Mapping the druggable targets displayed by human colonic enteroendocrine cells

Mapping the druggable targets displayed by human colonic enteroendocrine cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mapping the druggable targets displayed by human colonic enteroendocrine cells Gavin Bewick, Yuxian Lei, Bettina Bohl, Leah Meyer, Margot Jacobs, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5411079/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Enteroendocrine cells (EECs) are specialized intestinal hormone-secreting cells that play critical roles in metabolic homeostasis, digestion, and gut-brain communication. They detect diverse stimuli including endocrine, immune, neuronal, microbial, and dietary signals, through a complex array of receptors, ion channels, and transporters, to modulate the release of over 20 hormones. These molecular sensors serve as potential drug targets to modulate hormone secretion, but until recently, catalogues of such targets in human colonic EECs have not been produced. To address this gap, we performed bulk and single-cell RNA sequencing on fluorescently labelled EECs isolated from human colonic organoids, identifying and cataloguing potential druggable targets. This catalogue includes receptors, orphan GPCRs, transporters, and hormones not previously reported in human colonic EECs. Comparison with murine EECs highlighted interspecies similarities and differences, key data to facilitate the design and optimise the predictive accuracy of pre-clinical models. We also functionally validated two receptors not previously identified in human EECs: IL-13Rα1, was expressed in both peptide-producing EECs and serotonin producing Enterochromaffin cells (ECs), and its ligand IL-13 stimulated the secretion of glucagon-like peptide-1 (GLP-1) and serotonin measured in real-time, and GPR173, which was selectively expressed in ECs and, when activated by its agonist Phoenixin-20, also promoted serotonin release. These analyses provide a valuable resource for therapeutic interventions aimed at modulating gut hormone secretion, with potential applications in treating gastrointestinal, metabolic, and other related disorders. Biological sciences/Cell biology Health sciences/Endocrinology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Enteroendocrine cells (EECs) are vital sensors and signal transducers within the gastrointestinal epithelium. Although they comprise only ~ 1% of epithelial cells, EECs form an extensive hormone-producing network capable of converting luminal, neural, and immune signals into hormonal responses 1 . EECs have traditionally been classified by the predominant hormone they secrete, e.g., L-cells produce glucagon like peptide-1 (GLP-1), Glucagon like peptide-2 (GLP-2), and Peptide YY (PYY); K-cells secrete Gastric inhibitory polypeptide (GIP); and enterochromaffin cells (ECs) release serotonin (5-HT). Recent advances using fluorescent reporter mice revealed that EEC profiles are shaped by their differentiation trajectory, gut location, and positioning along Wnt and BMP gradients 2 , 3 , 4 . EECs respond to various luminal stimuli including nutrients, pH, and microbial metabolites, releasing over 20 peptide hormones to control that regulate digestion, metabolism, microbial-host cross talk, gut-brain communication and immune defence. Consequently, EEC dysregulation has been implicated in obesity and diabetes, gastrointestinal diseases, inflammation, autoimmune conditions, and certain cancers 5 , 6 . Given their diverse roles, EECs are valuable therapeutic targets and are thought to be important mediators of the metabolic benefits observed in bariatric surgery. Currently, several long-acting variants of individual or combined gut hormones such as GLP-1 and GIP are in development or approved as therapies for obesity and type 2 diabetes 7 . However, gaining deeper insights into EEC function and how these cells sense stimuli may uncover new druggable targets, enabling more precise control of therapeutic gut hormone secretion from specific EEC subtypes or gut regions. This could broaden therapeutic approaches and introduce new treatment options, including dietary interventions or small molecule drugs. Dietary strategies have the advantage of being more suitable for prevention or population-level interventions and could be more affordable for healthcare systems with limited funding. Despite their significance, studying human EECs has been difficult due to their sparse distribution and challenges of isolating and maintaining them in culture. Much of our understanding is based on murine models, where fluorescent labelling techniques have mapped EEC distribution. However, the development of organoid technology and CRISPR-Cas9 gene editing has revolutionized the field. These advances allow us to grow three-dimensional human intestinal organoids and study EECs in unprecedented detail 8 , 9 . We have taken advantage of this technology to map druggable targets expressed in human colonic EECs. We compared this dataset with data from mouse colonic EEC subtypes to probe interspecies similarities and differences and identified novel receptors enriched in human colonic EECs. Among those, we selected IL-13Rα1 and GPR173, and corroborated their effects on hormone secretion 10 . By providing a map of human colonic EEC cell surface targets we hope to facilitate the identification of novel EEC biology and therapeutic targets. Results Generation of human colonic CHGA reporter organoids Chromogranin A (CHGA) is a marker of neuroendocrine cells and marks EECs in the gut epithelium. We generated human CHGA-mNeon expressing colonoids using CRISPR–Cas9-mediated homology-independent transgenesis (CRISPR-HOT), as previously described 9 , 11 . The fluorescent tag mNeon was inserted 7 base pairs prior to the stop codon (TGA) of the endogenous CHGA gene (Fig. 1 a). To enrich for both peptide producing EECs and ECs, we combined two previously published differentiation protocols. This involved the use of differentiation medium which consisted of insulin-like growth factor 1 (IGF-1) and fibroblast growth factor 2 (FGF-2), with the removal of epidermal growth factor (EGF) 12 , 13 , and a 48-hour pulse treatment with a Notch inhibitor (iNotch), MEK inhibitor (iMEK), and ISX-9 14 (Fig. 1 b). This combination significantly increased EEC and EC differentiation. Differentiated CHGA-mNeon colonoids displayed scattered green-fluorescent cells throughout their structure (Fig. 1 c). Colocalization of CHGA and mNeon was confirmed by immunostaining (Fig. 1 d), indicating successful tagging of CHGA-expressing cells. We evaluated our differentiation method using time-course qPCR of whole colonoids (Fig. 1 e). The stem cell marker SOX9 and the endocrine progenitor markers NGN3 and PAX4 were upregulated early in the differentiation process, with the endocrine progenitor markers showing a slight delay relative to SOX9 . These markers were downregulated later in the differentiation time course. Conversely, markers of mature endocrine cells ( CHGA , NEUROD1 , tryptophan hydroxylase 1 ( TPH1 ), preproglucagon ( GCG )) increased over time, peaking on day 11. As expected, mNeon expression mirrored CHGA expression. Together the data suggested our protocol successfully enriched human colonoids with EECs and ECs. CHGA-mNeon positive cells were purified from differentiated colonoids using fluorescence-activated cell sorting (FACS), yielding two populations: fluorescent mNeon + ve and non-fluorescent mNeon − ve cells (Fig. 1 f). The mNeon + ve population was enriched for EEC markers ( CHGA , GCG ) and the EC marker TPH1 (Fig. 1 g). Conversely, mNeon − ve cells were enriched for Paneth cell ( LYZ ), intestinal stem cell ( LGR5 ), and goblet cell ( MUC2 ) markers. Proteomic analysis confirmed that mNeon + ve and mNeon − ve cells were significantly separated at the level of protein expression ( Fig. S1a ). mNeon + ve cells produced CHGA, TPH1 and GCG, whereas mNeon − ve cells produced proteins such as LYZ and the goblet cell marker TFF3 ( Fig. S1b ). These expression patterns demonstrate that mNeon + ve cells are human colonic EECs and ECs. Mapping the transcriptome of human colonic EEC and ECs. To characterize the transcriptome of mNeon + ve cells, we performed bulk RNA-sequencing (RNA-seq). Principal component analysis (PCA) confirmed clear transcriptional separation between mNeon + ve and mNeon − ve populations (Fig. 2 a). The mNeon + ve population exhibited EEC marker genes like CHGA , CHGB , and SCG5 (Fig. 2 b), and transcription factors (TFs) associated with EEC development including FEV , INSM , NKX2-2 , NKX2-1 ( Fig. S1c ). As expected, the enterocyte marker SLC26A3 , the Paneth cell markers CA4 , SPIB , and the goblet cell markers CLCA1 , MUC5AC , SPDEF , SPINK4 , and TFF3 were enriched in mNeon − ve cells. Hormone-encoding genes, including GCG , which produces the incretin GLP-1, and the anorexigenic peptide YY ( PYY ), were enriched in mNeon + ve cells. These are characteristic products of peptide producing EECs. Additionally, TPH1 , the rate-limiting enzyme in the synthesis of serotonin and a marker of ECs, was also enriched, along with transcription factors associated with EC differentiation such as PAX4 15 and RUNX1T1 2 . Other classical gut hormones, secretin ( SCT ) and neurotensin ( NTS ), were expressed at lower levels. Beyond the well-known gut hormones, our analysis of genes predicted to produce secreted products identified several additional neuropeptides and hormones not previously reported in human colonic peptide EECs or ECs ( Fig. S1c ). These included galanin ( GAL ), a potent inhibitor of gut hormone secretion (including GLP-1, PYY, and NTS) and a known regulator of energy homeostasis and intestinal motility 16 , 17 . Additionally, we found previously unreported expression of relaxin 1 ( RLN1 ), which modulates the reproductive system and gut motility 18 ; calcitonin gene-related peptide ( CALCB ), a neuropeptide with functions in the gut including the regulation of mucosal blood flow, epithelial homeostasis, exocrine and endocrine secretory processes, motor activity, and nociception 19 ; and neurokinin B ( TAC3 ), a member of the tachykinin family of neuropeptides involved in reproduction, gastric acid secretion, GI motor and sensory control, and inflammation 20 , 21 . Interestingly, our analysis also revealed the presence of follistatin-like 5 ( FSTL5 ), a secretory glycoprotein previously implicated in the olfactory system 22 . While its role in the gut is unclear, it has been shown to inhibit proliferation in liver hepatocellular carcinoma 23 . Among the top differentially expressed receptors (Fig. 2 c) were the bile acid receptor ( GPBAR1 ) and free fatty acid receptor 2 ( FFAR2 ), both implicated in the regulation of postprandial nutrient sensing and gut hormone secretion 24 . These receptors have been reported to be expressed by EECs and/or ECs. Several hormone receptors were also enriched, including the gastric inhibitory polypeptide receptor ( GIPR ). Its ligand, GIP, is an incretin secreted from K-cells in the small intestine. Our data corroborated the presence of the neuropeptide Y receptor type 1 ( NPY1R ), which has previously been identified on colonic goblet cells, endocrine cells, and enterocytes 25 . NPY1R mediates some effects of the NPY-like family of peptides on gut functions, including secretion of GLP-1, inflammation, barrier function, and absorption 26 – 29 . Additionally, the galanin receptor 1 ( GALR1 ) and the relaxin family peptide ( RXFP4 ) were also present. RXFP4 and its ligand, insulin-like peptide 5 (INSL-5), are thought to play a role in the regulation of food intake and gut motility. Recent data revealed co-localization of RXFP4 with serotonin in the lower gut of mice and single cell RNA-sequencing has suggested its presence in a subset of human colonic ECs 30 . EECs and ECs are strategically positioned to receive information from various sources, including enteric and vagal neurons. They can respond to classical neurotransmitters released by these neurons, and to dietary and microbial signals that in some instances act as ligands for neurotransmitter receptors. Our data demonstrate the presence of the γ-aminobutyric acid (GABA) receptor ( GABBR2 ), the cholinergic receptor ( CHRNB2 ) and the dopamine receptor D2 ( DRD2 ) in mNeon + ve cells (Fig. 2 c), extending previous findings in mice to humans 31 . Furthermore, we confirmed the presence of the olfactory receptors ( OR51E1 , OR51E2 ) in human colonic endocrine cells 8 . EECs and ECs play a crucial role in orchestrating gut epithelial immunity and inflammation. They possess multiple receptors which allow them to respond to immune mediators and can directly secrete cytokines and other modulators. For example, we found the receptors: interleukin-13 (IL-13) receptor IL13RA1 , ULBP1 , CLEC7A , KLRC3 , TNFRSF19 , and CD5 (Fig. 2 c), and cytokines including CCL5 , CCL15 , CSF1 , IL17 , IL11 , IL23 ( Fig. S1c ), and various tumour necrosis family members, enriched in mNeon + ve cells. The presence of IL13RA1 and IL17 corroborates the known role of IL-13 in controlling ECs in mice 32 and the previously reported production of IL-17C by EECs in humans 33 . We also identified differentially expressed transporters consistent with the chemosensing role of EECs and ECs ( Fig. S1c ), including SLC22A17 (iron transporter), SLC38A11 (amino acid transporter), SLC17A9 (ATP uniporter), SLC26A7 (anion exchange transporter), and SLC8A1 , a sodium/calcium exchanger. As expected, we identified numerous enriched ion channels associated with excitable cells, such as potassium channels ( KCTD12 , KCNJ3 , KCNJ6 , KCHN2 ), calcium channels ( CACNA2D1 , CACNA1A , CACNA1C ), and sodium channels ( SCNA3 , SCN3B , SCNA2 ). To complete our survey of druggable targets, we identified the top differentially expressed orphan G protein-coupled receptors ( Fig. S1c ), which to our knowledge have not been previously reported in either mouse or human colonic EECs or ECs. Interestingly, within this list of novel targets, GPR173 has recently been deorphanized and is proposed to be the receptor for Phoenixin (PNX), a recently discovered neuropeptide involved in the regulation of food intake, energy homeostasis, stress and inflammation 34 . Single-cell transcriptomic profiling of human colonic EECs and their receptor expression Enriching human EECs from primary tissues for single-cell profiling has been challenging due to reliance on antibodies and selection strategies that can lack specificity. To expand on our bulk sequencing data, we performed single cell RNA-sequencing on our CHGA reporter organoids, to profile mNeon + ve cells, focusing on the expression of druggable cell surface targets within specific cell populations. Data was generated and processed by sorting and robot-assisted transcriptome sequencing (SORT-seq 35 ) and analysed using Seurat 36 . Using shared nearest neighbour clustering based on highly differentially expressed genes, we constructed a map of CHGA-mNeon + ve cells. The map contained four populations of colonic EECs: progenitor cells, early EECs, peptide EECs and ECs (Fig. 2 d). These findings align with the two major branches of differentiated endocrine cells: TPH1-positive ECs and hormone expressing (e.g., GLP-1 positive) EECs, as previously documented 25 . Progenitors were identified by the expression of the stem cell markers SMOC2 , SOX9 and BMP4 , and LGALS4 , a marker of the early NGN3-to-EC transition 37 (Fig. 2 e). Early EECs were distinguished by the expression of PAX4 , SOX4 , GCH1 and RUNX1T1 8,37 ; this population likely harbours cells with the potential to become ECs as well as hormone producing EECs but with an EC bias due to the presence of FEV and PAX4 . Interestingly, PAX4 has recently been identified as a critical regulator in an endocrine transcription factor network controlling EC differentiation 15 and is one of the major targets of ISX-9, a component of our differentiation protocol. The late EC population was identified by enrichment for CHGA , CHGB and TPH1 and genes such as REG4, GC, RGS2 , and CRYBA2 which have previously been observed at the later stages of differentiation from progenitor to mature ECs in humans 37 . The EEC peptidergic cluster was identified based on the expression of classical markers, including GCG , PYY and NeuroD1 . Single-cell clustering identified the most highly expressed receptors and orphan GPCRs enriched in colonic EECs, with variable expression across cell subtypes (Fig. 2 f). Receptors most highly expressed in ECs included nuclear receptor subfamily 4 group A member 2 ( NR4A2 ), a regulator of inflammation in the gastrointestinal tract 38 , the olfactory receptor OR51E1 , and the Phoenixin receptor GPR173 . In contrast, receptors such as cadherin EGF LAG seven-pass G-type receptor 3 ( CELSR3 ), a key gene for planar cell polarity and the guidance of enteric neuronal projections 39 , the bile acid receptor ( GPBAR1 ), gastric inhibitory polypeptide receptor ( GIPR ), galanin receptor 1 ( GALR1 ), and the orphan receptor GPR108 were primarily enriched in peptide-producing EECs. Receptors with broader expression included GPR160 , an orphan receptor potentially activated by CART (cocaine and amphetamine-regulated transcript) peptide, a neuropeptide and hormone involved in appetite regulation 40 , gastrointestinal inflammation, and gut hormone secretion from the proximal bowel (GLP-1 and GIP) 41 , as well as IL13RA1 (interleukin-13 receptor subunit alpha-1). GPR155 showed the highest expression in progenitor cells and has been linked to cancer initiation and progression, particularly as a biomarker for hematogenous metastasis in gastrointestinal cancers 42 . However, its role in regulating intestinal stem cells (ISCs) or progenitor EEC proliferation and differentiation remains unclear. Additionally, 2-oxoglutarate receptor 1 ( OXGR1 ) was enriched in progenitor cells. Dietary and gut microbiota-derived 2-oxoglutarate is utilized by the epithelium for protein synthesis and oxidative metabolism and has been shown to restore intestinal barrier function by promoting stem cell activity in mice 43 . An integrated transcriptomic database of mouse and human colonic EECs. To compare interspecies differences and similarities between humans and mice, we conducted transcriptomic analysis of mouse colonic EECs. We used mouse TPH1 and PYY reporter cells, which are analogous to human colonic ECs and peptide hormone producing L-cells, respectively. TPH1 + ve cells were isolated from colonoids derived from Tph1-P2A-iCreERT2 mice 44 (Fig. 3 a). Principal component analysis (PCA) revealed two distinct CFP + ve and CFP − ve cell populations (Fig. 3 d). CFP + ve cells were enriched for the EC marker Tph1 , whilst CFP − ve cells were enriched with the Paneth cell marker Lyz and goblet cell marker Muc2 (Fig. 3 b, c). As expected, CFP + ve cells were also enriched with EEC lineage markers such as Neurog3 and Nkx2-2 (Fig. 3 e). These cells also showed enrichment for transcription factors driving EC differentiation, including Pax4 and Runx1t1 , along with TFs involved in serotonin biosynthesis, such as Lmx1a 45 and Ascl1 46 ( Fig. S2a ). As expected, the epithelial marker Slc26a3 , goblet cell differentiation factor Klf4 , and Paneth cell marker Mmp7 were enriched in CFP − ve cells (Fig. 3 e). Ninety receptors were differentially expressed in mouse CFP + ve ECs compared to CFP − ve cells, including known receptors involved in nutrient, gut hormone, and immune sensing. Among the top 40 differentially expressed receptors (Fig. 3 f), 10 were conserved between mouse and human (Fig. 3 g), including receptors such as FFAR2 , GABBR2 , and NPY1R , as well as several herein newly identified receptors, including IL13RA1 , CELSR3 , and GFRA1 . Glial cell line-derived neurotrophic factor (GDNF), the ligand for GFRA1 , is crucial for the development of the enteric nervous system 47 . Additionally, GFRA1 has been implicated in Hirschsprung’s disease and related enterocolitis in mice 48 . However, its expression and role in ECs has not been explored. Several hormone receptors were exclusively expressed in the mouse dataset (Fig. 3 g), including the vasoactive intestinal polypeptide receptor ( Vipr2 ), whose ligand VIP regulates nutrient absorption and gut motility 49 ; the somatostatin receptors ( Sstr5 , Sstr1 ), which play versatile roles in the gut, including chemosensing, mucus secretion, and inflammatory responses 50 ; and the CCK receptors ( Cckar , Cckbr ), which regulate gut motility and appetite. Selective species expression may indicate divergent hormonal control of ECs between mouse and humans. Among the orphan GPCRs ( Fig. S2b ), Gpr173 , Gpr162 , Gpr85 , Gpr146 , Gpr153 , Gpr6 , Gpr3 were conserved between mouse and human. Sphingosine-1-phosphate (S1P) has been identified as a likely ligand for Gpr6 , Gpr3 51 , and is implicated in intestinal barrier function 52 . In contrast, Gpr12 , Gpr27 , Gpr45 , Gpr161 and Gpr179 were selectively found in the mouse. Of these Gpr12 53 , Gpr27 54 , Gpr45 55 have been linked to obesity and/or metabolism, whereas Gpr161 has a role in intestinal immunity 56 . In the human mNeon + ve dataset, Gpr63 and Gpr68 were receptors with postulated ligands. For example, sphingosine-1-phosphate (S1P) and lysophosphatidic acid (LPA) have been suggested as low affinity agonists of Gpr63 57 . Gpr68 , also known as the proton-sensing ovarian cancer G-protein coupled receptor (OGR1), is a proton-activated GPCR that responds to acidic extracellular pH and in the intestine plays a role in tissue damage and inflammation 58 . There were also various receptors involved in gut inflammation and immune responses (Fig. 3 f, g), including the conserved receptor Il13ra1 and receptors uniquely expressed in mice, such as Pdcd1 (which encodes the programmed cell death protein 1), 59 , Il20ra 60 , Cd83 61 , Cd22 62 . Additionally, cytokines, such as the conserved Il17d, as well as mouse specific Ccl6, Ccl28, Mif are also implicated in these processes ( Fig. S2a ). Our analysis revealed both conserved and species-specific expression across other functional groups, highlighting the importance of understanding species-dependent expression to facilitate the optimisation of pre-clinical models for the validation of novel targets ( Fig. S2 ). In addition to the bulk transcriptome analysis of TPH1-CFP + ve cells, we also performed single cell RNA-sequencing using these cells and PYY-GFP cells, isolated from the colon of PYY-GFP mice 63 (Fig. 4 a). The CFP + ve ECs clustered into two groups: differentiated ECs and early ECs, mirroring clusters observed in human ECs derived from colonoids (Fig. 3 h). Differentiated ECs were identified by the expression of genes such as Foxa1 , S100a1 and Atf6 , which were observed during late EC lineage development in mice 2 (Fig. 3 i). Early ECs demonstrated markers like Neurog3 , Neurod2 and Sox4 . As expected, PYY-GFP cells were enriched for Pyy and Gcg expression and not for Tph1 or Lyz (Fig. 4 a, b). PYY-GFP cells clustered into peptide EECs, which highly expressed Chga , Gcg , and Pyy , and secretory progenitor cells, which expressed the markers Atoh1 64 , Spink4 , and Agr2 , and exhibited features of both goblet and Paneth cells (Fig. 4 c, d). The top differentially expressed receptors were compared between the TPH1 and PYY clusters (Fig. 4 e). Ffar2 was the most promiscuous, being expressed in all clusters whereas Gpbar1 was restricted to mature peptide EECs. When expression patterns were compared between human and mouse. Npy1r exhibited a conserved expression pattern, with predominant expression in ECs, whilst Ffar2 was broadly expressed across all EEC clusters in both mouse and humans. However, other receptors exhibited species-specific differences. For example, Gpr108 , an immune modulator, exhibited the highest expression in human peptide EECs but in the mouse was predominantly expressed in secretory progenitor cells and mature ECs. Il13ra1 also displayed differential expression, being primarily expressed in mouse secretory progenitor cells and early ECs, while in humans it was expressed across all stages of EC differentiation as well as in peptide EECs. Interestingly, the orphan receptor Gpr173 was undetected in mouse peptide EECs, consistent with its absence in human EECs, suggesting its expression is conserved and restricted to ECs. The complete lists of differentially expressed receptors, transcription factors, and cytokines in mouse ECs and human EECs are provided in Supplementary Tables S1 and S2. Additionally, the comparisons of average gene expression across all clusters of human EECs and mouse ECs are available in Supplementary Tables S3 and S4. Are receptors enriched on human EECs functionally significant? To evaluate whether we could identify receptors with functional significance in both ECs and peptide EECs, we selected IL-13Rα1 and GPR173. IL-13Rα1 has previously been shown to regulate serotonin secretion in mice, but its presence on human L-cells has not previously been reported. Whereas GPR173 has not been reported on ECs in either species. The expression of both receptors was confirmed by qPCR in human CHGA-mNeon + ve cells (Fig. 5 a). Additionally, co-expression of IL-13Rα1 with GLP-1 and 5-HT was further corroborated in human colon biopsies by immunohistochemistry (Fig. 5 c). Consistent with its expression in human EECs, Il13ra1 expression was significantly increased of in both mouse TPH1-CFP + ve cells (ECs) and PYY-GFP + ve cells (peptide EECs) (Fig. 5 d), while the expression of Gpr173 was enriched in mouse ECs but not in peptide EECs. The presence of IL-13Rα1 on L-cells suggests that the IL-13/IL-13Rα1 pathway might stimulate the secretion of GLP-1. In support of this, we found that IL-13 significantly increased GLP-1 secretion in colonoids derived from three separate donors (Fig. 5 e). IL-13 has been shown to stimulate serotonin secretion from BON-1 cells, a human carcinoid cell line that produces serotonin (5-HT) and other neurotransmitters and peptides 32 , 65 . However, it remains unknown whether IL-13 can stimulate serotonin secretion from primary human ECs. Accurately measuring serotonin secretion from primary human cells is challenging. Unlike secretion protocols for gut hormones, which rely on sensitive immunoassays, real-time serotonin measurement requires electrochemical techniques that necessitate placing electrodes near the secreting cells. This is impractical without a reporter to mark the cells of interest. Previously, 5-HT release from individual ECs in mice has been indirectly visualized by measuring Ca 2+ responses in fluorescently labelled ECs in organoids, or by indirectly measuring activation of biosensor cells expressing the serotonin-gated ion channel (5-HT 3 R) 66 . Here, we employed Fast Scan Cyclic Voltammetry (FSCV), an electrochemical technique used to simultaneously identify and quantify monoamines, to investigate whether the IL-13/IL-13Rα1 pathway stimulates serotonin secretion from primary human ECs. This method allows for real-time and label-free detection of extracellular 5-HT as a change in current at a carbon fibre microelectrode (CFM) (Fig. 6 a). Combining with CHGA-mNeon colonoids enabled the measurements to be performed in or near primary ECs. We compared the spontaneous (pre-drug) activity of CHGA-mNeon fluorescent cells to their post-drug stimulation. Representative current-time plots were generated for each organoid (Fig. 6 b). We quantified the frequency of 5-HT release events and observed significant increases following IL-13 stimulation (Fig. 6 c). Furthermore, we measured the peak amplitude corresponding to the amount of 5-HT released for each event and found IL-13 treatment increased peak amplitude (Fig. 6 d). These results suggest that IL-13 increases both the number of 5-HT release events, and the amount of 5-HT released in each event. To evaluate the functional significance of GPR173, which we found to be exclusively expressed by ECs in mouse and humans, we used its recently identified ligand PNX-20 and FSCV. The frequency of serotonin release events increased following PNX-20 treatment (Fig. 6 c). However, the peak amplitude, corresponding to the amount of 5-HT released, was not affected (Fig. 6 d). These results suggest that PNX increases the frequency of 5-HT release events but does not affect the amount of 5-HT secreted during each event. Discussion EECs are a potentially rich source of novel drug targets for the treatment of numerous diseases. However, human EEC biology and their therapeutic control have remained challenging to characterise beyond the well-studied incretin axes. By leveraging advanced methodologies, including CRISPR-Cas9 gene editing and organoid models, we and others have begun to address longstanding challenges in accessing and studying human EECs at high resolution 67 , 68 , 69 . By generating human CHGA-mNeon colonoids and developing a small molecule EEC differentiation protocol, we facilitated the isolation and visualization of EEC populations for transcriptomic profiling. In addition, we utilized TPH1-CFP and PYY-GFP transgenic mouse models to enable comparative analyses of EEC subtypes across species. This dual-species approach allowed us to map both conserved and species-specific features of EEC biology. By integrating bulk and single-cell RNA sequencing with functional assays we sought to delineate the molecular landscape of colonic EECs. This approach led to the identification of a broad array of previously unreported neuropeptides, hormones, and receptors in human colonic EECs. These findings expand our understanding of the signalling pathways that govern EEC function and highlight novel candidates for further exploration. Moreover, we corroborate the presence of key chemosensory receptors, such as the bile acid receptor GPBAR1 and free fatty acid receptor FFAR2, reaffirming the critical role of EECs in nutrient sensing and gut physiology. In addition to their roles in nutrient sensing, hormone secretion, and gut motility, EECs are increasingly recognized for mediating immune-gut communication. Though their immunological functions remain relatively underexplored, we show that human colonic EECs express receptors and signalling molecules associated with immune function, including cytokine and chemokine receptors. This suggests that EECs sense inflammatory signals and modulate immune responses via hormone and peptide release. For example, we demonstrate that IL-13 enhances GLP-1 and serotonin secretion, indicating that EECs may link immune regulation, particularly TH2 responses, with metabolic processes and or gut defence. Further research is needed to clarify this crosstalk and its role in disease. We employed fast-scan cyclic voltammetry (FSCV) for the first time in gut organoids to validate serotonin secretion from ECs. FSCV provides unparalleled temporal resolution, allowing for the detection of rapid and transient changes in serotonin release that are often undetectable with conventional methods such as ELISA. Using this approach will facilitate a deeper understanding of the finely tuned regulatory mechanisms governing serotonin secretion from ECs. For instance, we identified GPR173 as a novel receptor exclusively expressed by ECs. FSCV enabled us to demonstrate that the GPR173 ligand, Phoenixin-20, modulated serotonin release by increasing the frequency of release events. These findings suggest that GPR173 may influence gut motility, inflammation, or mood regulation, given the established role of serotonin in the gut-brain axis. Our interspecies transcriptomic comparisons revealed both conserved and divergent expression patterns, underscoring the limitations of exclusively using animal models for studying EEC biology. Conserved expression was observed for receptors such as NPY1R and FFAR2 whereas receptors like GPR3, GPR160, ULBP1, RXFP4 displayed human-specific expression, highlighting species-specific differences. This emphasizes the importance of conducting direct studies on human tissues to gain a comprehensive understanding of EEC function, particularly in the context of drug development. Additionally, the cross-species catalogue enables novel targets to be identified which can be further explored in mouse models of disease. Overall, we provide a transcriptomic catalogue of human colonic EECs and the functional validation of two novel receptors involved in EEC biology. The identification of previously uncharacterized receptors, channels and transporters in the database presents the field with an opportunity to identify novel therapeutic interventions in metabolic, gastrointestinal, and inflammatory diseases. Future research should prioritize in vivo validation of targets within disease-specific contexts where gut hormones play key regulatory roles. Finally, further investigation of the roles of IL-13Rα1 and GPR173 will lead to a deeper understanding of how these receptors influence gut homeostasis and their therapeutic potential in various pathologies. Methods Isolation and culture of human colonoids The isolation of human colonoids was adapted from previously described methods 14 , 70 . Three human colonoid lines were derived from colonoscopy biopsies of a 50-year-old male patient (unique identifier FG), a 75-year-old female patient (unique identifier MW) and 33-year-old female patient (unique identifier ZB), respectively, from Denmark Hill NHS Foundation Trust. The biopsies were rinsed with ice-cold D-PBS (D8537, Sigma-Aldrich) to remove any blood or debris and then incubated in 5 mL 10 mM 1,4-dithiothreitol (DTT; 10197777001, Sigma-Aldrich) for 5 min at room temperature, repeated once with fresh DTT. The biopsies were then incubated in 8 mM EDTA (15575-038, Invitrogen) in D-PBS on a rotator at 4°C for 1 hr, and subsequently vigorously shaken in cold D-PBS to release crypts. The supernatant containing crypts were collected and centrifuged at 400 rcf for 3 min and were washed in cold D-PBS for three times. 200 crypts per 25 µL were seeded in Cultrex Basement Membrane Extract (BME) (3536-005-02, Bio-Techne) in 48-well plate (Nunc). BME was polymerized for 15 min at 37°C, then overlaid with 250 µL/well IntestiCult™ Organoid Growth Medium (06010, STEMCELL) supplemented with 100 units/mL Pen-Strep and 10 µM Y-27632 (Y0503, Sigma-Aldrich). Plated organoids were maintained in a 37°C incubator with 5% CO 2 , and the media were changed every other day. When the crypts grew into organoids, they were maintained in the stem cell medium IFE. IFE medium consists of Advanced DMEM/F-12 (12634, ThermoFisher Scientific), 2 mM GlutaMAX (35050061, Gibco), 10 mM HEPES (15630056, Gibco), 100 units/mL Pen-Strep, 1× B27 supplement (17504044, Gibco), 1× N2 supplement (17502048, Gibco), 0.15 nM Wnt Surrogate-Fc Fusion Protein (N001, ImmunoPrecise Antibodies), 10% R-spondin-1 conditioned medium (in house), 1% Noggin-Fc fusion protein conditioned medium (N002, ImmunoPrecise Antibodies), 50 ng/mL recombinant human EGF (AF-100-15, PeproTech), 1.25 mM N-Acetylcysteine (A9165, Sigma-Aldrich), 10 nM Gastrin (G9145, Sigma-Aldrich), 500 nM A83-01 (2939, Bio-techne), 100 ng/mL recombinant human IGF-1 protein (590904, Biolegend) and 50 ng/mL recombinant human FGF-2 protein (100-18B, PeproTech). Maintained in IFE medium, the human colonoids were passaged every 7 days by mechanical dissociation, at a 1:6 split ratio. The differentiation of human colonoids was initiated on day 4 after splitting, when IFE medium was substituted by IF* medium till day 11. In the IF* medium, the Wnt Surrogate protein was reduced to 0.045 nM and EGF was removed to accelerate enteroendocrine differentiation. A 48-hr pulse treatment with a combination of small molecules was applied for enhanced differentiation. This treatment involved the use of 10 µM iNotch DAPT (D5942, Sigma-Aldrich), 500 nM iMEK PD0325901 (Sigma-Aldrich) and 40 µM ISX-9 (4439/10, Bio-Techne), which were applied 4 days before the end of the culture process 14 . After 48 hrs, the treatment was discontinued, and the medium was changed to IF* medium for an additional 48 hrs. Transgenic mice The transgenic PYY-GFP mice (a kind gift from Prof. Rodger Liddle, Duke University) 63 were maintained under regulated temperature (21–23°C) and light (12:12 hr light/dark cycle) conditions, with access to ad libitum water and chow diet. These mice were housed in compliance with Home Office UK regulations. Culture of mouse colonoids The transgenic Tph1-P2A-iCreERT2 mouse colonoids are a kind gift from Dr Cordelia Imig, University of Copenhagen 44 . Mouse colonoids were isolated from 8- to 12-week-old male mice and cultured as described previously 70 , 71 . The WENR medium consists of Advanced DMEM/F-12, 2 mM GlutaMAX, 10 mM HEPES, 100 units/mL Pen-Strep, 1× B27 supplement, 1× N2 supplement, 50 ng/mL recombinant human EGF, 1.25 mM N-Acetylcysteine, 1% Noggin-Fc fusion protein conditioned medium (N002, ImmunoPrecise Antibodies), 0.15nM Wnt Surrogate-Fc Fusion Protein (N001, ImmunoPrecise Antibodies) and 10% R-spondin-1 CM (in house). Maintained in WENR medium, the colonoids were passaged every 7 days by mechanical dissociation at a 1:6 split ratio. The differentiation of mouse colonoids was initiated on day 3 after splitting, when WENR medium was substituted by ENR medium till day 7. The ENR medium has the same composition as WENR, but without Wnt Surrogate. A 48-hr pulse treatment of 40 µM ISX-9 was applied from day 3 to day 5 in ENR. After 48 hrs, the treatment was discontinued, and the medium was changed to ENR medium for an additional 48 hrs. Generation of human CHGA-mNeon colonoids The human colonoids (unique identifier FG) were used to generate CHGA-mNeon reporter organoids by CRISPR-HOT. The three plasmids comprising the CRISPR-HOT system for targeting the endogenous CHGA gene locus include the target selector pSPgRNA (Addgene #47108), with sgRNA (CAGCTGCAGGCACTACGGCGGGG) inserted using a previously described protocol 72 , the frame selector pCas9-mCherry-Frame + 1 (Addgene plasmid #66940), and the universal donor pCRISPaint-mNeon (Addgene #174092) 9 . These three plasmids were kindly provided by Prof Hans Clevers’ group 8 . Additionally, an EF1α promoter-guided puromycin resistance gene cassette was amplified from the PB513B-1 plasmid (System biosciences) and cloned into the pCRISPaint-mNeon plasmid for antibiotic selection. Briefly, the pCRISPaint-mNeon plasmid was linearized by Q5® Hot Start High-Fidelity 2X Master Mix (M0494S, NEB). The EF1α promoter and the puromycin resistance gene fragments were amplified by touchdown PCR 73 using Q5® High-Fidelity DNA enzyme (M0491, New England BioLabs), each with a 20 bp extension complementary to the ends of the linearized pCRISPaint-mNeon plasmid. NEBuilder® HiFi DNA Assembly Master Mix (E2621S, New England BioLabs) was then used for ligation of the two DNA fragments. The ligated fragment was cloned into pCRISPaint-mNeon plasmid using the In-Fusion® HD Enzyme Premix (011614, Clontech). The generated plasmid was named pCRISPaint-mNeon-Efα-PuroR. PCR primers used in amplification and cloning are provided ( Table. S5 ). The three plasmids were transfected to human colonoids by electroporation, adapted from previously reported protocols 74 , 75 . Briefly, before electroporation, human colonoids were cultured in IFE medium supplemented with 10 µM Y-27632 and 5 µM Prostaglandin E2 (PGE2) (CAY14010, Cayman Chemical) 76 for 3–5 days after splitting. Ten wells of organoids were collected and mechanically broken by vigorous trituration with a p-200 micropipette. The pelleted organoids were then resuspended in 1 mL TrypLE Express Enzyme with 10 µM Y-27632, and incubated in 37°C water bath for 3 min to dissociate into clusters of 10–15 cells 75 . The dissociated organoids were washed in Opti-MEM (31985062, Gibco) twice. Every 1×10 5 to 5×10 5 cells were resuspended in 100 µL BTXpress buffer, containing 5 µg of each plasmid, 15µg in total. After that, the cell-plasmid mixture was placed into a 2-mm electroporation cuvette, and the electroporation performed immediately before the cells precipitated, using the NEPA21 electroporator (Nepa Gene). After electroporation, the cells were seeded in IFE medium with 10 µM Y-27632 and 5 µM PGE2. Five days after electroporation, cells were selected with 1 µg/mL puromycin in IFE medium. Whole mount immunostaining of colonoids Human colonoids embedded in BME were fixed in 4% formaldehyde (28908, Thermo Fisher) for 45 minutes at room temperature. After fixation, the organoids were washed in ice-cold D-PBS containing 2% BSA (A7906, Sigma-Aldrich). The organoids were incubated on a rotator at room temperature for 1 hr in blocking/permeabilization buffer, which consisted of 2% BSA, 5% donkey serum (D9663, Sigma-Aldrich) and 0.5% Triton X-100 (X100, Sigma) in D-PBS. After the blocking/permeabilization step, the organoids were incubated overnight at 4°C in a rotator with primary antibodies rabbit polyclonal anti-CHGA (1:800; ab15160, Abcam). On the next day, the organoids were washed and incubated with secondary antibodies, Alexa Fluor™ 568 donkey anti-rabbit (1:500, Invitrogen) in a rotator for 1 hr at room temperature. Nuclear counterstaining dye, Hoechst 33342 (5 µg/mL; H3570, Invitrogen) was added to the secondary antibody solution and incubated for another 15 min. Following thorough washes in D-PBS to remove all unbound antibodies, the organoids were mounted on a glass slide with a drop of mounting medium Fluoromount-G® (0100-01, Cambridge Bioscience) and covered with a coverslip. The slides were air-dried in the dark overnight at room temperature before being analysed by confocal microscopy. Immunohistochemical staining of colon biopsies Paraffin-embedded tissue blocks of human colon mucosal biopsies from healthy patients were provided by Dr Polychronis Pavlidis (King’s College Hospital, Denmark Hill). Tissue sections (5 µm) were mounted on positively coated Superfrost® Plus slides (MIC3040D2, Scientific Laboratory Supplies). Sections were deparaffinized and subsequently underwent heat-induced antigen retrieval by microwaving in 10 mM sodium citrate buffer (pH 6.0) for 10 min. After rinsing with water, slides were incubated at room temperature for 30 min in a blocking/permeabilization buffer containing 2% BSA, 5% donkey serum, and 0.5% Triton X-100 in D-PBS. Following blocking, sections were incubated overnight at 4°C in a humidified chamber with primary antibody IL-13Rα1 (1:200, rabbit; ab79277, Abcam) and GLP-1 (1:200, mouse; ab23468, Abcam) or 5-HT (1:200, goat; 20079, Immunostar). Slides were then washed in D-PBS and incubated with secondary antibodies (1:500, Alexa Fluor™, Invitrogen) for 1 hr at room temperature. Both primary and secondary antibodies were diluted in blocking/permeabilization buffer. After the secondary antibody incubation, slides were washed with D-PBS and mounted with DAPI Fluoromount-G® (0100 − 20, Cambridge Bioscience). The slides were air-dried in the dark overnight at room temperature before being analysed by confocal microscopy. FACS sorting fluorescent EECs After differentiation of the colonoids, they were dissociated to single cells in 2–4 mL TrypLE Express (Gibco), with 4 µg/mL DNase (79254, Qiagen) at 37°C for 15–20 min. Dissociated single cells were then filtered through a 40 µm cell strainer. The cells were then resuspended in FACS buffer (Advanced DMEM/F-12 medium, 2 mM GlutaMAX, 10 mM HEPES, 10 µM Y-27632, 2% FBS and 2 mM EDTA). For CHGA-mNeon and PYY-GFP cells, 0.1 µg/mL DAPI (10236276001, Merck) was used as the live/dead cell marker. For TPH1-CFP cells, 7-AAD (00-6993-50, Invitrogen™) at 0.25 µg per million cells was used. FACS was performed on the BD FACS Aria™ II (Beckton Dickinson) with a nozzle size of 100 µm. The gating strategy images were generated by FlowJo software. RNA extraction and real-time quantitative PCR (RT-qPCR) FACS-sorted cells were collected in a LoBind tube containing 500 µL of D-PBS. The cells were then centrifuged at 600 rcf, 4°C for 5 min. The total RNA extraction of the cells and cDNA transcription were performed using the SYBR™ Green Fast Advanced Cells-to-CT™ Kit (A35379, Invitrogen). RT-qPCR was performed using PowerUp SYBR Green Master Mix (A25742, Thermo Fisher) with QuantiTect primers or 500nM designed primers ( Table. S3 ) on LightCycler 96 (Roche). Total RNA extraction from the whole organoids was conducted using the RNeasy Kit (74106, Qiagen) after they were released from BME using Cell Recovery solution (11543560, Corning). On-column DNase I (79254, Qiagen) was used to remove residual genomic DNA. cDNA transcription was performed using Power High-Capacity cDNA Reverse Transcription Kit (4368813, Applied Biosystems). RT-qPCR was performed with QuantiFast SYBR Green PCR Kit (204057, Qiagen) with QuantiTect primers or 500nM designed primers ( Table. S3 ) on a LightCycler 96 (Roche). The relative gene expression levels were determined by averaging the Ct values from technical duplicates for each biological sample and then normalizing them against the expression of the reference gene beta-2-microglobulin ( B2M ). Single cell RNA-sequencing and data analysis Fluorescent single cells were sorted into 384-well cell capture plates (HSP3801, Bio-Rad) for SORT-seq. The samples were processed by Single Cell Discoveries (Utrecht, Netherlands) for library preparation, sequencing and alignment, following the published SORT-seq protocol 35 . The scRNA-seq analysis of raw counts was conducted in RStudio using the R package Seurat v4.3.0 36 . For human CHGA-mNeon cells, those with fewer than 1550 transcripts, unique feature counts exceeding 2500 or falling below 200, or mitochondrial counts exceeding 30% were excluded. The cells were normalized using SCTransform 77 . After normalization, distinct cell clusters were identified through shared nearest neighbour clustering optimization, based on the 4 most variable dimensions. A resolution of 0.4 was used during the clustering process. For mouse TPH1-CFP cells, those exceeding 4000 unique feature counts or falling below 200, or with mitochondrial counts surpassing 30%, were excluded. Following normalization using SCTransform, shared nearest neighbour clustering optimization was applied based on the 5 most variable dimensions, with a resolution of 0.4. This process identified three distinct cell clusters. However, one cluster was enriched with genes associated with cell death, indicating cell damage. Therefore, this population was removed. For mouse PYY-GFP cells, those with over 2500 unique feature counts or less than 200, along with mitochondrial counts exceeding 5%, were excluded. After SCTransform normalization, shared nearest neighbour clustering optimization with the 4 most variable dimensions and a resolution of 0.5 identified two distinct cell clusters. The cell clusters were plotted using UMAP dimension reduction. Expression of genes of interest across different cell clusters were plotted on heatmaps or distribution plots. Gene expression table of distinct clusters were generated by AverageExpression. Co-expression of two features was visualized by FeaturePlot. Bulk RNA-sequencing and data analysis Around 100,000 fluorescent and non-fluorescent single cells were FACS-sorted, pelleted and underwent RNA extraction with TRIzol™ LS Reagent (10296010, Invitrogen™). Three replicate samples were prepared for both fluorescent and non-fluorescent groups. The RNA samples were processed by Single Cell Discoveries for cDNA library preparation and sequencing. 75-bp paired-end sequencing of the cDNA libraries was performed on an Illumina NextSeq™ 500 platform. After sequencing, the paired-end reads were aligned to the human reference genome GRCh38/hg38 or mouse reference genome GRCm38/mm10 using Burrows-Wheeler Aligner (BWA) 78 . Differential gene expression analysis between the fluorescent and non-fluorescent groups (n = 3) was performed in RStudio using the Bioconductor package DESeq2 v1.38.1 79 . The PCA plot of differentially expressed genes was generated to demonstrate clusters of samples based on their similarity. Gene lists encoding receptors, orphan GPCRs, transporters, ion channels, gut hormones, transcription factors, cytokines were generated from the highly expressed gene list using QIAGEN Ingenuity Pathway Analysis (IPA) software, with log2FC > 1.5 and p-value < 0.05. Specifically, the genes with predicted protein location in the extracellular space were included in the gut hormone gene list. For display in heatmaps, genes were ranked by fold change compared against non-fluorescent cells. Differentially expressed genes with a p-value of 0 were replaced with 3x10 − 300 to enable visualisation in the volcano plot. Measurement of GLP-1 secretion in human colonoids Differentiated human colonoids were harvested with cell recovery solution and washed with D-PBS, with one well as one sample. The organoids were then incubated for 2 hrs at 37°C in 100 µL of saline secretion buffer containing 138 mM NaCl, 4.5 mM KCl, 4.2 mM NaHCO 3 , 1.2 mM NaH 2 PO 4 , 2.6 mM CaCl 2 , 1.2 mM MgCl 2 , 10 mM HEPES (pH 7.4), with freshly added 0.1% fatty acid free BSA (A6003, Sigma-Aldrich) and 50 µM DPP IV Inhibitor (DPP4, Sigma-Aldrich). Four groups were set up: a negative control, treatment of 10 µM Forskolin (FSK, F6886, Scientific Laboratory Supplies) and 10 µM IBMX (I5879, Merck), and treatments with 20ng/mL or 100 ng/mL recombinant human IL-13 protein (R&D Systems, 213-ILB-010). The supernatant and organoid lysates were collected to measure GLP-1 concentrations in secretion and total protein concentrations, respectively. Organoid lysates were prepared in 100 µL of D-PBS with protease inhibitors (A32955, Thermo Fisher) and sonicated on ice for 30 seconds at an amplitude of 10–14. The homogenates were then centrifuged at 5,000 rcf, 4°C for 5 min, and the supernatant was collected as organoid lysate samples. Both secretion and lysate samples were stored at -70°C. The GLP-1 concentrations of secretion and lysate samples were measured using GLP-1 ELISA kit (Merck, EGLP-35K). Total protein concentrations of organoid lysates were measured using Pierce™ BCA Protein Assay kit (23227, Thermo Fisher). The relative GLP-1 secretion levels were determined by averaging the GLP-1 concentrations from technical duplicates for each biological sample and then normalizing them against the concentrations of the total protein. FSCV cell measurements of 5-HT secretion in human colonoids, data acquisition and analysis CFM fabrication and FSCV data acquisition was performed as described previously 10 . Briefly, carbon fibre (T-650, 7 µm diameter; Goodfellow) was aspirated into a glass capillary (1.0 mm outer diameter, 0.58 mm inner diameter) and the capillary was pulled using PE-22 micropipette puller (Narishige Group) to create a seal around the fibre. The exposed carbon fibre was manually trimmed to 100 µm (+/- 2 µm) in length and back-connected using a pinned stainless-steel wire. The carbon surface was coated with Nafion™ (Liquion Solution, LQ-1105, 5% by weight Nafion, Ion Power) by applying 1 V (vs. Ag/AgCl) for 30 sec. All FSCV measurements were acquired in the saline secretion buffer as described above. Data collection and analysis was performed with WCCV 3.06 software (Knowmad Technologies), Dagan potentiostat (Dagan Corporation), and Pine Research headstage (Pine Research Instrumentation). A waveform optimized for 5-HT detection was applied (0.2 V to 1.0 V to -0.1 V to 0.2 V vs. Ag/AgCl at 1000 V/s) and measurements were taken at 10 Hz for 30 sec per file. For FSCV measurement, differentiated human CHGA-mNeon colonoids embedded in BME were mechanically detached from the plate using a p-1000 micropipette, washed in D-PBS to remove all culture medium, and then resuspended in 2 mL of saline secretion buffer. They were transferred to a 3.5 cm-dish, which was then affixed to the Bio Station IM (Nikon). EC cell-enriched structures were identified by mNeon green fluorescence before shielding the setup with aluminium foil grounded as a Faraday cage. The Ag/AgCl-reference electrode was placed in the dish and the CFM was positioned using a QUAD micromanipulator (Sutter Instruments). Repeated 30 sec-files were taken to record spontaneous activity for a total of 15 min. Subsequently, 100 nM PNX-20 or 100 ng/mL IL-13 was added, and recordings continued immediately for an additional 15 min. Events releasing 5-HT were identified by the 5-HT-specific oxidation peak in the cyclic voltammogram and quantified for each file. Frequency of 5-HT release event was calculated for each 30 sec-file, and peak amplitude was determined as the current difference immediately before release and the peak. Proteomics mass spectrometry measurement Around 100,000 sorted CHGA-mNeon + ve and mNeon − ve cells were pelleted and lysed with Branson sonifier 150 in 8.0M urea buffer. 20 µg of proteins from each sample were reduced with 7mM of 1,4-dithiothreitol for 1 hour at 56°C and alkylated with 12.5 mM iodoacetamide for 1 hour at room temperature in the dark. Samples were digested with trypsin enzyme at 1:50 ratio (enzyme:protein) and incubated at 37°C overnight. The digestion was quenched with 1% formic acid and tryptic peptides were purified and desalted by C18 spin column. Peptides were dried to completion by SpeedVac (Thermo) and resuspended in a loading buffer (2% acetonitrile in 0.1% formic acid) for LCMS analysis. The tryptic peptides were subjected to LCMS system for analysis. For liquid chromatography, a reverse phase Thermo Acclaim Pepmap trap column (2 cm length, 75 µm in diameter and 3 µm C18 beads) were connected to a nanoflow HPLC (RSLC Ultimate 3000) on an Easy-spray C18 nano column (50 cm length, 75 µm in diameter, ThermoFisherScientific). Buffer A (5% ACN, 0.1% formic acid) and buffer B (80% ACN, 0.1% formic acid) were used. Peptides were eluted with a linear gradient of 5–55% buffer B at a flow rate of 250 nl/min over 100 min at 45°C. Peptides were directly ionized within the easyspray ion source (Thermo) and injected into Orbitrap Fusion Lumos mass spectrometers (Thermo). MS data generated were collected within Xcalibur 4.4 to acquire MS data using a “Universal” method by defining a 3s cycle time between a full MS scan and MS/MS fragmentation. We acquired one full-scan MS spectrum at a resolution of 120,000 at 200 m/z with a normalized automatic gain control (AGC) target (%) of 250 and a scan range of 300 ~ 1600 m/z. The MS/MS fragmentation was conducted using CID collision energy (35%) with an orbitrap resolution of 30000 at 200 m/z. The AGC target (%) was set up as 200 with a max injection time of 128ms. A dynamic exclusion of 30s and 2–7 included charged states were defined within this method. Resulting raw files were searched again the human Uniprot fasta database within Thermo Proteome Discoverer (PD, version 2.5) allowing two missed trypsin cleavage sites and methionine oxidation. Carbamidomethylation on cysteine residues was set as a fixed modification. Precursor mass tolerance was set as 20 ppm and fragment ion tolerance was set as 0.6 Da. Peptides that met the false discovery rate cut-off of 1% based on the searching again a decoy database was considered for further analysis. Precursor ion intensities extracted from PD were applied for differential analysis using two-sample t-test algorithm embedded in Perseus software v2.1.1.0 using two-sample t-test algorithm 80 . Accession IDs were converted to gene IDs by SynGO 81 . Statistics The unpaired t-test was utilized to compare two unrelated groups when the test statistic followed a normal distribution. For comparisons involving multiple conditions, the one-way analysis of variance (ANOVA) was conducted. Statistical significance was accepted at p values < 0.05. The data were presented as mean ± SEM (n ≥ 3) and denoted as *p < 0.05, **p < 0.01, ***p < 0.001, or ****p < 0.0001 to indicate the level of significance. GraphPad Prism 10 was used for generating graphs and performing statistical analysis. Declarations Author contributions Y.L. and G.A.B. conceptualized the project and wrote the manuscript. Y.L. generated the reporter organoid model and analysed the EEC transcriptome database. B.B. and P.H. performed the FSCV serotonin secretion experiments and analysis. L.M. and K.G.M. participated in the editing of the manuscript. M.J. participated in bulk RNA analysis. N.H. participated in plasmid cloning and confocal imaging. X.Y. conducted the proteomics study. B.H. provided human biopsies for isolation of organoids. All authors reviewed the manuscript. Conflict of interests None declared. Acknowledgements We would like to thank Dr Joep Beumer and Dr Hans Clevers from Hubrecht Institute for providing the CRISPR-HOT plasmids, Dr Cordelia Imig from University of Copenhagen for providing TPH1-CFP mouse colonoids and Dr Polychronis Pavlidis from King’s College Hospital for providing paraffin-embedded tissue blocks of human colon mucosal biopsies. We appreciate the assistance from the staff in BRC Flow cytometry core, Guy's and St Thomas NHS Foundation Trust, Nikon Imaging Centre and Proteomics Facility at King’s College London. Y.L. acknowledges support from China Scholarship Council (K-CSC) and the Society for Endocrinology (Early Career Grant). K.G.M is supported by Diabetes UK (18/0005886, 20/0006295), the BBSRC (BB/W001497/1, BB/X017273/1). Both G.A.B and K.G.M are supported by the MRC (MR/Y013980/1) and the Wellcome Trust (310835/Z/24/Z). References Gribble FM, Reimann F. Function and mechanisms of enteroendocrine cells and gut hormones in metabolism. Nat Rev Endocrinol . 2019;15(4):226-237. doi:10.1038/s41574-019-0168-8 Gehart H, van Es JH, Hamer K, et al. Identification of Enteroendocrine Regulators by Real-Time Single-Cell Differentiation Mapping. Cell . 2019;176(5):1158-1173.e16. doi:10.1016/j.cell.2018.12.029 Goldspink DA, Reimann F, Gribble FM. Models and Tools for Studying Enteroendocrine Cells. Endocrinology . 2018;159(12):3874-3884. doi:10.1210/en.2018-00672 Beumer J, Artegiani B, Post Y, et al. Enteroendocrine cells switch hormone expression along the crypt-to-villus BMP signalling gradient. Nature Cell Biology . 2018;20(8):909-916. doi:10.1038/s41556-018-0143-y Yu Y, Yang W, Li Y, Cong Y. Enteroendocrine Cells: Sensing Gut Microbiota and Regulating Inflammatory Bowel Diseases. Inflamm Bowel Dis . 2020;26(1):11-20. doi:10.1093/ibd/izz217 Worthington JJ, Reimann F, Gribble FM. Enteroendocrine cells-sensory sentinels of the intestinal environment and orchestrators of mucosal immunity. Mucosal Immunol . 2018;11(1):3-20. doi:10.1038/mi.2017.73 Andersen A, Lund A, Knop FK, Vilsbøll T. Glucagon-like peptide 1 in health and disease. Nat Rev Endocrinol . 2018;14(7):390-403. doi:10.1038/s41574-018-0016-2 Beumer J, Puschhof J, Bauzá-Martinez J, et al. High-Resolution mRNA and Secretome Atlas of Human Enteroendocrine Cells. Cell . 2020;0(0). doi:10.1016/j.cell.2020.04.036 Schmid-Burgk JL, Höning K, Ebert TS, Hornung V. CRISPaint allows modular base-specific gene tagging using a ligase-4-dependent mechanism. Nature Communications . 2016;7(1):12338. doi:10.1038/ncomms12338 Holmes J, Lau T, Saylor R, et al. Voltammetric Approach for Characterizing the Biophysical and Chemical Functionality of Human Induced Pluripotent Stem Cell-Derived Serotonin Neurons. Anal Chem . 2022;94(25):8847-8856. doi:10.1021/acs.analchem.1c05082 Artegiani B, Hendriks D, Beumer J, et al. Fast and efficient generation of knock-in human organoids using homology-independent CRISPR-Cas9 precision genome editing. Nat Cell Biol . 2020;22(3):321-331. doi:10.1038/s41556-020-0472-5 Goldspink DA, Lu VB, Miedzybrodzka EL, et al. Labeling and Characterization of Human GLP-1-Secreting L-cells in Primary Ileal Organoid Culture. Cell Reports . 2020;31(13):107833. doi:10.1016/j.celrep.2020.107833 Fujii M, Matano M, Toshimitsu K, et al. Human Intestinal Organoids Maintain Self-Renewal Capacity and Cellular Diversity in Niche-Inspired Culture Condition. Cell Stem Cell . 2018;23(6):787-793.e6. doi:10.1016/j.stem.2018.11.016 Tsakmaki A, Fonseca Pedro P, Pavlidis P, Hayee B, Bewick GA. ISX-9 manipulates endocrine progenitor fate revealing conserved intestinal lineages in mouse and human organoids. Molecular Metabolism . 2020;34:157-173. doi:10.1016/j.molmet.2020.01.012 Lin L, DeMartino J, Wang D, et al. Unbiased transcription factor CRISPR screen identifies ZNF800 as master repressor of enteroendocrine differentiation. Science . 2023;382(6669):451-458. doi:10.1126/science.adi2246 Psichas A, Glass LL, Sharp SJ, Reimann F, Gribble FM. Galanin inhibits GLP-1 and GIP secretion via the GAL1 receptor in enteroendocrine L and K cells. Br J Pharmacol . 2016;173(5):888-898. doi:10.1111/bph.13407 Brzozowska M, Całka J. Review: Occurrence and Distribution of Galanin in the Physiological and Inflammatory States in the Mammalian Gastrointestinal Tract. Front Immunol . 2021;11:602070. doi:10.3389/fimmu.2020.602070 Garella R, Squecco R, Baccari MC. Site-related Effects of Relaxin in the Gastrointestinal Tract Through Nitric Oxide Signalling: An Updated Report. Curr Protein Pept Sci . 2017;18(12):1254-1262. doi:10.2174/1389203718666170612104719 Pendharkar SA, Walia M, Drury M, Petrov MS. Calcitonin gene-related peptide: neuroendocrine communication between the pancreas, gut, and brain in regulation of blood glucose. Annals of Translational Medicine . 2017;5(21):419-419. doi:10.21037/atm.2017.08.27 Topaloglu AK, Reimann F, Guclu M, et al. TAC3 and TACR3 Mutations in Familial Hypogonadotropic Hypogonadism Reveal a Key Role for Neurokinin B in the Central Control of Reproduction. Nat Genet . 2009;41(3):354-358. doi:10.1038/ng.306 Lecci A, Capriati A, Altamura M, Maggi CA. Tachykinins and tachykinin receptors in the gut, with special reference to NK2 receptors in human. Auton Neurosci . 2006;126-127:232-249. doi:10.1016/j.autneu.2006.02.014 Masuda T, Sakuma C, Nagaoka A, et al. Follistatin-like 5 is expressed in restricted areas of the adult mouse brain: Implications for its function in the olfactory system. Congenit Anom (Kyoto) . 2014;54(1):63-66. doi:10.1111/cga.12022 Li C, Dai L, Zhang J, et al. Follistatin‐like protein 5 inhibits hepatocellular carcinoma progression by inducing caspase‐dependent apoptosis and regulating Bcl‐2 family proteins. J Cell Mol Med . 2018;22(12):6190-6201. doi:10.1111/jcmm.13906 Husted AS, Trauelsen M, Rudenko O, Hjorth SA, Schwartz TW. GPCR-Mediated Signaling of Metabolites. Cell Metabolism . 2017;25(4):777-796. doi:10.1016/j.cmet.2017.03.008 Parikh K, Antanaviciute A, Fawkner-Corbett D, et al. Colonic epithelial cell diversity in health and inflammatory bowel disease. Nature . 2019;567(7746):49-55. doi:10.1038/s41586-019-0992-y Chichura KS, Elfers CT, Salameh TS, et al. A peptide triple agonist of GLP-1, neuropeptide Y1, and neuropeptide Y2 receptors promotes glycemic control and weight loss. Sci Rep . 2023;13(1):9554. doi:10.1038/s41598-023-36178-1 Adriaenssens AE, Gribble FM, Reimann F. The glucose-dependent insulinotropic polypeptide signaling axis in the central nervous system. Peptides . 2020;125:170194. doi:10.1016/j.peptides.2019.170194 Holzer P, Reichmann F, Farzi A. Neuropeptide Y, peptide YY and pancreatic polypeptide in the gut–brain axis. Neuropeptides . 2012;46(6):261-274. doi:10.1016/j.npep.2012.08.005 Lewis JE, Woodward ORM, Nuzzaci D, et al. Relaxin/insulin-like family peptide receptor 4 (Rxfp4) expressing hypothalamic neurons modulate food intake and preference in mice. Mol Metab . 2022;66:101604. doi:10.1016/j.molmet.2022.101604 Fernando SJA, Wang Q, Hay DL, Bathgate RAD, Shepherd PR, Lee KL. Evidence that RXFP4 is located in enterochromaffin cells and can regulate production and release of serotonin. Biosci Rep . 2023;43(4):BSR20221956. doi:10.1042/BSR20221956 Yang X, Lou J, Shan W, et al. Pathophysiologic Role of Neurotransmitters in Digestive Diseases. Front Physiol . 2021;12. doi:10.3389/fphys.2021.567650 Manocha M, Shajib MS, Rahman MM, et al. IL-13-mediated immunological control of enterochromaffin cell hyperplasia and serotonin production in the gut. Mucosal Immunol . 2013;6(1):146-155. doi:10.1038/mi.2012.58 Friedrich M, Diegelmann J, Schauber J, Auernhammer CJ, Brand S. Intestinal neuroendocrine cells and goblet cells are mediators of IL-17A-amplified epithelial IL-17C production in human inflammatory bowel disease. Mucosal Immunol . 2015;8(4):943-958. doi:10.1038/mi.2014.124 McIlwraith EK, Zhang N, Belsham DD. The Regulation of Phoenixin: A Fascinating Multidimensional Peptide. Journal of the Endocrine Society . 2022;6(2):bvab192. doi:10.1210/jendso/bvab192 Muraro MJ, Dharmadhikari G, Grün D, et al. A Single-Cell Transcriptome Atlas of the Human Pancreas. Cell Syst . 2016;3(4):385-394.e3. doi:10.1016/j.cels.2016.09.002 Stuart T, Butler A, Hoffman P, et al. Comprehensive Integration of Single-Cell Data. Cell . 2019;177(7):1888-1902.e21. doi:10.1016/j.cell.2019.05.031 Elmentaite R, Kumasaka N, Roberts K, et al. Cells of the human intestinal tract mapped across space and time. Nature . 2021;597(7875):250-255. doi:10.1038/s41586-021-03852-1 Han YF, Cao GW. Role of nuclear receptor NR4A2 in gastrointestinal inflammation and cancers. World J Gastroenterol . 2012;18(47):6865-6873. doi:10.3748/wjg.v18.i47.6865 Sasselli V, Boesmans W, Berghe PV, Tissir F, Goffinet AM, Pachnis V. Planar cell polarity genes control the connectivity of enteric neurons. J Clin Invest . 2013;123(4):1763-1772. doi:10.1172/JCI66759 Samson WK, Salvemini D, Yosten GLC. Overcoming Stress, Hunger, and Pain: Cocaine- and Amphetamine-Regulated Transcript Peptide’s Promise. Endocrinology . 2021;162(8):bqab108. doi:10.1210/endocr/bqab108 Shcherbina L, Lindqvist A, Thorén Fischer AH, et al. Intestinal CART is a regulator of GIP and GLP-1 secretion and expression. Molecular and Cellular Endocrinology . 2018;476:8-16. doi:10.1016/j.mce.2018.04.002 Shimizu D, Kanda M, Tanaka H, et al. GPR155 Serves as a Predictive Biomarker for Hematogenous Metastasis in Patients with Gastric Cancer. Sci Rep . 2017;7(1):42089. doi:10.1038/srep42089 Si X, Song Z, Liu N, Jia H, Liu H, Wu Z. α-Ketoglutarate Restores Intestinal Barrier Function through Promoting Intestinal Stem Cells-Mediated Epithelial Regeneration in Colitis. J Agric Food Chem . 2022;70(43):13882-13892. doi:10.1021/acs.jafc.2c04641 Shaaban A, Maaß F, Schwarze V, et al. Dissecting Functional, Structural, and Molecular Requirements for Serotonin Release from Mouse Enterochromaffin Cells. Published online May 29, 2021:2021.05.28.446100. doi:10.1101/2021.05.28.446100 Gross S, Garofalo DC, Balderes DA, et al. The novel enterochromaffin marker Lmx1a regulates serotonin biosynthesis in enteroendocrine cell lineages downstream of Nkx2.2. Development . 2016;143(14):2616-2628. doi:10.1242/dev.130682 Pattyn A, Simplicio N, van Doorninck JH, Goridis C, Guillemot F, Brunet JF. Ascl1/Mash1 is required for the development of central serotonergic neurons. Nat Neurosci . 2004;7(6):589-595. doi:10.1038/nn1247 Takahashi M. The GDNF/RET signaling pathway and human diseases. Cytokine & Growth Factor Reviews . 2001;12(4):361-373. doi:10.1016/S1359-6101(01)00012-0 Porokuokka LL, Virtanen HT, Lindén J, et al. Gfra1 Underexpression Causes Hirschsprung’s Disease and Associated Enterocolitis in Mice. Cell Mol Gastroenterol Hepatol . 2018;7(3):655-678. doi:10.1016/j.jcmgh.2018.12.007 Iwasaki M, Akiba Y, Kaunitz JD. Recent advances in vasoactive intestinal peptide physiology and pathophysiology: focus on the gastrointestinal system. F1000Res . 2019;8:F1000 Faculty Rev-1629. doi:10.12688/f1000research.18039.1 Shamsi BH, Chatoo M, Xu XK, Xu X, Chen XQ. Versatile Functions of Somatostatin and Somatostatin Receptors in the Gastrointestinal System. Front Endocrinol (Lausanne) . 2021;12:652363. doi:10.3389/fendo.2021.652363 K U, H G, E K. Sphingosine 1-phosphate is a ligand of the human gpr3, gpr6 and gpr12 family of constitutively active G protein-coupled receptors. Cellular signalling . 2002;14(11). doi:10.1016/s0898-6568(02)00041-4 Zou F, Wang S, Xu M, Wu Z, Deng F. The role of sphingosine-1-phosphate in the gut mucosal microenvironment and inflammatory bowel diseases. Front Physiol . 2023;14:1235656. doi:10.3389/fphys.2023.1235656 Bjursell M, Gerdin AK, Jönsson M, et al. G protein-coupled receptor 12 deficiency results in dyslipidemia and obesity in mice. Biochem Biophys Res Commun . 2006;348(2):359-366. doi:10.1016/j.bbrc.2006.07.090 Nath AK, Ma J, Chen ZZ, et al. Genetic deletion of gpr27 alters acylcarnitine metabolism, insulin sensitivity, and glucose homeostasis in zebrafish. FASEB J . 2020;34(1):1546-1557. doi:10.1096/fj.201901466R Cui J, Ding Y, Chen S, et al. Disruption of Gpr45 causes reduced hypothalamic POMC expression and obesity. J Clin Invest . 2016;126(9):3192-3206. doi:10.1172/JCI85676 Zong X, Wang H, Xiao X, et al. Enterotoxigenic Escherichia coli infection promotes enteric defensin expression via FOXO6-METTL3-m6A-GPR161 signalling axis. RNA Biology . 2020;18(4):576. doi:10.1080/15476286.2020.1820193 Niedernberg A, Tunaru S, Blaukat A, Ardati A, Kostenis E. Sphingosine 1-phosphate and dioleoylphosphatidic acid are low affinity agonists for the orphan receptor GPR63. Cell Signal . 2003;15(4):435-446. doi:10.1016/s0898-6568(02)00119-5 Maeyashiki C, Melhem H, Hering L, et al. Activation of pH-Sensing Receptor OGR1 (GPR68) Induces ER Stress Via the IRE1α/JNK Pathway in an Intestinal Epithelial Cell Model. Sci Rep . 2020;10(1):1438. doi:10.1038/s41598-020-57657-9 Chulkina M, Beswick EJ, Pinchuk IV. Role of PD-L1 in Gut Mucosa Tolerance and Chronic Inflammation. Int J Mol Sci . 2020;21(23):9165. doi:10.3390/ijms21239165 Chiriac MT, Hracsko Z, Günther C, et al. IL-20 controls resolution of experimental colitis by regulating epithelial IFN/STAT2 signalling. Gut . 2024;73(2):282-297. doi:10.1136/gutjnl-2023-329628 Riaz B, Islam SMS, Ryu HM, Sohn S. CD83 Regulates the Immune Responses in Inflammatory Disorders. Int J Mol Sci . 2023;24(3):2831. doi:10.3390/ijms24032831 Ballet R, Brennan M, Brandl C, et al. A CD22–Shp1 phosphatase axis controls integrin β7 display and B cell function in mucosal immunity. Nat Immunol . 2021;22(3):381-390. doi:10.1038/s41590-021-00862-z Bohórquez DV, Chandra R, Samsa LA, Vigna SR, Liddle RA. Characterization of basal pseudopod-like processes in ileal and colonic PYY cells. J Mol Hist . 2011;42(1):3-13. doi:10.1007/s10735-010-9302-6 Shroyer NF, Helmrath MA, Wang VY –C., Antalffy B, Henning SJ, Zoghbi HY. Intestine-Specific Ablation of Mouse atonal homolog 1 ( Math1 ) Reveals a Role in Cellular Homeostasis. Gastroenterology . 2007;132(7):2478-2488. doi:10.1053/j.gastro.2007.03.047 Shajib MdS, Wang H, Kim JJ, et al. Interleukin 13 and Serotonin: Linking the Immune and Endocrine Systems in Murine Models of Intestinal Inflammation. PLoS One . 2013;8(8):e72774. doi:10.1371/journal.pone.0072774 Bellono NW, Bayrer JR, Leitch DB, et al. Enterochromaffin Cells Are Gut Chemosensors that Couple to Sensory Neural Pathways. Cell . 2017;170(1):185-198.e16. doi:10.1016/j.cell.2017.05.034 Beumer J, Geurts MH, Geurts V, et al. Description and functional validation of human enteroendocrine cell sensors. Science . 2024;386(6719):341-348. doi:10.1126/science.adl1460 Guccio N, Alcaino C, Miedzybrodzka EL, et al. Molecular mechanisms underlying glucose-dependent insulinotropic polypeptide secretion in human duodenal organoids. Diabetologia . Published online October 23, 2024. doi:10.1007/s00125-024-06293-3 Miedzybrodzka EL, Foreman RE, Lu VB, et al. Stimulation of motilin secretion by bile, free fatty acids, and acidification in human duodenal organoids. Mol Metab . 2021;54:101356. doi:10.1016/j.molmet.2021.101356 Sato T, Stange DE, Ferrante M, et al. Long-term Expansion of Epithelial Organoids From Human Colon, Adenoma, Adenocarcinoma, and Barrett’s Epithelium. Gastroenterology . 2011;141(5):1762-1772. doi:10.1053/j.gastro.2011.07.050 Powell N, Pantazi E, Pavlidis P, et al. Interleukin-22 orchestrates a pathological endoplasmic reticulum stress response transcriptional programme in colonic epithelial cells. Gut . 2020;69(3):578-590. doi:10.1136/gutjnl-2019-318483 Ran FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering using the CRISPR-Cas9 system. Nat Protoc . 2013;8(11):2281-2308. doi:10.1038/nprot.2013.143 Korbie DJ, Mattick JS. Touchdown PCR for increased specificity and sensitivity in PCR amplification. Nat Protoc . 2008;3(9):1452-1456. doi:10.1038/nprot.2008.133 Fujii M, Matano M, Nanki K, Sato T. Efficient genetic engineering of human intestinal organoids using electroporation. Nat Protoc . 2015;10(10):1474-1485. doi:10.1038/nprot.2015.088 Gaebler AM, Hennig A, Buczolich K, et al. Universal and Efficient Electroporation Protocol for Genetic Engineering of Gastrointestinal Organoids. JoVE (Journal of Visualized Experiments) . 2020;(156):e60704. doi:10.3791/60704 Fan YY, Davidson LA, Callaway ES, Goldsby JS, Chapkin RS. Differential effects of 2- and 3-series E-prostaglandins on in vitro expansion of Lgr5+ colonic stem cells. Carcinogenesis . 2014;35(3):606-612. doi:10.1093/carcin/bgt412 Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology . 2019;20(1):296. doi:10.1186/s13059-019-1874-1 Schuhwerk H, Kleemann J, Gupta P, et al. The EMT transcription factor ZEB1 governs a fitness-promoting but vulnerable DNA replication stress response. Cell Reports . 2022;41(11):111819. doi:10.1016/j.celrep.2022.111819 Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology . 2014;15(12):550. doi:10.1186/s13059-014-0550-8 Tyanova S, Temu T, Sinitcyn P, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods . 2016;13(9):731-740. doi:10.1038/nmeth.3901 Koopmans F, Nierop P van, Andres-Alonso M, et al. SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse. Neuron . 2019;103(2):217-234.e4. doi:10.1016/j.neuron.2019.05.002 Additional Declarations There is NO Competing Interest. Supplementary Files Table1.HumanEECsdifferentialexpressionfunctionalcategories.xlsx Dataset 1 Table2.MouseECsdifferentialexpressionfunctionalcategories.xlsx Dataset 2 Table3.HumanEECsclusterexpressionfunctionalcategories.xlsx Dataset 3 Table4.MouseECsclusterexpressionfunctionalcategories.xlsx Dataset 4 Table5.PrimersincloningandqPCR.xlsx Dataset 5 supplementoryfigures.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5411079","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":391011637,"identity":"87269038-3678-4a6f-8293-dcfb150f6694","order_by":0,"name":"Gavin Bewick","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYJCCA2DEDIQfDjDIwMWI0sI44wADD1FaYCqYmXmI0SLvfsbwcAHDHTmD48yPjW3O2PEwsB9+wMxzBrcWwzM5BodnMDwzNjjMZpyccyOZh4EnzYCZ5wYeLQ1pCYd5GA4nbjvMw3w458MBoMNyGJh5PuDR0v8MrKUerMUCpIX/DX4t8hLJB0BaEsyAWpIZbgC1SIBsweMwA4nHQC0Gzwz3A/1i2HMmmYdN4pnBwTl4vC/fn9j8mafijrxk/+HHEj+O2cnx8yc/fPDmGB5bDoBJJBE2BgIRKd+AT3YUjIJRMApGAQgAAAxKUdcqEZvqAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4335-8403","institution":"King's College London","correspondingAuthor":true,"prefix":"","firstName":"Gavin","middleName":"","lastName":"Bewick","suffix":""},{"id":391011638,"identity":"3c055327-f318-4384-94d3-3c40bde6efa8","order_by":1,"name":"Yuxian Lei","email":"","orcid":"https://orcid.org/0000-0003-0280-3168","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Yuxian","middleName":"","lastName":"Lei","suffix":""},{"id":391011639,"identity":"4988c8bc-116e-4607-b3f1-230319c9b8b7","order_by":2,"name":"Bettina Bohl","email":"","orcid":"https://orcid.org/0000-0003-0373-7836","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Bettina","middleName":"","lastName":"Bohl","suffix":""},{"id":391011640,"identity":"f35e072a-56a6-4cf1-97d3-cf424fd2669e","order_by":3,"name":"Leah Meyer","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Leah","middleName":"","lastName":"Meyer","suffix":""},{"id":391011641,"identity":"49c6489b-928d-411b-9815-6a301c5d3226","order_by":4,"name":"Margot Jacobs","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Margot","middleName":"","lastName":"Jacobs","suffix":""},{"id":391011642,"identity":"2f8bca37-d258-4b74-a8e7-609b8be5e22f","order_by":5,"name":"Naila Haq","email":"","orcid":"https://orcid.org/0000-0003-1887-5449","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Naila","middleName":"","lastName":"Haq","suffix":""},{"id":391011643,"identity":"0d27fae0-a57f-4162-b451-c79fd858cbce","order_by":6,"name":"Xiaoping Yang","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Yang","suffix":""},{"id":391011644,"identity":"6776f7d2-b444-4b3b-80e1-a53d7580a161","order_by":7,"name":"Bu’ Hussain Hayee","email":"","orcid":"","institution":"King's College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bu’","middleName":"Hussain","lastName":"Hayee","suffix":""},{"id":391011645,"identity":"0f3c67a9-6738-4a25-b363-b61cb1a7e5c6","order_by":8,"name":"Kevin Murphy","email":"","orcid":"https://orcid.org/0000-0002-3417-0310","institution":"Mr","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Murphy","suffix":""},{"id":391011646,"identity":"2fdd87fa-4998-4955-970a-c1a3fbba098f","order_by":9,"name":"Parastoo Hashemi","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Parastoo","middleName":"","lastName":"Hashemi","suffix":""}],"badges":[],"createdAt":"2024-11-07 15:36:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5411079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5411079/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71763649,"identity":"517cf7f5-ca0f-4493-8f38-13f7f31aad1b","added_by":"auto","created_at":"2024-12-18 11:23:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2987753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneration of human CHGA-mNeon reporter colonoids. a,\u003c/strong\u003e Schematic showing mNeon insertion into the CHGA gene by CRISPR–Cas9-mediated homology-independent organoid transgenesis (CRISPR-HOT). \u003cstrong\u003eb,\u003c/strong\u003e Schematic demonstrating differentiation protocol of human colonoids to enrich enteroendocrine cells (EECs). \u003cstrong\u003ec,\u003c/strong\u003e Live image of CHGA-mNeon colonoids showing the mNeon green fluorescence reporter expression. Scale bar, 50 µm. \u003cstrong\u003ed,\u003c/strong\u003e Confocal images of immunofluorescent staining of CHGA-mNeon human colonoids for CHGA (red), which colocalized with mNeon green fluorescence. Scale bar, 50 µm. \u003cstrong\u003ee,\u003c/strong\u003e qPCR analysis of key EEC developmental markers in the time course differentiation of CHGA-mNeon human colonoids. Data are represented as mean ± SEM. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ****p \u0026lt; 0.0001 by one-way ANOVA tests.\u0026nbsp; \u003cstrong\u003ef,\u003c/strong\u003e Representative fluorescence-activated cell sort (FACS) plot of 500,000 events. mNeon-positive and -negative cells were sorted based on mNeon fluorescence, with DAPI-negative gating. \u003cstrong\u003eg,\u003c/strong\u003e qPCR analysis of mNeon\u003csup\u003e-ve\u003c/sup\u003e and mNeon\u003csup\u003e+ve\u003c/sup\u003e sorted cells. Data are represented as mean ± SEM. *p \u0026lt; 0.05, ****p \u0026lt; 0.0001 by unpaired t tests.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/b33e24c3ec02c7631d5e2517.png"},{"id":71761734,"identity":"c43436b5-87ef-4c60-aeb7-be3abf8edf59","added_by":"auto","created_at":"2024-12-18 11:07:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2500860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of CHGA-mNeon cells on bulk and single-cell scales.\u003c/strong\u003e \u003cstrong\u003ea,\u003c/strong\u003e Principal component analysis (PCA) plot comparing mNeon positive and negative cell populations by bulk RNA-sequencing (n=3). \u003cstrong\u003eb,\u003c/strong\u003e Volcano plot showing differential expression of selected genes. \u003cstrong\u003ec,\u003c/strong\u003e Heatmap showing differentially expressed receptor genes with the highest fold change and a p value \u0026lt; 0.01. \u003cstrong\u003ed,\u003c/strong\u003e UMAP showing segregation and annotation of CHGA-mNeon cells by single-cell RNA-sequencing. \u003cstrong\u003ee,\u003c/strong\u003eHeatmap showing relative expression of selected key developmental markers for the EECs across different cell clusters. \u003cstrong\u003ef,\u003c/strong\u003e Dot plot of genes coding receptors and orphan GPCRs, identified across different human colonic EEC clusters. Size of the circles represents percentage of cells expressing the gene and color of the circles represents average expression of indicated genes partitioned by clusters.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/545d667cdd171dbf45162bed.png"},{"id":71761736,"identity":"1cc02f2c-e728-4b89-9153-8b4ced58d4f6","added_by":"auto","created_at":"2024-12-18 11:07:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3312963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of mouse colonic Tph1-CFP cells on bulk and single-cell scales.\u003c/strong\u003e \u003cstrong\u003ea,\u003c/strong\u003e Live image of mouse TPH1-CFP colonoids showing the CFP fluorescence reporter expression. Scale bar, 50µm. \u003cstrong\u003eb,\u003c/strong\u003e FACS output plot of 450,000 events, showing the sorting of CFP-positive and -negative cells, with 7AAD-negative gating. \u003cstrong\u003ec,\u003c/strong\u003e qPCR analysis of CFP\u003csup\u003e-\u003c/sup\u003e and CFP\u003csup\u003e+\u003c/sup\u003e sorted cells. Data are represented as mean ± SEM. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ****p \u0026lt; 0.0001 by unpaired t tests. \u003cstrong\u003ed,\u003c/strong\u003e PCA plot of CFP-positive and -negative cell populations by bulk RNA-sequencing (n=3). \u003cstrong\u003ee,\u003c/strong\u003e Volcano plot showing differential expression of selected genes. \u003cstrong\u003ef,\u003c/strong\u003e Heatmap showing differentially expressed receptor genes with the highest fold change and a p value \u0026lt; 0.01. \u003cstrong\u003eg,\u003c/strong\u003e Venn diagram showing the conserved and uniquely expressed top receptor genes between human EECs and mouse ECs. \u003cstrong\u003eh,\u003c/strong\u003e UMAP showing segregation and annotation of Tph1-CFP cells by single-cell RNA-sequencing. \u003cstrong\u003ei,\u003c/strong\u003e Heatmap showing relative expression of selected key markers in Tph1-CFP cells across different cell clusters.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/5f9fe93186a2d92533fb098b.png"},{"id":71763097,"identity":"92a7ddde-fe62-4de1-8f6c-06240f2e3656","added_by":"auto","created_at":"2024-12-18 11:15:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2367041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of mouse colonic Pyy-GFP cells. a, \u003c/strong\u003eFACS output plot of 500,000 events, showing the sorting of PYY-GFP-positive and -negative cells, with DAPI-negative gating.\u003cstrong\u003e b, \u003c/strong\u003eqPCR analysis of GFP\u003csup\u003e-\u003c/sup\u003e and GFP\u003csup\u003e+\u003c/sup\u003e sorted cells. Data are represented as mean ± SEM. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ****p \u0026lt; 0.0001 by unpaired t tests. \u003cstrong\u003ec, \u003c/strong\u003eUMAP showing segregation and annotation of Pyy-GFP cells by single-cell RNA-sequencing\u003cstrong\u003e d,\u003c/strong\u003e Heatmap showing relative expression of selected key markers in Pyy-GFP cells across different cell clusters. \u003cstrong\u003ee,\u003c/strong\u003e Dot plot of genes coding human receptors and orphan GPCRs were aligned across different mouse colonic EEC clusters. Size of the circles represents percentage of cells expressing the gene and color of the circles represents average expression of indicated genes partitioned by clusters.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/aedc7021bb88c836cc79a720.png"},{"id":71763095,"identity":"9116118f-ea2a-479d-afc0-0c9c05068915","added_by":"auto","created_at":"2024-12-18 11:15:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3145109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActivation of IL13RA1 induced GLP-1 secretion\u003c/strong\u003e. \u003cstrong\u003ea\u003c/strong\u003e, qPCR analysis showing enrichment of receptor genes \u003cem\u003eGPR173\u003c/em\u003e and \u003cem\u003eIL13RA1 \u003c/em\u003ein human CHGA-mNeon cells. \u003cstrong\u003eb,\u003c/strong\u003e UMAP displaying co-expression of receptor genes \u003cem\u003eGPR173\u003c/em\u003e and \u003cem\u003eIL13RA1\u003c/em\u003ewith hormone genes \u003cem\u003eTPH1\u003c/em\u003e and \u003cem\u003eGCG\u003c/em\u003e in human colonic EECs on the single-cell level. Bars display color-coded unique transcript expression in logarithmic scale. \u003cstrong\u003ec,\u003c/strong\u003e Immunohistochemical staining showing colocalization of IL13RA1 with GLP-1 or 5-HT in human colon mucosal biopsy sections. Scale bar 10 µm. \u003cstrong\u003ed,\u003c/strong\u003e qPCR analysis showing enrichment of \u003cem\u003eGPR173\u003c/em\u003ein mouse Tph1-CFP cells, \u003cem\u003eIL13RA1\u003c/em\u003e in both Pyy-GFP and Tph1-CFP cells. \u003cstrong\u003ee,\u003c/strong\u003e GLP-1 secretions in response to 20 and 100 ng/mL IL-13, and 10 µM forskolin (FSK) plus 10 µM 3-isobutyl-1-methylxanthine (IBMX) were measured in supernatants by ELISA. Values were normalized against the total protein concentrations. Data are represented as mean ± SEM. Unpaired t tests were performed, with *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/7ef72fa42edbe11ad7656be0.png"},{"id":71761751,"identity":"59ef5586-94db-4b05-9c41-ccf1db50f541","added_by":"auto","created_at":"2024-12-18 11:07:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1587661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActivation of IL13RA1 and GPR173 induced serotonin (5-HT) secretion.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e, Schematic showing fast cyclic voltammetry (FSCV) measuring real-time 5HT secretion from CHGA-mNeon fluorescent cells in human colonoids, where extracellular 5-HT is detected as a change in current at a carbon fibre microelectrode (CFM). \u003cstrong\u003eb\u003c/strong\u003e, Representative current-time plots of each organoid, after treatment of 10 µM FSK/IBMX, 100 nM Phoenixin-20, or 100 ng/mL IL-13. Vehicle control was measured in secretion buffer without organoids. \u003cstrong\u003ec\u003c/strong\u003e, Quantified frequencies of 5-HT release events. \u003cstrong\u003ed,\u003c/strong\u003e The peak amplitude indicating the amount of 5-HT released per event.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/ca56d8a2c4b783da44cb4885.png"},{"id":71764701,"identity":"cd426374-24f7-472a-bda7-84092b151de9","added_by":"auto","created_at":"2024-12-18 11:31:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16328375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/eff700f7-777d-43c7-b479-ee3c4d3b618b.pdf"},{"id":71763653,"identity":"f63a1962-12a2-4fac-b613-abcee4d65ceb","added_by":"auto","created_at":"2024-12-18 11:23:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":324919,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 1\u003c/p\u003e","description":"","filename":"Table1.HumanEECsdifferentialexpressionfunctionalcategories.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/bad768cc1c6453fc28250429.xlsx"},{"id":71761732,"identity":"8ccff416-eae4-4cfd-8826-177165d2012f","added_by":"auto","created_at":"2024-12-18 11:07:14","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":187226,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 2\u003c/p\u003e","description":"","filename":"Table2.MouseECsdifferentialexpressionfunctionalcategories.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/4d3098e45c090f129434f65d.xlsx"},{"id":71763110,"identity":"8b1cf6bc-3a34-4ad9-9320-958b0228ade1","added_by":"auto","created_at":"2024-12-18 11:15:14","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":893329,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 3\u003c/p\u003e","description":"","filename":"Table3.HumanEECsclusterexpressionfunctionalcategories.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/9919e31a88d590aca34c1cd7.xlsx"},{"id":71763107,"identity":"637ed07c-67c1-4c6d-9fcf-779fb1b425f5","added_by":"auto","created_at":"2024-12-18 11:15:14","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":868468,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 4\u003c/p\u003e","description":"","filename":"Table4.MouseECsclusterexpressionfunctionalcategories.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/c15d8f5cba280331547e3a41.xlsx"},{"id":71761741,"identity":"81dc2b21-c715-4982-a410-d3d3e69b4b87","added_by":"auto","created_at":"2024-12-18 11:07:14","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11866,"visible":true,"origin":"","legend":"\u003cp\u003eDataset 5\u003c/p\u003e","description":"","filename":"Table5.PrimersincloningandqPCR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/35aa8e34541589ed725c56b3.xlsx"},{"id":71761754,"identity":"0bf721ee-6835-473c-ae68-17e27ca0c02d","added_by":"auto","created_at":"2024-12-18 11:07:14","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1884251,"visible":true,"origin":"","legend":"","description":"","filename":"supplementoryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5411079/v1/506604af635fb7d8da97be6e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mapping the druggable targets displayed by human colonic enteroendocrine cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnteroendocrine cells (EECs) are vital sensors and signal transducers within the gastrointestinal epithelium. Although they comprise only\u0026thinsp;~\u0026thinsp;1% of epithelial cells, EECs form an extensive hormone-producing network capable of converting luminal, neural, and immune signals into hormonal responses \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. EECs have traditionally been classified by the predominant hormone they secrete, e.g., L-cells produce glucagon like peptide-1 (GLP-1), Glucagon like peptide-2 (GLP-2), and Peptide YY\u003c/p\u003e \u003cp\u003e(PYY); K-cells secrete Gastric inhibitory polypeptide (GIP); and enterochromaffin cells (ECs) release serotonin (5-HT). Recent advances using fluorescent reporter mice revealed that EEC profiles are shaped by their differentiation trajectory, gut location, and positioning along Wnt and BMP gradients \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. EECs respond to various luminal stimuli including nutrients, pH, and microbial metabolites, releasing over 20 peptide hormones to control that regulate digestion, metabolism, microbial-host cross talk, gut-brain communication and immune defence. Consequently, EEC dysregulation has been implicated in obesity and diabetes, gastrointestinal diseases, inflammation, autoimmune conditions, and certain cancers \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven their diverse roles, EECs are valuable therapeutic targets and are thought to be important mediators of the metabolic benefits observed in bariatric surgery. Currently, several long-acting variants of individual or combined gut hormones such as GLP-1 and GIP are in development or approved as therapies for obesity and type 2 diabetes \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, gaining deeper insights into EEC function and how these cells sense stimuli may uncover new druggable targets, enabling more precise control of therapeutic gut hormone secretion from specific EEC subtypes or gut regions. This could broaden therapeutic approaches and introduce new treatment options, including dietary interventions or small molecule drugs. Dietary strategies have the advantage of being more suitable for prevention or population-level interventions and could be more affordable for healthcare systems with limited funding.\u003c/p\u003e \u003cp\u003eDespite their significance, studying human EECs has been difficult due to their sparse distribution and challenges of isolating and maintaining them in culture. Much of our understanding is based on murine models, where fluorescent labelling techniques have mapped EEC distribution. However, the development of organoid technology and CRISPR-Cas9 gene editing has revolutionized the field. These advances allow us to grow three-dimensional human intestinal organoids and study EECs in unprecedented detail \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe have taken advantage of this technology to map druggable targets expressed in human colonic EECs. We compared this dataset with data from mouse colonic EEC subtypes to probe interspecies similarities and differences and identified novel receptors enriched in human colonic EECs. Among those, we selected IL-13Rα1 and GPR173, and corroborated their effects on hormone secretion \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. By providing a map of human colonic EEC cell surface targets we hope to facilitate the identification of novel EEC biology and therapeutic targets.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeneration of human colonic CHGA reporter organoids\u003c/h2\u003e \u003cp\u003eChromogranin A (CHGA) is a marker of neuroendocrine cells and marks EECs in the gut epithelium. We generated human CHGA-mNeon expressing colonoids using CRISPR\u0026ndash;Cas9-mediated homology-independent transgenesis (CRISPR-HOT), as previously described \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The fluorescent tag mNeon was inserted 7 base pairs prior to the stop codon (TGA) of the endogenous \u003cem\u003eCHGA\u003c/em\u003e gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). To enrich for both peptide producing EECs and ECs, we combined two previously published differentiation protocols. This involved the use of differentiation medium which consisted of insulin-like growth factor 1 (IGF-1) and fibroblast growth factor 2 (FGF-2), with the removal of epidermal growth factor (EGF) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and a 48-hour pulse treatment with a Notch inhibitor (iNotch), MEK inhibitor (iMEK), and ISX-9 \u003csup\u003e14\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). This combination significantly increased EEC and EC differentiation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferentiated CHGA-mNeon colonoids displayed scattered green-fluorescent cells throughout their structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Colocalization of CHGA and mNeon was confirmed by immunostaining (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), indicating successful tagging of CHGA-expressing cells. We evaluated our differentiation method using time-course qPCR of whole colonoids (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The stem cell marker \u003cem\u003eSOX9\u003c/em\u003e and the endocrine progenitor markers \u003cem\u003eNGN3\u003c/em\u003e and \u003cem\u003ePAX4\u003c/em\u003e were upregulated early in the differentiation process, with the endocrine progenitor markers showing a slight delay relative to \u003cem\u003eSOX9\u003c/em\u003e. These markers were downregulated later in the differentiation time course. Conversely, markers of mature endocrine cells (\u003cem\u003eCHGA\u003c/em\u003e, \u003cem\u003eNEUROD1\u003c/em\u003e, tryptophan hydroxylase 1 (\u003cem\u003eTPH1\u003c/em\u003e), preproglucagon (\u003cem\u003eGCG\u003c/em\u003e)) increased over time, peaking on day 11. As expected, \u003cem\u003emNeon\u003c/em\u003e expression mirrored \u003cem\u003eCHGA\u003c/em\u003e expression. Together the data suggested our protocol successfully enriched human colonoids with EECs and ECs.\u003c/p\u003e \u003cp\u003eCHGA-mNeon positive cells were purified from differentiated colonoids using fluorescence-activated cell sorting (FACS), yielding two populations: fluorescent mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e and non-fluorescent mNeon\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). The mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e population was enriched for EEC markers (\u003cem\u003eCHGA\u003c/em\u003e, \u003cem\u003eGCG\u003c/em\u003e) and the EC marker \u003cem\u003eTPH1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). Conversely, mNeon\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells were enriched for Paneth cell (\u003cem\u003eLYZ\u003c/em\u003e), intestinal stem cell (\u003cem\u003eLGR5\u003c/em\u003e), and goblet cell (\u003cem\u003eMUC2\u003c/em\u003e) markers. Proteomic analysis confirmed that mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e and mNeon\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells were significantly separated at the level of protein expression (\u003cb\u003eFig. S1a\u003c/b\u003e). mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells produced CHGA, TPH1 and GCG, whereas mNeon\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells produced proteins such as LYZ and the goblet cell marker TFF3 (\u003cb\u003eFig. S1b\u003c/b\u003e). These expression patterns demonstrate that mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells are human colonic EECs and ECs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMapping the transcriptome of human colonic EEC and ECs.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo characterize the transcriptome of mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells, we performed bulk RNA-sequencing (RNA-seq). Principal component analysis (PCA) confirmed clear transcriptional separation between mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e and mNeon\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e population exhibited EEC marker genes like \u003cem\u003eCHGA\u003c/em\u003e, \u003cem\u003eCHGB\u003c/em\u003e, and \u003cem\u003eSCG5\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), and transcription factors (TFs) associated with EEC development including \u003cem\u003eFEV\u003c/em\u003e, \u003cem\u003eINSM\u003c/em\u003e, \u003cem\u003eNKX2-2\u003c/em\u003e, \u003cem\u003eNKX2-1\u003c/em\u003e (\u003cb\u003eFig. S1c\u003c/b\u003e). As expected, the enterocyte marker \u003cem\u003eSLC26A3\u003c/em\u003e, the Paneth cell markers \u003cem\u003eCA4\u003c/em\u003e, \u003cem\u003eSPIB\u003c/em\u003e, and the goblet cell markers \u003cem\u003eCLCA1\u003c/em\u003e, \u003cem\u003eMUC5AC\u003c/em\u003e, \u003cem\u003eSPDEF\u003c/em\u003e, \u003cem\u003eSPINK4\u003c/em\u003e, and \u003cem\u003eTFF3\u003c/em\u003e were enriched in mNeon\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells. Hormone-encoding genes, including \u003cem\u003eGCG\u003c/em\u003e, which produces the incretin GLP-1, and the anorexigenic peptide YY (\u003cem\u003ePYY\u003c/em\u003e), were enriched in mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells. These are characteristic products of peptide producing EECs. Additionally, \u003cem\u003eTPH1\u003c/em\u003e, the rate-limiting enzyme in the synthesis of serotonin and a marker of ECs, was also enriched, along with transcription factors associated with EC differentiation such as \u003cem\u003ePAX4\u003c/em\u003e \u003csup\u003e15\u003c/sup\u003e and \u003cem\u003eRUNX1T1\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e. Other classical gut hormones, secretin (\u003cem\u003eSCT\u003c/em\u003e) and neurotensin (\u003cem\u003eNTS\u003c/em\u003e), were expressed at lower levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeyond the well-known gut hormones, our analysis of genes predicted to produce secreted products identified several additional neuropeptides and hormones not previously reported in human colonic peptide EECs or ECs (\u003cb\u003eFig. S1c\u003c/b\u003e). These included galanin (\u003cem\u003eGAL\u003c/em\u003e), a potent inhibitor of gut hormone secretion (including GLP-1, PYY, and NTS) and a known regulator of energy homeostasis and intestinal motility \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Additionally, we found previously unreported expression of relaxin 1 (\u003cem\u003eRLN1\u003c/em\u003e), which modulates the reproductive system and gut motility \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e; calcitonin gene-related peptide (\u003cem\u003eCALCB\u003c/em\u003e), a neuropeptide with functions in the gut including the regulation of mucosal blood flow, epithelial homeostasis, exocrine and endocrine secretory processes, motor activity, and nociception \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e; and neurokinin B (\u003cem\u003eTAC3\u003c/em\u003e), a member of the tachykinin family of neuropeptides involved in reproduction, gastric acid secretion, GI motor and sensory control, and inflammation \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Interestingly, our analysis also revealed the presence of follistatin-like 5 (\u003cem\u003eFSTL5\u003c/em\u003e), a secretory glycoprotein previously implicated in the olfactory system \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While its role in the gut is unclear, it has been shown to inhibit proliferation in liver hepatocellular carcinoma \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong the top differentially expressed receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) were the bile acid receptor (\u003cem\u003eGPBAR1\u003c/em\u003e) and free fatty acid receptor 2 (\u003cem\u003eFFAR2\u003c/em\u003e), both implicated in the regulation of postprandial nutrient sensing and gut hormone secretion \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These receptors have been reported to be expressed by EECs and/or ECs. Several hormone receptors were also enriched, including the gastric inhibitory polypeptide receptor (\u003cem\u003eGIPR\u003c/em\u003e). Its ligand, GIP, is an incretin secreted from K-cells in the small intestine. Our data corroborated the presence of the neuropeptide Y receptor type 1 (\u003cem\u003eNPY1R\u003c/em\u003e), which has previously been identified on colonic goblet cells, endocrine cells, and enterocytes \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. NPY1R mediates some effects of the NPY-like family of peptides on gut functions, including secretion of GLP-1, inflammation, barrier function, and absorption \u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Additionally, the galanin receptor 1 (\u003cem\u003eGALR1\u003c/em\u003e) and the relaxin family peptide (\u003cem\u003eRXFP4\u003c/em\u003e) were also present. RXFP4 and its ligand, insulin-like peptide 5 (INSL-5), are thought to play a role in the regulation of food intake and gut motility. Recent data revealed co-localization of RXFP4 with serotonin in the lower gut of mice and single cell RNA-sequencing has suggested its presence in a subset of human colonic ECs \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEECs and ECs are strategically positioned to receive information from various sources, including enteric and vagal neurons. They can respond to classical neurotransmitters released by these neurons, and to dietary and microbial signals that in some instances act as ligands for neurotransmitter receptors. Our data demonstrate the presence of the γ-aminobutyric acid (GABA) receptor (\u003cem\u003eGABBR2\u003c/em\u003e), the cholinergic receptor (\u003cem\u003eCHRNB2\u003c/em\u003e) and the dopamine receptor D2 (\u003cem\u003eDRD2\u003c/em\u003e) in mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), extending previous findings in mice to humans \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Furthermore, we confirmed the presence of the olfactory receptors (\u003cem\u003eOR51E1\u003c/em\u003e, \u003cem\u003eOR51E2\u003c/em\u003e) in human colonic endocrine cells \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEECs and ECs play a crucial role in orchestrating gut epithelial immunity and inflammation. They possess multiple receptors which allow them to respond to immune mediators and can directly secrete cytokines and other modulators. For example, we found the receptors: interleukin-13 (IL-13) receptor \u003cem\u003eIL13RA1\u003c/em\u003e, \u003cem\u003eULBP1\u003c/em\u003e, \u003cem\u003eCLEC7A\u003c/em\u003e, \u003cem\u003eKLRC3\u003c/em\u003e, \u003cem\u003eTNFRSF19\u003c/em\u003e, and \u003cem\u003eCD5\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), and cytokines including \u003cem\u003eCCL5\u003c/em\u003e, \u003cem\u003eCCL15\u003c/em\u003e, \u003cem\u003eCSF1\u003c/em\u003e, \u003cem\u003eIL17\u003c/em\u003e, \u003cem\u003eIL11\u003c/em\u003e, \u003cem\u003eIL23\u003c/em\u003e (\u003cb\u003eFig. S1c\u003c/b\u003e), and various tumour necrosis family members, enriched in mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells. The presence of \u003cem\u003eIL13RA1\u003c/em\u003e and \u003cem\u003eIL17\u003c/em\u003e corroborates the known role of IL-13 in controlling ECs in mice \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and the previously reported production of IL-17C by EECs in humans \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe also identified differentially expressed transporters consistent with the chemosensing role of EECs and ECs (\u003cb\u003eFig. S1c\u003c/b\u003e), including \u003cem\u003eSLC22A17\u003c/em\u003e (iron transporter), \u003cem\u003eSLC38A11\u003c/em\u003e (amino acid transporter), \u003cem\u003eSLC17A9\u003c/em\u003e (ATP uniporter), \u003cem\u003eSLC26A7\u003c/em\u003e (anion exchange transporter), and \u003cem\u003eSLC8A1\u003c/em\u003e, a sodium/calcium exchanger. As expected, we identified numerous enriched ion channels associated with excitable cells, such as potassium channels (\u003cem\u003eKCTD12\u003c/em\u003e, \u003cem\u003eKCNJ3\u003c/em\u003e, \u003cem\u003eKCNJ6\u003c/em\u003e, \u003cem\u003eKCHN2\u003c/em\u003e), calcium channels (\u003cem\u003eCACNA2D1\u003c/em\u003e, \u003cem\u003eCACNA1A\u003c/em\u003e, \u003cem\u003eCACNA1C\u003c/em\u003e), and sodium channels (\u003cem\u003eSCNA3\u003c/em\u003e, \u003cem\u003eSCN3B\u003c/em\u003e, \u003cem\u003eSCNA2\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eTo complete our survey of druggable targets, we identified the top differentially expressed orphan G protein-coupled receptors (\u003cb\u003eFig. S1c\u003c/b\u003e), which to our knowledge have not been previously reported in either mouse or human colonic EECs or ECs. Interestingly, within this list of novel targets, GPR173 has recently been deorphanized and is proposed to be the receptor for Phoenixin (PNX), a recently discovered neuropeptide involved in the regulation of food intake, energy homeostasis, stress and inflammation \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSingle-cell transcriptomic profiling of human colonic EECs and their receptor expression\u003c/h3\u003e\n\u003cp\u003eEnriching human EECs from primary tissues for single-cell profiling has been challenging due to reliance on antibodies and selection strategies that can lack specificity. To expand on our bulk sequencing data, we performed single cell RNA-sequencing on our CHGA reporter organoids, to profile mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells, focusing on the expression of druggable cell surface targets within specific cell populations. Data was generated and processed by sorting and robot-assisted transcriptome sequencing (SORT-seq \u003csup\u003e35\u003c/sup\u003e) and analysed using Seurat \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Using shared nearest neighbour clustering based on highly differentially expressed genes, we constructed a map of CHGA-mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells. The map contained four populations of colonic EECs: progenitor cells, early EECs, peptide EECs and ECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eThese findings align with the two major branches of differentiated endocrine cells: TPH1-positive ECs and hormone expressing (e.g., GLP-1 positive) EECs, as previously documented \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Progenitors were identified by the expression of the stem cell markers \u003cem\u003eSMOC2\u003c/em\u003e, \u003cem\u003eSOX9\u003c/em\u003e and \u003cem\u003eBMP4\u003c/em\u003e, and \u003cem\u003eLGALS4\u003c/em\u003e, a marker of the early NGN3-to-EC transition \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Early EECs were distinguished by the expression of \u003cem\u003ePAX4\u003c/em\u003e, \u003cem\u003eSOX4\u003c/em\u003e, \u003cem\u003eGCH1\u003c/em\u003e and \u003cem\u003eRUNX1T1\u003c/em\u003e \u003csup\u003e8,37\u003c/sup\u003e; this population likely harbours cells with the potential to become ECs as well as hormone producing EECs but with an EC bias due to the presence of \u003cem\u003eFEV\u003c/em\u003e and \u003cem\u003ePAX4\u003c/em\u003e. Interestingly, \u003cem\u003ePAX4\u003c/em\u003e has recently been identified as a critical regulator in an endocrine transcription factor network controlling EC differentiation \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and is one of the major targets of ISX-9, a component of our differentiation protocol. The late EC population was identified by enrichment for \u003cem\u003eCHGA\u003c/em\u003e, \u003cem\u003eCHGB\u003c/em\u003e and \u003cem\u003eTPH1\u003c/em\u003e and genes such as \u003cem\u003eREG4, GC, RGS2\u003c/em\u003e, and \u003cem\u003eCRYBA2\u003c/em\u003e which have previously been observed at the later stages of differentiation from progenitor to mature ECs in humans \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The EEC peptidergic cluster was identified based on the expression of classical markers, including \u003cem\u003eGCG\u003c/em\u003e, \u003cem\u003ePYY\u003c/em\u003e and \u003cem\u003eNeuroD1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eSingle-cell clustering identified the most highly expressed receptors and orphan GPCRs enriched in colonic EECs, with variable expression across cell subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Receptors most highly expressed in ECs included nuclear receptor subfamily 4 group A member 2 (\u003cem\u003eNR4A2\u003c/em\u003e), a regulator of inflammation in the gastrointestinal tract \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, the olfactory receptor \u003cem\u003eOR51E1\u003c/em\u003e, and the Phoenixin receptor \u003cem\u003eGPR173\u003c/em\u003e. In contrast, receptors such as cadherin EGF LAG seven-pass G-type receptor 3 (\u003cem\u003eCELSR3\u003c/em\u003e), a key gene for planar cell polarity and the guidance of enteric neuronal projections \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, the bile acid receptor (\u003cem\u003eGPBAR1\u003c/em\u003e), gastric inhibitory polypeptide receptor (\u003cem\u003eGIPR\u003c/em\u003e), galanin receptor 1 (\u003cem\u003eGALR1\u003c/em\u003e), and the orphan receptor \u003cem\u003eGPR108\u003c/em\u003e were primarily enriched in peptide-producing EECs. Receptors with broader expression included \u003cem\u003eGPR160\u003c/em\u003e, an orphan receptor potentially activated by CART (cocaine and amphetamine-regulated transcript) peptide, a neuropeptide and hormone involved in appetite regulation \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, gastrointestinal inflammation, and gut hormone secretion from the proximal bowel (GLP-1 and GIP) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, as well as \u003cem\u003eIL13RA1\u003c/em\u003e (interleukin-13 receptor subunit alpha-1). \u003cem\u003eGPR155\u003c/em\u003e showed the highest expression in progenitor cells and has been linked to cancer initiation and progression, particularly as a biomarker for hematogenous metastasis in gastrointestinal cancers \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, its role in regulating intestinal stem cells (ISCs) or progenitor EEC proliferation and differentiation remains unclear. Additionally, 2-oxoglutarate receptor 1 (\u003cem\u003eOXGR1\u003c/em\u003e) was enriched in progenitor cells. Dietary and gut microbiota-derived 2-oxoglutarate is utilized by the epithelium for protein synthesis and oxidative metabolism and has been shown to restore intestinal barrier function by promoting stem cell activity in mice \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAn integrated transcriptomic database of mouse and human colonic EECs.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo compare interspecies differences and similarities between humans and mice, we conducted transcriptomic analysis of mouse colonic EECs. We used mouse TPH1 and PYY reporter cells, which are analogous to human colonic ECs and peptide hormone producing L-cells, respectively. TPH1\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells were isolated from colonoids derived from Tph1-P2A-iCreERT2 mice \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Principal component analysis (PCA) revealed two distinct CFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e and CFP\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). CFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells were enriched for the EC marker \u003cem\u003eTph1\u003c/em\u003e, whilst CFP\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells were enriched with the Paneth cell marker \u003cem\u003eLyz\u003c/em\u003e and goblet cell marker \u003cem\u003eMuc2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, c). As expected, CFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells were also enriched with EEC lineage markers such as \u003cem\u003eNeurog3\u003c/em\u003e and \u003cem\u003eNkx2-2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). These cells also showed enrichment for transcription factors driving EC differentiation, including \u003cem\u003ePax4\u003c/em\u003e and \u003cem\u003eRunx1t1\u003c/em\u003e, along with TFs involved in serotonin biosynthesis, such as \u003cem\u003eLmx1a\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eAscl1\u003c/em\u003e \u003csup\u003e46\u003c/sup\u003e (\u003cb\u003eFig. S2a\u003c/b\u003e). As expected, the epithelial marker \u003cem\u003eSlc26a3\u003c/em\u003e, goblet cell differentiation factor \u003cem\u003eKlf4\u003c/em\u003e, and Paneth cell marker \u003cem\u003eMmp7\u003c/em\u003e were enriched in CFP\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNinety receptors were differentially expressed in mouse CFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e ECs compared to CFP\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells, including known receptors involved in nutrient, gut hormone, and immune sensing. Among the top 40 differentially expressed receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), 10 were conserved between mouse and human (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), including receptors such as \u003cem\u003eFFAR2\u003c/em\u003e, \u003cem\u003eGABBR2\u003c/em\u003e, and \u003cem\u003eNPY1R\u003c/em\u003e, as well as several herein newly identified receptors, including \u003cem\u003eIL13RA1\u003c/em\u003e, \u003cem\u003eCELSR3\u003c/em\u003e, and \u003cem\u003eGFRA1\u003c/em\u003e. Glial cell line-derived neurotrophic factor (GDNF), the ligand for \u003cem\u003eGFRA1\u003c/em\u003e, is crucial for the development of the enteric nervous system \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Additionally, \u003cem\u003eGFRA1\u003c/em\u003e has been implicated in Hirschsprung\u0026rsquo;s disease and related enterocolitis in mice \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, its expression and role in ECs has not been explored. Several hormone receptors were exclusively expressed in the mouse dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), including the vasoactive intestinal polypeptide receptor (\u003cem\u003eVipr2\u003c/em\u003e), whose ligand VIP regulates nutrient absorption and gut motility \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e; the somatostatin receptors (\u003cem\u003eSstr5\u003c/em\u003e, \u003cem\u003eSstr1\u003c/em\u003e), which play versatile roles in the gut, including chemosensing, mucus secretion, and inflammatory responses \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e; and the CCK receptors (\u003cem\u003eCckar\u003c/em\u003e, \u003cem\u003eCckbr\u003c/em\u003e), which regulate gut motility and appetite. Selective species expression may indicate divergent hormonal control of ECs between mouse and humans.\u003c/p\u003e \u003cp\u003eAmong the orphan GPCRs (\u003cb\u003eFig. S2b\u003c/b\u003e), \u003cem\u003eGpr173\u003c/em\u003e, \u003cem\u003eGpr162\u003c/em\u003e, \u003cem\u003eGpr85\u003c/em\u003e, \u003cem\u003eGpr146\u003c/em\u003e, \u003cem\u003eGpr153\u003c/em\u003e, \u003cem\u003eGpr6\u003c/em\u003e, \u003cem\u003eGpr3\u003c/em\u003e were conserved between mouse and human. Sphingosine-1-phosphate (S1P) has been identified as a likely ligand for \u003cem\u003eGpr6\u003c/em\u003e, \u003cem\u003eGpr3\u003c/em\u003e \u003csup\u003e51\u003c/sup\u003e, and is implicated in intestinal barrier function \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In contrast, \u003cem\u003eGpr12\u003c/em\u003e, \u003cem\u003eGpr27\u003c/em\u003e, \u003cem\u003eGpr45\u003c/em\u003e, \u003cem\u003eGpr161\u003c/em\u003e and \u003cem\u003eGpr179\u003c/em\u003e were selectively found in the mouse. Of these \u003cem\u003eGpr12\u003c/em\u003e \u003csup\u003e53\u003c/sup\u003e, \u003cem\u003eGpr27\u003c/em\u003e \u003csup\u003e54\u003c/sup\u003e, \u003cem\u003eGpr45\u003c/em\u003e \u003csup\u003e55\u003c/sup\u003e have been linked to obesity and/or metabolism, whereas \u003cem\u003eGpr161\u003c/em\u003e has a role in intestinal immunity \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. In the human mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e dataset, \u003cem\u003eGpr63\u003c/em\u003e and \u003cem\u003eGpr68\u003c/em\u003e were receptors with postulated ligands. For example, sphingosine-1-phosphate (S1P) and lysophosphatidic acid (LPA) have been suggested as low affinity agonists of \u003cem\u003eGpr63\u003c/em\u003e \u003csup\u003e57\u003c/sup\u003e. \u003cem\u003eGpr68\u003c/em\u003e, also known as the proton-sensing ovarian cancer G-protein coupled receptor (OGR1), is a proton-activated GPCR that responds to acidic extracellular pH and in the intestine plays a role in tissue damage and inflammation \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere were also various receptors involved in gut inflammation and immune responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, g), including the conserved receptor Il13ra1 and receptors uniquely expressed in mice, such as Pdcd1 (which encodes the programmed cell death protein 1), \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eIl20ra\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eCd83\u003c/em\u003e \u003csup\u003e61\u003c/sup\u003e, \u003cem\u003eCd22\u003c/em\u003e \u003csup\u003e62\u003c/sup\u003e. Additionally, cytokines, such as the conserved Il17d, as well as mouse specific Ccl6, Ccl28, Mif are also implicated in these processes (\u003cb\u003eFig. S2a\u003c/b\u003e). Our analysis revealed both conserved and species-specific expression across other functional groups, highlighting the importance of understanding species-dependent expression to facilitate the optimisation of pre-clinical models for the validation of novel targets (\u003cb\u003eFig. S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the bulk transcriptome analysis of TPH1-CFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells, we also performed single cell RNA-sequencing using these cells and PYY-GFP cells, isolated from the colon of PYY-GFP mice \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The CFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e ECs clustered into two groups: differentiated ECs and early ECs, mirroring clusters observed in human ECs derived from colonoids (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). Differentiated ECs were identified by the expression of genes such as \u003cem\u003eFoxa1\u003c/em\u003e, \u003cem\u003eS100a1\u003c/em\u003e and \u003cem\u003eAtf6\u003c/em\u003e, which were observed during late EC lineage development in mice \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ei). Early ECs demonstrated markers like \u003cem\u003eNeurog3\u003c/em\u003e, \u003cem\u003eNeurod2\u003c/em\u003e and \u003cem\u003eSox4\u003c/em\u003e. As expected, PYY-GFP cells were enriched for \u003cem\u003ePyy\u003c/em\u003e and \u003cem\u003eGcg\u003c/em\u003e expression and not for \u003cem\u003eTph1\u003c/em\u003e or \u003cem\u003eLyz\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). PYY-GFP cells clustered into peptide EECs, which highly expressed \u003cem\u003eChga\u003c/em\u003e, \u003cem\u003eGcg\u003c/em\u003e, and \u003cem\u003ePyy\u003c/em\u003e, and secretory progenitor cells, which expressed the markers \u003cem\u003eAtoh1\u003c/em\u003e \u003csup\u003e64\u003c/sup\u003e, \u003cem\u003eSpink4\u003c/em\u003e, and \u003cem\u003eAgr2\u003c/em\u003e, and exhibited features of both goblet and Paneth cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, d). The top differentially expressed receptors were compared between the TPH1 and PYY clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). \u003cem\u003eFfar2\u003c/em\u003e was the most promiscuous, being expressed in all clusters whereas \u003cem\u003eGpbar1\u003c/em\u003e was restricted to mature peptide EECs. When expression patterns were compared between human and mouse. \u003cem\u003eNpy1r\u003c/em\u003e exhibited a conserved expression pattern, with predominant expression in ECs, whilst \u003cem\u003eFfar2\u003c/em\u003e was broadly expressed across all EEC clusters in both mouse and humans. However, other receptors exhibited species-specific differences. For example, \u003cem\u003eGpr108\u003c/em\u003e, an immune modulator, exhibited the highest expression in human peptide EECs but in the mouse was predominantly expressed in secretory progenitor cells and mature ECs. \u003cem\u003eIl13ra1\u003c/em\u003e also displayed differential expression, being primarily expressed in mouse secretory progenitor cells and early ECs, while in humans it was expressed across all stages of EC differentiation as well as in peptide EECs. Interestingly, the orphan receptor \u003cem\u003eGpr173\u003c/em\u003e was undetected in mouse peptide EECs, consistent with its absence in human EECs, suggesting its expression is conserved and restricted to ECs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe complete lists of differentially expressed receptors, transcription factors, and cytokines in mouse ECs and human EECs are provided in Supplementary Tables S1 and S2. Additionally, the comparisons of average gene expression across all clusters of human EECs and mouse ECs are available in Supplementary Tables S3 and S4.\u003c/p\u003e\n\u003ch3\u003eAre receptors enriched on human EECs functionally significant?\u003c/h3\u003e\n\u003cp\u003eTo evaluate whether we could identify receptors with functional significance in both ECs and peptide EECs, we selected IL-13Rα1 and GPR173. IL-13Rα1 has previously been shown to regulate serotonin secretion in mice, but its presence on human L-cells has not previously been reported. Whereas GPR173 has not been reported on ECs in either species. The expression of both receptors was confirmed by qPCR in human CHGA-mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Additionally, co-expression of IL-13Rα1 with GLP-1 and 5-HT was further corroborated in human colon biopsies by immunohistochemistry (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Consistent with its expression in human EECs, \u003cem\u003eIl13ra1\u003c/em\u003e expression was significantly increased of in both mouse TPH1-CFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells (ECs) and PYY-GFP\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e cells (peptide EECs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), while the expression of \u003cem\u003eGpr173\u003c/em\u003e was enriched in mouse ECs but not in peptide EECs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe presence of IL-13Rα1 on L-cells suggests that the IL-13/IL-13Rα1 pathway might stimulate the secretion of GLP-1. In support of this, we found that IL-13 significantly increased GLP-1 secretion in colonoids derived from three separate donors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). IL-13 has been shown to stimulate serotonin secretion from BON-1 cells, a human carcinoid cell line that produces serotonin (5-HT) and other neurotransmitters and peptides \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. However, it remains unknown whether IL-13 can stimulate serotonin secretion from primary human ECs. Accurately measuring serotonin secretion from primary human cells is challenging. Unlike secretion protocols for gut hormones, which rely on sensitive immunoassays, real-time serotonin measurement requires electrochemical techniques that necessitate placing electrodes near the secreting cells. This is impractical without a reporter to mark the cells of interest. Previously, 5-HT release from individual ECs in mice has been indirectly visualized by measuring Ca\u003csup\u003e2+\u003c/sup\u003e responses in fluorescently labelled ECs in organoids, or by indirectly measuring activation of biosensor cells expressing the serotonin-gated ion channel (5-HT\u003csub\u003e3\u003c/sub\u003eR) \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we employed Fast Scan Cyclic Voltammetry (FSCV), an electrochemical technique used to simultaneously identify and quantify monoamines, to investigate whether the IL-13/IL-13Rα1 pathway stimulates serotonin secretion from primary human ECs. This method allows for real-time and label-free detection of extracellular 5-HT as a change in current at a carbon fibre microelectrode (CFM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Combining with CHGA-mNeon colonoids enabled the measurements to be performed in or near primary ECs. We compared the spontaneous (pre-drug) activity of CHGA-mNeon fluorescent cells to their post-drug stimulation. Representative current-time plots were generated for each organoid (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). We quantified the frequency of 5-HT release events and observed significant increases following IL-13 stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Furthermore, we measured the peak amplitude corresponding to the amount of 5-HT released for each event and found IL-13 treatment increased peak amplitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). These results suggest that IL-13 increases both the number of 5-HT release events, and the amount of 5-HT released in each event.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the functional significance of GPR173, which we found to be exclusively expressed by ECs in mouse and humans, we used its recently identified ligand PNX-20 and FSCV. The frequency of serotonin release events increased following PNX-20 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). However, the peak amplitude, corresponding to the amount of 5-HT released, was not affected (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). These results suggest that PNX increases the frequency of 5-HT release events but does not affect the amount of 5-HT secreted during each event.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEECs are a potentially rich source of novel drug targets for the treatment of numerous diseases. However, human EEC biology and their therapeutic control have remained challenging to characterise beyond the well-studied incretin axes. By leveraging advanced methodologies, including CRISPR-Cas9 gene editing and organoid models, we and others have begun to address longstanding challenges in accessing and studying human EECs at high resolution \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. By generating human CHGA-mNeon colonoids and developing a small molecule EEC differentiation protocol, we facilitated the isolation and visualization of EEC populations for transcriptomic profiling. In addition, we utilized TPH1-CFP and PYY-GFP transgenic mouse models to enable comparative analyses of EEC subtypes across species. This dual-species approach allowed us to map both conserved and species-specific features of EEC biology. By integrating bulk and single-cell RNA sequencing with functional assays we sought to delineate the molecular landscape of colonic EECs. This approach led to the identification of a broad array of previously unreported neuropeptides, hormones, and receptors in human colonic EECs. These findings expand our understanding of the signalling pathways that govern EEC function and highlight novel candidates for further exploration. Moreover, we corroborate the presence of key chemosensory receptors, such as the bile acid receptor GPBAR1 and free fatty acid receptor FFAR2, reaffirming the critical role of EECs in nutrient sensing and gut physiology.\u003c/p\u003e \u003cp\u003eIn addition to their roles in nutrient sensing, hormone secretion, and gut motility, EECs are increasingly recognized for mediating immune-gut communication. Though their immunological functions remain relatively underexplored, we show that human colonic EECs express receptors and signalling molecules associated with immune function, including cytokine and chemokine receptors. This suggests that EECs sense inflammatory signals and modulate immune responses via hormone and peptide release. For example, we demonstrate that IL-13 enhances GLP-1 and serotonin secretion, indicating that EECs may link immune regulation, particularly TH2 responses, with metabolic processes and or gut defence. Further research is needed to clarify this crosstalk and its role in disease.\u003c/p\u003e \u003cp\u003eWe employed fast-scan cyclic voltammetry (FSCV) for the first time in gut organoids to validate serotonin secretion from ECs. FSCV provides unparalleled temporal resolution, allowing for the detection of rapid and transient changes in serotonin release that are often undetectable with conventional methods such as ELISA. Using this approach will facilitate a deeper understanding of the finely tuned regulatory mechanisms governing serotonin secretion from ECs. For instance, we identified GPR173 as a novel receptor exclusively expressed by ECs. FSCV enabled us to demonstrate that the GPR173 ligand, Phoenixin-20, modulated serotonin release by increasing the frequency of release events. These findings suggest that GPR173 may influence gut motility, inflammation, or mood regulation, given the established role of serotonin in the gut-brain axis.\u003c/p\u003e \u003cp\u003eOur interspecies transcriptomic comparisons revealed both conserved and divergent expression patterns, underscoring the limitations of exclusively using animal models for studying EEC biology. Conserved expression was observed for receptors such as NPY1R and FFAR2 whereas receptors like GPR3, GPR160, ULBP1, RXFP4 displayed human-specific expression, highlighting species-specific differences. This emphasizes the importance of conducting direct studies on human tissues to gain a comprehensive understanding of EEC function, particularly in the context of drug development. Additionally, the cross-species catalogue enables novel targets to be identified which can be further explored in mouse models of disease.\u003c/p\u003e \u003cp\u003eOverall, we provide a transcriptomic catalogue of human colonic EECs and the functional validation of two novel receptors involved in EEC biology. The identification of previously uncharacterized receptors, channels and transporters in the database presents the field with an opportunity to identify novel therapeutic interventions in metabolic, gastrointestinal, and inflammatory diseases. Future research should prioritize \u003cem\u003ein vivo\u003c/em\u003e validation of targets within disease-specific contexts where gut hormones play key regulatory roles. Finally, further investigation of the roles of IL-13Rα1 and GPR173 will lead to a deeper understanding of how these receptors influence gut homeostasis and their therapeutic potential in various pathologies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIsolation and culture of human colonoids\u003c/h2\u003e \u003cp\u003eThe isolation of human colonoids was adapted from previously described methods \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Three human colonoid lines were derived from colonoscopy biopsies of a 50-year-old male patient (unique identifier FG), a 75-year-old female patient (unique identifier MW) and 33-year-old female patient (unique identifier ZB), respectively, from Denmark Hill NHS Foundation Trust.\u003c/p\u003e \u003cp\u003eThe biopsies were rinsed with ice-cold D-PBS (D8537, Sigma-Aldrich) to remove any blood or debris and then incubated in 5 mL 10 mM 1,4-dithiothreitol (DTT; 10197777001, Sigma-Aldrich) for 5 min at room temperature, repeated once with fresh DTT. The biopsies were then incubated in 8 mM EDTA (15575-038, Invitrogen) in D-PBS on a rotator at 4\u0026deg;C for 1 hr, and subsequently vigorously shaken in cold D-PBS to release crypts. The supernatant containing crypts were collected and centrifuged at 400 rcf for 3 min and were washed in cold D-PBS for three times. 200 crypts per 25 \u0026micro;L were seeded in Cultrex Basement Membrane Extract (BME) (3536-005-02, Bio-Techne) in 48-well plate (Nunc). BME was polymerized for 15 min at 37\u0026deg;C, then overlaid with 250 \u0026micro;L/well IntestiCult\u0026trade; Organoid Growth Medium (06010, STEMCELL) supplemented with 100 units/mL Pen-Strep and 10 \u0026micro;M Y-27632 (Y0503, Sigma-Aldrich). Plated organoids were maintained in a 37\u0026deg;C incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e, and the media were changed every other day.\u003c/p\u003e \u003cp\u003eWhen the crypts grew into organoids, they were maintained in the stem cell medium IFE. IFE medium consists of Advanced DMEM/F-12 (12634, ThermoFisher Scientific), 2 mM GlutaMAX (35050061, Gibco), 10 mM HEPES (15630056, Gibco), 100 units/mL Pen-Strep, 1\u0026times; B27 supplement (17504044, Gibco), 1\u0026times; N2 supplement (17502048, Gibco), 0.15 nM Wnt Surrogate-Fc Fusion Protein (N001, ImmunoPrecise Antibodies), 10% R-spondin-1 conditioned medium (in house), 1% Noggin-Fc fusion protein conditioned medium (N002, ImmunoPrecise Antibodies), 50 ng/mL recombinant human EGF (AF-100-15, PeproTech), 1.25 mM N-Acetylcysteine (A9165, Sigma-Aldrich), 10 nM Gastrin (G9145, Sigma-Aldrich), 500 nM A83-01 (2939, Bio-techne), 100 ng/mL recombinant human IGF-1 protein (590904, Biolegend) and 50 ng/mL recombinant human FGF-2 protein (100-18B, PeproTech). Maintained in IFE medium, the human colonoids were passaged every 7 days by mechanical dissociation, at a 1:6 split ratio.\u003c/p\u003e \u003cp\u003eThe differentiation of human colonoids was initiated on day 4 after splitting, when IFE medium was substituted by IF* medium till day 11. In the IF* medium, the Wnt Surrogate protein was reduced to 0.045 nM and EGF was removed to accelerate enteroendocrine differentiation. A 48-hr pulse treatment with a combination of small molecules was applied for enhanced differentiation. This treatment involved the use of 10 \u0026micro;M iNotch DAPT (D5942, Sigma-Aldrich), 500 nM iMEK PD0325901 (Sigma-Aldrich) and 40 \u0026micro;M ISX-9 (4439/10, Bio-Techne), which were applied 4 days before the end of the culture process \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. After 48 hrs, the treatment was discontinued, and the medium was changed to IF* medium for an additional 48 hrs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTransgenic mice\u003c/h3\u003e\n\u003cp\u003eThe transgenic PYY-GFP mice (a kind gift from Prof. Rodger Liddle, Duke University) \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e were maintained under regulated temperature (21\u0026ndash;23\u0026deg;C) and light (12:12 hr light/dark cycle) conditions, with access to \u003cem\u003ead libitum\u003c/em\u003e water and chow diet. These mice were housed in compliance with Home Office UK regulations.\u003c/p\u003e\n\u003ch3\u003eCulture of mouse colonoids\u003c/h3\u003e\n\u003cp\u003eThe transgenic Tph1-P2A-iCreERT2 mouse colonoids are a kind gift from Dr Cordelia Imig, University of Copenhagen \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Mouse colonoids were isolated from 8- to 12-week-old male mice and cultured as described previously \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The WENR medium consists of Advanced DMEM/F-12, 2 mM GlutaMAX, 10 mM HEPES, 100 units/mL Pen-Strep, 1\u0026times; B27 supplement, 1\u0026times; N2 supplement, 50 ng/mL recombinant human EGF, 1.25 mM N-Acetylcysteine, 1% Noggin-Fc fusion protein conditioned medium (N002, ImmunoPrecise Antibodies), 0.15nM Wnt Surrogate-Fc Fusion Protein (N001, ImmunoPrecise Antibodies) and 10% R-spondin-1 CM (in house). Maintained in WENR medium, the colonoids were passaged every 7 days by mechanical dissociation at a 1:6 split ratio.\u003c/p\u003e \u003cp\u003eThe differentiation of mouse colonoids was initiated on day 3 after splitting, when WENR medium was substituted by ENR medium till day 7. The ENR medium has the same composition as WENR, but without Wnt Surrogate. A 48-hr pulse treatment of 40 \u0026micro;M ISX-9 was applied from day 3 to day 5 in ENR. After 48 hrs, the treatment was discontinued, and the medium was changed to ENR medium for an additional 48 hrs.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeneration of human CHGA-mNeon colonoids\u003c/h2\u003e \u003cp\u003eThe human colonoids (unique identifier FG) were used to generate CHGA-mNeon reporter organoids by CRISPR-HOT. The three plasmids comprising the CRISPR-HOT system for targeting the endogenous CHGA gene locus include the target selector pSPgRNA (Addgene #47108), with sgRNA (CAGCTGCAGGCACTACGGCGGGG) inserted using a previously described protocol \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, the frame selector pCas9-mCherry-Frame\u0026thinsp;+\u0026thinsp;1 (Addgene plasmid #66940), and the universal donor pCRISPaint-mNeon (Addgene #174092) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These three plasmids were kindly provided by Prof Hans Clevers\u0026rsquo; group \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Additionally, an EF1α promoter-guided puromycin resistance gene cassette was amplified from the PB513B-1 plasmid (System biosciences) and cloned into the pCRISPaint-mNeon plasmid for antibiotic selection. Briefly, the pCRISPaint-mNeon plasmid was linearized by Q5\u0026reg; Hot Start High-Fidelity 2X Master Mix (M0494S, NEB). The EF1α promoter and the puromycin resistance gene fragments were amplified by touchdown PCR \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e using Q5\u0026reg; High-Fidelity DNA enzyme (M0491, New England BioLabs), each with a 20 bp extension complementary to the ends of the linearized pCRISPaint-mNeon plasmid. NEBuilder\u0026reg; HiFi DNA Assembly Master Mix (E2621S, New England BioLabs) was then used for ligation of the two DNA fragments. The ligated fragment was cloned into pCRISPaint-mNeon plasmid using the In-Fusion\u0026reg; HD Enzyme Premix (011614, Clontech). The generated plasmid was named pCRISPaint-mNeon-Efα-PuroR. PCR primers used in amplification and cloning are provided (\u003cb\u003eTable. S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe three plasmids were transfected to human colonoids by electroporation, adapted from previously reported protocols \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Briefly, before electroporation, human colonoids were cultured in IFE medium supplemented with 10 \u0026micro;M Y-27632 and 5 \u0026micro;M Prostaglandin E2 (PGE2) (CAY14010, Cayman Chemical) \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e for 3\u0026ndash;5 days after splitting. Ten wells of organoids were collected and mechanically broken by vigorous trituration with a p-200 micropipette. The pelleted organoids were then resuspended in 1 mL TrypLE Express Enzyme with 10 \u0026micro;M Y-27632, and incubated in 37\u0026deg;C water bath for 3 min to dissociate into clusters of 10\u0026ndash;15 cells \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. The dissociated organoids were washed in Opti-MEM (31985062, Gibco) twice. Every 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e to 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells were resuspended in 100 \u0026micro;L BTXpress buffer, containing 5 \u0026micro;g of each plasmid, 15\u0026micro;g in total. After that, the cell-plasmid mixture was placed into a 2-mm electroporation cuvette, and the electroporation performed immediately before the cells precipitated, using the NEPA21 electroporator (Nepa Gene). After electroporation, the cells were seeded in IFE medium with 10 \u0026micro;M Y-27632 and 5 \u0026micro;M PGE2. Five days after electroporation, cells were selected with 1 \u0026micro;g/mL puromycin in IFE medium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWhole mount immunostaining of colonoids\u003c/h2\u003e \u003cp\u003eHuman colonoids embedded in BME were fixed in 4% formaldehyde (28908, Thermo Fisher) for 45 minutes at room temperature. After fixation, the organoids were washed in ice-cold D-PBS containing 2% BSA (A7906, Sigma-Aldrich). The organoids were incubated on a rotator at room temperature for 1 hr in blocking/permeabilization buffer, which consisted of 2% BSA, 5% donkey serum (D9663, Sigma-Aldrich) and 0.5% Triton X-100 (X100, Sigma) in D-PBS. After the blocking/permeabilization step, the organoids were incubated overnight at 4\u0026deg;C in a rotator with primary antibodies rabbit polyclonal anti-CHGA (1:800; ab15160, Abcam). On the next day, the organoids were washed and incubated with secondary antibodies, Alexa Fluor\u0026trade; 568 donkey anti-rabbit (1:500, Invitrogen) in a rotator for 1 hr at room temperature. Nuclear counterstaining dye, Hoechst 33342 (5 \u0026micro;g/mL; H3570, Invitrogen) was added to the secondary antibody solution and incubated for another 15 min. Following thorough washes in D-PBS to remove all unbound antibodies, the organoids were mounted on a glass slide with a drop of mounting medium Fluoromount-G\u0026reg; (0100-01, Cambridge Bioscience) and covered with a coverslip. The slides were air-dried in the dark overnight at room temperature before being analysed by confocal microscopy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical staining of colon biopsies\u003c/h2\u003e \u003cp\u003eParaffin-embedded tissue blocks of human colon mucosal biopsies from healthy patients were provided by Dr Polychronis Pavlidis (King\u0026rsquo;s College Hospital, Denmark Hill). Tissue sections (5 \u0026micro;m) were mounted on positively coated Superfrost\u0026reg; Plus slides (MIC3040D2, Scientific Laboratory Supplies). Sections were deparaffinized and subsequently underwent heat-induced antigen retrieval by microwaving in 10 mM sodium citrate buffer (pH 6.0) for 10 min. After rinsing with water, slides were incubated at room temperature for 30 min in a blocking/permeabilization buffer containing 2% BSA, 5% donkey serum, and 0.5% Triton X-100 in D-PBS. Following blocking, sections were incubated overnight at 4\u0026deg;C in a humidified chamber with primary antibody IL-13Rα1 (1:200, rabbit; ab79277, Abcam) and GLP-1 (1:200, mouse; ab23468, Abcam) or 5-HT (1:200, goat; 20079, Immunostar). Slides were then washed in D-PBS and incubated with secondary antibodies (1:500, Alexa Fluor\u0026trade;, Invitrogen) for 1 hr at room temperature. Both primary and secondary antibodies were diluted in blocking/permeabilization buffer. After the secondary antibody incubation, slides were washed with D-PBS and mounted with DAPI Fluoromount-G\u0026reg; (0100\u0026thinsp;\u0026minus;\u0026thinsp;20, Cambridge Bioscience). The slides were air-dried in the dark overnight at room temperature before being analysed by confocal microscopy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFACS sorting fluorescent EECs\u003c/h2\u003e \u003cp\u003eAfter differentiation of the colonoids, they were dissociated to single cells in 2\u0026ndash;4 mL TrypLE Express (Gibco), with 4 \u0026micro;g/mL DNase (79254, Qiagen) at 37\u0026deg;C for 15\u0026ndash;20 min. Dissociated single cells were then filtered through a 40 \u0026micro;m cell strainer. The cells were then resuspended in FACS buffer (Advanced DMEM/F-12 medium, 2 mM GlutaMAX, 10 mM HEPES, 10 \u0026micro;M Y-27632, 2% FBS and 2 mM EDTA). For CHGA-mNeon and PYY-GFP cells, 0.1 \u0026micro;g/mL DAPI (10236276001, Merck) was used as the live/dead cell marker. For TPH1-CFP cells, 7-AAD (00-6993-50, Invitrogen\u0026trade;) at 0.25 \u0026micro;g per million cells was used. FACS was performed on the BD FACS Aria\u0026trade; II (Beckton Dickinson) with a nozzle size of 100 \u0026micro;m. The gating strategy images were generated by FlowJo software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and real-time quantitative PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eFACS-sorted cells were collected in a LoBind tube containing 500 \u0026micro;L of D-PBS. The cells were then centrifuged at 600 rcf, 4\u0026deg;C for 5 min. The total RNA extraction of the cells and cDNA transcription were performed using the SYBR\u0026trade; Green Fast Advanced Cells-to-CT\u0026trade; Kit (A35379, Invitrogen). RT-qPCR was performed using PowerUp SYBR Green Master Mix (A25742, Thermo Fisher) with QuantiTect primers or 500nM designed primers (\u003cb\u003eTable. S3\u003c/b\u003e) on LightCycler 96 (Roche).\u003c/p\u003e \u003cp\u003eTotal RNA extraction from the whole organoids was conducted using the RNeasy Kit (74106, Qiagen) after they were released from BME using Cell Recovery solution (11543560, Corning). On-column DNase I (79254, Qiagen) was used to remove residual genomic DNA. cDNA transcription was performed using Power High-Capacity cDNA Reverse Transcription Kit (4368813, Applied Biosystems). RT-qPCR was performed with QuantiFast SYBR Green PCR Kit (204057, Qiagen) with QuantiTect primers or 500nM designed primers (\u003cb\u003eTable. S3\u003c/b\u003e) on a LightCycler 96 (Roche).\u003c/p\u003e \u003cp\u003eThe relative gene expression levels were determined by averaging the Ct values from technical duplicates for each biological sample and then normalizing them against the expression of the reference gene beta-2-microglobulin (\u003cem\u003eB2M\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell RNA-sequencing and data analysis\u003c/h2\u003e \u003cp\u003eFluorescent single cells were sorted into 384-well cell capture plates (HSP3801, Bio-Rad) for SORT-seq.\u0026nbsp;The samples were processed by Single Cell Discoveries (Utrecht, Netherlands) for library preparation, sequencing and alignment, following the published SORT-seq protocol \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The scRNA-seq analysis of raw counts was conducted in RStudio using the R package Seurat v4.3.0 \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor human CHGA-mNeon cells, those with fewer than 1550 transcripts, unique feature counts exceeding 2500 or falling below 200, or mitochondrial counts exceeding 30% were excluded. The cells were normalized using SCTransform \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. After normalization, distinct cell clusters were identified through shared nearest neighbour clustering optimization, based on the 4 most variable dimensions. A resolution of 0.4 was used during the clustering process. For mouse TPH1-CFP cells, those exceeding 4000 unique feature counts or falling below 200, or with mitochondrial counts surpassing 30%, were excluded. Following normalization using SCTransform, shared nearest neighbour clustering optimization was applied based on the 5 most variable dimensions, with a resolution of 0.4. This process identified three distinct cell clusters. However, one cluster was enriched with genes associated with cell death, indicating cell damage. Therefore, this population was removed. For mouse PYY-GFP cells, those with over 2500 unique feature counts or less than 200, along with mitochondrial counts exceeding 5%, were excluded. After SCTransform normalization, shared nearest neighbour clustering optimization with the 4 most variable dimensions and a resolution of 0.5 identified two distinct cell clusters.\u003c/p\u003e \u003cp\u003eThe cell clusters were plotted using UMAP dimension reduction. Expression of genes of interest across different cell clusters were plotted on heatmaps or distribution plots. Gene expression table of distinct clusters were generated by AverageExpression. Co-expression of two features was visualized by FeaturePlot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBulk RNA-sequencing and data analysis\u003c/h2\u003e \u003cp\u003eAround 100,000 fluorescent and non-fluorescent single cells were FACS-sorted, pelleted and underwent RNA extraction with TRIzol\u0026trade; LS Reagent (10296010, Invitrogen\u0026trade;). Three replicate samples were prepared for both fluorescent and non-fluorescent groups. The RNA samples were processed by Single Cell Discoveries for cDNA library preparation and sequencing. 75-bp paired-end sequencing of the cDNA libraries was performed on an Illumina NextSeq\u0026trade; 500 platform. After sequencing, the paired-end reads were aligned to the human reference genome GRCh38/hg38 or mouse reference genome GRCm38/mm10 using Burrows-Wheeler Aligner (BWA) \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDifferential gene expression analysis between the fluorescent and non-fluorescent groups (n\u0026thinsp;=\u0026thinsp;3) was performed in RStudio using the Bioconductor package DESeq2 v1.38.1 \u003csup\u003e79\u003c/sup\u003e. The PCA plot of differentially expressed genes was generated to demonstrate clusters of samples based on their similarity. Gene lists encoding receptors, orphan GPCRs, transporters, ion channels, gut hormones, transcription factors, cytokines were generated from the highly expressed gene list using QIAGEN Ingenuity Pathway Analysis (IPA) software, with log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Specifically, the genes with predicted protein location in the extracellular space were included in the gut hormone gene list. For display in heatmaps, genes were ranked by fold change compared against non-fluorescent cells. Differentially expressed genes with a p-value of 0 were replaced with 3x10\u003csup\u003e\u0026minus;\u0026thinsp;300\u003c/sup\u003e to enable visualisation in the volcano plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of GLP-1 secretion in human colonoids\u003c/h2\u003e \u003cp\u003eDifferentiated human colonoids were harvested with cell recovery solution and washed with D-PBS, with one well as one sample. The organoids were then incubated for 2 hrs at 37\u0026deg;C in 100 \u0026micro;L of saline secretion buffer containing 138 mM NaCl, 4.5 mM KCl, 4.2 mM NaHCO\u003csub\u003e3\u003c/sub\u003e, 1.2 mM NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 2.6 mM CaCl\u003csub\u003e2\u003c/sub\u003e, 1.2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 10 mM HEPES (pH 7.4), with freshly added 0.1% fatty acid free BSA (A6003, Sigma-Aldrich) and 50 \u0026micro;M DPP IV Inhibitor (DPP4, Sigma-Aldrich). Four groups were set up: a negative control, treatment of 10 \u0026micro;M Forskolin (FSK, F6886, Scientific Laboratory Supplies) and 10 \u0026micro;M IBMX (I5879, Merck), and treatments with 20ng/mL or 100 ng/mL recombinant human IL-13 protein (R\u0026amp;D Systems, 213-ILB-010). The supernatant and organoid lysates were collected to measure GLP-1 concentrations in secretion and total protein concentrations, respectively. Organoid lysates were prepared in 100 \u0026micro;L of D-PBS with protease inhibitors (A32955, Thermo Fisher) and sonicated on ice for 30 seconds at an amplitude of 10\u0026ndash;14. The homogenates were then centrifuged at 5,000 rcf, 4\u0026deg;C for 5 min, and the supernatant was collected as organoid lysate samples. Both secretion and lysate samples were stored at -70\u0026deg;C. The GLP-1 concentrations of secretion and lysate samples were measured using GLP-1 ELISA kit (Merck, EGLP-35K). Total protein concentrations of organoid lysates were measured using Pierce\u0026trade; BCA Protein Assay kit (23227, Thermo Fisher). The relative GLP-1 secretion levels were determined by averaging the GLP-1 concentrations from technical duplicates for each biological sample and then normalizing them against the concentrations of the total protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFSCV cell measurements of 5-HT secretion in human colonoids, data acquisition and analysis\u003c/h2\u003e \u003cp\u003eCFM fabrication and FSCV data acquisition was performed as described previously \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Briefly, carbon fibre (T-650, 7 \u0026micro;m diameter; Goodfellow) was aspirated into a glass capillary (1.0 mm outer diameter, 0.58 mm inner diameter) and the capillary was pulled using PE-22 micropipette puller (Narishige Group) to create a seal around the fibre. The exposed carbon fibre was manually trimmed to 100 \u0026micro;m (+/- 2 \u0026micro;m) in length and back-connected using a pinned stainless-steel wire. The carbon surface was coated with Nafion\u0026trade; (Liquion Solution, LQ-1105, 5% by weight Nafion, Ion Power) by applying 1 V (vs. Ag/AgCl) for 30 sec. All FSCV measurements were acquired in the saline secretion buffer as described above. Data collection and analysis was performed with WCCV 3.06 software (Knowmad Technologies), Dagan potentiostat (Dagan Corporation), and Pine Research headstage (Pine Research Instrumentation). A waveform optimized for 5-HT detection was applied (0.2 V to 1.0 V to -0.1 V to 0.2 V vs. Ag/AgCl at 1000 V/s) and measurements were taken at 10 Hz for 30 sec per file.\u003c/p\u003e \u003cp\u003eFor FSCV measurement, differentiated human CHGA-mNeon colonoids embedded in BME were mechanically detached from the plate using a p-1000 micropipette, washed in D-PBS to remove all culture medium, and then resuspended in 2 mL of saline secretion buffer. They were transferred to a 3.5 cm-dish, which was then affixed to the Bio Station IM (Nikon). EC cell-enriched structures were identified by mNeon green fluorescence before shielding the setup with aluminium foil grounded as a Faraday cage. The Ag/AgCl-reference electrode was placed in the dish and the CFM was positioned using a QUAD micromanipulator (Sutter Instruments). Repeated 30 sec-files were taken to record spontaneous activity for a total of 15 min. Subsequently, 100 nM PNX-20 or 100 ng/mL IL-13 was added, and recordings continued immediately for an additional 15 min. Events releasing 5-HT were identified by the 5-HT-specific oxidation peak in the cyclic voltammogram and quantified for each file. Frequency of 5-HT release event was calculated for each 30 sec-file, and peak amplitude was determined as the current difference immediately before release and the peak.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProteomics mass spectrometry measurement\u003c/h2\u003e \u003cp\u003eAround 100,000 sorted CHGA-mNeon\u003csup\u003e+\u0026thinsp;ve\u003c/sup\u003e and mNeon\u003csup\u003e\u0026minus;\u0026thinsp;ve\u003c/sup\u003e cells were pelleted and lysed with Branson sonifier 150 in 8.0M urea buffer. 20 \u0026micro;g of proteins from each sample were reduced with 7mM of 1,4-dithiothreitol for 1 hour at 56\u0026deg;C and alkylated with 12.5 mM iodoacetamide for 1 hour at room temperature in the dark. Samples were digested with trypsin enzyme at 1:50 ratio (enzyme:protein) and incubated at 37\u0026deg;C overnight. The digestion was quenched with 1% formic acid and tryptic peptides were purified and desalted by C18 spin column. Peptides were dried to completion by SpeedVac (Thermo) and resuspended in a loading buffer (2% acetonitrile in 0.1% formic acid) for LCMS analysis.\u003c/p\u003e \u003cp\u003eThe tryptic peptides were subjected to LCMS system for analysis. For liquid chromatography, a reverse phase Thermo Acclaim Pepmap trap column (2 cm length, 75 \u0026micro;m in diameter and 3 \u0026micro;m C18 beads) were connected to a nanoflow HPLC (RSLC Ultimate 3000) on an Easy-spray C18 nano column (50 cm length, 75 \u0026micro;m in diameter, ThermoFisherScientific). Buffer A (5% ACN, 0.1% formic acid) and buffer B (80% ACN, 0.1% formic acid) were used. Peptides were eluted with a linear gradient of 5\u0026ndash;55% buffer B at a flow rate of 250 nl/min over 100 min at 45\u0026deg;C. Peptides were directly ionized within the easyspray ion source (Thermo) and injected into Orbitrap Fusion Lumos mass spectrometers (Thermo).\u003c/p\u003e \u003cp\u003eMS data generated were collected within Xcalibur 4.4 to acquire MS data using a \u0026ldquo;Universal\u0026rdquo; method by defining a 3s cycle time between a full MS scan and MS/MS fragmentation. We acquired one full-scan MS spectrum at a resolution of 120,000 at 200 m/z with a normalized automatic gain control (AGC) target (%) of 250 and a scan range of 300\u0026thinsp;~\u0026thinsp;1600 m/z. The MS/MS fragmentation was conducted using CID collision energy (35%) with an orbitrap resolution of 30000 at 200 m/z. The AGC target (%) was set up as 200 with a max injection time of 128ms. A dynamic exclusion of 30s and 2\u0026ndash;7 included charged states were defined within this method.\u003c/p\u003e \u003cp\u003eResulting raw files were searched again the human Uniprot fasta database within Thermo Proteome Discoverer (PD, version 2.5) allowing two missed trypsin cleavage sites and methionine oxidation. Carbamidomethylation on cysteine residues was set as a fixed modification. Precursor mass tolerance was set as 20 ppm and fragment ion tolerance was set as 0.6 Da. Peptides that met the false discovery rate cut-off of 1% based on the searching again a decoy database was considered for further analysis. Precursor ion intensities extracted from PD were applied for differential analysis using two-sample t-test algorithm embedded in Perseus software v2.1.1.0 using two-sample t-test algorithm \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Accession IDs were converted to gene IDs by SynGO \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eThe unpaired t-test was utilized to compare two unrelated groups when the test statistic followed a normal distribution. For comparisons involving multiple conditions, the one-way analysis of variance (ANOVA) was conducted.\u003c/p\u003e \u003cp\u003eStatistical significance was accepted at p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM (n\u0026thinsp;\u0026ge;\u0026thinsp;3) and denoted as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, or ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 to indicate the level of significance. GraphPad Prism 10 was used for generating graphs and performing statistical analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eY.L. and G.A.B. conceptualized the project and wrote the manuscript. Y.L. generated the reporter organoid model and analysed the EEC transcriptome database. B.B. and P.H. performed the FSCV serotonin secretion experiments and analysis. L.M. and K.G.M. participated in the editing of the manuscript. M.J. participated in bulk RNA analysis. N.H. participated in plasmid cloning and confocal imaging. X.Y. conducted the proteomics study. B.H. provided human biopsies for isolation of organoids. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConflict of interests\u003c/h2\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to thank Dr Joep Beumer and Dr Hans Clevers from Hubrecht Institute for providing the CRISPR-HOT plasmids, Dr Cordelia Imig from University of Copenhagen for providing TPH1-CFP mouse colonoids and Dr Polychronis Pavlidis from King’s College Hospital for providing paraffin-embedded tissue blocks of human colon\u0026nbsp;mucosal biopsies. We appreciate the assistance from the staff in BRC Flow cytometry core, Guy's and St Thomas NHS Foundation Trust, Nikon Imaging Centre and Proteomics Facility at King’s College London. Y.L. acknowledges support from China Scholarship Council (K-CSC) and the Society for Endocrinology (Early Career Grant). K.G.M is supported by Diabetes UK (18/0005886, 20/0006295), the BBSRC (BB/W001497/1, BB/X017273/1). Both G.A.B and K.G.M are supported by the MRC (MR/Y013980/1) and the Wellcome Trust (310835/Z/24/Z).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGribble FM, Reimann F. Function and mechanisms of enteroendocrine cells and gut hormones in metabolism. \u003cem\u003eNat Rev Endocrinol\u003c/em\u003e. 2019;15(4):226-237. doi:10.1038/s41574-019-0168-8\u003c/li\u003e\n \u003cli\u003eGehart H, van Es JH, Hamer K, et al. Identification of Enteroendocrine Regulators by Real-Time Single-Cell Differentiation Mapping. \u003cem\u003eCell\u003c/em\u003e. 2019;176(5):1158-1173.e16. doi:10.1016/j.cell.2018.12.029\u003c/li\u003e\n \u003cli\u003eGoldspink DA, Reimann F, Gribble FM. Models and Tools for Studying Enteroendocrine Cells. \u003cem\u003eEndocrinology\u003c/em\u003e. 2018;159(12):3874-3884. doi:10.1210/en.2018-00672\u003c/li\u003e\n \u003cli\u003eBeumer J, Artegiani B, Post Y, et al. Enteroendocrine cells switch hormone expression along the crypt-to-villus BMP signalling gradient. \u003cem\u003eNature Cell Biology\u003c/em\u003e. 2018;20(8):909-916. doi:10.1038/s41556-018-0143-y\u003c/li\u003e\n \u003cli\u003eYu Y, Yang W, Li Y, Cong Y. Enteroendocrine Cells: Sensing Gut Microbiota and Regulating Inflammatory Bowel Diseases. \u003cem\u003eInflamm Bowel Dis\u003c/em\u003e. 2020;26(1):11-20. doi:10.1093/ibd/izz217\u003c/li\u003e\n \u003cli\u003eWorthington JJ, Reimann F, Gribble FM. Enteroendocrine cells-sensory sentinels of the intestinal environment and orchestrators of mucosal immunity. \u003cem\u003eMucosal Immunol\u003c/em\u003e. 2018;11(1):3-20. doi:10.1038/mi.2017.73\u003c/li\u003e\n \u003cli\u003eAndersen A, Lund A, Knop FK, Vilsb\u0026oslash;ll T. Glucagon-like peptide 1 in health and disease. \u003cem\u003eNat Rev Endocrinol\u003c/em\u003e. 2018;14(7):390-403. doi:10.1038/s41574-018-0016-2\u003c/li\u003e\n \u003cli\u003eBeumer J, Puschhof J, Bauz\u0026aacute;-Martinez J, et al. High-Resolution mRNA and Secretome Atlas of Human Enteroendocrine Cells. \u003cem\u003eCell\u003c/em\u003e. 2020;0(0). doi:10.1016/j.cell.2020.04.036\u003c/li\u003e\n \u003cli\u003eSchmid-Burgk JL, H\u0026ouml;ning K, Ebert TS, Hornung V. CRISPaint allows modular base-specific gene tagging using a ligase-4-dependent mechanism. \u003cem\u003eNature Communications\u003c/em\u003e. 2016;7(1):12338. doi:10.1038/ncomms12338\u003c/li\u003e\n \u003cli\u003eHolmes J, Lau T, Saylor R, et al. Voltammetric Approach for Characterizing the Biophysical and Chemical Functionality of Human Induced Pluripotent Stem Cell-Derived Serotonin Neurons. \u003cem\u003eAnal Chem\u003c/em\u003e. 2022;94(25):8847-8856. doi:10.1021/acs.analchem.1c05082\u003c/li\u003e\n \u003cli\u003eArtegiani B, Hendriks D, Beumer J, et al. Fast and efficient generation of knock-in human organoids using homology-independent CRISPR-Cas9 precision genome editing. \u003cem\u003eNat Cell Biol\u003c/em\u003e. 2020;22(3):321-331. doi:10.1038/s41556-020-0472-5\u003c/li\u003e\n \u003cli\u003eGoldspink DA, Lu VB, Miedzybrodzka EL, et al. Labeling and Characterization of Human GLP-1-Secreting L-cells in Primary Ileal Organoid Culture. \u003cem\u003eCell Reports\u003c/em\u003e. 2020;31(13):107833. doi:10.1016/j.celrep.2020.107833\u003c/li\u003e\n \u003cli\u003eFujii M, Matano M, Toshimitsu K, et al. Human Intestinal Organoids Maintain Self-Renewal Capacity and Cellular Diversity in Niche-Inspired Culture Condition. \u003cem\u003eCell Stem Cell\u003c/em\u003e. 2018;23(6):787-793.e6. doi:10.1016/j.stem.2018.11.016\u003c/li\u003e\n \u003cli\u003eTsakmaki A, Fonseca Pedro P, Pavlidis P, Hayee B, Bewick GA. ISX-9 manipulates endocrine progenitor fate revealing conserved intestinal lineages in mouse and human organoids. \u003cem\u003eMolecular Metabolism\u003c/em\u003e. 2020;34:157-173. doi:10.1016/j.molmet.2020.01.012\u003c/li\u003e\n \u003cli\u003eLin L, DeMartino J, Wang D, et al. Unbiased transcription factor CRISPR screen identifies ZNF800 as master repressor of enteroendocrine differentiation. \u003cem\u003eScience\u003c/em\u003e. 2023;382(6669):451-458. doi:10.1126/science.adi2246\u003c/li\u003e\n \u003cli\u003ePsichas A, Glass LL, Sharp SJ, Reimann F, Gribble FM. Galanin inhibits GLP-1 and GIP secretion via the GAL1 receptor in enteroendocrine L and K cells. \u003cem\u003eBr J Pharmacol\u003c/em\u003e. 2016;173(5):888-898. doi:10.1111/bph.13407\u003c/li\u003e\n \u003cli\u003eBrzozowska M, Całka J. Review: Occurrence and Distribution of Galanin in the Physiological and Inflammatory States in the Mammalian Gastrointestinal Tract. \u003cem\u003eFront Immunol\u003c/em\u003e. 2021;11:602070. doi:10.3389/fimmu.2020.602070\u003c/li\u003e\n \u003cli\u003eGarella R, Squecco R, Baccari MC. Site-related Effects of Relaxin in the Gastrointestinal Tract Through Nitric Oxide Signalling: An Updated Report. \u003cem\u003eCurr Protein Pept Sci\u003c/em\u003e. 2017;18(12):1254-1262. doi:10.2174/1389203718666170612104719\u003c/li\u003e\n \u003cli\u003ePendharkar SA, Walia M, Drury M, Petrov MS. Calcitonin gene-related peptide: neuroendocrine communication between the pancreas, gut, and brain in regulation of blood glucose. \u003cem\u003eAnnals of Translational Medicine\u003c/em\u003e. 2017;5(21):419-419. doi:10.21037/atm.2017.08.27\u003c/li\u003e\n \u003cli\u003eTopaloglu AK, Reimann F, Guclu M, et al. TAC3 and TACR3 Mutations in Familial Hypogonadotropic Hypogonadism Reveal a Key Role for Neurokinin B in the Central Control of Reproduction. \u003cem\u003eNat Genet\u003c/em\u003e. 2009;41(3):354-358. doi:10.1038/ng.306\u003c/li\u003e\n \u003cli\u003eLecci A, Capriati A, Altamura M, Maggi CA. Tachykinins and tachykinin receptors in the gut, with special reference to NK2 receptors in human. \u003cem\u003eAuton Neurosci\u003c/em\u003e. 2006;126-127:232-249. doi:10.1016/j.autneu.2006.02.014\u003c/li\u003e\n \u003cli\u003eMasuda T, Sakuma C, Nagaoka A, et al. Follistatin-like 5 is expressed in restricted areas of the adult mouse brain: Implications for its function in the olfactory system. \u003cem\u003eCongenit Anom (Kyoto)\u003c/em\u003e. 2014;54(1):63-66. doi:10.1111/cga.12022\u003c/li\u003e\n \u003cli\u003eLi C, Dai L, Zhang J, et al. Follistatin‐like protein 5 inhibits hepatocellular carcinoma progression by inducing caspase‐dependent apoptosis and regulating Bcl‐2 family proteins. \u003cem\u003eJ Cell Mol Med\u003c/em\u003e. 2018;22(12):6190-6201. doi:10.1111/jcmm.13906\u003c/li\u003e\n \u003cli\u003eHusted AS, Trauelsen M, Rudenko O, Hjorth SA, Schwartz TW. GPCR-Mediated Signaling of Metabolites. \u003cem\u003eCell Metabolism\u003c/em\u003e. 2017;25(4):777-796. doi:10.1016/j.cmet.2017.03.008\u003c/li\u003e\n \u003cli\u003eParikh K, Antanaviciute A, Fawkner-Corbett D, et al. Colonic epithelial cell diversity in health and inflammatory bowel disease. \u003cem\u003eNature\u003c/em\u003e. 2019;567(7746):49-55. doi:10.1038/s41586-019-0992-y\u003c/li\u003e\n \u003cli\u003eChichura KS, Elfers CT, Salameh TS, et al. A peptide triple agonist of GLP-1, neuropeptide Y1, and neuropeptide Y2 receptors promotes glycemic control and weight loss. \u003cem\u003eSci Rep\u003c/em\u003e. 2023;13(1):9554. doi:10.1038/s41598-023-36178-1\u003c/li\u003e\n \u003cli\u003eAdriaenssens AE, Gribble FM, Reimann F. The glucose-dependent insulinotropic polypeptide signaling axis in the central nervous system. \u003cem\u003ePeptides\u003c/em\u003e. 2020;125:170194. doi:10.1016/j.peptides.2019.170194\u003c/li\u003e\n \u003cli\u003eHolzer P, Reichmann F, Farzi A. Neuropeptide Y, peptide YY and pancreatic polypeptide in the gut\u0026ndash;brain axis. \u003cem\u003eNeuropeptides\u003c/em\u003e. 2012;46(6):261-274. doi:10.1016/j.npep.2012.08.005\u003c/li\u003e\n \u003cli\u003eLewis JE, Woodward ORM, Nuzzaci D, et al. Relaxin/insulin-like family peptide receptor 4 (Rxfp4) expressing hypothalamic neurons modulate food intake and preference in mice. \u003cem\u003eMol Metab\u003c/em\u003e. 2022;66:101604. doi:10.1016/j.molmet.2022.101604\u003c/li\u003e\n \u003cli\u003eFernando SJA, Wang Q, Hay DL, Bathgate RAD, Shepherd PR, Lee KL. Evidence that RXFP4 is located in enterochromaffin cells and can regulate production and release of serotonin. \u003cem\u003eBiosci Rep\u003c/em\u003e. 2023;43(4):BSR20221956. doi:10.1042/BSR20221956\u003c/li\u003e\n \u003cli\u003eYang X, Lou J, Shan W, et al. Pathophysiologic Role of Neurotransmitters in Digestive Diseases. \u003cem\u003eFront Physiol\u003c/em\u003e. 2021;12. doi:10.3389/fphys.2021.567650\u003c/li\u003e\n \u003cli\u003eManocha M, Shajib MS, Rahman MM, et al. IL-13-mediated immunological control of enterochromaffin cell hyperplasia and serotonin production in the gut. \u003cem\u003eMucosal Immunol\u003c/em\u003e. 2013;6(1):146-155. doi:10.1038/mi.2012.58\u003c/li\u003e\n \u003cli\u003eFriedrich M, Diegelmann J, Schauber J, Auernhammer CJ, Brand S. Intestinal neuroendocrine cells and goblet cells are mediators of IL-17A-amplified epithelial IL-17C production in human inflammatory bowel disease. \u003cem\u003eMucosal Immunol\u003c/em\u003e. 2015;8(4):943-958. doi:10.1038/mi.2014.124\u003c/li\u003e\n \u003cli\u003eMcIlwraith EK, Zhang N, Belsham DD. The Regulation of Phoenixin: A Fascinating Multidimensional Peptide. \u003cem\u003eJournal of the Endocrine Society\u003c/em\u003e. 2022;6(2):bvab192. doi:10.1210/jendso/bvab192\u003c/li\u003e\n \u003cli\u003eMuraro MJ, Dharmadhikari G, Gr\u0026uuml;n D, et al. A Single-Cell Transcriptome Atlas of the Human Pancreas. \u003cem\u003eCell Syst\u003c/em\u003e. 2016;3(4):385-394.e3. doi:10.1016/j.cels.2016.09.002\u003c/li\u003e\n \u003cli\u003eStuart T, Butler A, Hoffman P, et al. Comprehensive Integration of Single-Cell Data. \u003cem\u003eCell\u003c/em\u003e. 2019;177(7):1888-1902.e21. doi:10.1016/j.cell.2019.05.031\u003c/li\u003e\n \u003cli\u003eElmentaite R, Kumasaka N, Roberts K, et al. Cells of the human intestinal tract mapped across space and time. \u003cem\u003eNature\u003c/em\u003e. 2021;597(7875):250-255. doi:10.1038/s41586-021-03852-1\u003c/li\u003e\n \u003cli\u003eHan YF, Cao GW. Role of nuclear receptor NR4A2 in gastrointestinal inflammation and cancers. \u003cem\u003eWorld J Gastroenterol\u003c/em\u003e. 2012;18(47):6865-6873. doi:10.3748/wjg.v18.i47.6865\u003c/li\u003e\n \u003cli\u003eSasselli V, Boesmans W, Berghe PV, Tissir F, Goffinet AM, Pachnis V. Planar cell polarity genes control the connectivity of enteric neurons. \u003cem\u003eJ Clin Invest\u003c/em\u003e. 2013;123(4):1763-1772. doi:10.1172/JCI66759\u003c/li\u003e\n \u003cli\u003eSamson WK, Salvemini D, Yosten GLC. Overcoming Stress, Hunger, and Pain: Cocaine- and Amphetamine-Regulated Transcript Peptide\u0026rsquo;s Promise. \u003cem\u003eEndocrinology\u003c/em\u003e. 2021;162(8):bqab108. doi:10.1210/endocr/bqab108\u003c/li\u003e\n \u003cli\u003eShcherbina L, Lindqvist A, Thor\u0026eacute;n Fischer AH, et al. Intestinal CART is a regulator of GIP and GLP-1 secretion and expression. \u003cem\u003eMolecular and Cellular Endocrinology\u003c/em\u003e. 2018;476:8-16. doi:10.1016/j.mce.2018.04.002\u003c/li\u003e\n \u003cli\u003eShimizu D, Kanda M, Tanaka H, et al. GPR155 Serves as a Predictive Biomarker for Hematogenous Metastasis in Patients with Gastric Cancer. \u003cem\u003eSci Rep\u003c/em\u003e. 2017;7(1):42089. doi:10.1038/srep42089\u003c/li\u003e\n \u003cli\u003eSi X, Song Z, Liu N, Jia H, Liu H, Wu Z. \u0026alpha;-Ketoglutarate Restores Intestinal Barrier Function through Promoting Intestinal Stem Cells-Mediated Epithelial Regeneration in Colitis. \u003cem\u003eJ Agric Food Chem\u003c/em\u003e. 2022;70(43):13882-13892. doi:10.1021/acs.jafc.2c04641\u003c/li\u003e\n \u003cli\u003eShaaban A, Maa\u0026szlig; F, Schwarze V, et al. Dissecting Functional, Structural, and Molecular Requirements for Serotonin Release from Mouse Enterochromaffin Cells. Published online May 29, 2021:2021.05.28.446100. doi:10.1101/2021.05.28.446100\u003c/li\u003e\n \u003cli\u003eGross S, Garofalo DC, Balderes DA, et al. The novel enterochromaffin marker Lmx1a regulates serotonin biosynthesis in enteroendocrine cell lineages downstream of Nkx2.2. \u003cem\u003eDevelopment\u003c/em\u003e. 2016;143(14):2616-2628. doi:10.1242/dev.130682\u003c/li\u003e\n \u003cli\u003ePattyn A, Simplicio N, van Doorninck JH, Goridis C, Guillemot F, Brunet JF. Ascl1/Mash1 is required for the development of central serotonergic neurons. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2004;7(6):589-595. doi:10.1038/nn1247\u003c/li\u003e\n \u003cli\u003eTakahashi M. The GDNF/RET signaling pathway and human diseases. \u003cem\u003eCytokine \u0026amp; Growth Factor Reviews\u003c/em\u003e. 2001;12(4):361-373. doi:10.1016/S1359-6101(01)00012-0\u003c/li\u003e\n \u003cli\u003ePorokuokka LL, Virtanen HT, Lind\u0026eacute;n J, et al. Gfra1 Underexpression Causes Hirschsprung\u0026rsquo;s Disease and Associated Enterocolitis in Mice. \u003cem\u003eCell Mol Gastroenterol Hepatol\u003c/em\u003e. 2018;7(3):655-678. doi:10.1016/j.jcmgh.2018.12.007\u003c/li\u003e\n \u003cli\u003eIwasaki M, Akiba Y, Kaunitz JD. Recent advances in vasoactive intestinal peptide physiology and pathophysiology: focus on the gastrointestinal system. \u003cem\u003eF1000Res\u003c/em\u003e. 2019;8:F1000 Faculty Rev-1629. doi:10.12688/f1000research.18039.1\u003c/li\u003e\n \u003cli\u003eShamsi BH, Chatoo M, Xu XK, Xu X, Chen XQ. Versatile Functions of Somatostatin and Somatostatin Receptors in the Gastrointestinal System. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e. 2021;12:652363. doi:10.3389/fendo.2021.652363\u003c/li\u003e\n \u003cli\u003eK U, H G, E K. Sphingosine 1-phosphate is a ligand of the human gpr3, gpr6 and gpr12 family of constitutively active G protein-coupled receptors. \u003cem\u003eCellular signalling\u003c/em\u003e. 2002;14(11). doi:10.1016/s0898-6568(02)00041-4\u003c/li\u003e\n \u003cli\u003eZou F, Wang S, Xu M, Wu Z, Deng F. The role of sphingosine-1-phosphate in the gut mucosal microenvironment and inflammatory bowel diseases. \u003cem\u003eFront Physiol\u003c/em\u003e. 2023;14:1235656. doi:10.3389/fphys.2023.1235656\u003c/li\u003e\n \u003cli\u003eBjursell M, Gerdin AK, J\u0026ouml;nsson M, et al. G protein-coupled receptor 12 deficiency results in dyslipidemia and obesity in mice. \u003cem\u003eBiochem Biophys Res Commun\u003c/em\u003e. 2006;348(2):359-366. doi:10.1016/j.bbrc.2006.07.090\u003c/li\u003e\n \u003cli\u003eNath AK, Ma J, Chen ZZ, et al. Genetic deletion of gpr27 alters acylcarnitine metabolism, insulin sensitivity, and glucose homeostasis in zebrafish. \u003cem\u003eFASEB J\u003c/em\u003e. 2020;34(1):1546-1557. doi:10.1096/fj.201901466R\u003c/li\u003e\n \u003cli\u003eCui J, Ding Y, Chen S, et al. Disruption of \u003cem\u003eGpr45\u003c/em\u003e causes reduced hypothalamic POMC expression and obesity. \u003cem\u003eJ Clin Invest\u003c/em\u003e. 2016;126(9):3192-3206. doi:10.1172/JCI85676\u003c/li\u003e\n \u003cli\u003eZong X, Wang H, Xiao X, et al. Enterotoxigenic Escherichia coli infection promotes enteric defensin expression via FOXO6-METTL3-m6A-GPR161 signalling axis. \u003cem\u003eRNA Biology\u003c/em\u003e. 2020;18(4):576. doi:10.1080/15476286.2020.1820193\u003c/li\u003e\n \u003cli\u003eNiedernberg A, Tunaru S, Blaukat A, Ardati A, Kostenis E. Sphingosine 1-phosphate and dioleoylphosphatidic acid are low affinity agonists for the orphan receptor GPR63. \u003cem\u003eCell Signal\u003c/em\u003e. 2003;15(4):435-446. doi:10.1016/s0898-6568(02)00119-5\u003c/li\u003e\n \u003cli\u003eMaeyashiki C, Melhem H, Hering L, et al. Activation of pH-Sensing Receptor OGR1 (GPR68) Induces ER Stress Via the IRE1\u0026alpha;/JNK Pathway in an Intestinal Epithelial Cell Model. \u003cem\u003eSci Rep\u003c/em\u003e. 2020;10(1):1438. doi:10.1038/s41598-020-57657-9\u003c/li\u003e\n \u003cli\u003eChulkina M, Beswick EJ, Pinchuk IV. Role of PD-L1 in Gut Mucosa Tolerance and Chronic Inflammation. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2020;21(23):9165. doi:10.3390/ijms21239165\u003c/li\u003e\n \u003cli\u003eChiriac MT, Hracsko Z, G\u0026uuml;nther C, et al. IL-20 controls resolution of experimental colitis by regulating epithelial IFN/STAT2 signalling. \u003cem\u003eGut\u003c/em\u003e. 2024;73(2):282-297. doi:10.1136/gutjnl-2023-329628\u003c/li\u003e\n \u003cli\u003eRiaz B, Islam SMS, Ryu HM, Sohn S. CD83 Regulates the Immune Responses in Inflammatory Disorders. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2023;24(3):2831. doi:10.3390/ijms24032831\u003c/li\u003e\n \u003cli\u003eBallet R, Brennan M, Brandl C, et al. A CD22\u0026ndash;Shp1 phosphatase axis controls integrin \u0026beta;7 display and B cell function in mucosal immunity. \u003cem\u003eNat Immunol\u003c/em\u003e. 2021;22(3):381-390. doi:10.1038/s41590-021-00862-z\u003c/li\u003e\n \u003cli\u003eBoh\u0026oacute;rquez DV, Chandra R, Samsa LA, Vigna SR, Liddle RA. Characterization of basal pseudopod-like processes in ileal and colonic PYY cells. \u003cem\u003eJ Mol Hist\u003c/em\u003e. 2011;42(1):3-13. doi:10.1007/s10735-010-9302-6\u003c/li\u003e\n \u003cli\u003eShroyer NF, Helmrath MA, Wang VY \u0026ndash;C., Antalffy B, Henning SJ, Zoghbi HY. Intestine-Specific Ablation of \u003cem\u003eMouse atonal homolog 1\u003c/em\u003e (\u003cem\u003eMath1\u003c/em\u003e) Reveals a Role in Cellular Homeostasis. \u003cem\u003eGastroenterology\u003c/em\u003e. 2007;132(7):2478-2488. doi:10.1053/j.gastro.2007.03.047\u003c/li\u003e\n \u003cli\u003eShajib MdS, Wang H, Kim JJ, et al. Interleukin 13 and Serotonin: Linking the Immune and Endocrine Systems in Murine Models of Intestinal Inflammation. \u003cem\u003ePLoS One\u003c/em\u003e. 2013;8(8):e72774. doi:10.1371/journal.pone.0072774\u003c/li\u003e\n \u003cli\u003eBellono NW, Bayrer JR, Leitch DB, et al. Enterochromaffin Cells Are Gut Chemosensors that Couple to Sensory Neural Pathways. \u003cem\u003eCell\u003c/em\u003e. 2017;170(1):185-198.e16. doi:10.1016/j.cell.2017.05.034\u003c/li\u003e\n \u003cli\u003eBeumer J, Geurts MH, Geurts V, et al. Description and functional validation of human enteroendocrine cell sensors. \u003cem\u003eScience\u003c/em\u003e. 2024;386(6719):341-348. doi:10.1126/science.adl1460\u003c/li\u003e\n \u003cli\u003eGuccio N, Alcaino C, Miedzybrodzka EL, et al. Molecular mechanisms underlying glucose-dependent insulinotropic polypeptide secretion in human duodenal organoids. \u003cem\u003eDiabetologia\u003c/em\u003e. Published online October 23, 2024. doi:10.1007/s00125-024-06293-3\u003c/li\u003e\n \u003cli\u003eMiedzybrodzka EL, Foreman RE, Lu VB, et al. Stimulation of motilin secretion by bile, free fatty acids, and acidification in human duodenal organoids. \u003cem\u003eMol Metab\u003c/em\u003e. 2021;54:101356. doi:10.1016/j.molmet.2021.101356\u003c/li\u003e\n \u003cli\u003eSato T, Stange DE, Ferrante M, et al. Long-term Expansion of Epithelial Organoids From Human Colon, Adenoma, Adenocarcinoma, and Barrett\u0026rsquo;s Epithelium. \u003cem\u003eGastroenterology\u003c/em\u003e. 2011;141(5):1762-1772. doi:10.1053/j.gastro.2011.07.050\u003c/li\u003e\n \u003cli\u003ePowell N, Pantazi E, Pavlidis P, et al. Interleukin-22 orchestrates a pathological endoplasmic reticulum stress response transcriptional programme in colonic epithelial cells. \u003cem\u003eGut\u003c/em\u003e. 2020;69(3):578-590. doi:10.1136/gutjnl-2019-318483\u003c/li\u003e\n \u003cli\u003eRan FA, Hsu PD, Wright J, Agarwala V, Scott DA, Zhang F. Genome engineering using the CRISPR-Cas9 system. \u003cem\u003eNat Protoc\u003c/em\u003e. 2013;8(11):2281-2308. doi:10.1038/nprot.2013.143\u003c/li\u003e\n \u003cli\u003eKorbie DJ, Mattick JS. Touchdown PCR for increased specificity and sensitivity in PCR amplification. \u003cem\u003eNat Protoc\u003c/em\u003e. 2008;3(9):1452-1456. doi:10.1038/nprot.2008.133\u003c/li\u003e\n \u003cli\u003eFujii M, Matano M, Nanki K, Sato T. Efficient genetic engineering of human intestinal organoids using electroporation. \u003cem\u003eNat Protoc\u003c/em\u003e. 2015;10(10):1474-1485. doi:10.1038/nprot.2015.088\u003c/li\u003e\n \u003cli\u003eGaebler AM, Hennig A, Buczolich K, et al. Universal and Efficient Electroporation Protocol for Genetic Engineering of Gastrointestinal Organoids. \u003cem\u003eJoVE (Journal of Visualized Experiments)\u003c/em\u003e. 2020;(156):e60704. doi:10.3791/60704\u003c/li\u003e\n \u003cli\u003eFan YY, Davidson LA, Callaway ES, Goldsby JS, Chapkin RS. Differential effects of 2- and 3-series E-prostaglandins on in vitro expansion of Lgr5+ colonic stem cells. \u003cem\u003eCarcinogenesis\u003c/em\u003e. 2014;35(3):606-612. doi:10.1093/carcin/bgt412\u003c/li\u003e\n \u003cli\u003eHafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. \u003cem\u003eGenome Biology\u003c/em\u003e. 2019;20(1):296. doi:10.1186/s13059-019-1874-1\u003c/li\u003e\n \u003cli\u003eSchuhwerk H, Kleemann J, Gupta P, et al. The EMT transcription factor ZEB1 governs a fitness-promoting but vulnerable DNA replication stress response. \u003cem\u003eCell Reports\u003c/em\u003e. 2022;41(11):111819. doi:10.1016/j.celrep.2022.111819\u003c/li\u003e\n \u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biology\u003c/em\u003e. 2014;15(12):550. doi:10.1186/s13059-014-0550-8\u003c/li\u003e\n \u003cli\u003eTyanova S, Temu T, Sinitcyn P, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. \u003cem\u003eNat Methods\u003c/em\u003e. 2016;13(9):731-740. doi:10.1038/nmeth.3901\u003c/li\u003e\n \u003cli\u003eKoopmans F, Nierop P van, Andres-Alonso M, et al. SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse. \u003cem\u003eNeuron\u003c/em\u003e. 2019;103(2):217-234.e4. doi:10.1016/j.neuron.2019.05.002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5411079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5411079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnteroendocrine cells (EECs) are specialized intestinal hormone-secreting cells that play critical roles in metabolic homeostasis, digestion, and gut-brain communication. They detect diverse stimuli including endocrine, immune, neuronal, microbial, and dietary signals, through a complex array of receptors, ion channels, and transporters, to modulate the release of over 20 hormones. These molecular sensors serve as potential drug targets to modulate hormone secretion, but until recently, catalogues of such targets in human colonic EECs have not been produced.\u003c/p\u003e \u003cp\u003eTo address this gap, we performed bulk and single-cell RNA sequencing on fluorescently labelled EECs isolated from human colonic organoids, identifying and cataloguing potential druggable targets. This catalogue includes receptors, orphan GPCRs, transporters, and hormones not previously reported in human colonic EECs. Comparison with murine EECs highlighted interspecies similarities and differences, key data to facilitate the design and optimise the predictive accuracy of pre-clinical models. We also functionally validated two receptors not previously identified in human EECs: IL-13Rα1, was expressed in both peptide-producing EECs and serotonin producing Enterochromaffin cells (ECs), and its ligand IL-13 stimulated the secretion of glucagon-like peptide-1 (GLP-1) and serotonin measured in real-time, and GPR173, which was selectively expressed in ECs and, when activated by its agonist Phoenixin-20, also promoted serotonin release.\u003c/p\u003e \u003cp\u003eThese analyses provide a valuable resource for therapeutic interventions aimed at modulating gut hormone secretion, with potential applications in treating gastrointestinal, metabolic, and other related disorders.\u003c/p\u003e","manuscriptTitle":"Mapping the druggable targets displayed by human colonic enteroendocrine cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 11:07:09","doi":"10.21203/rs.3.rs-5411079/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"47800694-6366-4076-96c2-37fd7d276cb4","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":41674446,"name":"Biological sciences/Cell biology"},{"id":41674447,"name":"Health sciences/Endocrinology"}],"tags":[],"updatedAt":"2025-03-12T06:35:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 11:07:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5411079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5411079","identity":"rs-5411079","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}