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[Preprint]. 2025 Sep 5:2025.09.01.673548.
doi: 10.1101/2025.09.01.673548.

Gene-Expression Programs in Salivary Gland Adenoid Cystic Carcinoma Analyzed Using Single-Cell and Spatial Transcriptomics

Ifeoma Ebinumoliseh  1 , Gopikrishnan Bijukumar  1 , Kendall Hoff  1   2 , Kathryn J Brayer  3 , Elaine L Bearer  4 , Scott A Ness  3 , Jeremy S Edwards  1   3
Affiliations

Gene-Expression Programs in Salivary Gland Adenoid Cystic Carcinoma Analyzed Using Single-Cell and Spatial Transcriptomics

Ifeoma Ebinumoliseh et al. bioRxiv. .

Abstract

Adenoid cystic carcinoma of the salivary gland (SGACC) is a highly aggressive malignancy characterized by poor patient survival outcomes. While several studies have analyzed the transcriptome of the salivary gland at the bulk and single-cell level, no spatial transcriptomic analyses of this tissue have been published. Most of the existing publications on SGACC have predominantly relied on bulk and single cell RNA sequencing approaches, which do not resolve the spatially localized transcriptional heterogeneity nor have the resolution for defining molecular markers within tumor subpopulations. SGACC is clinically notable for the presence of multiple tumor clones, distinct spatial phenotypes, and its indolent yet invasive nature coupled with a high propensity for distant metastasis. These features may reflect co-expression of tumor-associated markers across diverse cellular niches, and a resultant biological complexity which causes standard treatment such as surgical resection, radiation therapy, and chemotherapy to be largely ineffective in significantly improving long-term survival, and highlights the need for more precise, targeted therapeutic strategies. Herein, we analyzed single cell (n = 4) and high-resolution spatial transcriptomics samples (n = 5) to characterize cancer cell populations in MYB- and non-MYB-expressing cell states, delineated gene expression signatures, and identified critical molecular interactions specific to SGACC. We used Visum HD to obtain spatial transcriptomics data at 2μm squared high resolution. This allowed a multi-omics approach comprising single cell and spatial transcriptomic methods to enable the discovery of novel transcriptional signatures and microenvironmental features not captured by conventional methods. Spatial mapping revealed marked cellular heterogeneity and demonstrated how tissue environments influence cellular transcriptomics. To tumor heterogeneity, we focused on tumorigenic cell populations, profiled plasma and T cell enrichment within the tumor microenvironment and identified key pathways and transcriptional drivers including the MYB-NFIB fusion underlying the tumor cluster formation. Our findings indicate an upregulation of genes involved in extracellular matrix remodeling, autophagy, and reactive stromal cell populations. We further found evidence of partial epithelial-mesenchymal transition (P-EMT) programming within MYB-expressing tumor clusters. Pathway analysis revealed that mutations in the spatial query sample prominently affect the PI3K-AKT and IL-17 signaling pathways, together with a downregulation of canonical Wnt signaling in some regions of the tissue architecture adjacent to immune cells. Collectively, these results underscore the complex regulatory landscape of SGACC and offer insights into its cellular dynamics and possible therapeutic vulnerabilities.

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Conflict of interest statement

Competing interests KH and JSE declare that they are affiliated with Centrillion Biosciences. JSE has consulted for Sentieon, EquiSeq, Armonica, Centrillion, and Roche.

Figures

Figure 1.
Figure 1.. Identification and classification of cell populations using UMAP and histology.
(A) UMAP visualization of cell clusters in the spatial query dataset. (B)UMAP highlighting MYB-expressing versus non-MYB-expressing cell clusters. (C). UMAP of integrated single-cell reference datasets. (D) H&E image of the SGACC spatial transcriptomics sample (ACC1)
Figure 1.
Figure 1.. Identification and classification of cell populations using UMAP and histology.
(A) UMAP visualization of cell clusters in the spatial query dataset. (B)UMAP highlighting MYB-expressing versus non-MYB-expressing cell clusters. (C). UMAP of integrated single-cell reference datasets. (D) H&E image of the SGACC spatial transcriptomics sample (ACC1)
Figure 1.
Figure 1.. Identification and classification of cell populations using UMAP and histology.
(A) UMAP visualization of cell clusters in the spatial query dataset. (B)UMAP highlighting MYB-expressing versus non-MYB-expressing cell clusters. (C). UMAP of integrated single-cell reference datasets. (D) H&E image of the SGACC spatial transcriptomics sample (ACC1)
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 2.
Figure 2.
RNA sequencing analysis of spatial SGACC versus normal control salivary glands. (A) Heatmap of top 1000 differentially expressed genes of normal control (N1) samples (n =2) vs SGACC (ACC1 -ACC5) samples (n = 5). (B) Heatmap of top 60 differentially expressed genes of normal control (N1 and N2) vs SGACC (ACC1 -ACC5) samples (n = 7). Green and red bars indicate gene upregulation and downregulation, respectively. (C) Heatmap of top markers for each cluster considering 50 bins from each cluster. (D) Box plots of top differentially expressed collagens and matrix metalloproteinases between normal salivary gland and SGACC. Statistical significance is indicated by asterisks: * P < 0.05, **P < 0.01, and ***P < 0.001.
Figure 3.
Figure 3.. Identification of cell clusters in the spatial transcriptomics sample along with their relative abundance.
(A). Spatial map showing absence of MYB-expressing cell clusters. (B). Co-localization of MYB-expressing cell clusters.(C). Visualization of intercellular crosstalk highlighting stromal cell populations
Figure 3.
Figure 3.. Identification of cell clusters in the spatial transcriptomics sample along with their relative abundance.
(A). Spatial map showing absence of MYB-expressing cell clusters. (B). Co-localization of MYB-expressing cell clusters.(C). Visualization of intercellular crosstalk highlighting stromal cell populations
Figure 4.
Figure 4.
Kegg pathway and biological process. (A) KEGG pathway enrichment analysis in MYB-expressing and non-MYB-expressing clusters showing IL-7 and PI3K pathway upregulation (B). GO biological process enrichment analysis in MYB-expressing clusters. (C). GO biological process enrichment in non-MYB-expressing clusters.
Figure 4.
Figure 4.
Kegg pathway and biological process. (A) KEGG pathway enrichment analysis in MYB-expressing and non-MYB-expressing clusters showing IL-7 and PI3K pathway upregulation (B). GO biological process enrichment analysis in MYB-expressing clusters. (C). GO biological process enrichment in non-MYB-expressing clusters.
Figure 5.
Figure 5.. Identification of immune cells interactions with cell populations in the spatial transcriptomics sample along with their relative abundance.
(A) Visualization of plasma and T cell interactions with MYB-expressing clusters. (B) Spatial distribution of an active stromal cell population within the tumor core. (C) Plasma cell migration toward tumor regions along fibroblastic and endothelial scaffolds (D) Spatial abundance of annotated cell types across the tissue section
Figure 5.
Figure 5.. Identification of immune cells interactions with cell populations in the spatial transcriptomics sample along with their relative abundance.
(A) Visualization of plasma and T cell interactions with MYB-expressing clusters. (B) Spatial distribution of an active stromal cell population within the tumor core. (C) Plasma cell migration toward tumor regions along fibroblastic and endothelial scaffolds (D) Spatial abundance of annotated cell types across the tissue section
Figure 5.
Figure 5.. Identification of immune cells interactions with cell populations in the spatial transcriptomics sample along with their relative abundance.
(A) Visualization of plasma and T cell interactions with MYB-expressing clusters. (B) Spatial distribution of an active stromal cell population within the tumor core. (C) Plasma cell migration toward tumor regions along fibroblastic and endothelial scaffolds (D) Spatial abundance of annotated cell types across the tissue section
Figure 6.
Figure 6.
Enrichment of disease-related pathways and angiogenic processes. (A). Upregulated disease-associated pathways in MYB-expressing cell clusters. (B) Upregulated disease-associated pathways in non-MYB-expressing cell clusters.
Figure 7.
Figure 7.
Trajectory and pseudotime analysis of MYB-expressing cell clusters. (A). Monocle-inferred trajectory of key MYB-expressing cell clusters. (B). Pseudotime estimation across principal MYB-expressing populations. (C). Spatial mapping of pseudotime progression within the tissue architecture. (D) UMAP visualization of selected genes identified from trajectory analysis.
Figure 7.
Figure 7.
Trajectory and pseudotime analysis of MYB-expressing cell clusters. (A). Monocle-inferred trajectory of key MYB-expressing cell clusters. (B). Pseudotime estimation across principal MYB-expressing populations. (C). Spatial mapping of pseudotime progression within the tissue architecture. (D) UMAP visualization of selected genes identified from trajectory analysis.
Figure 8.
Figure 8.
Differential gene expression between MYB and non-MYB expressing cell types. (A) Pairwise comparison of stromal cells expressing MYB vs. non-MYB. (B) Pairwise comparison of epithelial cells expressing MYB vs. non-MYB. (C) Pairwise comparison of basal cells expressing MYB vs. non-MYB.
Figure 8.
Figure 8.
Differential gene expression between MYB and non-MYB expressing cell types. (A) Pairwise comparison of stromal cells expressing MYB vs. non-MYB. (B) Pairwise comparison of epithelial cells expressing MYB vs. non-MYB. (C) Pairwise comparison of basal cells expressing MYB vs. non-MYB.
Figure 8.
Figure 8.
Differential gene expression between MYB and non-MYB expressing cell types. (A) Pairwise comparison of stromal cells expressing MYB vs. non-MYB. (B) Pairwise comparison of epithelial cells expressing MYB vs. non-MYB. (C) Pairwise comparison of basal cells expressing MYB vs. non-MYB.
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