Figure 1A) (Siegmund and Korge, 2001; Hartenstein, 2006). Their axon terminals form neurohemal sites through which hormones are released into the circulation or locally on peripheral targets such as the crop. Collectively, the NSC in the brain form a major, yet distributed, neuroendocrine network that is functionally analogous to the hypothalamus (Nässel and Zandawala, 2020). We identified all brain NSC which project via the NCC in the FlyWire connectome by isolating the nerve bundle containing their axons (Figure 1B). In total, we independently identified 80 brain NSC, in agreement with our companion studies (Dorkenwald et al., 2024; Schlegel et al., 2024). We propose and utilize a systematic nomenclature for all brain NSC based on their location and neuropeptide identity (Table 1).

In addition to the FlyWire connectome, two additional comprehensive Drosophila connectomes, the brain and nerve cord (BANC) and male central nervous system (maleCNS) connectomes, are now available (Berg et al., 2025; Bates et al., 2026). Since NSC have also been identified in these datasets, we have classified them according to our scheme (Table 1 and Figure 1 Supplement 1) to facilitate comparison.

NSC classes can be classified based on their morphology, neuropeptide identity, and synaptic connectivity

Interestingly, the number of NSC identified in the FlyWire connectome is larger than the number of NSC characterized in the neural connectome of the first instar larvae (Huckesfeld et al., 2021) (Table 1). In larvae, two groups of m-NSC express myosuppressin (m-NSC^DMS) and diuretic hormone 44 (m-NSC^DH44). A third group of m-NSC express insulin-like peptides 2, 3, and 5 (m-NSC^DILP), and are commonly referred to as insulin-producing cells. In addition, there are five groups of l-NSC which express ion-transport peptide (l-NSC^ITP), corazonin (l-NSC^CRZ), diuretic hormone 31 (l-NSC^DH31), prothoracicotropic hormone and eclosion hormone. The latter two populations undergo apoptosis soon after adult eclosion and are thus not found in mature adults (Nässel and Zandawala, 2019; Scott et al., 2020). Lastly, the SEZ-NSC include two groups which express CAPA (SEZ-NSC^CAPA) and Hugin (SEZ-NSC^Hugin) neuropeptides. While 14 CRZ neurons are present in adults, it is not clear if all of these are bona fide NSC and release CRZ into the circulation. Since the number of neurons comprising the remaining NSC classes is thought to remain constant across development, the identity of additional NSC found in adults needed to be clarified.

With this aim in mind, we sought to classify the adult NSC into different classes based on their neuropeptide identity. All SEZ-NSC and some l-NSC classes can easily be identified based on their morphology and location (Figure 1A). However, this approach is not feasible for m-NSC since they are clustered together in the superior medial protocerebrum and appear similar based on gross morphology.

Therefore, we asked whether clustering NSC based on cosine similarity between their synaptic inputs (Schlegel et al., 2021) can help distinguish and identify different NSC populations in the FlyWire connectome. We have recently utilized a similar approach to successfully classify neurons of the circadian clock (Reinhard et al., 2024). As expected, SEZ-NSC^Hugin, SEZ-NSC^CAPA, and l-NSC^DH31 form three separate clusters (Figure 1C). Most l-NSC^ITP do not have any input synapses in this dataset and were thus excluded from the analysis. However, the 8 l-NSC^ITP are easily recognizable based on their unique morphology (Figure 1D-E). Notably, our clustering analysis resulted in two clades of m-NSC each comprising 6 neurons (Figure 1C). These clusters likely represent m-NSC^DMS (addressed below) and m-NSC^DH44. We also obtained two additional clusters of m-NSC comprised of 18 and 10 neurons, with the latter having low similarity between the neurons forming that cluster. We suspected that the cluster comprised of 18 m-NSC represents m-NSC^DILP as we expected at least 14 m-NSC^DILP in the connectome. To clarify the number of m-NSC^DILP in adults, we quantified the number of cells using various combinations of m-NSC^DILP-specific drivers and antibodies against DILP2 or DILP3 (Figure 2A and Figure 2 Supplement 1). On average, we detected 15-16 m-NSC^DILP with antibodies against DILPs, with several preparations labelling 18 neurons and all of them labelling greater than 10 neurons. Hence, the largest m-NSC cluster represents m-NSC^DILP. While there are 18 m-NSC^DILP in the FlyWire connectome, there are 16 such cells in BANC and maleCNS connectomes, indicating inter-individual differences. Additionally, we also quantified the number of m-NSC^DMS since we did not retrieve any clusters with 4 neurons as was anticipated for m-NSC^DMS. DMS-T2A-Gal4 drives GFP expression in 6 pars intercerebralis neurons on average which project via the NCC (Figure 2B-C). Surprisingly, we also detected weak GFP expression in SEZ-NSC^CAPA (Figure 2B and 2D). Thus, there are additional m-NSC^DILP and m-NSC^DMS neurons in adults compared to larvae, and circulating/hormonal DMS can originate from two NSC classes.

Figure 2.

Intriguingly, one of the m-NSC clusters comprising 6 neurons represents m-NSC^DMS while the other represents m-NSC^DH44. Since our clustering was based on input synapses, we reasoned that the differences in their pre-synaptic partners could enable us to identify these clusters. To explore this possibility, we performed retrograde trans-synaptic labelling of m-NSC^DH44 neurons with retro-Tango (Sorkac et al., 2023) using a highly-specific DH44-Gal4 for m-NSC^DH44 (Figure 2 Supplement 2A). The majority of the input to m-NSC^DH44 originates from neurons in the SEZ which are consistently labelled across samples (Figure 2 Supplement 3). Since no specific Gal4 drivers for m-NSC^DMS are currently available, we could not clearly map their presynaptic partners using retro-Tango. We next compared the retro-Tango output with the in silico retrograde tracing of 6 neurons belonging to each of the two m-NSC clusters (Figure 2 Supplement 2B-C). Surprisingly, both sets of m-NSC receive a majority of their inputs from neurons in the SEZ which have similar location and morphology. However, one set of m-NSC receives inputs from a group of central neurons (Figure 2 Supplement 2C) that are not visible with DH44 > retro-Tango. We therefore refer to this cluster as m-NSC^DMS (Figure 2 Supplement 2C) and the other cluster as m-NSC^DH44 (Figure 2 Supplement 2B). To obtain additional support for our classification, we next focused on their ultrastructural features, specifically their dense core vesicles (DCVs). A recent study has shown that vesicles for different neurotransmitters can exhibit slight but significant differences that are visible in electron micrographs (Eckstein et al., 2024). We thus asked whether visual or morphological differences in DCVs are also observed for cells expressing DH44 and DMS. We addressed this by first identifying a pair of descending neurons (DNp32 cell type) in the connectome (Figure 2 Supplement 2D) which have previously been shown to express DMS (Carlsson et al., 2010). DCVs within these neurons are clearly visible in the soma (Figure 2 Supplement 2G). These DCVs appear to be heterogenous as they are of different sizes and contrast, suggesting presence of another neuromodulator in addition to DMS. Nonetheless, the higher contrast of some DCVs in the DMS descending neuron is more similar to that of DCVs in m-NSC^DMS (Figure 2 Supplement 2F) compared to m-NSC^DH44 (Figure 2 Supplement 2E). This agrees with our hypothesis that DCVs containing the same neuromodulator appear similar visually across different cell types. To quantify these differences, we calculated the mean gray value based on at least 50 DCVs per cell for m-NSC^DMS, m-NSC^DH44 and DMS-expressing DNp32 in FlyWire, BANC and maleCNS connectomes (Figure 2 Supplement 2H-J). For consistency, we examined the cross section of each cell where the diameter of the nucleus was largest. Our analysis shows that mean gray values of putative m-NSC^DMS and DMS descending neurons in FlyWire and maleCNS connectomes are not significantly different, whereas the mean gray values of m-NSC^DH44 are significantly higher. Taken together, our synaptic connectivity and DCV analyses lend strong support to our classification of m-NSC^DH44 and m-NSC^DMS.

Lastly, we could only reliably identify 6 out of the expected 14 l-NSC^CRZ (Figure 1C and Table 1) (Kapan et al., 2012). Our inability to identify the remaining 8 CRZ neurons prompted us to examine if these adult-specific CRZ neurons are indeed neurosecretory. Previous work has shown that some of the CRZ neurons express Gr64a and Gr43a gustatory receptors (Miyamoto and Amrein, 2014; Fujii et al., 2015). Using Gr64a-Gal4 to label the adult-specific CRZ neurons (Fujii et al., 2015), we showed that there are only 6 adult l-NSC^CRZ which project via the NCC (Figure 2E). These neurons correspond to the ones present in larvae (Huckesfeld et al., 2021). The 8 adult-specific CRZ neurons, labelled by Gr64a-Gal4, do not project via the NCC and are thus not neurosecretory. Hence, our clustering analysis accounts for all the NSC that persist into adulthood. Further, we uncovered 8-10 additional putative m-NSC and 9-14 additional putative l-NSC across the three adult brain connectomes (Table 1, Figure 1C-E and Figure 1 Supplement 1). These neurons, especially l-NSC^unknown, have relatively fewer DCVs than neurons such as l-NSC^ITP (data not shown). Hence, the type (neuropeptide, biogenic amine, or fast-acting neurotransmitter) and the identity of the signaling molecules within these neurons remain unknown. Taken together, some NSC classes have expanded in number in adults, along with an additional population of l-NSC and m-NSC (Table 1).

NSC classes comprise heterogenous cell types

Having classified NSC into 10 classes, we next compared their morphological characteristics including their cable length, surface area, cell size, and nuclei volume (Figure 2 Supplement 4). SEZ-NSC^CAPA are about twice as large compared to other NSC types (Figure 2 Supplement 4C); however, the function of pyrokinin neuropeptide produced by these cells is still unknown (Wegener et al., 2006). Additionally, we performed principal component analysis of the four morphological features, which revealed that the NSC of a given class generally cluster together (Figure 2 Supplement 4E). However, we also observed high variability within l-NSC^CRZ, l-NSC^DH31, m-NSC^DH44, and m-NSC^DILP populations, indicating that they are comprised of morphologically heterogenous subpopulations. Clustering based on synaptic connectivity also supports this heterogeneity as we observed multiple subclades for these NSC classes (Figure 1C). For instance, the 6 l-NSC^CRZ cluster into two separate subclades as they represent a heterogeneous population both anatomically and functionally (Oh et al., 2019; Zandawala et al., 2021). Thus, some of the 10 NSC classes identified here are heterogenous.

Descending neurons provide input to NSC

NSC represent a conduit through which information processed by the nervous system is relayed to peripheral tissues via different peptide hormones. As such, several neural pathways are expected to converge onto NSC. To comprehensively elucidate the inputs to NSC, we first mapped the location of input synapses for each NSC type (Figure 3A and Figure 3 Supplement 1). Dendritic regions for the majority of NSC are found in the protocerebrum and SEZ. Interestingly, none of the 8 l-NSC^ITP had more than five synapses in the FlyWire connectome, which was the threshold used for identifying significant connections. Accordingly, their input synapses may be located outside the brain and/or the major inputs to these neurons are likely to be paracrine or hormonal in nature. We next examined the major neuronal classes providing inputs to NSC. Surprisingly, only 20 sensory neurons, all of which project to the SEZ, provide inputs despite most NSC having dendrites in that region (Figure 3B). This sensory input is directed to SEZ-NSC^CAPA, l-NSC^CRZ, and m-NSC^DMS (Figure 3C and Figure 3 Supplement 2A). Instead, a majority of the inputs to NSC arise from neurons in the central brain and ascending neurons from the ventral nerve cord (Figure 3D and Figure 3 Supplement 2A). l-NSC^DH31, m-NSC^DH44, and m-NSC^unknown almost exclusively receive inputs from central neurons (Figure 3 Supplement 2A). Interestingly, several classes of NSC receive inputs from descending neurons which are generally associated with the sensory-motor pathways that control locomotion and other behaviors (Rossignol et al., 2006; Namiki et al., 2018). This suggests that descending neurons regulate both hormonal and motor output to synchronize physiological changes with appropriate behaviors, which could explain the inhibition of m-NSC^DILP during locomotion (Liessem et al., 2023). Lastly, l-NSC^DH31 (not shown) receive small but significant direct input from ITP-expressing visual projection neurons that are part of the circadian clock network (Kurogi et al., 2023; Reinhard et al., 2024). Overall, m-NSC^DH44 and m-NSC^DILP receive the largest number of inputs, and l-NSC^ITP and SEZ-NSC^Hugin receive the least synaptic inputs (Figure 3 Supplement 2B). The types of inputs to NSC, as well as their relative proportions, are consistent across the three datasets (Figure 3 Supplement 3), suggesting that the broad trends observed here are not specific to an individual or sex.

Figure 3.

Next, we examined the neurotransmitters expressed in the neurons presynaptic to NSC (Figure 3 Supplement 4). For this, we used electron microscopy-based neurotransmitter predictions determined previously (Eckstein et al., 2024). We only focused on fast-acting neurotransmitters (i.e. acetylcholine, glutamate, and GABA) since those predictions were generally more reliable compared to other neurotransmitters such as serotonin (Eckstein et al., 2024). Both l-NSC^DH31 and m-NSC^DH44 receive strong glutamatergic inputs (Figure 3 Supplement 4A). Out of the three fast-acting neurotransmitters, GABA provides the least inputs (Figure 3 Supplement 4B) consistent with the proportional usage of the three neurotransmitters across the brain (Eckstein et al., 2024).

Since NSC receive inputs from several different cell types whose functions are yet unknown, we focused on cells that provide strong inputs to NSC. First, we examined the number of strong input connections (ζ 50 synapses) to each NSC class (Figure 3E). l-NSC^DH31 receive the largest number of inputs via 16 strong connections, and these are comprised of over 1500 synapses in total (Figure 3E). These inputs originate from neurons in the SEZ and the lateral horn (Figure 3F). SEZ-NSC^CAPA, m-NSC^DH44, and m-NSC^DILP also receive substantial inputs via strong connections (Figure 3E-F). In the case of SEZ-NSC^CAPA, the strong presynaptic connections include sensory neurons and GABAergic olfactory projection neurons (Figure 3 Supplement 2A and Figure 3 Supplement 4A). For both m-NSC^DH44 and m-NSC^DILP, the strong inputs are exclusively from the SEZ. In summary, these results suggest that sensory pathways could have a major effect on NSC activity and will be explored in detail later.

Synaptic architecture supports orchestration of physiology by multiple hormones

Several studies have previously characterized neuroendocrine pathways which regulate different aspects of Drosophila physiology including metabolism, reproduction, and osmotic homeostasis (Lee et al., 2015; Kubrak et al., 2016; Oh et al., 2019; Hadjieconomou et al., 2020; Koyama et al., 2021; Zandawala et al., 2021; Kurogi et al., 2023; Lee et al., 2023; Gera et al., 2025). Based on these and other studies, it is becoming increasingly evident that multiple hormonal systems interact to orchestrate specific physiological processes rather than individual hormones operating in isolation. For instance, DH31, DH44, CAPA, tachykinin, and ITP, which can all be released from brain NSC, influence osmotic homeostasis via direct actions on kidney-like Malpighian tubules (Kahsai et al., 2010; Halberg et al., 2015; Zandawala et al., 2018a; Agard et al., 2024; Gera et al., 2025). While these hormones could be released individually under specific contexts, we anticipate some of them to be co-released to elicit an additive or synergistic response (Paluzzi et al., 2012; Zandawala et al., 2018a). As such, NSC producing these hormones could be regulated by common pre-synaptic partners. In total, we identified 76 neurons, belonging to 42 unique cell types, which provide inputs to more than one type of NSC, with m-NSC^DH44 receiving inputs from most of these neurons (Figure 3G and Figure 3 Supplement 5). This is in line with the role of DH44 in multiple processes including feeding, reproduction, metabolism, and osmotic homeostasis (Dus et al., 2015; Lee et al., 2015; Lee et al., 2023). Out of the 76 neurons, 53 neurons provide inputs to two types of NSC (Figure 3H) and 22 neurons provide inputs to three types of NSC (Figure 3I). In addition, one neuron (CB3500 cell type) influences four types of NSC (Figure 3J). Three other neurons of the CB3500 cell type are pre-synaptic to 2-3 NSC types. Therefore, CB3500 and other neurons that provide inputs to multiple NSC could potentially integrate information from various pathways to orchestrate the release of hormones in different combinations. Taken together, this analysis provides the basis to investigate the neural control of hormonal networks regulating various physiological processes.

NSC receive direct and indirect synaptic inputs from various sensory modalities

Sensory to endocrine pathways enable animals to maintain homeostasis by adjusting physiological processes in response to changing external environments. Since only 20 sensory neurons lie directly upstream of NSC (Figure 3B), we delineated both monosynaptic and disynaptic sensory-endocrine pathways in further detail. For simplicity, we refer to interneurons mediating these connections as sensory interneurons. By extension, neurons that provide inputs to NSC but don’t receive direct sensory input are called non-sensory interneurons (Figure 4A). Focusing first on the direct sensory input to NSC, only 2 gustatory receptor neurons (GRN) and 2 mechanosensory neurons are presynaptic to m-NSC^DH44, SEZ-NSC^CAPA, and l-NSC^CRZ respectively (Figure 4A-B). The remaining 16 sensory neurons that provide input to m-NSC^DMS are enteric neurons (Figure 4A-B) (Giakoumas et al., 2025). These enteric neurons can be further classified into at least five subtypes based on morphology and synaptic connectivity (Figure 4 Supplement 1). Enteric neurons project via the pharyngeal nerve to the SEZ and some of these neurons have recently been implicated in regulating sucrose, sodium or general food intake (Cui et al., 2024; Gao et al., 2024; Kim et al., 2024a). Thus, nutrient and mineral content of an ingested meal could influence the activity of m-NSC^DMS.

Figure 4.

Next, we examined indirect sensory inputs to each NSC type. Almost all types of NSC receive disynaptic inputs from GRN, enteric and mechanosensory neurons, with m-NSC^DMS and m-NSC^DH44 receiving inputs from the largest number of neurons (Figure 4A and 4C). Although l-NSC^unknown is the second largest cluster after m-NSC^DILP, they hardly receive any input from sensory neurons (Figure 4A). Looking further at the categories of sensory neurons, NSC receive inputs from all the major sub-classes of GRN as well as mechanosensory neurons in the head bristle (Figure 4D). The latter neurons likely regulate grooming behavior (Zhang et al., 2020); however, the link between grooming and endocrine signaling remains to be explored. Further, only 4 labellar sugar/water taste neurons are disynaptically connected to NSC. We expected sweet taste to be a major regulator of insulin, DH44, and CRZ signaling since these hormones have known roles in feeding and glucose homeostasis (Dus et al., 2015; Kubrak et al., 2016; Nässel and Zandawala, 2019). This prompted us to explore pathways from external GRN in other structures such as the legs (Figure 4E) (Freeman and Dahanukar, 2015). We identified 4 tarsal GRN in the connectome based on anatomical similarity (Thoma et al., 2016). Although tarsal GRN are not directly connected to NSC, they do provide indirect inputs to l-NSC^CRZ, m-NSC^DH44, and m-NSC^DMS (Figure 4E). Hence, tarsal and labellar GRN could regulate NSC activity to some extent. Intriguingly, the majority of the taste-related input to NSC stems from enteric neurons (i.e., neurons in the hypocerebral ganglion which are associated with the anterior gut (Min et al., 2021)) as well as neurons projecting via the accessory pharyngeal nerve (Figure 4D). This suggests that internal taste neurons in the pharynx and those associated with the gut, which are activated upon feeding rather than by external taste, are more important for neurosecretion. Interestingly, gustatory, mechanosensory, and enteric neurons are the only sensory neurons disynaptically upstream of NSC. Therefore, other sensory modalities such as vision and olfaction require additional layers of connectivity.

We next focused on olfactory pathways to NSC because pheromones and odors can have a profound impact on hormonal activity and resultant physiology (Lushchak et al., 2015; He et al., 2022; Zhang et al., 2022). For instance, acute exposure to food odors alone can trigger an anticipatory endocrine response (Lushchak et al., 2015). To obtain novel insights into olfactory modulation of NSC activity, we explored the shortest path from olfactory receptor neurons (ORN) to NSC. The canonical olfactory pathway in Drosophila and other insects begins with the ORN in the antennae and maxillary palps (Figure 5A). ORN of a given type all project to a single glomerulus in the antennal lobe (Vosshall et al., 2000). From here, uni-glomerular and multi-glomerular projection neurons (PN) transmit olfactory information to higher-order brain centers such as the mushroom body for learning and memory and the lateral horn which controls innate behaviors (Schlegel et al., 2021). Since NSC are not located within these brain regions, additional interneurons likely transmit the information from PN to NSC. In addition, local interneurons (LN) in the antennal lobes innervate multiple glomeruli and modulate olfactory pathways. Our analysis identified a total of 321 ORN which provide inputs to 8 NSC via this canonical pathway (Figure 5B). Moreover, we grouped the different ORN based on their behavioral significance (Zheng et al., 2022). ORN such as V, DL4 and DL5 that detect aversive odors comprise the largest group which provides input to NSC (Figure 5B-E). Food odor-related ORN such as DP1l, VA6, DL2d, VL2a, and VM7d make up the second largest group (Figure 5B-E). Olfactory information from the antennal lobe is relayed via 10 uni-glomerular PN and 3 multi-glomerular PN (Figure 5F and 5G). Interestingly, while all SEZ-NSC^CAPA are indirectly downstream of ORN, only 2 out of the 18 m-NSC^DILP and 2 out of the 6 l-NSC^DH31 receive olfactory inputs (Figure 5H), further emphasizing the heterogeneity within these clusters. Intriguingly, 11 pheromonal and 2 egg-laying related ORN provide input to m-NSC^DILP via this 3-hop pathway (Figure 5I). This pathway is distinct from a male-specific pathway identified earlier whereby pheromonal inputs from the leg ppk23 neurons activate m-NSC^DILP to inhibit courtship drive (Zhang et al., 2022). In addition, the strongest olfactory inputs are directed to l-NSC^DH31 (Figure 5J). These inputs stem from all types of ORN, with aversive and food-related ORN providing the majority of inputs. Since DP1l and M_lvPNm35 express the excitatory neurotransmitter acetylcholine, food odors likely promote the release of DH31 and ITP from l-NSC^DH31 (Gera et al., 2025). Since both hormones, albeit from different sources, have previously been implicated in feeding (Lin et al., 2022; Gera et al., 2025), l-NSC^DH31 could also have a complementary role in feeding-related behaviors and physiological processes. Lastly, SEZ-NSC^CAPA also receive strong inputs from aversive ORN via cholinergic PN and GABAergic LHPV10c1 interneurons (Figure 5K). Although our analysis was based on the shortest 3-hop pathway, if we consider an additional layer of neurons and account for 4 hops, more than 91% of all ORN provide input to m-NSC^DILP, l-NSC^DH31 and SEZ-NSC^CAPA (not shown). At this level of connectivity, where most ORN are connected to NSC, it is difficult to decipher specific pathways. This is not surprising since the average shortest path length between any two neurons in the entire connectome is about 4 hops (Lin et al., 2024). In summary, olfactory inputs to NSC appear to be relatively sparse, and pheromonal and aversive odors could modulate hormonal signaling.

Figure 5.

Enteric neurons have a stronger influence on NSC compared to other sensory neurons

While a circuit diagram depicts which neurons are connected and how strongly (i.e. the number of synapses), they cannot assess the impact of one set of neurons on another set. This is especially true for neuron sets that are connected indirectly via multiple synaptic pathways. Moreover, specific synaptic inputs to a given neuron need to be weighted in relation to the receiving neuron’s total synaptic input to obtain a more accurate representation of the impact on the receiving neuron. Thus, to obtain further insight into sensory pathways regulating NSC activity, we used a recently established linear dynamical modeling-based approach (Bates et al., 2026) to quantify the influence of various sensory source neurons on different NSC cell classes (Figure 6A). First, we stimulated different types of sensory neurons including gustatory, olfactory, mechanosensory, enteric, hygrosensory and thermosensory neurons in the FlyWire dataset. Consistent with our olfactory pathway analysis (Figure 5J-K), SEZ-NSC^CAPA and l-NSC^DH31 are influenced by various types of olfactory inputs, and more strongly than other types of NSC (Figure 6B). However, olfaction has a much weaker influence on NSC in relation to peripheral gustatory neurons and enteric neurons. Enteric neurons, specifically ENS4, have the strongest influence on m-NSC^DMS, consistent with their direct connectivity (Figure 4A). Hygro-and thermo-sensation predominantly influence SEZ-NSC^CAPA, consistent with the role of CAPA in modulating desiccation and cold tolerance (Terhzaz et al., 2015). Since influence scores are relative rather than absolute values, we next assessed the influence of sensory inputs on NSC in relation to other cell types (Figure 6C-D). In relation to other cell types in the olfactory pathway (e.g. antennal lobe projection neurons and Kenyon cells) (Figure 5A), the influence of olfaction on NSC is quite low (Figure 6C). However, enteric neurons, especially ENS4, do have a strong influence on m-NSC, and this is ranked top in relation to their influence on other cell types (Figure 6D). We also observed similar trends when examining sensory to NSC pathways in the BANC connectome (Figure 6 Supplement 1). Taken together, this analysis provides a framework to generate testable hypotheses regarding novel functional connections between sensory neurons and NSC.

Figure 6.

NSC have sparse synaptic output

While NSC predominantly release hormones into the circulation following their activation, some NSC types can also signal to other neurons within the brain (King et al., 2017). With this in mind, we examined synaptic output from NSC. Output synapses have been predicted for most NSC in the FlyWire connectome, with l-NSC^CRZ and l-NSC^unknown having the largest number (Figure 7A and Figure 7 Supplement 1). Similar to the location of their dendrites, these output synapses are situated in the protocerebrum and SEZ. Although all types of NSC form output synapses, most of these comprise a connection which does not meet our threshold of 5 synapses. Hence, l-NSC^CRZ and l-NSC^unknown are the only NSC types which provide significant output to other neurons within the brain volume (Figure 7B). The output from l-NSC^unknown is primarily directed to cholinergic and glutamatergic central neurons (Figure 7B-D and Figure 7G) whereas two pairs of l-NSC^CRZ provide strong output to DNg27 descending neurons (Figure 7B-C and Figure 7E-F). DNg27 neurons, whose function is yet unknown, contain DCVs and innervate the SEZ, intermediate tectulum, lower tectulum and wing tectulum (Court et al., 2020). This suggests that they could regulate various functions including feeding and flight (Figure 7E-F) through synaptic and paracrine signaling. We also explored the output from NSC after lowering the threshold of significant connections to 2 synapses (Figure 7 Supplement 2). Although the output from NSC increases substantially at this threshold, most of this output is directed to undefined cells which include partial fragments as well as non-neuronal cells (Figure 7 Supplement 2A-C). Nonetheless, additional output to central, endocrine, and descending neurons is also observed, some of which could be biologically significant even though only a few synapses mediate these connections. To determine if any of these connections are biologically relevant, we also explored synaptic output from NSC in BANC (Figure 7 Supplement 3A-B) and maleCNS (Figure 7 Supplement 3C-D) datasets. In BANC, the only connection observed is that between l-NSC^CRZ and DNg27 (Figure 7 Supplement 3A-B). The l-NSC^CRZ to DNg27 connection is also present in maleCNS, but additional connectivity between NSC and central brain neurons, as well as NSC interconnectivity, is observed (Figure 7 Supplement 3C-D). It remains to be seen if this additional connectivity observed in the maleCNS connectome compared to the FlyWire and BANC connectomes (both from a female fly) is due to sexual differences and/or differences in synapse prediction accuracy across datasets. In conclusion, sparse synaptic output from NSC agrees with the expectation that they mainly signal in a paracrine and endocrine manner. Since l-NSC^CRZ to DNg27 is the only connection common across the datasets, we validated the functions of these neurons.

Figure 7.

l-NSC^CRZ and DNg27 regulate egg-laying

Previous work has shown that l-NSC^CRZ co-express CRZ and short neuropeptide F (sNPF) neuropeptides (Kapan et al., 2012), and these neurons regulate feeding, metabolism and osmotic homeostasis (Kubrak et al., 2016; Oh et al., 2019; Zandawala et al., 2021). However, these effects are primarily mediated via modulation of other hormonal pathways or peripheral tissues. Our connectome analysis here reveals that l-NSC^CRZ also exhibit synaptic output in addition to their known roles via volume transmission. Since the synaptic output from l-NSC^CRZ is targeted to DNg27 descending neurons with unknown function, we tested the function of both cell types in various behaviors and physiology. We examined behaviors that are primarily regulated by the brain (i.e. feeding) as well as those that are governed by the VNC (i.e. flight and egg-laying). Genetically silencing all CRZ neurons including l-NSC^CRZ with Kir2.1, an inwardly rectifying potassium channel, resulted in reduced food intake (Figure 8A) and increased starvation survival (Figure 8B). Hence, our genetic manipulation of l-NSC^CRZ can recapitulate the phenotypes observed following reduced CRZ signaling (Kubrak et al., 2016). In contrast, silencing DNg27 neurons had no effect on food intake (Figure 8D) or starvation survival (Figure 8E). Thus, the effect of l-NSC^CRZ on feeding and metabolic physiology appears to be independent of DNg27.

Figure 8.

Research in Drosophila and other insects has also implicated CRZ or CRZ-expression neurons in various reproductive behaviors (Bergland et al., 2012; Xi et al., 2025; Zhang et al., 2026). In addition, male-specific CRZ neurons in the ventral nerve cord affect fertility by regulating sperm transfer to females during mating (Tayler et al., 2012). Therefore, we asked whether female CRZ neurons also regulate fertility, and if this is dependent on DNg27. To address this, we mated experimental or control females flies with wild-type males and monitored the number of eggs laid over two weeks. Females with genetically-silenced CRZ neurons lay less eggscompared to their genetic controls starting from day 4 (Figure 8C). Similarly, females with silenced DNg27 neurons also lay less eggs; however, this effect is only visible from day 12 onwards (Figure 8F). This suggests that CRZ from l-NSC^CRZ regulates egg-laying, and this effect may partially be mediated via DNg27 neurons.

Finally, our connectome analysis suggests that DNg27 neurons could regulate flight, since they innervate wing power motor neurons. We utilized an optogenetic strategy established previously (Stupski and van Breugel, 2024) to test the role of both CRZ and DNg27 neurons in a free-flight wind tunnel (Figure 8 Supplement 1A). During free-flight, activation of neither CRZ nor DNg27 neurons in female flies demonstrated significant alterations in flight trajectory parameters, including course direction, horizontal velocity, and flight altitude during or after optogenetic illumination, compared with no-LED flash sham controls (Figure 8 Supplement 1D-F). Trajectory parameters were overall similar to those of the corresponding empty-Gal4 and empty split-Gal4 genetic controls. CRZ-Gal4 females did demonstrate a slightly increased startle response during red LED stimulation, with a small increase in trajectories oriented towards π rad/180° and a larger peak in horizontal velocity 100-200 milliseconds after illumination onset (Figure 8 Supplement 1D-E, top rows). CRZ neurons express other neuropeptides and acetylcholine (Oh et al., 2019) and CRZ-GAL4 also drives strong expression in the optic lobe neurons (Figure 8 Supplement 2). Thus, release of multiple neuronal signaling molecules following stimulation with CRZ-GAL4 may increase a visual startle effect often observed during optogenetic activation in the flight chamber.

We independently confirmed that activation of CRZ neurons in the abdominal ganglion of males can recapitulate copulation-like behaviors (Figure 8 Supplement 1B-C) reported previously (Inagaki et al., 2014). Optogenetic activation of CRZ neurons in males elicited significantly more copulatory-related behaviors (genital eversion with or without abdominal curling and targeted genital grooming) than genetic controls (Figure 8 Supplement 1C). Hence, the lack of an observable phenotype following CRZ neuron activation during free flight is likely not due to insufficient activation. However, we cannot rule this possibility out for DNg27 since no functions are currently known for these neurons, and the driver targeting DNg27 drove CsChrimson-mVenus expression very weakly (Figure 8 Supplement 2).

Together, our functional characterization of CRZ and DNg27 neurons identifies a novel pathway via which l-NSC^CRZ could regulate egg-laying in females. Since l-NSC^CRZ to DNg27 connections also exist in males, these neurons could regulate other behaviors besides egg-laying.

Peptide hormone receptors are broadly expressed in NSC and peripheral tissues

The availability of large-scale single-cell transcriptome datasets now enables us to identify and explore transcriptomes of rare cell types such as NSC. We have recently used this strategy to determine the modulatory inputs to m-NSC^DILP (Held et al., 2025), l-NSC^ITP (Gera et al., 2025), and some other NSC types (Reinhard et al., 2024). Here, we expand this approach to first identify single-cell transcriptomes of all NSC classes based on previously established markers (Figure 9A). Consistent with our anatomical mapping (Figure 2D), Capa and Ms are co-expressed in SEZ-NSC^CAPA. Given the proximity of all NSC axon terminations, it is extremely likely that a hormone released from a given NSC will influence the activity of other NSC types if its receptor is expressed in those cells. In fact, we have previously shown CRZ to inhibit CAPA release from a different set of neurosecretory cells much further away in the ventral nerve cord (Zandawala et al., 2021). Therefore, we examined the expression of hormone receptors in all the NSC transcriptomes to determine the molecular substrates for paracrine interaction between different NSC types (Figure 9B). Consistent with previous studies, CrzR is expressed in SEZ-NSC^CAPA (Zandawala et al., 2021), sNPF-R and Lkr in m-NSC^DILP (Kapan et al., 2012; Zandawala et al., 2018c) and Dh44-R2 in SEZ-NSC^Hugin (Mizuno et al., 2021). While receptors for CAPA and ITP were not detected in any transcriptomes, receptors for other hormones were expressed in varying amounts. l-NSC^DH31 express receptors for several hormones and thus appear to be heavily modulated (Figure 9B). Further, InR (insulin receptor) is expressed in all NSC classes consistent with the role of insulin in promoting cellular glucose uptake (O’Neill, 2013). Having mapped the expression of peptide hormones and their receptors in different types of NSC, we sought to explore the strength of putative paracrine connections based on their expression levels. Thus, higher expression of both the hormone and receptor implies a stronger connection. Using this approach, we show the extent of putative paracrine connectivity between different hormonal systems (Figure 9C and Figure 9 Supplement 1). We used a conservative approach (see methods for details) to reduce false prediction by setting a stringent expression threshold for the hormone levels (Figure 9 Supplement 1I). Our analysis reveals that paracrine signaling can greatly enhance the interactions between different hormonal pathways.

Figure 9.

Since all NSC have their release sites on or near adipokinetic hormone (AKH) producing cells of the CC, various hormones can influence the release of AKH (Oh et al., 2019; Koyama et al., 2021). Glucagon-like AKH, along with DILPs, is a major regulator of metabolic homeostasis and associated behaviors (Isabel et al., 2005; Yu et al., 2016; Nässel and Zandawala, 2019). Consequently, modulation of AKH release is one way of regulating metabolic physiology. We thus examined single-cell transcriptomes of AKH cells for expression of hormone receptors (Figure 9D). While sNPF-R expression in AKH cells was demonstrated previously (Oh et al., 2019), we additionally show the presence of receptors for DMS, DH44, DH31 and tachykinin. Thus, these neuropeptides could regulate metabolic homeostasis via AKH-signaling in addition to their known roles in feeding-related processes (Song et al., 2014; Dus et al., 2015; Hadjieconomou et al., 2020).

Previously, Nässel and Zandawala (2019) catalogued the expression of neuropeptide receptors across all Drosophila tissues. However, that analysis was based on a microarray-based dataset (Chintapalli et al., 2007) and therefore lacks the sensitivity and resolution offered by current sequencing technologies. We sought to fill this gap by cataloguing the expression of peptide hormone receptors using Fly Cell Atlas, a single-cell transcriptomic resource of all cells of the adult fly. Expression of hormone receptors in salivary glands, nervous system, Malpighian tubules, heart, gut and fat body (Figure 9E and Figure 9 Supplement 2-4) is largely consistent with the previous analysis. For instance, expression of CapaR, Dh31-R and Dh44-R2 in Malpighian tubules and CrzR, Dh31-R, Dh44-R2 and TkR99D in the heart was reported previously (Nässel and Zandawala, 2019). Interestingly, we now additionally detect TkR86C and TkR99D expression in the Malpighian tubules and MsR2 expression in the heart. Examining expression of these receptors at cellular resolution reveals that TkR99D is highly expressed in stellate cells of the Malpighian tubules and MsR2 is strongly expressed in the alary muscles of the heart (Figure 9 Supplement 3). Insights from a similar analysis were recently used to characterize the effects of tachykinin on stellate cells (Agard et al., 2024). Our analysis here suggests that DMS can modulate heart contractility via activation of MsR2 on the alary muscles. Hence, this approach can uncover novel cellular targets of various hormones. Importantly, Fly Cell Atlas also includes tissues such as trachea, leg, wing, haltere, proboscis and maxillary palp where expression of peptide hormone receptors has not been explored comprehensively. We reveal expression of several receptors including CrzR, MsR1, MsR2, Dh44-R1 and Dh44-R2 in sensory neurons of the antenna and other tissues (Figure 9 Supplement 3). Thus, m-NSC^DMS, m-NSC^DH44, and l-NSC^CRZ not only receive direct sensory inputs (Figure 4A) but they could also modulate other types of sensory neurons, potentially forming sensory-endocrine feedback loops. Taken together, our analysis presents an important resource to functionally characterize novel hormonal targets.

Discussion

Here, we describe the first connectome of a neurosecretory network in the brain of an adult animal. This connectome is based on 72-80 NSC (depending on the connectome) which can be subclassified into 10 categories based on neuropeptide expression, morphological similarity and/or synaptic connectivity (Figure 10A). Moreover, our integration of connectomics with anatomical analyses provides a comprehensive view of the NSC landscape and their connectivity in the adult Drosophila brain. Our analyses reveal a functionally diverse, yet highly interconnected neuroendocrine system which provides the basis to perform comparisons with other neuroendocrine connectomes from larval Platynereis (Williams et al., 2017) and Drosophila (Huckesfeld et al., 2021) established previously.

Figure 10.

Comparison of neuroendocrine connectomes across development, sex and species

The composition of the adult NSC is different compared to larvae. We observed an expansion of m-NSC^DILP and m-NSC^DMS clusters along with the presence of two additional populations of putative NSC whose identity and function remain to be explored. The expansion in the number of identified NSC classes from larvae to adults underscores developmental changes that might reflect the life-history adaptation of these cells to new physiological demands or environmental challenges. For instance, adults are considerably larger than larvae and comprise more cells in general. Glucose uptake by these cells is modulated by DILPs via InR. Hence, additional m-NSC^DILP may be needed for increased DILP production and release to compensate for this size increase. The increase in NSC number is not merely a quantitative change but suggests functional diversification, as evident by the presence of previously unidentified m-NSC and l-NSC populations in adults. The connectome datasets examined here do not include the complete axonal projections of these neurons outside the brain volume. Hence, it is unclear whether these neurons innervate the CC and CA for regulating release of AKH and juvenile hormone, respectively, or if they release hormones into the circulation via neurohemal sites. However, given their smaller size compared to other NSC populations, they might influence the release of other hormones via local actions rather than hormonal regulation of peripheral tissues.

Similar to the Drosophila and Platynereis larval neuroendocrine connectomes, NSC in adult Drosophila have limited synaptic output in the brain (Williams et al., 2017; Huckesfeld et al., 2021). Eclosion hormone-expressing NSC are the only cells with synaptic output in larvae (Huckesfeld et al., 2021). However, these cells undergo apoptosis soon after eclosion and are thus not found in mature adults (Scott et al., 2020). In contrast, l-NSC^CRZ and l-NSC^unknown provide the majority of synaptic output in the FlyWire connectome. l-NSC^CRZ are especially of interest here because only 4 out of the 6 neurons in this cluster lie upstream of DNg27 descending neurons that primarily innervate the wing neuropil. The remaining two l-NSC^CRZ are internal glucose sensors which signal via sNPF to influence DILP and AKH release, which in turn regulate glucose homeostasis (Oh et al., 2019) (Figure 10C). Furthermore, DILPs suppress starvation-induced food search whereas AKH promotes this behavior (Yu et al., 2016). Therefore, it is plausible that one subset of l-NSC^CRZ affect starvation-dependent locomotor activity via AKH and DILPs, while the other subset modulate flight via DNg27 in response to starvation. However, activation of CRZ neurons or DNg27 did not elicit any obvious phenotypes during free-flight. The function of both neuron types during flight is still unknown. Interestingly, silencing either of these neuron types in females did cause a significant reduction in the number of eggs laid. Since DNg27 do not have direct synaptic output to any known oviposition-regulating neurons in the BANC dataset, they could modulate egg-laying indirectly or via volume transmission. Lastly, the l-NSC^CRZ to DNg27 pathway could also regulate other behaviors or physiology besides egg-laying since this circuit motif is also found in males.

In addition to l-NSC^CRZ, other NSC types including m-NSC^DILP, m-NSC^DH44 and l-NSC^DH31 also exhibit heterogeneity in their morphology, synaptic inputs, and gene expression. This heterogeneity may reflect an adaptive mechanism allowing for fine-tuned responses to environmental and physiological cues. In support of this, only a subset of m-NSC^DILP and m-NSC^DH44 express the mechanosensitive channel Piezo (Wang et al., 2020; Oh et al., 2021). Similar functional differences between l-NSC^DH31 subtypes remain to be explored. Regarding non-synaptic output, neuropeptides expressed in the Platynereis neuroendocrine center are largely distinct from those found in the Drosophila brain NSC, with insulin-like peptides, tachykinin and sulfakinin being the only hormones that are common across both species (Williams et al., 2017; Huckesfeld et al., 2021). The latter was previously shown to be expressed in a subset of m-NSC^DILP (Soderberg et al., 2012); however, we were unable to detect it in our single-cell transcriptomic analysis, likely due to low expression. Interestingly, cholecystokinin, the vertebrate ortholog of sulfakinin, is also expressed in the hypothalamus (Williams et al., 2017; Nässel and Wu, 2022). Thus, the expression of cholecystokinin/sulfakinin in neuroendocrine centers and their function in regulating satiety are conserved across evolution. Examination of NSC in other species can shed light on other conserved hormonal systems.

Elucidation of sensory inputs to NSC suggests novel roles for conserved peptide hormones

Our analysis of sensory input pathways revealed that only 20 sensory neurons, primarily enteric neurons, provide direct inputs to NSC in the FlyWire connectome. While this number is much smaller than the corresponding number in larvae (Huckesfeld et al., 2021), that study utilized a synaptic threshold of only one. Perhaps one synapse may be sufficient to modulate the activity of NSC on slower timescales. However, this number also includes some transient connections which may not persist due to context-dependent synaptic plasticity. Therefore, we used a higher threshold in line with other studies using the same dataset (Dorkenwald et al., 2024; Reinhard et al., 2024). To account for multiple indirect pathways between sensory neurons and NSC, we used a linear dynamical modelling approach to calculate the ‘influence’ of sensory neuron stimulation on NSC activity. Our analysis indicates that SEZ-NSC^CAPA, which express the mammalian neuromedin U homolog CAPA, receive and integrate inputs from multiple sensory modalities. For instance, hey receive thermosensory and hygrosensory inputs which could be important for CAPA neuropeptide to regulate desiccation and cold stress tolerance (Terhzaz et al., 2015). In addition, they also receive inputs from mechanosensory and olfactory pathways. This cross-sensory integration could allow an animal to comprehensively assess the environment such as during odor-guided navigation where both wind and odor inputs are important. These sensory modalities could also be important during feeding to assess food quality and texture, where gustatory and enteric inputs would also be relevant. Our olfactory circuit tracing analysis also revealed olfactory inputs to l-NSC^DH31. Interestingly, l-NSC^DH31 respond to both pheromonal and food-odor inputs. Integration of these odors can attract flies of both sexes to food sources that are suitable for mating (Kohl et al., 2015). Based on this circuit motif, we would expect these cells to play a role in feeding, mating and/or courtship. Consistent with this prediction, calcitonin-like DH31 from these neurons targets the CA to suppress juvenile hormone signaling (Kurogi et al., 2023), which in turn influences egg maturation, courtship and sex pheromone production (Bilen et al., 2013). l-NSC^DH31 also express ITP which is important for feeding and metabolic homeostasis (Gera et al., 2025). Moreover, l-NSC^DH31 could thus be part of a feedback loop where they receive pheromonal and food-related odor inputs to regulate pheromone production and food intake. Since olfactory inputs have a much weaker influence on all NSC compared to other sensory modalities, we predict that olfactory inputs alone are likely not sufficient to induce or inhibit hormone release (Figure 10B). In comparison to olfaction, internal enteric neurons and peripheral gustatory neurons are predicted to have a greater influence on NSC activity. This is not surprising given that multiple hormones produced by NSC regulate feeding-associated behaviors and metabolic physiology (Kahsai et al., 2010; Kapan et al., 2012; Dus et al., 2015; Yu et al., 2016; Oh et al., 2019; Gera et al., 2025; Qin et al., 2026). Importantly, contents of the ingested meal sensed by enteric neurons (Cui et al., 2024; Kim et al., 2024a) have a stronger influence on NSC compared to inputs from peripheral gustatory receptors. This circuit architecture could ensure that systemic physiological changes orchestrated by hormones are only activated once the food has been consumed. Together, our findings suggest that different NSC classes could integrate information from multiple sensory modalities to regulate crucial interdependent behaviors to regulate systemic homeostasis.

Interactions between hormonal pathways

Our analyses highlight extensive putative interactions between hormonal systems via both their synaptic inputs and hormonal output. Multiple NSC classes receive inputs from the same set of pre-synaptic neurons, indicating that different hormonal pathways do not operate in isolation but rather interact within a complex network. m-NSC^DH44 are of particular interest here since they receive the most extensive input from neurons which also provide inputs to other types of NSC. Consequently, circuits which influence other hormones could also affect DH44 release. In addition, m-NSC^DH44 can function as cell-autonomous glucose and amino acid sensors (Dus et al., 2015; Yang et al., 2018). m-NSC^DH44 in females can also integrate inputs regarding the quality of their male partner’s ejaculate by directly sensing a phospho-galactoside present in the male ejaculate (Kim et al., 2024b). Further, these cells are also intrinsically mechanosensitive and can monitor the feeding state of the animal based on crop distension (Oh et al., 2021). Thus, various inputs regulate the activity of m-NSC^DH44 and DH44 could act as a major co-coordinator of physiology and behavior. For instance, DH44 could be co-released with DMS and/or DILPs to orchestrate feeding and reproductive processes (Nässel and Zandawala, 2019; Hadjieconomou et al., 2020). In addition to the regulation of hormonal pathways by common synaptic inputs, the various hormones can also modulate each other’s release via paracrine signaling. Hence, sulfakinin and DILPs from m-NSC^DILP can interact with other signaling pathways as their receptors are expressed in most NSC types. Moreover, l-NSC^DH31 and AKH-producing cells are also heavily modulated by different hormones. Therefore, in agreement with their major roles in metabolic physiology, several endocrine pathways converge on m-NSC^DILP (Held et al., 2025) and AKH-producing cells.

Limitations of our approach

Our connectomics and transcriptomics-based approach to decipher synaptic and paracrine connectivity of the NSC has some general limitations. Firstly, despite providing several lines of anatomical evidence, our assignment of m-NSC^DMS and m-NSC^DH44 cell classes should be treated with caution since the connectome datasets examined here lack molecular markers to identify these cell classes unambiguously. Similarly, the novel synaptic and paracrine pathways identified here are only putative until experimentally verified using functional connectivity or behavioral analyses. Moreover, our analyses underestimate the connectivity for several reasons: 1) the algorithm used for synapse prediction was not 100% effective, 2) we used a fairly stringent threshold (ζ 5 synapses) for assessing connectivity, 3) some neurons have not yet been proofread and 4) NSC could form synapses with each other near their release sites and outside the brain volume examined here. Moreover, NSC can also couple electrically via gap junctions which are not accounted for here (Orchard and Shivers, 1986; Alvarado Alvarez et al., 1993). Further, the connectome depicts a static snapshot of connectivity which we anticipate changing with age as well as with the mating and feeding status of the animal. Hence, weaker connections should be treated with caution. However, since we observed similar NSC connectivity across three different datasets, we believe that the inter-individual variability is not drastic when using a threshold of 5 synapses for significant connections. Lastly, we only focus on the brain NSC that project via the NCC. Other brain NSC, namely serotonin-producing DNg28 (Yao and Scott, 2022) and CB2748 cell types, which project via other nerve bundles, are not addressed here.

Conclusion and future directions

Future research should focus on functional validation of the observed synaptic and paracrine connectivity patterns, particularly how specific sensory inputs to NSC are translated into physiological responses (Figure 10D). Moreover, a more detailed analysis of the neuroendocrine connectome in the male brain can provide insights into sexual dimorphism within the neuroendocrine pathways. Identification and functional characterization of neuronal messengers in novel NSC (l-NSC^unknown and m-NSC^unknown) and comprehensive identification of NSC in the ventral nerve cord can provide a more complete outlook on how the neuroendocrine system integrates different inputs to regulate systemic physiology. Taken together, our characterization of the adult Drosophila brain neuroendocrine network connectome provides a foundation to experimentally disrupt endocrine pathways and establish causal relationships with disorders such as diabetes, hypertension, and infertility. In addition, it provides a blueprint for understanding complex hormonal networks and how they orchestrate animal behaviors and physiology.

Materials and methods

Fly strains

Drosophila melanogaster strains used in this study are listed in Supplementary Table 1. Fly lines were mostly obtained from the Bloomington Drosophila Stock Center (BDSC). Flies were reared at 25°C under LD12:12 on a standard Drosophila medium containing 8.0% malt extract, 8.0% corn flour, 2.2% sugar beet molasses, 1.8% yeast, 1.0% soy flour, 0.8% agar and 0.3% hydroxybenzoic acid. We used 13-15 day old adults for retro-Tango analyses.

For CRZ and DNg27 neuron inactivation experiments with Kir2.1, flies were reared on a diet containing 5% w/v cornmeal, 2% w/v yeast, 0.5% w/v agar, 1.35% w/v dextrose, 3% v/v saccharose syrup, 0.75% v/v propionic acid, hydroxybenzoate and 1.125% ethanol.

For the optogenetic testing and validation of CRZ-Gal4 F1 genotype experiments, parental flies were reared in a humidity-controlled incubator (25°C; 60% relative humidity; LD16:8) in plastic Drosophila colony vials (Genesee Scientific Flystuff, #32-117), filled with standard molasses-containing Drosophila medium (Genesee Scientific Nutri-Fly MF, #66-44). Crosses between male (empty-Gal4, CRZ-Gal4, empty split-Gal4, DNg27-split-Gal4), and virgin female (20xUAS-CsChrimson::mVenus) flies were performed in vials containing the same Drosophila medium which also incorporated 0.35 mM all-trans retinal (Sigma; R2500). The 20xUAS-CsChrimson::mVenus flies were created by injecting the transgene into the attp2 site of wild-type Berlin flies, and have a X chromosome, including a wild-type white gene. Newly eclosed F1 adult flies were transferred to fresh vials containing molasses Drosophila medium supplemented by 200 µl of 0.8 mM all-trans retinal deposited on the food surface.

Immunohistochemistry and confocal imaging

Immunostainings were performed as described previously (Gera et al., 2025). Briefly, whole female flies were fixed in 4% paraformaldehyde in phosphate-buffered saline (PBS) with 0.5% Triton X-100 (PBS-T) for 2.5 h on a nutator at room temperature (RT). Fixed flies were washed four times with PBS-T before dissecting their brains. The samples were blocked in PBS-T containing 5% normal goat serum for 1 hour at RT and subsequently incubated in primary antibodies at 4°C for 48 h. Following four washes with PBS-T, the brains were incubated in secondary antibodies at 4°C for 48 h. Lastly, the samples were washed four times in PBS-T and mounted using either Fluoromount-G^TM (Invitrogen, Thermo Fisher) or Vectashield mounting medium (Vector Laboratories, Burlingame, CA, USA). Images were acquired using Leica SPE and SP8 confocal microscopes (Leica Microsystems) using 20x or 40x objectives. All images presented in this study are representative images based on at least 5 independent samples.

All primary and secondary antibodies are listed in Supplementary Table 2.

Connectome datasets and NSC identification

We used the v783 snapshot of the FlyWire whole brain connectome and its annotations (annotations last updated: 12 June 2024) for all the analyses (Dorkenwald et al., 2024; Schlegel et al., 2024). Enteric neurons (ENS1 to ENS5), previously classified as unknown sensory neurons, were recently annotated in the FlyWire connectome. We therefore updated the classification of the relevant unknown sensory neurons to ENS1-5. To identify NSC in the FlyWire connectome, we first identified NSC subsets that have a characteristic morphology and location, e.g. l-NSC^ITP. We then identified the nerve bundle (nervii corpora cardiaca, NCC) through which l-NSC^ITP axons exit the brain. This nerve bundle contains all the axons for NSC in the Drosophila brain. We manually assessed other axons in NCC to identify the remaining NSC in the connectome. Independently, we also examined the cross-section of the soma of all putative NSC for the presence of dense core vesicles, which suggests that they contain neuropeptides/neuromodulators.

We used the v888 snapshot of the BANC dataset and its annotations (downloaded from Harvard dataverse or bancr package: 28 May 2026). For maleCNS, we used the v0.9 snapshot of the dataset and its annotations (downloaded from malecns package: 2 April 2026).

Cell IDs of NSC identified in FlyWire, BANC and maleCNS are provided in Supplementary Table 3.

Neurotransmitter predictions

Neurotransmitter predictions for the FlyWire connectome are based on Eckstein et al. (2024). We only considered neurotransmitter prediction scores for fast-acting neurotransmitters (i.e., acetylcholine, glutamate, and GABA) that were greater than 62%.

DCV brightness quantification

The electron micrographs for all m-NSC^DH44 (6 neurons), m-NSC^DMS (6 neurons) and DNp32 descending neurons (2 neurons) from Flywire, BANC and maleCNS connectomes were obtained using neuroglancer (Dorkenwald et al., 2022). For consistency, we examined the cross section of each cell where the diameter of nuclei was the largest. We quantified the mean gray value of at least 50 DCVs per cell using the Fiji software. The individual who performed these analyses was blind to the neuron identity.

Data visualization

Data was visualized using ggplot2 (v 3.5.1, Wickham, 2016) and circlize (v 0.4.16) for R (v 4.4.1) in RStudio (2024.04.2+764) (Gu et al., 2014). FlyWire NSC reconstructions were downloaded using the navis library (v 1.0.4, https://github.com/navis-org) and cloud-volume library (v 8.10.0, https://github.com/seung-lab/cloud-volume) for Python (v 3.8.5), and visualized using blender (v 3.01, Community, B. O. 2018). BANC and maleCNS reconstructions for NSCs were created in R using natverse and rgl libraries. All other neuron reconstructions were visualized using FlyWire neuroglancer (Dorkenwald et al., 2022). Links to each visualization in figures are included on Zenodo.

Connectivity analyses

Natverse libraries (v 0.2.4) for R in RStudio (Bates et al., 2020) were used to analyze the connectivity data. NSC were clustered based on all their synaptic inputs with coconatfly (v 0.1.0.9000) for R (Schlegel et al., 2024). Enteric neurons in the FlyWire connectome (previously classified as “unknown sensory”) were clustered based on all their synaptic inputs and outputs. Filtered synapses for the FlyWire connectome were retrieved in Python using the navis and pandas (v 1.1.3) libraries. Connectivity data for BANC and maleCNS was retrieved using bancr (v 0.3.2) and malecns (v 0.4.1) packages, respectively. Unless stated otherwise, a threshold of 5 synapses was used to determine significant connections. Our analyses were based on a custom code generated previously (Reinhard et al., 2024).

Influence analysis

Influence analysis was conducted in Python (v 3.13.1) using the connectome influence calculator (v 0.4: https://github.com/DrugowitschLab/ConnectomeInfluenceCalculator).

For analysis using FlyWire, we downloaded the meta data tables (cell types, classification) and unfiltered connections based on Princeton synapse predictions (v783: updated 08 July 2025) from Codex (14 Nov 2025) and used these to run the analysis in Python. Output and results were then processed and visualized in R. We appended any updates from the cell types or classifications from Codex (v783: updated 24 Feb 2026). For analysis using BANC, we downloaded the meta and connectivity (simple_v2) data from the Harvard dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/7WTH1N), using v888 associated with the published manuscript. To match cell types across datasets, we filtered the cell types in BANC according to the sensory cell types we used in FlyWire for the influence analysis.

For both datasets, we followed the analysis pipeline used by (Bates et al., 2026). We used a lenient constant c of 24 for adjusted influence that corresponds to the minimum accepted influence of 3.78e-11 and normalized the influence scores by both the number of source and target neurons. We scaled the influence values to be between 0 and 1 for ease of interpretation.

Starvation survival

To monitor starvation survival, female flies in groups of 20 were placed in glass vials containing 2% agar and maintained at 25°C with 60% relative humidity under a 12:12 LD cycle. Dead flies were quantified visually at regular intervals of 4 hours during daytime until no flies remained alive. Survival curves were generated based on at least 120 flies per genotypes.

Feeding assay

Capillary feeding (CAFE) assay was performed to measure total food intake as well as to monitor the preference between nutritive and non-nutritive sugars. For total food intake, ad libitum-fed female flies were anesthetized with CO₂ and individually transferred into 2 ml tubes. A 5 µl capillary tube, filled with 10% sucrose, 10% yeast and 0.1% propionic acid was inserted through a hole into the lid of the 2 ml tubes. Similar tubes without flies were used as controls for evaporation. All the prepared tubes were then kept inside a moist chamber within a standard fly incubator at 25°C on a 12:12 LD cycle. Flies were allowed to feed for 24 hours and the drop in the volume of the food remaining within the capillary was recorded over time. This was then used to calculate the amount of food consumed.

Egg-laying assay

Virgin females of different genotypes were allowed to mate with wild-type males for 2 days to eliminate any potential effects of genetic manipulation on male fertility. Following mating, females were isolated and transferred to fresh food vials (20 females per vial). Females were flipped into fresh food vials every 24 hours, and eggs were quantified daily for 14 consecutive days. Egg-laying curves were generated based on at least 120 flies per genotype.

Optogenetic activation in free-flight

The behavioral paradigm was mostly as described previously (Stupski and van Breugel, 2024) with some modifications. Flies were tested in free-flight within a wind-tunnel (1.0 x 0.5 x 0.5 m³), housed in a room maintained at 22° C and 60% relative humidity. Ambient light was from blue (STN-BBLU-B6A-08C1M-24V, Super Bright LEDs Inc. St. Louis, MO, USA), white, and infrared (PN: 7031, Waveform Lighting, Vancouver, WA, USA) LEDs. A constant-speed laminar wind was generated continuously with a measured wind speed of 0.4 m/s, calibrated using a hot-wire anemometer (Alnor Velometer AVM410; TSI Inc.). When flies entered a defined trigger volume, one of two events was triggered at random: either a flash event, during which red LEDs (Triple Output High Power RGB LEDs, COM-15200, https://www.sparkfun.com/; λ = 620 nm) were activated for 2000 milliseconds, or a control sham event without any additional LED activation. Regardless of event type, the trajectories of each fly were recorded. The trigger volume dimensions differed somewhat from (Stupski and van Breugel, 2024), having larger dimensions along the tunnel length, width, and height, resulting in a greater number of recorded trajectories. Comparing sham controls with responses to flashes was used to determine the effects of the activation of CRZ and DNg27 neurons on free-flight characteristics.

The total illumination of the tunnel chamber was measured along the midline (y-axis) at multiple points across the length of the tunnel (x-axis) and at multiple altitude levels (z-axis). Illumination was measured using a light meter (Dr. meter LX 1330B) positioned horizontally to measure light coming from above the sensor, which includes all of the red LEDs. As lux values across the x-axis positions did not vary significantly, we averaged along the tunnel length, resulting in mean lux values for red, white, and blue LEDs at the 3 z-axis positions: 13, 23, and 33 centimeters above the tunnel floor. For each LED type, we converted the lux values to mean irradiance E_irr (μW/mm²) using the equation:

E_lux(y) is the mean lux values at z-position y, and 683 ∗ V(λ) represents the luminous efficacy, which is the efficiency of conversion of a light source’s power to visible light, consistent with human cone physiology (CVRL: http://www.cvrl.org/lumindex.htm). Adding the irradiance for individual LED types, the ambient irradiance (white and blue LEDs) ranged from 4.8 - 10.7 μW/mm² from the lowest to highest altitudes, respectively, whereas ambient light plus optogenetic stimulation from red LEDs varied from 12-52 μW/mm².

The morning of an experiment, female F1 flies (2-5 days old) were anesthetized at 4°C on a cold plate, and 20-26 females were placed in an empty tube with a moist Kimwipe to prevent desiccation. We then starved flies in their preparation tubes for 6-8 hours in darkness prior to any experimentation to encourage flight and food seeking. In the evening (16-19:00) we placed experimental flies into the wind tunnel behavioral system without any further anesthesia. Flies were permitted to fly about the tunnel volume overnight until the following morning at which point the data collection was terminated. Flies from each F1 genotype were tested on three independent, overnight flight sessions, and the data from each experiment pooled by genotype. We analyzed the data using a custom Python software pipeline, similar to (Stupski and van Breugel, 2024). Trajectories were filtered for length, minimum distance travelled along the x-axis, and for those which stayed entirely within the wind tunnel volume. We also eliminated any trajectories that re-triggered sham or flash events. From the filtered trajectories, we calculated histograms of ground-speed (x-y) and angular velocities.

Optogenetic activation of CRZ neurons in males to trigger copulation behaviors

We validated our optogenetic setup by phenocopying a behavior known to be controlled by CRZ neurons (Inagaki et al., 2014). Male flies (2-4 days old) of both genetic control and experimental genotypes were positioned on their backs and glued to glass microscope slides using UV-sensitive glue (Bondic) by the wings and pronotum. Each slide had equal numbers of both genotypes mounted. The slides were held almost vertically in a custom-made holder with the flies at a position of approximately 63 x 26 x 37 cm in the wind tunnel (as measured from bottom, rear, downwind corner of the flight space).

Similar to the free-flight experiments described above, light levels were measured by holding a light meter at both horizontal and vertical positions at the approximate location of the glued files, to capture light from the LEDs mounted above the wind tunnel (which include the red LEDs for optogenetic stimulation), as well as LEDs from both the top and bottom of the flight space illuminating the flies from the downwind tunnel portion. Under ambient, non-optogenetic stimulation (illumination with blue and white LEDs only), light intensity from above measured 666-668 lux from the top, and 303-305 lux from the side. Under bright optogenetic and ambient stimulation (blue, white, and red LED illumination), top-down luminance was 10,800-10,820 lux and 1823-1825 lux from the side. As the flies were 4-5 cm higher than the highest light measurement position for the free-flight experiments, we can place a minimum bound of 10.7 and 52 μW/mm² from top-down illumination for ambient and ambient plus optogenetic stimulation irradiance, respectively. From the side, we can place minimum mean irradiance at 4.8 and 11 μW/mm² for ambient alone, and ambient with optogenetic light stimulation, respectively.

Slides were recorded under constant ambient light from blue, white, and infrared LEDs described previously, and optogenetic stimulation was elicited by red LEDs using the following protocol: 30 s baseline with ambient LEDs only, followed by 15 s of red LED stimulation in addition to ambient, then ended by a second 30 s period of ambient-only LED exposure. For each slide, the protocol was repeated 5 times. During each stimulation trial, the flies were recorded using a USB digital camera (acA2040-120um, Basler AG, Ahrensburg, Germany) connected to a laptop, running the manufacturer’s video software (pylon Viewer). Flies were viewed at 200% magnification, using a manual exposure setting allowing good contrast and detail during high-intensity red LED illumination.

We developed an abdominal ethogram of observed behaviors, including those involved in copulation. The following behaviors were chosen for analysis: (1) abdominal curling (without eversion of male genitalia), (2) abdominal curling with genital eversion, (3) genital eversion without curling, (4) targeted grooming of genitalia of ≥ 1 s in duration, (5) ejaculation with visible sperm emission. The responses to optogenetic stimulation were quantified in both control and experimental F1 flies (n = 20 for each genotype).

Statistical analyses

Starvation survival: survival over time was visualized by converting cumulative death counts to percent survival. Plots show mean percent survival ± one standard error of the mean (SEM) at each time point using the ggplot2 package (v 4.0.3) in R-Studio (v 4.5.3). To conduct statistical analysis of survival between genotypes, cumulative death counts were used to assign each fly a time of death corresponding to the hour at which it was first observed dead. Flies still alive at the final check were censored at that timepoint. Kaplan-Meier survival curves were estimated using the survfit function in the survival package (v 3.8-6). Overall differences in survival across genotypes were assessed using the log-rank test, and pairwise comparisons between genotypes were performed using the log-rank test with Bonferroni correction for multiple comparisons (pairwise_survdiff, survminer package v 0.5.2).

Feeding assay: total feeding volume per fly (μL/fly) was compared across genotypes using one-way analysis of variance (ANOVA) from the stats package in R-Studio (v 4.5.3). Pairwise post hoc comparisons were performed using Tukey’s honestly significant difference (HSD) test as implemented in the rstatix package (v 0.7.3). Significance brackets were added to plots using the ggpubr package (v 0.6.3).

Egg-laying assay: to assess the effects of genotype and time on egg-laying, we fit a linear mixed-effects model using the lmer function in the lme4 package (v 2.0-1) in R-Studio (v 4.5.3). Egg count was modeled as a function of genotype, day, and their interaction (fixed effects), with a random intercept for replicate to account for repeated measurements taken from the same flies across days. Day was treated as a categorical factor rather than a continuous predictor, so that the model estimated genotype-specific means at each timepoint. Degrees of freedom and p-values for fixed effects were estimated using the Satterthwaite approximation as implemented in the lmerTest package (v 3.2-1). The genotype ξ day interaction tested whether the pattern of differences between genotypes varied across days. Post hoc pairwise comparisons between genotypes within each day were performed on the estimated marginal means using the emmeans package (v 2.0.3), with p-values adjusted using Tukey’s method.

Prediction of paracrine and endocrine networks

Single-cell transcriptomes of AKH-producing cells and all tissues (stringent version) were obtained from the Fly Cell Atlas (Li et al., 2022). We manually reclassified the cell clusters for different tissues since some tissues included artefacts or cells that are not unique to a particular tissue (e.g., hemocytes). Cell types present in multiple tissues were classified as “general”. Unannotated and artefact clusters were excluded. Head and body clusters were also excluded since they included cell types that were present in individual tissues.

NSC transcriptomes were identified from the brain single-cell transcriptomes generated previously (Davie et al., 2018). The parameters used to identify the different NSC types were based on previous studies and provided below (Kahsai et al., 2010; Kapan et al., 2012; Miyamoto and Amrein, 2014; Cannell et al., 2016; Yang et al., 2018; Nässel and Zandawala, 2019; Oh et al., 2019; Hadjieconomou et al., 2020; Mizuno et al., 2021; Zandawala et al., 2021; Gera et al., 2025).

l-NSC^DH31 (6 cells): ITP > 2 & Dh31 > 4 & amon > 0 & Phm > 0

l-NSC^CRZ (4 cells): Crz > 3 & sNPF > 3 & Dh44 == 0 & ITP == 0 & ChAT == 0 & Gr64a == 0 & Phm > 0

l-NSC^ITP (7 cells): Tk > 1 & sNPF > 1 & ITP > 1 & ImpL2 > 1 & Crz == 0

m-NSC^DH44 (6 cells): Dh44 > 2 & CG13248 > 0 & CG13743 > 0 & Lkr > 0 & Phm > 0

m-NSC^DILP (14 cells): Ilp2 > 3 & Ilp3 > 3 & Ilp5 > 3 & ChAT == 0

m-NSC^DMS (5 cells): Ms > 2 & EcR > 0 & rk > 0 & amon > 0 & Phm > 0

SEZ-NSC^CAPA (3 cells): Capa > 0 & CrzR > 0 & trp > 0 & amon > 0 & Phm > 0

SEZ-NSC^Hugin (15 cells): Hug > 3 & Dh44-R2 > 0 & amon > 0 & Phm > 0

To determine the strength of paracrine connections between NSC classes, we multiplied the composite expression scores (see text below for calculation) of each neuropeptide with those of its corresponding receptor. Some neuropeptides mediate their effects via two receptors. If both receptors were expressed in a given cell-type, we only considered the one with higher expression for the sake of simplicity. To minimize false positives, neuropeptides were subjected to a two-step filtering process. First, only those expressed in at least 50% of the cells within a given cluster were retained. Second, a composite expression score was calculated for each neuropeptide by multiplying its average expression by its percent detection. These values were normalized to the maximum observed signal across the dataset, and only hormone-cluster pairs maintaining a relative score of 0.25 or higher were included in the final analysis. This stringent filtering approach was implemented to focus the analysis on dominant neuropeptides and to exclude contamination from ambient RNA. To account for lower abundance of receptor transcripts, we used a more permissive threshold for receptors by retaining those expressed in at least 5% of the cells within a cluster. Unlike the neuropeptides, no secondary relative-score filtering was applied to the receptors to ensure that biologically relevant signaling targets were not prematurely excluded due to low transcript density. We used this thresholding criteria to align previous anatomical studies with our single-cell expression analysis. Since m-NSC^DILP produce DILP2, DILP3, and DILP5, all of which target the same receptor, we used their average expression for all analyses.

Analyses were performed in R-Studio (v 2024.04.2+764) using the Seurat package (v 4.4.0 (Hao et al., 2021)), unless specified otherwise. Analyses presented in Figure 9E and Figure 9 Supplement 2-4 were performed using Seurat package (v 5.4.0) as the dataset was large.

Figure supplements

Figure 1 Supplement 1.

Figure 2 Supplement 1.

Figure 2 Supplement 2.

Figure 2 Supplement 3.

Figure 2 Supplement 4.

Figure 3 Supplement 1.

Figure 3 Supplement 2.

Figure 3 Supplement 3.

Figure 3 Supplement 4.

Figure 3 Supplement 5.

Figure 4 Supplement 1.

Figure 6 Supplement 1.

Figure 7 Supplement 1.

Figure 7 Supplement 2.

Figure 7 Supplement 3.

Figure 8 Supplement 1.

Figure 8 Supplement 2.

Figure 9 Supplement 1.

Figure 9 Supplement 2.

Figure 9 Supplement 3.

Figure 9 Supplement 4.

Supplementary Tables

Supplementary Table 1.

Supplementary Table 2.

Data availability

Custom code used to analyze and visualize the data is available at: https://github.com/Zandawala-lab/McKim-et-al-2026-Synaptic-connectome-of-a-neurosecretory-network-in-the-Drosophila-brain. Custom code and output files are available at: https://doi.org/10.5281/zenodo.20795211

Acknowledgements

We are thankful to Emilia Derksen and Zeyu Chang for providing technical assistance, Dick Nässel and Anthony Crown for helpful feedback during preparation of this manuscript and Gilad Barnea, Dan Chuntao and Vivek Jayaraman for fly lines. We thank the Princeton FlyWire team and members of the Murthy and Seung labs, as well as members of the Allen Institute for Brain Science, for development and maintenance of FlyWire (supported by BRAIN Initiative grants MH117815 and NS126935 to Murthy and Seung). Development of the natverse including the coconatfly and fafbseg packages has been supported by the NIH BRAIN Initiative (grant 1RF1MH120679-01), NSF/MRC Neuronex2 (NSF 2014862/MC_EX_MR/T046279/1) and core funding from the Medical Research Council (MC_U105188491). We acknowledge members of the Princeton FlyWire team and the FlyWire consortium for neuron proofreading and annotation. J.G. was supported by funding from the University of Würzburg. M.Z. was supported by funding from the University of Würzburg, German Research Foundation (DFG; ZA1296/1-1), FWO Odysseus Type I grant (WIRED2FEED; G0ASQ25N) and NV INBRE grant from the National Institute of General Medical Sciences (GM103440). Research reported in this publication used the Cellular and Molecular Imaging core facility supported by the National Institute of General Medical Sciences (P30 GM145646). G.M. and N.R. were supported by German Research Foundation grants (FO 207/21 and FO 207/16-1) to C.H.F. We also acknowledge funding from the DFG for the Leica TCS SP8 microscope (251610680, INST 93/809-1 FUGG).

Additional information

Author contributions

MZ conceived the study. MZ, FVB, PC and CHF supervised the project. JG, GM, KC, JS and SH performed the experimental work. THM, NR, AJG and MZ performed computational analyses. All authors contributed to data visualization. MZ wrote the manuscript with input from THM. All authors read, provided feedback and approved the final manuscript.

Funding

Fonds Wetenschappelijk Onderzoek (FWO) (G0ASQ25N)

• Meet Zandawala

Deutsche Forschungsgemeinschaft (DFG) (ZA1296/1-1)

• Meet Zandawala

HHS | NIH | National Institute of General Medical Sciences (NIGMS) (GM103440)

• Meet Zandawala

Deutsche Forschungsgemeinschaft (DFG) (FO 207/21)

• Charlotte Helfrich-Foerster

Deutsche Forschungsgemeinschaft (DFG) (FO 207/16-1)

• Charlotte Helfrich-Foerster

Deutsche Forschungsgemeinschaft (DFG) (INST 93/809-1 FUGG)

• Charlotte Helfrich-Foerster

HHS | NIH | National Institute of General Medical Sciences (NIGMS) (P30 GM145646)

• Meet Zandawala

Additional files

Supplementary Table 3. Cell IDs of NSC identified in FlyWire, BANC and maleCNS connectomes.