Spatial transcriptomic analysis of mouse parathyroid gland cells expressing an activating variant of Gcm2

Priyanka Bolel; Jeremie Oliver Piña; Fabio R Faucz; James R Iben; Wafa Abbas; Smita Jha; William F Simonds; Lee S Weinstein; Sunita K Agarwal

doi:10.1093/jbmrpl/ziaf147

. 2025 Sep 9;9(10):ziaf147. doi: 10.1093/jbmrpl/ziaf147

Spatial transcriptomic analysis of mouse parathyroid gland cells expressing an activating variant of Gcm2

Priyanka Bolel ¹, Jeremie Oliver Piña ^2,³, Fabio R Faucz ^4,⁵, James R Iben ⁶, Wafa Abbas ⁷, Smita Jha ⁸, William F Simonds ⁹, Lee S Weinstein ¹⁰, Sunita K Agarwal ^11,^✉

¹ Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States

² Section on Craniofacial Genetic Disorders, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, United States

³ Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, United States

⁴ Molecular Genomics Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, United States

⁵ Hospital for Endocrine Surgery Research Center, HCA Research Institute, Tampa, FL, 33615, United States

⁶ Molecular Genomics Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, United States

⁷ Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States

⁸ Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States

⁹ Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States

¹⁰ Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States

¹¹ Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States

^✉

Corresponding author: Sunita K. Agarwal, Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bldg. 10 Room 8C101, Bethesda, MD 20892, United States (sunitaa@mail.nih.gov; sunitaa@amb.niddk.nih.gov).

Roles

Priyanka Bolel: Investigation, Methodology, Visualization, Writing - review & editing

Jeremie Oliver Piña: Data curation, Formal analysis, Investigation, Methodology, Resources, Visualization, Writing - review & editing

Fabio R Faucz: Investigation, Methodology, Resources, Visualization, Writing - review & editing

James R Iben: Data curation, Resources, Software, Writing - review & editing

Wafa Abbas: Resources

Smita Jha: Resources, Writing - review & editing

William F Simonds: Resources, Writing - review & editing

Lee S Weinstein: Resources, Supervision, Writing - review & editing

Sunita K Agarwal: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing - original draft, Writing - review & editing

PMCID: PMC12476839 PMID: 41025096

Abstract

Glial cells missing 2 (GCM2) is an essential transcription factor for the development of parathyroid glands. Germline GCM2 variants that repress or enhance transcriptional activity predispose a subset of patients to hypoparathyroidism or hyperparathyroidism, respectively. A recurrent germline heterozygous activating missense variant of GCM2, p.Y394S has been identified in some patients with primary hyperparathyroidism. A genetically engineered knock-in mouse model of this variant corresponding to p.Y392S in the mouse Gcm2 gene (Gcm2^+/Y392S) did not show obvious parathyroid tumors. However, in GCM2-binding site mediated luciferase reporter assays in HEK293 cells, the mouse and the human variant both exhibited enhanced transcriptional activity. Therefore, we assessed the effect of this variant on gene expression in vivo in parathyroid glands from Gcm2^+/Y392S and WT mice. Using the 10x Genomics Visium platform, spatially resolved transcriptomic analysis was performed on formalin-fixed and paraffin-embedded (FFPE) tracheal tissue sections of Gcm2^+/Y392S and WT mice to capture RNA from parathyroid glands together with other cell types in the tissue sections. Transcriptome sequence data analysis detected 8 different clusters in the tissue sections based on similarity of gene expression profiles. Cluster-1, which contained parathyroid gland cells expressing Pth and Gcm2, was further evaluated for transcripts that were differentially expressed more than 2-fold in Gcm2^+/Y392S compared to WT. Increased transcript level of Lgals3 (galectin-3) was seen in Gcm2^+/Y392S parathyroid gland cells which is among markers of parathyroid carcinoma. Galectin-3 protein was detected in available FFPE human parathyroid samples of patients with germline heterozygous activating GCM2 variants, p.Y394S (n = 4/10) or p.L379Q (n = 2/2). These results indicate a potential for growth and malignancy of parathyroid glands expressing GCM2 variants. The transcriptomic data of mouse parathyroid gland cells generated in this study can serve as a valuable resource for investigating genes and pathways in normal or abnormal parathyroid gland growth and physiology.

Keywords: galectin-3; GCM2, gene; expression; LGALS3; parathyroid; primary hyperparathyroidism; p.Y394S; spatial transcriptomics; Visium

Graphical Abstract

Introduction

The transcription factor glial cells missing 2 (GCM2) is essential for the embryonic development of parathyroid glands and is predominantly expressed in developing and mature parathyroid gland cells.^1–3 Parathyroid glands are crucial for regulating and maintaining calcium homeostasis through the production and secretion of parathyroid hormone (PTH).⁴^,⁵ Germline inactivating variants in the GCM2 gene are seen in a subset of patients with isolated hypoparathyroidism characterized by hypocalcemia, hyperphosphatemia, and deficient or absent circulating PTH due to abnormal parathyroid gland development.^6–13 Conversely, germline activating variants in the GCM2 gene are seen in a subset of patients with primary hyperparathyroidism characterized by hypercalcemia and elevated levels of circulating PTH due to parathyroid tumor development with associated increased proliferation of parathyroid gland cells.¹²^,^14–25

In functional assays, GCM2 proteins with inactivating variants fail to activate the transcription of a luciferase reporter construct with GCM2-binding sites.⁶^,¹⁰^,¹¹ In similar functional assays, GCM2 proteins with activating variants enhance transcription.¹²^,¹⁴^,¹⁵^,¹⁹ Further functional analysis in mouse models has shown that inactivation of the Gcm2 gene by targeted deletion leads to lack of parathyroid glands causing premature death in 30% mice, while the remaining mice develop hypoparathyroidism.¹^,²^,²⁶ However, obvious parathyroid tumors or primary hyperparathyroidism were not seen in a knock-in mouse model with an activating variant in the Gcm2 gene, p.Y392S (Gcm2^+/Y392S).²⁷ The mouse Gcm2 variant p.Y392S corresponds to the human GCM2 variant p.Y394S, which is a recurrent variant that is seen in a subset of patients with familial primary hyperparathyroidism.¹²^,^14–18^,²⁰^,^22–25 The mouse Gcm2 variant p.Y392S and the human GCM2 variant p.Y394S, both enhance transcription in luciferase reporter assays.²⁷ Therefore, to assess gene expression changes in vivo from this activated form of Gcm2, we performed spatial transcriptomics of parathyroid glands from Gcm2^+/Y392S and WT mice. Parathyroid glands are very small in mice and are surrounded by other tissue types in the neck making it difficult to locate and isolate the glands to obtain sufficient cells for molecular analysis. Spatial transcriptomics can capture RNA from even a small number of cells in intact tissue sections that are either frozen or formalin-fixed and paraffin-embedded (FFPE), facilitating the detection of gene expression in tissues together with the spatial localization of cells.

Materials and methods

Mouse model and tissues

All animal experiments were performed according to the animal study protocols approved by the NIDDK Institutional Animal Care and Use Committee (Animal Study Protocol number: K070-MDB-24). The generation and characterization of a CRISPR/Cas9-mediated genetically engineered germline knock-in mouse model of the Gcm2 variant p.Y392S (Gcm2^+/Y392S) in the C57BL/6 background was previously described.²⁷ Formalin-fixed and paraffin-embedded tracheal tissue blocks of Gcm2^+/Y392S and WT mice from similar numbers of males and females at age 18-24 mo were previously prepared and described.²⁷ These FFPE tracheal blocks contained the esophagus, trachea, thyroid, and parathyroid together. Hematoxylin and eosin (H&E) staining was performed on 5 μm serial sections to locate the region in the tissue block that contained parathyroid glands. Freshly cut sections were used for spatial transcriptomics.

Human tissues

Archived FFPE parathyroid tissue sections (5 μm) of 12 patients who underwent parathyroid gland surgery at the National Institutes of Health (NIH) Clinical Center were obtained from the National Cancer Institute (NCI) pathology lab. The genetic status of these patients was previously determined: germline heterozygous for GCM2 p.Y394S (n = 10) or GCM2 p.L379Q (n = 2), and a parathyroid cancer positive control sample (n = 1, germline heterozygous variant in FLCN c.1285insC and negative for germline or somatic GCM2 variant).¹⁴^,²⁸ Written informed consent was obtained from all patients under IRB-approved protocol “Natural History Study of Parathyroid Disorders” (NCT04969926) conducted at the NIH Clinical Center.

Spatial transcriptomics

Protocols for spatial transcriptomic analysis were used from the 10x Genomics User guide CG000407 Rev D, Visium Spatial Gene Expression Reagent Kits for FFPE. The following paragraph provides a brief description of the workflow.

Each FFPE tracheal tissue block was cut on a microtome, and 2-3 sections (5 μm) per block were directly placed on 10x Genomics Visium slides containing 2 or 4 capture areas of 6.5 × 6.5 mm². Each 6.5 × 6.5 mm² capture area consisted of 5000 gene expression spots (~55 μm in diameter to capture ~10 cells per spot) coated with primers for spatial location, RNA/probe capture and sequencing. All primers include a 34-nucleotide sequencing adapter (i5+TruSeq Read 1), 16-nucleotide spatial barcode (all primers in a specific spot share the same spatial barcode), 12-nucleotide unique molecular identifier (UMI), and 30-nucleotide poly(dT) sequence (to capture the probe ligation product). The FFPE tissue sections on Visium slides were processed for deparaffinization, decrosslinking, mouse transcriptome probe hybridization (~20 000 genes; 2 split probes per targeted mRNA), ligation, RNA digestion, permeabilization to release the ligated probes and subsequent capture on the bar-coded primers on the Visium slide surface, probe extension and elution, and sequencing library preparation. Sequencing was performed using the Illumina NovaSeq 6000 for at least 25 000 paired end reads per spot of the Visium slide capture area.

Spatial transcriptomics data processing

Following initial processing with 10X Spaceranger software, transcriptome library sequence data analysis was performed by Quick Biology, Inc. They analyzed the spatial transcriptome data using R and the indicated Bioconductor packages, as per the workflow description that they provided as follows. The basic quality metrics, including UMI and gene counts per spot, were estimated using the scater package. Spots with low library size and low numbers of genes were filtered out using the quickPerCellQC function in scater package. The cutoff was 500 UMI/spot and 200 genes/spot. Batch effects were removed, and datasets from each sample were integrated using the harmony method in the harmony R package. Hypervariable expressed genes were identified by the getTopHVGs function in the scran package. The top 500 genes with the highest expression variances were selected for dimension reduction using the t-SNE (t-distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) algorithm. Clusters (spots with similar gene expression profile) were identified using the spatialCluster function in the BayesSpace package. Marker genes for the clusters were determined by combined pairwise comparisons (t-Tests) using the findMarkers function of the scran package. Significant differentially expressed genes were selected by a combined p-value below .05. Functional annotation and overrepresentation analysis of these gene sets was done by goana function in the limma package. The gene differential analysis of the same cluster/spot between different sample/group was performed using the edgeR package.

Immunostaining

Immunohistochemistry (IHC) was performed in 5 μm FFPE tissue sections using EnVision FLEX IHC kit (Agilent Cat# K8000). Antibodies used were as follows: mouse anti-PTH mouse monoclonal antibody (Bio-Rad Cat# 7170-6216) and galectin-3/LGALS3 (E7B6R) rabbit monoclonal antibody (Cell Signaling Technology Cat# 76015). Immunohistochemistry with the secondary antibody alone, anti-mouse or anti-rabbit, served as a negative control (from the EnVision IHC kit). A semi-quantitative visual scoring was done for evaluating relative galectin-3 staining as negative, weak (diffuse staining distinguishable above background level of sample scored as negative), moderate (patchy heterogeneous staining), or strong (dark homogeneous staining). Immunohistochemistry to assess GCM2 staining in FFPE tissue sections was not successful with three different anti-GCM2 antibodies that could detect GCM2 on immunoblots (Figure S1) (Santa Cruz Biotechnology Cat# sc-79496 and Cat# sc-390603, and one generated in-house) or that captured GCM2 by chromatin immunoprecipitation (sc-79496).²⁹

Microscopy and imaging

Formalin-fixed and paraffin-embedded tissue sections that were processed for H&E staining and IHC were imaged on the Keyence BZ-X700 microscope (Keyence), or by digital slide scanning (Histoserv).

Galectin-3 promoter activity

As per sequence information from the UCSC Genome Browser (GRCm38/mm10), primers were designed to cover the promoter signature and enhancer region of mouse Lgals3 (mLgals3), chr14:47373667-47374242 (576 bp), primers mForward: GCGCgagctcCCTGACAGGCAGCTTCAGAC and mReverse: GCGCctcgagAGCTACAAGGTGGATCTCCGTA. Promoter signature: chr14:47373799-47373949 and GCM2-binding site containing enhancer region chr14:47374053-47374287.

As per sequence information from the UCSC Genome Browser (GRCh38/hg38), primers were designed to cover the human LGALS3 (hLGALS3) promoter signature and GCM2-binding enhancer region reported in Jung et al., ²⁹ chr14:55128871-55129550 (680 bp), primers hForward: GCGCgagctcCTAGGGAATAGACATTGAAAAG and hReverse: GCGCctcgagGTCTCGTAAGGTGCTGCCAACCT. Promoter signature: chr14:55129017-55129338 and GCM2-binding site containing enhancer region chr14:55128989-55129489.

Promoter/enhancer region primers of mLgals3 and hLGALS3 were used to amplify a 576 and 680 bp region from mouse and human genomic DNA, respectively, by PCR and cloned into the promoter-less luciferase reporter vector pGL4.10 (Promega) in the SacI and XhoI site. The insert in the pGL4.10-mLgals3 and pGL4.10-hLGALS3 plasmids was confirmed by sequencing.

Luciferase reporter assays were performed in HEK-293 cells (ATCC) as described previously.²⁷ HEK-293 cells were transfected using lipofectamine 2000 (Thermo Fisher) with reporter plasmids pGL4.10 vector, pGL4.10-mLgals3, or pGL4.10-hLGALS3 together with 3xFlag-tagged vector or 3x-Flag-tagged mammalian expression plasmids¹⁴ Flag-tagged-GCM2(WT), Flag-tagged-GCM2(Y394S), or Flag-tagged-GCM2(L379Q). Luciferase assay was performed 24 h post-transfection as per the manufacturer (Promega) using a Luminometer (Lumat LB 9507, Berthold Technologies). The mean ratio of the luciferase activity counts over pGL4.10 vector from three independent experiments were plotted, representing fold transcriptional activity. GraphPad Prism software (version 10) was used to calculate mean and SD, to plot graphs, Student’s t-test, and p < .05 was considered significant.

The expression of transfected Flag-tagged proteins was detected using standard immunoblot assay with mouse-Anti-Flag-tag (Sigma-Aldrich Cat# F3165) and mouse-Anti-GAPDH (Santa Cruz Biotechnology Cat# sc-47724) was used as the loading control. The immunoblots were developed by ECL (Millipore-Sigma) and imaged with a G-BOX imaging system (Syngene).

Results

Spatial transcriptomics captured RNA from mouse parathyroid gland cells

Formalin-fixed and paraffin-embedded tracheal tissue blocks (n = 8) from 2 male and 2 female mice each of Gcm2^+/Y392S (MUT) and WT mice were selected such that they contained parathyroid tissue as per H&E staining and IHC for PTH.²⁷ Cross sections of FFPE tracheal tissue contained the esophagus, trachea, thyroid, and parathyroid together because mouse parathyroid glands are very small (approximately 0.2 × 0.5 mm), and not easily visible nor sufficiently discrete to allow for isolation and separation from other surrounding tissues prior to block preparation.

Spatial transcriptomics was performed on tissue sections of the 8 FFPE tracheal tissue blocks using the 10x Genomics Visium Spatial Gene Expression platform (Figure 1A). Formalin-fixed and paraffin-embedded tissue sections were processed on Visium slides that contain spots coated with barcoded primers to capture RNA from tissue/cells that cover the spots (~55 μm in diameter to capture ~10 cells per spot, and all primers in a specific spot sharing the same spatial barcode). Transcriptome libraries were prepared after the Visium RNA capture procedure and sequenced for downstream analysis. Unsupervised clustering of the transcriptome sequencing data was used to classify cell types captured in the spots based on similarities in gene expression profiles. The clustering analysis produced 8 clusters (Figure 1B and C) corresponding to the various tissue/cell types in the tracheal region (Figure 1D). Visualization of the 8 clusters in spatially resolved gene expression data (Figure S2) showed enrichment of transcripts for Pth (parathyroid hormone) exclusively in spots contained in cluster-1 (Figure 1E and F). Other transcripts that were highly enriched in cluster-1 include chromogranin A (Chga), Gcm2, and Guanine nucleotide-binding protein G(s) subunit alpha (Gnas) (Figure 1B). The spatial location of cluster-1 matched with the location of parathyroid glands in the serial section’s H&E and PTH staining (Figure 1D). The other seven clusters were not incorporated in downstream analysis because the purpose of using spatial transcriptomics for this study was to assist in locating the parathyroid gland cells that are embedded in other tissue types in the neck.

Spatial transcriptomics of mouse FFPE tracheal tissue sections. (A) The10x Genomics Visium platform captures gene expression in the architecture of intact tissues with an on-slide cDNA synthesis approach. A FFPE tracheal tissue block is shown; the two dark areas correspond to the thyroid lobes. The block is processed for sectioning and 2-3 sections per block are placed on the 6.5 mm² capture areas of the Visium gene expression slide and treated for RNA capture and sequencing. Each capture area has ~5000 gene expression spots, and each spot is coated with primers. Each primer has nucleotide (nt) sequences for: 34 nt Illumina TruSeq read 1 (for sequencing), 16 nt spatial barcode (all primers in a specific spot share the same spatial barcode), 12 nt unique molecular identifier (UMI), and 30 nt poly(dT) (captures paired probes for each targeted gene with poly(A) that are hybridized to the mRNA in the tissue). Unique molecular identifiers help to track the number of unique mRNA molecules in each spatial location (spot) on the tissue section. This is done to eliminate PCR duplicates, thereby providing an accurate representation of gene expression. The spatially barcoded probes (corresponding to mRNAs) are collected from the Visium gene expression slide and processed for sequencing. The barcode sequence in the sequencing reads maps the spatial position in the intact tissues and the rest of the sequencing reads are mapped to the genome to identify the transcripts and their expression level (spatial transcriptome). (B) Gene expression heat map of the 8 different clusters identified by unsupervised clustering analysis of spatial transcriptome sequencing data. Gene expression profile of top 40 cluster marker genes (rows) is shown. Each column represents a single spatial feature (transcripts in a spot), and features are grouped by their assigned cluster number (1-8) based on similar gene expression profile. Cluster-1 contains spots of parathyroid gland cells enriched for the expression of *Chga*, *Gcm2*, *Gnas*, and *Pth*. Color scale shows log-transformed normalized gene expression. (C) Summary of the clustering analysis showing the number of spots for each cluster in the FFPE tracheal tissue sections of 4 *Gcm2*^+/Y392S (MUT) and 4 WT mice processed for Visium spatial transcriptomics. Parathyroid gland cells are in the spots of cluster-1. (D) Histology of a representative mouse FFPE tracheal tissue section stained with H&E showing the various tissue types. The parathyroid gland tissue/cells are indicated with a dotted outline and the arrow. Inset, top right: magnified image of the parathyroid gland tissue area, in the section that was processed for IHC with anti-PTH (cytoplasmic brown granular staining). (E) Spot based visualization of a representative tracheal region tissue section showing the spatial distribution of 8 different clusters based on similar gene expression profiles identified by spatial transcriptomics are color-coded, corresponding to the various tissue/cell types in the section. The parathyroid gland tissue/cells are indicated with a dotted outline and the arrow. (F) Spot based visualization of the tracheal region tissue section in (C), showing expression of *Pth* transcript, a marker that identifies parathyroid gland tissue. The parathyroid gland tissue/cells are indicated with a dotted outline and the arrow.

Spatial visualization of Pth (Figure 2A-D) and Gcm2 (Figure 2E-H) transcripts in tissue sections from MUT and WT mice helped in the identification of four samples (MUT-1, MUT-2, WT-1, and WT-2) for further analysis of the spatial transcriptomic sequencing data of cluster-1. The other MUT and WT samples (MUT-3, MUT-4, WT-3, and WT-4) were not used for further analysis as determined by spatial data quality assessment (Figures S3 and S4). The spatial localization of Pth and Gcm2 transcripts in 3 samples (MUT-3, MUT-4, and WT-3) was scattered and not adequately resolved in the expected location of parathyroid glands perhaps due to technical issues in the Visium slide processing procedure or spatial data processing (Figure S3). Also, compared to WT-1 and WT-2, the expression of Pth was low in WT-4 and many spots corresponding to cluster-1 in sample WT-4 did not match with the location of Pth transcripts (Figure S4A-F). Finally, data from one male and one female each for MUT and WT were of good quality for further analysis (Figure 2A-H).

Spatial mapping of marker genes in parathyroid gland cells of *Gcm2*^+/Y392S and WT mice. Expression level of representative marker genes *Pth* (A-D) and *Gcm2* (E-H) in spots corresponding to parathyroid gland cells is mapped in FFPE tracheal tissue sections of 2 *Gcm2*^+/Y392S mice (MUT) and 2 WT mice. F indicates female and M indicates male. Color scale shows log-transformed normalized gene expression.

The Visium spatial transcriptomics method successfully captured RNA from the limited number of mouse parathyroid gland cells in the FFPE tracheal tissue sections. The parathyroid gland-related transcript data separated in cluster-1. Therefore, to study gene expression changes in parathyroid glands, transcriptomic sequence data from cluster-1 was used for further analysis.

Differentially expressed genes and functional annotation

Characterization of cluster-1 through differential gene expression analysis was performed using spatial transcriptomic data of 4 samples (2 MUT vs 2 WT) (Figure 3A and B). Among the 264 down-regulated and 406 up-regulated genes with more than 2-fold change in gene expression and significant p-values, the top 5 down-regulated genes were Pck1 (phosphoenolpyruvate carboxykinase 1), Cox8b (cytochrome c oxidase subunit VIIIb), Ucp1 (uncoupling protein 1), Pdk4 (pyruvate dehydrogenase kinase, isoenzyme 4), and Cidea (cell death-inducing DNA fragmentation factor, alpha subunit-like effector A). The top 5 up-regulated genes were Lgals3 (lectin galactose binding soluble 3), Cd44 (CD44 antigen), Nkx2.2 (NK2 homeobox 2), Ptprc (protein tyrosine phosphatase, receptor type, C; also known as Cd45), and Sprr1a (small proline-rich protein 1A). Lgals3 (galectin-3) was of particular interest being the top up-regulated gene based on fold-gene expression change with a significant p-value, and also because overexpression of galectin-3 is included among IHC markers for parathyroid carcinoma to distinguish from benign and other parathyroid tumors.^30–34 Differential gene expression data of cluster-1 were also evaluated for any effect of the Gcm2 p.Y392S variant on the expression of genes typically screened in hyperparathyroidism gene panels (Blueprint Genetics) or genes associated with parathyroid gland development³⁵ (Table 1). No significant difference was observed in the expression of these genes between MUT and WT.

Differentially expressed genes in Cluster-1. (A) Heat map representation of the top 100 genes with the most variable expression in group-2 (*Gcm2*^+/Y392S mice (MUT)) vs group-1 (WT). The heat map was generated by using genes in cluster-1 that contained parathyroid gland cells, from 2 MUT and 2 WT samples. The other samples did not show proper spatial localization of parathyroid glands and the expression of *Pth* transcripts (see Figures S3 and S4) perhaps due to technical issues in sample processing or spatial data processing. (B) Volcano plot of differentially expressed genes in MUT vs WT (p < .05, fold change >1.5) showing significantly down-regulated and up-regulated genes.

Table 1.

Expression level of genes related to hyperparathyroidism and parathyroid gland development.

Gene	Name	MUT-1	MUT-2	WT-1	WT-2
Expression level of genes related to hyperparathyroidism (transcript counts per million)
*Aire*	Autoimmune regulator	2	0	0	4
*Ap2s1*	Adaptor related protein complex 2 subunit sigma 1	n/d	n/d	n/d	n/d
*Casr*	Calcium-sensing receptor	2358	2845	3415	3464
*Ccnd1*	Cyclin D1	74	20	39	12
*Cdc73*	Cell division cycle 73	1	59	0	93
*Cdkn1a*	Cyclin-dependent kinase inhibitor 1A (p21)	103	95	43	62
*Cdkn1b*	Cyclin-dependent kinase inhibitor 1B (p27)	76	69	51	61
*Cdkn2b*	Cyclin-dependent kinase inhibitor 2B (p15)	5	7	6	6
*Cdkn2c*	Cyclin-dependent kinase inhibitor 2C (p18)	42	24	23	31
*Flcn*	Folliculin	52	86	45	57
*Gcm2*	Glial cells missing homolog 2	488	580	887	696
*Gna11*	Guanine nucleotide binding protein, alpha 11	62	81	91	77
*Max*	Myc associated factor X	34	39	37	35
*Men1*	Multiple endocrine neoplasia 1	46	44	53	50
*Pth*	Parathyroid hormone	6197	31333	11056	48060
*Ret*	Ret proto-oncogene	22	7	6	12
*Trpv6*	Transient receptor potential cation channel, subfamily V, member 6	3	0	4	7
Expression level of genes related to parathyroid gland development (transcript counts per million)
*Eya1*	Eyes absent 1 homolog	82	108	134	194
*Gata3*	GATA binding protein 3	247	475	492	476
*Gcm2*	Glial cells missing homolog 2	488	580	887	696
*Hoxa3*	Homeobox A3	16	17	16	19
*Mafb*	v-maf musculoaponeurotic fibrosarcoma oncogene homolog B	507	869	1107	803
*Pax1*	Paired box 1	68	166	130	120
*Pax3*	Paired box 3	4	0	2	4
*Pax9*	Paired box 9	143	257	249	219
*Pbx1*	Pre B cell leukemia homeobox 1	143	105	88	111
*Six1*	Sine oculis-related homeobox 1	190	304	350	338
*Sox3*	SRY (sex determining region Y)-box 3	1	7	0	3
*Tbx1*	T-box 1	107	144	220	166

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Expression level in “transcript counts per million” is shown for cluster-1 that contained parathyroid gland cells. MUT-1 and MUT-2 are Gcm2^+/Y392S mice, and WT-1 and WT-2 are WT mice. Ap2s1 transcripts were not detected (n/d). Gene expression analysis of MUT vs WT did not show statistically significant fold-changes (>2-fold) in any of these genes.

Functional annotation of the differentially expressed genes for KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway categories showed PI3K-Akt and AMPK signaling pathways among the top 5 pathways represented in the dataset (Figure 4A). Also, Gene Set Enrichment Analysis (GSEA) was performed, which queries activation or suppression of gene sets in biological functional groups obtained from Gene Ontology, KEGG, Hallmarks, and other databases. Epithelial to mesenchymal transition was among the gene sets 2-fold activated in the differentially expressed genes of MUT vs WT (Figure 4B). Among the top 5 gene sets that were suppressed, HNF3A and HNF3B pathways were 1.7-fold suppressed in MUT vs WT (Figure 4B).

Functional annotation of differentially expressed genes. (A) KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis of differentially expressed genes in cluster-1 of MUT vs WT parathyroid gland cells. The number of differentially expressed genes in the enriched pathway is indicated by the circle area, and the circle color indicates p-values. (B) GSEA (Gene Set Enrichment Analysis) of differentially expressed genes in cluster-1 of MUT vs WT for the enrichment of gene sets of specific phenotypes or diseases in the Human Molecular Signatures Database (MSigDB) for functional groups, such as REACTOME, HALLMARK, KEGG, and other. Bars indicate enrichment score of activated gene sets and suppressed gene sets.

The functional annotation of differentially expressed genes suggests that the mouse Gcm2 variant p.Y392S in parathyroid gland cells affects pathways indicative of cellular transformation, such as PI3K-Akt, epithelial to mesenchymal transition, and up-regulation of Lgals3.

Positive galectin-3 staining in parathyroid tumors of patients with activating germline GCM2 variants

The biological relevance of increased Lgals3 transcripts in parathyroid gland cells of Gcm2^+/Y392S mice was explored in human parathyroid tumor samples. IHC for galectin-3 was performed on FFPE parathyroid tumor sections from 12 patients who previously tested positive for germline heterozygous GCM2 variants p.Y394S (n = 10) or p.L379Q (n = 2), and a positive control case of parathyroid cancer with germline heterozygous FLCN mutation.¹⁴^,¹⁵^,²⁸ In parathyroid tumors with the GCM2 variant p.Y394S, galectin-3 staining was negative in 3/10, weak in 3/10, moderate in 3/10, and strong in 1/10. Galectin-3 staining was strong in 2 of 2 parathyroid tumors with the GCM2 variant p.L379Q. Representative images of negative, weak, moderate, and strong staining are shown in Figure S5. Strong staining in a tumor sample with GCM2 variant p.Y394S and a tumor sample with p.L379Q are shown in Figure 5A and B, respectively. Also, galectin-3 staining was strong in the FLCN-related positive control parathyroid cancer sample (Figure 5C). These data indicate a potential for progression to carcinoma in parathyroid glands with GCM2 variants.

Galectin-3 staining in human parathyroid tumors. Hematoxylin and eosin staining and “strong” immunostaining for galectin-3 and PTH in representative FFPE parathyroid tumor sections is shown from patients with (A) germline heterozygous *GCM2* variant p.Y394S, (B) germline heterozygous *GCM2* variant p.L379Q, and (C) a positive control parathyroid cancer sample (*FLCN* c.1285insC). Galectin-3 (brown staining) was localized to the cytoplasm and nucleus in addition to plasma membrane (as described in the human protein atlas, proteinatlas.org), PTH staining was mainly seen in the cytoplasm (brown staining), and the blue staining is for hematoxylin. Scale bar = 50 μm. Additional brightfield microscopy images are shown in Figure S5 for negative control and staining observed as negative, weak, moderate, or strong.

Galectin-3 promoter activity is not affected by GCM2 or the activating variants of GCM2

To determine whether GCM2 activating variants were regulating the galectin-3 promoter to increase gene expression, luciferase reporter assays were conducted using the promoter and GCM2-binding site containing enhancer region of the mouse and human galectin-3 encoding gene (Figure S6A). In HEK293 cells, luciferase reporter-constructs containing mLgals3 or hLGALS3 promoter/enhancer region showed 8-fold and 20-fold increase in transcriptional activity, respectively, compared to the promoter-less reporter vector pGL4.10 (Figure S6B and C). However, further increase in the transcriptional activity of the mLgals3 and hLGALS3 promoter/enhancer-reporter-constructs was not observed with the expression of GCM2 or GCM2 activating variants p.Y394S or p.L379Q (Figure S6B and C). Therefore, a direct effect of GCM2 or the activating variants of GCM2 on the galectin-3 promoter/enhancer to increase gene expression was not detected.

Discussion

Technological advances in spatially resolved transcriptome analysis (Spatial Transcriptomics) have enabled characterization of gene expression based on the position of cell types in a tissue.³⁶ Also, gene expression from within a small number of cells can be easily detected by spatial transcriptomics methods even from FFPE tissue sections. Mouse parathyroid glands are very small and embedded in the thyroid tissue, which hinders the isolation and procurement of sufficient parathyroid gland cells for molecular studies. To study mouse parathyroid glands, generally FFPE tissue blocks are prepared that contain intact parathyroid glands together with the surrounding tissues (esophagus, trachea, and thyroid). Therefore, to capture gene expression from parathyroid glands, we have used the spatial transcriptomics Visium platform from10x Genomics that enabled spatially resolved RNA analysis from mouse FFPE tracheal tissue sections.

Previously, we generated a mouse model to study the effect of a recurrent germline heterozygous variant of the parathyroid-specific transcription factor GCM2 that is associated with parathyroid tumor development in isolated primary hyperparathyroidism.²⁷ These mice did not develop parathyroid tumors. Interestingly, both the human variant (GCM2 p.Y394S) and corresponding mouse variant (Gcm2 p.Y392S) enhanced transcription in luciferase reporter assays mediated by GCM2-binding sites in HEK293 cells.²⁷ We have investigated whether this enhanced transcription translates into gene expression changes in parathyroid gland cells in vivo by spatial transcriptomic analysis of FFPE tracheal tissue blocks from Gcm2^+/Y392S (MUT) and WT mice.

Spatially resolved transcriptomic analysis revealed 8 different clusters based on similar gene expression as expected for the various cell types in the FFPE tracheal tissue sections of MUT and WT, where cluster-1 belonged to parathyroid gland cells that were enriched for Pth and Gcm2 transcripts. The purpose of using spatial transcriptomics for this study was to assist in locating the small parathyroid glands that are embedded in the tissues of the neck/tracheal region; therefore, the 7 clusters that belong to the other neck/tracheal tissues were not used in downstream analysis. Further analysis of cluster-1 transcripts for the functional characterization of differentially expressed genes between MUT and WT parathyroid gland cells revealed enrichment of pathways and gene sets in MUT indicative of cellular signaling that enhance growth (PI3K-Akt and AMPK signaling pathways) and a potential for invasion (epithelial-to-mesenchymal transition, EMT pathway).³⁷^,³⁸ Also, GSEA showed suppression of HNF3A (FOXA1) and HNF3B (FOXA2) pathway gene sets. The FOXA proteins belong to the forkhead domain transcription factor family, which, among other biological roles, is necessary for maintaining the epithelial phenotype. In this capacity, FOXA1 and FOXA2 act as antagonists of the EMT pathway in some cell types.³⁹^,⁴⁰ These data suggest a potential for growth and invasion of parathyroid gland cells expressing the p.Y392S variant of Gcm2.

Lgals3 (galectin-3) was the top hit among the significantly up-regulated genes in the differential gene expression analysis of cluster-1 transcripts of MUT compared to WT. Galectin-3 expression is included among IHC markers that are associated with parathyroid carcinoma.^30–34^,⁴¹ We found moderate to strong galectin-3 staining in parathyroid tumors of patients with germline heterozygous GCM2 variants p.Y394S (n = 4/10) and p.L379Q (n = 2/2), indicating a potential for parathyroid carcinoma. The uniformly strong galectin-3 staining observed in both parathyroid tumors with the GCM2 variant p.L379Q suggests that this genotype may have a particularly high malignant potential, but much more research and clinical observation will be required to substantiate such a hypothesis. Whether galectin-3 is directly regulated by GCM2 or GCM2 activating variant expression remains to be determined. In HEK293 cells, GCM2 or GCM2 activating variants did not enhance the transcriptional activity of the promoter/enhancer region of the galectin-3 encoding mouse Lgals3 or human LGALS3 gene. Studies in cell lines derived from parathyroid gland cells may help to evaluate whether the GCM2 protein requires other partners to regulate LGALS3 expression. It is possible that the relevant GCM2 binding regions are located elsewhere in the LGALS3 gene, for example, GCM2-binding was detected in enhancer regions within introns of the CASR gene.²⁹ It would be of interest to further explore whether genes in the cellular growth and invasion pathways that are enriched in Gcm2^+/Y392S parathyroid gland cells are direct targets of GCM2 and its activating variants.

A limitation of our previous study was that Gcm2^+/Y392S mice were followed to the age of 24 mo for the development of parathyroid tumors, and following mice beyond this age was not possible as that is the average lifespan.²⁷ Also, we did not study the effect of this variant in other mouse backgrounds that may show parathyroid tumor development. A limitation of our current study is that spatial transcriptomics was not performed on human parathyroid tumors that express activating GCM2 variants due to the lack of counterpart normal parathyroid tissue samples. Mouse model studies helped to procure transcriptomic data from both normal Gcm2 and Gcm2 variant expressing parathyroid gland cells. Comparison of mouse and human dataset will be critical to confirm the biological significance of these findings.

Parathyroid carcinoma or atypical parathyroid adenoma has been reported in a few patients with the germline heterozygous GCM2 p.Y394S variant.²⁵^,⁴² This is consistent with the findings of the spatial transcriptomic analysis of parathyroid glands from Gcm2^+/Y392S vs WT mice that revealed up-regulation of Lgals3, enrichment of pathways associated with cellular growth, and gene sets associated with the epithelial-mesenchymal transition state, indicating that the Gcm2 variant p.Y392S may contribute to carcinoma progression of parathyroid gland cells. Positive galectin-3 staining in human parathyroid tumor specimens also indicates a potential for carcinoma progression from activating GCM2 variants. The transcriptomic data of mouse parathyroid gland cells generated in this study can serve as a valuable resource for facilitating the investigation of genes and pathways in normal or abnormal parathyroid gland growth and physiology.

Patient consent

Written informed consent was obtained from all patients under IRB-approved “Natural History Study of Parathyroid Disorders” (NCT04969926) conducted at the National Institutes of Health (NIH) Clinical Center, Bethesda, MD, United States.

Supplementary Material

Supplementray_data_ziaf147

supplementray_data_ziaf147.pdf^{(5.3MB, pdf)}

Acknowledgments

We thank Annya Lopez of the NCI pathology lab for providing human FFPE parathyroid tissue blocks. This research was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) within the National Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.

Contributor Information

Priyanka Bolel, Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States.

Jeremie Oliver Piña, Section on Craniofacial Genetic Disorders, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, United States; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, 84112, United States.

Fabio R Faucz, Molecular Genomics Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, United States; Hospital for Endocrine Surgery Research Center, HCA Research Institute, Tampa, FL, 33615, United States.

James R Iben, Molecular Genomics Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, United States.

Wafa Abbas, Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States.

Smita Jha, Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States.

William F Simonds, Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States.

Lee S Weinstein, Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States.

Sunita K Agarwal, Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892, United States.

Author contributions

Priyanka Bolel (Investigation, Methodology, Visualization, Writing—review & editing), Jeremie Oliver Piña (Investigation, Data curation, Formal analysis, Methodology, Resources, Visualization, Writing—review & editing), Fabio R. Faucz (Investigation, Methodology, Resources, Visualization, Writing—review & editing), James R. Iben (Data curation, Resources, Software, Writing—review & editing), Wafa Abbas (Resources), Smita Jha (Resources, Writing—review & editing), William F. Simonds (Resources, Writing—review & editing), Lee S. Weinstein (Supervision, Resources, Writing—review & editing), and Sunita K. Agarwal (Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing—original draft, Writing—review & editing)

Priyanka Bolel and Jeremie Oliver Piña contributed equally to this work.

Funding

This work was supported by the Intramural Research Program of the NIH: National Institute of Diabetes and Digestive and Kidney Diseases, grant number 1ZIADK075035 (S.K.A.) and 1ZIADK043006 (S.J.), and National Institute of Child Health and Human Development, Intramural Research Training Award (J.O.P.).

Conflicts of interest

The authors have no conflicts of interest to declare.

Data availability

Spatial transcriptomics data files have been deposited in the National Center of Biotechnology Information’s Gene Expression Omnibus (GEO) database under accession number GSE298115 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE298115).

Ethics approval

All animal experiments were performed according to the animal study protocols approved by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Institutional Animal Care and Use Committee (Animal Study Protocol number: K070-MDB-24).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementray_data_ziaf147

supplementray_data_ziaf147.pdf^{(5.3MB, pdf)}

Data Availability Statement

[ref1] 1. Gunther T, Chen ZF, Kim J, et al. Genetic ablation of parathyroid glands reveals another source of parathyroid hormone. Nature. 2000;406(6792):199–203. [DOI] [PubMed] [Google Scholar]

[ref2] 2. Liu Z, Yu S, Manley NR. Gcm2 is required for the differentiation and survival of parathyroid precursor cells in the parathyroid/thymus primordia. Dev Biol. 2007;305(1):333–346. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref3] 3. Yamada T, Tatsumi N, Anraku A, et al. Gcm2 regulates the maintenance of parathyroid cells in adult mice. PLoS One. 2019;14(1):e0210662. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref4] 4. Brown EM. Control of parathyroid hormone secretion by its key physiological regulators. In: Bilezikian JP, ed. The Parathyroids. Third ed. London: Academic Press; 2015:101–118. [Google Scholar]

[ref5] 5. Jha S, Simonds WF. Molecular and clinical spectrum of primary hyperparathyroidism. Endocr Rev. 2023;44(5):779–818. 10.1210/endrev/bnad009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref6] 6. Ding C, Buckingham B, Levine MA. Familial isolated hypoparathyroidism caused by a mutation in the gene for the transcription factor GCMB. J Clin Invest. 2001;108(8):1215–1220. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref7] 7. Baumber L, Tufarelli C, Patel S, et al. Identification of a novel mutation disrupting the DNA binding activity of GCM2 in autosomal recessive familial isolated hypoparathyroidism. J Med Genet. 2005;42(5):443–448. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref8] 8. Thomee C, Schubert SW, Parma J, et al. GCMB mutation in familial isolated hypoparathyroidism with residual secretion of parathyroid hormone. J Clin Endocrinol Metab. 2005;90(5):2487–2492. [DOI] [PubMed] [Google Scholar]

[ref9] 9. Mannstadt M, Bertrand G, Muresan M, et al. Dominant-negative GCMB mutations cause an autosomal dominant form of hypoparathyroidism. J Clin Endocrinol Metab. 2008;93(9):3568–3576. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref10] 10. Maret A, Ding C, Kornfield SL, Levine MA. Analysis of the GCM2 gene in isolated hypoparathyroidism: a molecular and biochemical study. J Clin Endocrinol Metab. 2008;93(4):1426–1432. [DOI] [PubMed] [Google Scholar]

[ref11] 11. Bowl MR, Mirczuk SM, Grigorieva IV, et al. Identification and characterization of novel parathyroid-specific transcription factor glial cells missing homolog B (GCMB) mutations in eight families with autosomal recessive hypoparathyroidism. Hum Mol Genet. 2010;19(10):2028–2038. [DOI] [PubMed] [Google Scholar]

[ref12] 12. Canaff L, Guarnieri V, Kim Y, et al. Novel glial cells Missing-2 (GCM2) variants in parathyroid disorders. Eur J Endocrinol. 2022;186(3):351–366. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref13] 13. Singhania P, Ghosh A, Das D, Bhattacharjee R, Roy A, Chowdhury S. Glial cell missing homolog 2 mutation causing severe hypoparathyroidism: report of two cases with novel mutations. J Endocr Soc. 2022;7(1):bvac166. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref14] 14. Guan B, Welch JM, Sapp JC, et al. GCM2-activating mutations in familial isolated hyperparathyroidism. Am J Hum Genet. 2016;99(5):1034–1044. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref15] 15. Guan B, Welch JM, Vemulapalli M, et al. Ethnicity of patients with germline GCM2-activating variants and primary hyperparathyroidism. J Endocr Soc. 2017;1(5):488–499. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref16] 16. Riccardi A, Aspir T, Shen L, et al. Analysis of activating GCM2 sequence variants in sporadic parathyroid adenomas. J Clin Endocrinol Metab. 2019;104(6):1948–1952. [DOI] [PubMed] [Google Scholar]

[ref17] 17. Cetani F, Pardi E, Aretini P, et al. Whole exome sequencing in familial isolated primary hyperparathyroidism. J Endocrinol Investig. 2020;43(2):231–245. [DOI] [PubMed] [Google Scholar]

[ref18] 18. Coppin L, Dufosse M, Romanet P, et al. Should the GCM2 gene be tested when screening for familial primary hyperparathyroidism? Eur J Endocrinol. 2020;182(1):57–65. [DOI] [PubMed] [Google Scholar]

[ref19] 19. Song A, Yang Y, Wang Y, et al. Germline GCM2 mutation screening in Chinese primary hyperparathyroidism patients. Endocr Pract. 2020;26(10):1093–1104. [DOI] [PubMed] [Google Scholar]

[ref20] 20. Garcia-Castano A, Madariaga L, Gomez-Conde S, et al. Five patients with disorders of calcium metabolism presented with GCM2 gene variants. Sci Rep. 2021;11(1):2968. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref21] 21. Park HS, Lee YH, Hong N, Won D, Rhee Y. Germline mutations related to primary hyperparathyroidism identified by next-generation sequencing. Front Endocrinol (Lausanne). 2022;13:853171. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref22] 22. Vincze S, Peters NV, Kuo CL, et al. GCM2 variants in familial and multiglandular primary hyperparathyroidism. J Clin Endocrinol Metab. 2022;107(5):e2021–e2026. [DOI] [PubMed] [Google Scholar]

[ref23] 23. Tolkin L, Klein V, Frankel M, Altarescu G, Beeri R, Munter G. Variant Tyr 394Ser in the GCM2 gene is rare in a cohort of Ashkenazi Jews with primary hyperparathyroidism. J Endocr Soc. 2023;7(7):bvad086. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ref24] 24. Szalat A, Shpitzen S, Pollack R, Mazeh H, Durst R, Meiner V. GCM2 p.Tyr394Ser variant in Ashkenazi Israeli patients with suspected familial isolated hyperparathyroidism. Front Endocrinol (Lausanne). 2023;14:1254156. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Spatial transcriptomic analysis of mouse parathyroid gland cells expressing an activating variant of Gcm2

Priyanka Bolel

Jeremie Oliver Piña

Fabio R Faucz

James R Iben

Wafa Abbas

Smita Jha

William F Simonds

Lee S Weinstein

Sunita K Agarwal

Roles

Abstract

Graphical Abstract

Graphical Abstract.

Introduction

Materials and methods

Mouse model and tissues

Human tissues

Spatial transcriptomics

Spatial transcriptomics data processing

Immunostaining

Microscopy and imaging

Galectin-3 promoter activity

Results

Spatial transcriptomics captured RNA from mouse parathyroid gland cells

Figure 1.

Figure 2.

Differentially expressed genes and functional annotation

Figure 3.

Table 1.

Figure 4.

Positive galectin-3 staining in parathyroid tumors of patients with activating germline GCM2 variants

Figure 5.

Galectin-3 promoter activity is not affected by GCM2 or the activating variants of GCM2

Discussion

Patient consent

Supplementary Material

Acknowledgments

Contributor Information

Author contributions

Funding

Conflicts of interest

Data availability

Ethics approval

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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