A comparative transcriptomic analysis of Glucagon-like peptide-1 receptor- and Glucose-dependent insulinotropic polypeptide receptor-expressing cells in the hypothalamus

Objective The hypothalamus is a key region of the brain implicated in homeostatic regulation, and is an integral centre for the control of feeding behaviour. Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are incretin hormones with potent glucoregulatory function through engagement of their respective cognate receptors, GLP-1R and GIPR. Recent evidence indicates that there is a synergistic effect of combining GIP- and GLP-1-based pharmacology on appetite and body weight. The mechanisms underlying the enhanced weight loss exhibited by GIPR/GLP-1R co-agonism are unknown. Gipr and Glp1r are expressed in the hypothalamus in both rodents and human. To better understand incretin receptor-expressing cell populations, we compared the cell types and expression profiles of Gipr- and Glp1r- expressing hypothalamic cells using single-cell RNA sequencing. Methods Using Glp1r-Cre or Gipr-Cre transgenic mouse lines, fluorescent reporters were introduced into either Glp1r- or Gipr-expressing cells, respectively, upon crossing with a ROSA26-EYFP reporter strain. From the hypothalami of these mice, fluorescent Glp1rEYFP+ or GiprEYFP+ cells were FACS purified and sequenced using single-cell RNA sequencing. Transcriptomic analysis provided a survey of both non-neuronal and neuronal cells, and comparisons between Glp1rEYFP+ and GiprEYFP+ populations were made. Results A total of 14,091 Glp1rEYFP+ and GiprEYFP+ cells were isolated, sequenced and taken forward for bioinformatic analysis. Both Glp1rEYFP+ and GiprEYFP+ hypothalamic populations were transcriptomically highly heterogeneous, representing vascular cell types, oligodendrocytes, astrocytes, microglia, and neurons. The majority of GiprEYFP+ cells were non-neuronal, whereas the Glp1rEYFP+ population was evenly split between neuronal and non-neuronal cell types. Both Glp1rEYFP+ and GiprEYFP+ oligodendrocytes express markers for mature, myelin-forming oligodendrocytes. While mural cells are represented in both Glp1rEYFP+ and GiprEYFP+ populations, Glp1rEYFP+ mural cells are largely smooth muscle cells, while the majority of GiprEYFP+ mural cells are pericytes. The co-expression of regional markers indicate that clusters of Glp1rEYFP+ and GiprEYFP+ neurons have been isolated from the arcuate, ventromedial, lateral, tuberal, suprachiasmatic, and premammillary nuclei of the hypothalamus. Conclusions We have provided a detailed comparison of Glp1r and Gipr cells of the hypothalamus with single-cell resolution. This resource will provide mechanistic insight into how engaging Gipr and Glp1r cells of the hypothalamus may result in changes in feeding behaviour and energy balance.


Introduction
The hypothalamus is a key region of the brain implicated in homeostatic regulation, and is integral to the tight control of an organism's energy balance. Studies in preclinical models have identified key circuits within the hypothalamus that integrate nutritive and metabolic cues from the periphery with neural networks that regulate appetite, feeding behaviour, and energy expenditure (Yeo & Heisler, 2012). This circuitry lies within discrete nuclei of the hypothalamus that are critical for maintaining energy homeostasis (Gao & Horvath, 2007). In addition to neuronal cells within these nuclei, it is becoming increasingly apparent that non-neuronal cells share in the task of integrating information regarding an organism's metabolic state (Butiaeva et al., 2021;Djogo et al., 2016;Kohnke et al., 2021;Reiner et al., 2016). With increasing genetic evidence indicating that obesity is driven by aberrations in signalling pathways of the central nervous system (CNS), cell surface receptors and protein effectors involved in signalling cascades acting through different cell types located within these hypothalamic nuclei may represent promising targets for the development of therapeutics (Locke et al., 2015).
Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are incretin hormones with potent stimulatory effects on postprandial insulin release (Drucker, 2018). GLP-1 receptor (GLP-1R) agonism is an established therapeutic strategy for treating and managing type 2 diabetes (T2D). In addition to bringing glucoregulatory benefit, the GLP-1 receptor (GLP-1R) agonist (GLP1-RA) drug class has recently been licensed for the treatment of obesity, owing to the anorexigenic properties of GLP-1R engagement in the CNS (Gabery et al., 2020;Secher et al., 2014;Wilding et al., 2021). Central mechanisms underlying the effect of GLP-1R agonism on body weight and feeding are thought to involve recruitment of pathways triggering malaise and emesis in the dorsal vagal complex and parabrachial nucleus, reducing the reward value of food through engaging the mesolimbic reward system, as well as further suppressing appetite through activating hypothalamic nuclei (Alhadeff et al., 2014;Alhadeff et al., 2012;Borner et al., 2021;Richard et al., 2014;Secher et al., 2014).
The next generation of therapeutics for the treatment of metabolic disease includes multifunctional GLP-1RA-based drugs designed with the aim to simultaneously target multiple signalling pathways to improve the therapeutic index. The GIP receptor (GIPR) signalling axis has proven to be an effective co-target, with both GIPR agonists and antagonists demonstrating enhanced weight loss or prevention of weight gain when combined with GLP-1R agonism in preclinical models (Coskun et al., 2018;Lu et al., 2021). The advancement of GIPR/GLP-1R dual agonists to late-stage clinical trials has further highlighted the therapeutic potential of GIPR agonism, with dual incretin receptor agonists demonstrating enhanced glycaemic control and weight loss in patients with T2D (Frias et al., 2018). The mechanisms underlying the enhanced weight loss exhibited by GIPR/GLP-1R co-agonism are yet to be fully characterised. Recent reports have highlighted the ability of GIPR agonism to attenuate nausea, emesis, and aversion associated with GLP-1RA treatment (Borner et al., 2021;Costa et al., 2022). In preclinical models GIPR / GLP-1R co-agonism has synergistic effects on appetite suppression (Coskun et al., 2018), suggesting a central mechanism of action regulating appetite and feeding behaviour.
Gipr and Glp1r are expressed in cell populations in the hypothalamus in both rodents and humans (Adriaenssens et al., 2019). In rodents, central expression of Gipr is necessary for the ability of GIPR/GLP-1R dual agonism to reduce body weight beyond that achieved by GLP-1R agonism alone, and chemogenetic activation of hypothalamic Gipr neurons acutely suppresses food intake (Adriaenssens et al., 2019;Zhang et al., 2021). Similarly, Glp1r expression in neurons of the CNS is necessary for GLP-1RA-mediated anorexia and body weight loss (Sisley et al., 2014). Blockade of GLP-1R in the arcuate nucleus of the hypothalamus attenuates the ability of a long-acting GLP-1RA to suppress food intake and body weight (Secher et al., 2014). Collectively these data demonstrate the potential importance of hypothalamic Giprand Glp1r-expressing cell populations to the regulation of pathways that control feeding and energy balance.
Recent advances in single-cell transcriptomics have enhanced our understanding of the heterogeneity of brain cells acting within molecularly-defined neurocircuits as well as nonneuronal cells that contribute to brain tissue maintenance and function. In this report, to better characterise hypothalamic Gipr and Glp1r populations and understand their relative similarities and differences, we used a transgenic approach combined with fluorescence activated cell sorting (FACS) to purify Giprand Glp1rexpressing hypothalamic cells and perform single-cell RNA sequencing (scRNAseq). Through downstream bioinformatic analysis, we have detailed and compared the composition and expression profiles of Giprand Glp1rexpressing hypothalamic cell populations, providing an in-depth analysis of both non-neuronal and neuronal cell types expressing these incretin receptors. Gipr-Cre (Adriaenssens et al., 2019) or Glp1r-Cre (Richards et al., 2014) mice were crossed with a ROSA26-EYFP reporter strain to enable fluorescent detection of cells expressing Gipr or Glp1r, producing Gipr EYFP or Glp1r EYFP mice, respectively. Cre lines and reporter strains were on a mixed C57B6J/N genetic background.

Flow Cytometry
Single cell suspensions were prepared from hypothalamic tissue pooled from two to six Gipr EYFP or Glp1r EYFP mice that were four to six weeks old as described previously (Adriaenssens et al., 2019). Both male and female mice were used. Briefly, mice were sacrificed by cervical dislocation, and tissue from the hypothalamus located ventrally caudal of the optical nerve chiasm (~Bregma -0.3 to -2.92 mm) was dissected into Hibernate-A medium without calcium (BrainBits). The tissue was digested with 20 U/ml Papain (Worthington) for 30 min at 37°C, followed by trituration in Hibernate-A medium (Thermo Fisher Scientific) containing 0.005% (w/v) DNase 1 (Worthington). The cell suspension was filtered through a 40 μm cell strainer into a fresh tube.

Single Cell RNA Sequencing
Single cell cDNA libraries from purified Gipr EYFP+ or Glp1r EYFP+ cells were generated using the 10× Genomics Chromium Instrument and single-cell 3' Reagent kit(V2 or V3 ; 10X Genomics). Pooled libraries were sequenced on an Illumina NovaSeq 6000 instrument (28-bp first read, 91-bp second read). Library preparation and sequencing was performed by the Genomics Core, Cancer Research UK Cambridge Institute. For downstream analysis, previously published sequencing data from hypothalamic Gipr EYFP+ cells (Adriaenssens et al., 2019) were included to increase the Gipr EYFP+ sample size.
Sequencing reads were aligned to an amended annotation of the mouse genome (GRCm39). The 3' untranslated regions (UTRs) of Glp1r, Ghsr, Prokr2 and Prlhr were compared against PolyA_DB v3 (Wang et al., 2018), and extended where necessary to include the furthermost 3' UTRs in keeping with previous studies (Ludwig et al., 2021;Zhang et al., 2021). Downstream analyses on the unfiltered count matrices were performed using the Seurat v4 R package (Hao et al., 2021). Each sample was analysed individually prior to integration. Cells were filtered from the analysis if they expressed fewer than 500 unique genes or their total number of reads originating from mitochondrial genes was greater than 20%. There were a total of 14,091 cells in the filtered dataset, consisting of 1,614 and 1,210 from the two female Gipr EYFP datasets, 8,527 from the male Gipr EYFP dataset, and 2,740 from the male Glp1r EYFP dataset.
Dimensionality reduction was performed on SCTransform (SCT)-normalised data, regressed for percentage of mitochondrial reads, percentage of ribosomal reads, and percentage of Xist reads. Uniform manifold approximation and projection (UMAP) were generated using the top 30 principle components from principle component analysis (PCA), run on variable features detected by SCT. Clustering was performed using the Louvain algorithm with resolution 0.8-1.0, and the k-parameter (k=15-30) for neighbour detection was reduced to the point where the smallest cluster visible was detected. Gene markers for each cluster were cross referenced against other bulk and single cell RNA sequencing databases RNAseq and in situ hybridisation (ISH) databases to assign cell type identities for each cluster (Campbell et al., 2017;Chen et al., 2017;Marques et al., 2016;Lein et al., 2007;Mickelsen et al., 2019;Mickelsen et al., 2020;Moffitt et al., 2018;He et al., 2016;Zeisel et al., 2018).
Where differential expression analysis was performed, marker genes were identified for all clusters using a negative binomial regression model, implemented by Seurat's FindAllMarkers function. Datasets were integrated using canonical correlation analysis (CCA), approximate nearest neighbours (ANN), and n = 2,000 integration features.

Gipr and Glp1r expressing populations of the hypothalamus are heterogeneous
While multiple reports have used scRNAseq to survey and characterise neurons within the hypothalamus, Gipr+ and Glp1r+ populations represented in these studies are low in number, likely owing to difficulties in detecting lowly expressed transcripts at the sequencing depth afforded by single-cell and single-nucleus sequencing approaches. We therefore used Gipr-Cre and Glp1r-Cre transgenic mice crossed with a ROSA26-EYFP reporter strain to produce Gipr EYFP and Glp1r EYFP mice, allowing for the purification and enrichment of Gipr EYFP+ and Glp1r EYFP+ cells based on EYFP expression for detailed transcriptomic characterisation of these hypothalamic cell types.
Single-cell preparations isolated from hypothalamic samples of Gipr EYFP or Glp1r EYFP mice were FACS purified and encapsulated to create sequencing libraries for Gipr EYFP+ and Glp1r EYFP+ cells, respectively. We integrated Gipr EYFP+ -and Glp1r EYFP+ data with a previously-published Gipr EYFP+ dataset to increase power for comparative analysis. Three sequencing libraries for Gipr EYFP+ cells and one sequencing library for Glp1r EYFP+ cells yielded a total of 11,351 Gipr EYFP+ cells and 2,740 Glp1r EYFP+ cells for subsequent scRNAseq analysis (Supp. Fig. 1A).
Unsupervised clustering analyses stratified these cells into 9 different cell type groupings (Fig 1 A). Cell type identities were assigned based on the expression of canonical marker genes (Figure 1 B Supp. Fig. 1A), indicating that both Gipr EYFP+ and Glp1r EYFP+ populations are highly heterogeneous, with both neuronal and non-neuronal cell types represented (Campbell et al., 2017;He et al., 2016;Ludwig et al., 2021;Marques et al., 2016).
We explored the number of cells expressing Gipr and Glp1r transcript in each cell type cluster ( Supplementary Fig 2A-C). Cells containing Gipr transcript mapped to all clusters except ependymocytes and macrophages, while cells containing Glp1r transcript mapped to all clusters except VLMCs. That not all cells contain Gipr or Glp1r transcript is expected given the difficulty in detecting low-expressing gene transcripts in scRNAseq data. In contrast, expression of Cre-dependent EYFP showed extensive coverage in the majority of cells in all clusters (Supplementary Fig 2D).
To investigate the proportion of Gipr EYFP+ or Glp1r EYFP+ cells represented by each cell type, the percentage of cells originating from the Gipr EYFP+ or Glp1r EYFP+ datasets within each cell type was calculated ( Figure 1C). The Glp1r EYFP+ dataset accounted for the majority of neurons, while pericytes were only found in the Gipr EYFP+ datasets. About equal numbers of SMCs were detected from Gipr EYFP+ or Glp1r EYFP+ datasets, and the Gipr EYFP+ datasets accounted for the majority of both ODs and VLMCs. Considering that the Gipr EYFP+ datasets account for more than 4x the number of cells compared to Glp1r EYFP+ in the integrated dataset, adjusting for sample sizes suggests that neurons and SMCs may be more heavily dominated by Glp1r EYFP+ cells (Supplementary. Fig. 2E).
Of the six SMC clusters, identified by Acta2 expression, SMC-6 expressed significantly higher levels of cell adhesion and proliferation gene Ccn1, and higher levels of transcription factors Atf3, Fos, Jun, Junb and Jund ( Figure 2D). Each of these transcription factors have previously been shown to be enriched in arterial versus venous SMCs (Vanlandewijck et al., 2018). A subset of cells in SMC-1, -2, and-3 expressed higher levels of Rgs5, a marker for venous mural cells, as well as olfactory receptor Olfr558, and chemokine receptor Ackr3 ( Figure 2D). Rgs5 was significantly enriched in the Gipr EYFP+ cells versus Glp1r EYFP+ in SMC-5 and SMC-6 (Supplementary. Fig. 3B). Olfr558 was significantly enriched in the Gipr EYFP+ cells versus Glp1rEYFP+ in SMC-5 (Supplementary Fig 3B). These results suggest separation within the SMCs based on arteriovenous zonation, supported by the observation that venous SMC marker Car4 was significantly enriched in the Gipr EYFP+ cells versus Glp1r EYFP+ in SMC-5, and arterial SMC markers Cnn1 and Tinagl1 were both enriched in Glp1r EYFP+ cells versus Gipr EYFP+ cells in SMC-6. Tinagl1 was further enriched in Glp1r EYFP+ cells versus Gipr EYFP+ cells in SMC-1, -2, -4, -5 (Supplementary Figure 3C).

Neurons
To perform detailed analysis of the neurons represented in our data, cells were filtered for expression of Syt1 and Snap25. Contaminating pericytes, SMCs, and ODs were eliminated based on expression of Abcc9, Acta2, and Mal, respectively. 397 Gipr EYFP+ and 944 Glp1r EYFP+ resultant neurons clustered into 12 distinct sub-populations following dimensionality reduction (Fig 4A).
The tissue preparation enabling scRNAseq results in a loss of the spatial context of neurons represented in our sample. To infer the anatomical distribution of Gipr EYFP+ and Glp1r EYFP+ neuronal clusters to discrete hypothalamic nuclei, the top 15 cluster-enriched gene markers extracted from differential gene expression analysis were compared to available nucleus-specific scRNAseq datasets and were mapped to the Allan Brain Atlas Europe PMC Funders Author Manuscripts (Campbell et al., 2017;Chen et al., 2017;Lein et al., 2007;Mickelsen et al., 2019;Mickelsen et al., 2020;Moffitt et al., 2018) (SCN.1, SCN.2), one cluster from the lateral hypothalamus (LH), two clusters from the premammillary nucleus (PMv, PMd), one cluster from the medial tuberal nucleus (MTu), and one cluster that remains unassigned (NK.1) (Fig 4B). Eight of the 12 clusters were predominantly composed of Glp1r EYFP+ neurons, 3 were predominantly represented by Gipr EYFP+ neurons, and one had equal representation of Glp1r EYFP+ and Gipr EYFP+ neurons (Fig 4B).
In addition to regional markers, the neuronal clusters were stratified by their relative enrichment for neurotransmitters and genes encoding secreted products and cell surface receptors (Fig 4C)

Discussion
Rich insight into the heterogeneity and identity of cells is afforded by single cell transcriptomic approaches, though limitations in sensitivity can be problematic for target cell populations identified by lowly expressed genes. Expression levels for Gipr and Glp1r are lower than for genes encoding neuropeptides and secreted hormones, presenting a barrier to mining existing transcriptomic datasets for cell types defined by either Gipr or Glp1r where their representation is either extremely low or not at single-cell resolution (Biglari et al., 2021;Lam et al., 2017). Through the use of transgenic mouse models, our approach enabled the purification and enrichment of Gipr EYFP+ and Glp1r EYFP+ populations, resulting in a dataset of over 14,000 cells.
While we have previously shown that Gipr EYFP+ cells of the hypothalamus are composed of both neuronal and non-neuronal cell types, our present analysis revels that Glp1r EYFP+ cells are similarly heterogeneous. In particular Glp1r EYFP+ and Gipr EYFP+ cells were found in a variety of vascular cell types. Gipr EYFP+ cells localised to all observed vascular cell clusters, including pericytes, SMCs, VLMCs, and ECs. In contrast, Glp1r EYFP+ cells were found in VLMCs, ECs, SMCs, but not pericyte clusters. Pericytes have wide-ranging effects on the cerebrovasculature, from modulating endothelial transcytosis and the polarization of astrocyte end-feet, to regulating neuroinflammatory responses, cerebral blood flow and the integrity of the vascular basement membrane (Armulik et al., 2010;Hall et al., 2014;Török et al., 2021;Vanlandewijck et al., 2018). Additionally, gene ontology analysis has previously shown that genes encoding transmembrane transporters are organotypic for brain pericytes over pericyte cells isolated from peripheral regions, suggesting that pericytes of the brain are directly involved in molecular transport at the blood brain barrier (BBB) (Vanlandewijck et al., 2018). Whether acute manipulation of pericyte activity via GIPR engagement affects the transport and uptake of nutrients, peptides, and other regulatory factors from the periphery across the BBB should be investigated.
The absence of Glp1r EYFP+ pericytes is likely due to the arteriovenous zonation of pericytes, which are transcriptomically more similar to venous SMCs (Vanlandewijck et al., 2018). This is supported by the observation that the Glp1r EYFP+ SMCs express higher levels of arterial gene markers, and appear transcriptomically distinct from the Gipr EYFP+ SMCs, which instead express higher levels of venous gene markers. This is further supported by the increased expression of arterial endothelial cell markers in the Glp1r EYFP+ vascular cells compared to Gipr EYFP+ vascular cells. These data suggest that GLP-1 may signal principally through arterial cells whereas GIP may target venous cells of the cerebrovasculature.
Glp1r has previously been shown to be expressed in vascular smooth muscle cells of large and small arteries in the periphery (Richards et al., 2014). Systemic administration of GLP-1RAs has been demonstrated to protect brain tissue from ischaemia-induced damage, supporting a role for GLP-1R engagement in modulating perfusion of brain tissue (Hakon et al., 2015). While these protective effects of GLP-1RAs may be attributable to engagement of GLP-1R in the carotid body (Pauza et al., 2022), in acute brain slices, application of a short-acting GLP-1RA had a strong dilatory effect on cortical arterioles and GLP-1R agonism elicited increased cerebral blood flow in vivo, indicating that direct GLP-1R engagement at the level of the cerebrovasculature may also be important for regulating brain tissue perfusion and oxygenation (Nizari et al., 2021). Given that the majority of Gipr EYFP+ cells in the hypothalamus express markers for vascular cells, GIPR activation may also modulate cerebral blood flow. Though exogenous GIP infusion has previously been reported Europe PMC Funders Author Manuscripts to increase blood flow in the periphery in adipose tissue (Asmar et al., 2016;Asmar et al., 2017), effects of GIPR agonism on brain perfusion are yet to be demonstrated.
All oligodendrocyte clusters in our data were predominantly composed of Gipr EYFP+ cells. Expression of Gipr transcript has been previously reported in oligodendrocytes from both the hypothalamus and the dorsal vagal complex (Dowsett et al., 2021;Kohnke et al., 2021). Oligodendrocytes are acutely sensitive to changes in nutritional signals, with fasting triggering their rapid proliferation and differentiation (Kohnke, S Cell Rep, 2021). In the hindbrain, oligodendrocyte remodelling in response to fasting was driven by changes in Tcf7l2 expression (Dowsett et al., 2021). Whether GIPR agonism induces transcriptional changes and elicits cell differentiation in oligodendrocytes elicits is currently unknown and warrants further exploration.
The majority of oligodendrocytes in our data expressed markers indicative of mature and myelinating oligodendrocytes. It is important to note that the tissue samples from which our data were collected were isolated from young mice (four to six weeks in age), potentially before myelination is complete. Whether the relative representation of oligodendrocyte precursor cells verses mature oligodendrocytes expressing incretin receptors changes with maturity should be evaluated.
In this study we find that a small population of astrocytes express Glp1r. While these astrocytes demonstrate low Gfap expression, they instead express Aqp4, a universal marker for astrocytes encoding the water channel, aquaporin 4 that is located on astrocyte vascular end-feet (Zeisel et al., 2018). The fact that incretin receptor-expressing astrocytes are low in Gfap suggests that they are parenchymal protoplasmic astrocytes, rather than fibrous astrocytes known for their high levels of Gfap expression (Zeisel et al., 2018). A role for Glp1r in glial and astrocyte biology is supported by studies examining GLP-1R pharmacology in these cell types (Cui et al., 2022). Specifically, in calcium imaging studies one third of astrocytes in the dorsal vagal complex were responsive to GLP-1R agonism, and pharmacological blockade of astrocytes in the NTS attenuated GLP-1RA-dependent anorexia (Reiner et al., 2016). Due to their relative low abundance, it is likely that incretin receptor-expressing astrocytes work in conjunction with other Gipr and Glp1r expressing cell types to mediate changes in energy balance following pharmacological activation.
We were able to identify Gipr EYFP+ and Glp1r EYFP+ neuronal cells originating from discrete hypothalamic nuclei through mapping transcriptomic cluster signatures to ISH references. Localisation of Glp1r+ neurons to the ARH, VMH, LH, SCN and premammillary nuclei is in good agreement with previous immunohistochemical studies (Cork et al., 2015;Jensen et al., 2018). Of note, no Gipr EYFP+ or Glp1r EYFP+ clusters were mapped to the paraventricular hypothalamus (PVH) or dorsomedial hypothalamus (DMH). Previous immunohistochemistry studies indicate that Gipr+ and Glp1r+ neurons are present in these nuclei (Adriaenssens et al., 2019;Cork et al., 2015). The cluster, NK.1 may represent cells from the PVH or DMH, though the transcriptional markers for this cluster largely failed to specifically map this cluster. Our data build on previous immunohistochemical studies through Identifying key neuropeptide and cell surface identifiers for Glp1r EYFP+ and Gipr EYFP+ clusters, enriching our knowledge of factors that distinguish Glp1rand Gipr-expressing cells residing in separate hypothalamic nuclei.

Limitations of Study
Our study relies on a transgenic approach where Glp1rand Gipr-expressing cells have been labelled with an endogenous fluorescent reporter using Glp1r-Cre and Gipr-Cre mice, respectively. Transgenic Cre drivers can result in aberrant Cre expression or lineage tracing artefacts. Additionally, mRNA expression does not always mirror protein translation, which has been demonstrated for GLP-1R in pancreatic delta cells where Glp1r transcript was detected, but did not correlate with GLP-1R protein levels as measured using antibody staining (Gray et al., 2020). Recently, however Jensen et al described a specific GLP-1R monoclonal antibody (mAb) for immunohistochemical (IHC) analysis of mouse brain tissue. In this study the authors compared the distribution of GLP-1R mAb staining to a report by Cork et al where the Glp1r EYFP mouse model we present here was used to map Cre-dependent EYFP expression in mouse brain (Cork et al., 2015;Jensen et al., 2018). Jensen et al reported strong correlation between GLP-1R monoclonal antibody staining with Glp1r-Cre-dependent EYFP staining. Importantly, no regions that were positive for Glp1r-Cre-dependent EYFP staining were found to be negative for GLP-1R mAb staining (Jensen et al., 2018). At present, no GIPR antibodies have been validated for use in IHC in whole tissue. The Gipr-Cre model used in this study is a knock-in Cre, which is less likely to result in aberrant Cre expression compared to transgenic models using randomly integrated constructs. While we cannot exclude that some cells may report Cre activity in the absence of active Gipr transcription, in our previous work we confirmed that Gipr-Credependent EYFP expression in the hypothalamus corroborated the detection of Gipr mRNA using single molecule fluorescent in situ hybridization (Adriaenssens et al., 2019). Future studies should further confirm the anatomical phenotyping provided in this report using cell type-specific smFISH markers and functional assays measuring GIPR and GLP-1R activity.
The Glp1r-Cre model used in this report employs a strategy relying on the integration of a bacterial artificial chromosome (BAC) (Richards et al., 2014). Two additional transgenic Glp1r-Cre mouse lines have since been created, both relying on a knock-in approach to introduce Cre-recombinase expression under the control of the Glp1r promotor Williams et al., 2016). Discrepancy in peripheral cell types labelled by the knock-in versus the BAC approach has been reported, with pancreatic acinar cells labelled by a knock-in model whereas no exocrine pancreatic cells were labelled using the BAC model Richards et al., 2014). While this divergence may reflect the low expression level of Glp1r in exocrine tissue, ideally similar studies to those presented in this work should be performed using a Glp1r-Cre knock-in model to explore whether discordance exists at the transcript level in brain cells expressing Glp1r.

Summary
Here we provide transcriptomic molecular identification of neuronal and non-neuronal cells in the hypothalamus that express receptors for the incretin hormones, GIP and GLP-1. This census of cell types expressing Glp1r EYFP+ and Gipr EYFP+ will be an important resource for understanding the molecular fingerprint of these cells, and provides insight for future functional studies.

Supplementary Material
Refer to Web version on PubMed Central for supplementary material.