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functional imaging ○ human liver tissue microarray construction ○ human liver xenium spatial transcriptomics • quantification  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc functional imaging ○ human liver tissue microarray construction ○ human liver xenium spatial transcriptomics • quantification
    Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • Quantification, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/tissue+microarray+images/differences+imaging+in+xenium/pm41435820-258-85-97
    Average 86 stars, based on 1 article reviews
    functional imaging ○ human liver tissue microarray construction ○ human liver xenium spatial transcriptomics • quantification - by Bioz Stars, 2026-10
    86/100 stars

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    Related Articles

    Imaging:

    Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images
    Article Snippet: .. Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images. ..

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Sequencing:

    Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images
    Article Snippet: .. Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images. ..

    Gene Expression:

    Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images
    Article Snippet: .. Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images. ..

    Fat:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Microarray:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Functional Assay:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Immunofluorescence:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Western Blot:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Isolation:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Transduction:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Spatial Transcriptomics:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..

    Control:

    Article Title: Hepatic adaptation to chronic metabolic stress primes tumorigenesis.
    Article Snippet: .. STAR★METHODS Detailed methods are provided in the online version of this paper and include the following: • KEY RESOURCES TABLE • EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS ○ Mouse Husbandry and High Fat Diet ○ Human Banked Samples for Tissue Microarray • METHOD DETAILS ○ Mouse Functional Testing and Histology ○ Multiplexed Immunofluorescence ○ Immunoblotting ○ Lipidomics ○ Live-Tissue Dissociation for scRNA-seq ○ Nuclei Isolation for snRNA-seq + snATAC-seq ○ Lentiviral Production ○ Lentiviral Transduction ○ Liver Cell Lipid Culture ○ Liver Cell scRNA-seq and Functional Imaging ○ Human Liver Tissue Microarray Construction ○ Human Liver Xenium Spatial Transcriptomics • QUANTIFICATION AND STATISTICAL ANALYSES ○ sc/snRNA-seq QC, Filtering, and Annotation ○ snATAC-seq QC, Filtering, and Annotation ○ Gene Program Derivation ○ Cancer Signature Induction in Stress Response ○ Longitudinal Epigenetic Alterations & Priming ○ MATCHA Transcription Factor Inference ○ Control Analyses of RELB CNVs on Programs ○ Xenium Spatial Transcriptomics ll OPEN ACCESS Cell 189, 1–26, January 22, 2026 17 Please cite this article in press as: Tzouanas et al., Hepatic adaptation to chronic metabolic stress primes tumorigenesis, Cell (2026), https:// doi.org/10.1016/j.cell.2025.11.031 Article SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j.cell. ..



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    AR activity drives fibroblast’s response to hormone-replacement therapy (A) UMAP depicting fibroblast subclusters (top) in snRNA-seq data (matrix 1 and 2, matrix-type fibroblasts; lipo-f, lipo-fibroblasts; vasc-f, vascular-like fibroblasts) and the distribution of patient samples in each gender identity (bottom). (B) Heatmap shows scaled average expression of snRNA-seq markers identified for each of the fibroblast subclusters. (C) Motif footprints for AR (AR-CisBP M03389_2.00) among trans-male and cis-female fibroblast cells. Top panel shows the transposase bias-corrected signal, and the bottom panel shows the transposase bias. (D) Right panel shows the enrichment of motifs among unique accessible chromatin peaks of fibroblast cells from cis-female and trans-male samples. Left panel shows the fraction of the peaks of the corresponding cells that overlap with the motif. (E) Average ratio of AR staining intensity in fibroblast nucleus compared with the cytoplasm in tissue regions of each sample type (p value, Wilcoxon; 1.2 × 10 −8 ). (F) Left panel boxplots show the fraction of cis-female (purple) and trans-male (orange) cells corresponding to five different classes of fibroblasts detected in tissue regions of <t>CODEX</t> <t>microarray</t> data (p values, Wilcoxon: fibr-main = 0.00011, fibr-epi = 0.0046). Right panel shows the scaled staining intensities of various markers that distinguish the five subtypes of fibroblasts. (G) Boxplots show per-region average distance for each of the five subtypes of fibroblasts to the most proximal epithelial cell. (H) Violin plots show the RNA expression of laminins LAMA2 (top) and LAMB1 (bottom) in fibroblast subclusters, split by sample type (adjusted p values, MAST: LAMA2 in lipo-f = 2.60 × 10 −63 , matrix 1 = 5.11 × 10 −175 , matrix 2 = 1.03 × 10 −69 , vasc-f = 1.99 × 10 −10 ; LAMB1 in lipo-f = 3.71 × 10 −35 , matrix 1 = 1.46 × 10 −55 , matrix 2 = 3.08 × 10 −87 , vasc-f = 2.58 × 10 −13 ). (I) AR binding sites (red markers) across genomic regions of LAMA2 and LAMB1. Gene bodies are shown (light green) with the promoter (arrow) and exon boundaries (dark green). Genomic window shows chromatin accessibility in cis-female (purple) and trans-male (orange) fibroblasts. (J) Boxplots show per-region average LAMA2 (left) and LAMB1 (right) staining intensities in the LAMB1 + fibroblast subtype on CODEX microarray data (p value, Wilcoxon: LAMA2 = 0.64, LAMB1 = 0.0079). (K) Boxplots show the average RNA expression of ITGB1 among luminal-HR + , luminal-HR – , and basal epithelial cells (adjusted p value, MAST in basal <2.22 × 10 −16 ).
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    AR activity drives fibroblast’s response to hormone-replacement therapy (A) UMAP depicting fibroblast subclusters (top) in snRNA-seq data (matrix 1 and 2, matrix-type fibroblasts; lipo-f, lipo-fibroblasts; vasc-f, vascular-like fibroblasts) and the distribution of patient samples in each gender identity (bottom). (B) Heatmap shows scaled average expression of snRNA-seq markers identified for each of the fibroblast subclusters. (C) Motif footprints for AR (AR-CisBP M03389_2.00) among trans-male and cis-female fibroblast cells. Top panel shows the transposase bias-corrected signal, and the bottom panel shows the transposase bias. (D) Right panel shows the enrichment of motifs among unique accessible chromatin peaks of fibroblast cells from cis-female and trans-male samples. Left panel shows the fraction of the peaks of the corresponding cells that overlap with the motif. (E) Average ratio of AR staining intensity in fibroblast nucleus compared with the cytoplasm in tissue regions of each sample type (p value, Wilcoxon; 1.2 × 10 −8 ). (F) Left panel boxplots show the fraction of cis-female (purple) and trans-male (orange) cells corresponding to five different classes of fibroblasts detected in tissue regions of CODEX microarray data (p values, Wilcoxon: fibr-main = 0.00011, fibr-epi = 0.0046). Right panel shows the scaled staining intensities of various markers that distinguish the five subtypes of fibroblasts. (G) Boxplots show per-region average distance for each of the five subtypes of fibroblasts to the most proximal epithelial cell. (H) Violin plots show the RNA expression of laminins LAMA2 (top) and LAMB1 (bottom) in fibroblast subclusters, split by sample type (adjusted p values, MAST: LAMA2 in lipo-f = 2.60 × 10 −63 , matrix 1 = 5.11 × 10 −175 , matrix 2 = 1.03 × 10 −69 , vasc-f = 1.99 × 10 −10 ; LAMB1 in lipo-f = 3.71 × 10 −35 , matrix 1 = 1.46 × 10 −55 , matrix 2 = 3.08 × 10 −87 , vasc-f = 2.58 × 10 −13 ). (I) AR binding sites (red markers) across genomic regions of LAMA2 and LAMB1. Gene bodies are shown (light green) with the promoter (arrow) and exon boundaries (dark green). Genomic window shows chromatin accessibility in cis-female (purple) and trans-male (orange) fibroblasts. (J) Boxplots show per-region average LAMA2 (left) and LAMB1 (right) staining intensities in the LAMB1 + fibroblast subtype on CODEX microarray data (p value, Wilcoxon: LAMA2 = 0.64, LAMB1 = 0.0079). (K) Boxplots show the average RNA expression of ITGB1 among luminal-HR + , luminal-HR – , and basal epithelial cells (adjusted p value, MAST in basal <2.22 × 10 −16 ).

    Journal: Cell Genomics

    Article Title: The molecular consequences of androgen activity in the human breast

    doi: 10.1016/j.xgen.2023.100272

    Figure Lengend Snippet: AR activity drives fibroblast’s response to hormone-replacement therapy (A) UMAP depicting fibroblast subclusters (top) in snRNA-seq data (matrix 1 and 2, matrix-type fibroblasts; lipo-f, lipo-fibroblasts; vasc-f, vascular-like fibroblasts) and the distribution of patient samples in each gender identity (bottom). (B) Heatmap shows scaled average expression of snRNA-seq markers identified for each of the fibroblast subclusters. (C) Motif footprints for AR (AR-CisBP M03389_2.00) among trans-male and cis-female fibroblast cells. Top panel shows the transposase bias-corrected signal, and the bottom panel shows the transposase bias. (D) Right panel shows the enrichment of motifs among unique accessible chromatin peaks of fibroblast cells from cis-female and trans-male samples. Left panel shows the fraction of the peaks of the corresponding cells that overlap with the motif. (E) Average ratio of AR staining intensity in fibroblast nucleus compared with the cytoplasm in tissue regions of each sample type (p value, Wilcoxon; 1.2 × 10 −8 ). (F) Left panel boxplots show the fraction of cis-female (purple) and trans-male (orange) cells corresponding to five different classes of fibroblasts detected in tissue regions of CODEX microarray data (p values, Wilcoxon: fibr-main = 0.00011, fibr-epi = 0.0046). Right panel shows the scaled staining intensities of various markers that distinguish the five subtypes of fibroblasts. (G) Boxplots show per-region average distance for each of the five subtypes of fibroblasts to the most proximal epithelial cell. (H) Violin plots show the RNA expression of laminins LAMA2 (top) and LAMB1 (bottom) in fibroblast subclusters, split by sample type (adjusted p values, MAST: LAMA2 in lipo-f = 2.60 × 10 −63 , matrix 1 = 5.11 × 10 −175 , matrix 2 = 1.03 × 10 −69 , vasc-f = 1.99 × 10 −10 ; LAMB1 in lipo-f = 3.71 × 10 −35 , matrix 1 = 1.46 × 10 −55 , matrix 2 = 3.08 × 10 −87 , vasc-f = 2.58 × 10 −13 ). (I) AR binding sites (red markers) across genomic regions of LAMA2 and LAMB1. Gene bodies are shown (light green) with the promoter (arrow) and exon boundaries (dark green). Genomic window shows chromatin accessibility in cis-female (purple) and trans-male (orange) fibroblasts. (J) Boxplots show per-region average LAMA2 (left) and LAMB1 (right) staining intensities in the LAMB1 + fibroblast subtype on CODEX microarray data (p value, Wilcoxon: LAMA2 = 0.64, LAMB1 = 0.0079). (K) Boxplots show the average RNA expression of ITGB1 among luminal-HR + , luminal-HR – , and basal epithelial cells (adjusted p value, MAST in basal <2.22 × 10 −16 ).

    Article Snippet: To study the staining patterns around epithelial cells in detail, we applied morphological operations to specifically isolate individual acini structures within the CODEX tissue microarray images ( C).

    Techniques: Activity Assay, Expressing, Staining, Microarray, RNA Expression, Binding Assay

    Epithelial cells without hormone responsiveness lose contractile functions upon androgen therapy (A) Images from CODEX data showing mammary acini structures from cis-female (top) and trans-male (bottom) tissues marked by expression of ACTA2 (basal cells, purple), TP63 (basal cell nuclei, blue), and KRT8 (luminal cells, green). (B) Average area of acinar structures (left panel) and average area of acini border that was filled with ACTA2 signal (see <xref ref-type=Figure S7 C and ) among cis-female and trans-male tissues (p values, Wilcoxon: area = 0.026, ACTA2 coverage = 0.012). (C) UMAP of basal cell subclusters in snRNA-seq data (top) and the distribution of trans-male and cis-female cells across them (bottom). (D) RNA expression of ACTA2, OXTR (lactation markers), and TP63 in basal cells of trans-male and cis-female samples (adjusted p values, MAST: ACTA2 = 8.86 × 10 −296 , OXTR = 9.59 × 10 −262 , TP63 = 1.16 × 10 −96 ). (E) Module scores of enriched pathways overlaid on the basal cell UMAP (REAC, Reactome; KEGG, Kyoto Encyclopedia of Genes and Genomes). (F) Right panel shows the enrichment of motifs among unique accessible chromatin peaks from trans-male and cis-female basal cells. Left panel shows the fraction of peaks from the corresponding cells that overlap with the motif. (G) Kernel density estimation of module scores for selected altered structural pathways in luminal-HR – cells (p values, Wilcoxon: KEGG, adherens junction = 4.13 × 10 −285 ; KEGG, focal adhesion = 1.42 × 10 −255 ; KEGG, regulation of actin cytoskeleton <1.42 × 10 −255 ). (H) Average RNA expression (top) of integrin receptors from the “KEGG: regulation of actin cytoskeleton” pathway in luminal-HR – cells (adjusted p values, MAST: ITGA2 = 4.89 × 10 −201 , ITGB8 = 6.40 × 10 −267 ) and average expression of the ITGA2 and ITGB8 ligand FN1 in fibroblast subclusters and lymphatic endothelial cells (bottom) from trans-male and cis-female samples (adjusted p values, MAST: matrix 1 = 1.66 × 10 −54 , matrix 2 = non-significant [n.s.], lipo-f = 1.32 × 10 −16 , vasc-f = n.s., lymph. EC = 3.13 × 10 −99 ). (I) Fisher exact test odds ratio (x axis) and –log 10 p value (y axis) corresponding to enrichment of each motif among the chromatin accessibility peaks for the genes of the "WikiPathways: focal adhesion pathway.” Colors indicate log 2 fold change in gene expression of transcription factors corresponding to each motif. Gray motifs represent transcription factors without differential gene expression among luminal-HR – cells. Right panel shows the fraction of genes (left) and genes annotated within the focal adhesion pathway (right) that contain a chromatin peak with an ESRRG sequence motif (cisBP ESRRG_697). (J) Spatial distribution of epithelial, stromal, immune, and endothelial cells in an example breast tissue region from cis-female (top) and trans-male (bottom) samples. (K) Ratios of stromal to epithelial cells in the epithelial neighborhood (see Figure S8 C) among regions of cis-female and transgender male tissue in CODEX microarray data (p value, Wilcoxon: 0.0052). " width="100%" height="100%">

    Journal: Cell Genomics

    Article Title: The molecular consequences of androgen activity in the human breast

    doi: 10.1016/j.xgen.2023.100272

    Figure Lengend Snippet: Epithelial cells without hormone responsiveness lose contractile functions upon androgen therapy (A) Images from CODEX data showing mammary acini structures from cis-female (top) and trans-male (bottom) tissues marked by expression of ACTA2 (basal cells, purple), TP63 (basal cell nuclei, blue), and KRT8 (luminal cells, green). (B) Average area of acinar structures (left panel) and average area of acini border that was filled with ACTA2 signal (see Figure S7 C and ) among cis-female and trans-male tissues (p values, Wilcoxon: area = 0.026, ACTA2 coverage = 0.012). (C) UMAP of basal cell subclusters in snRNA-seq data (top) and the distribution of trans-male and cis-female cells across them (bottom). (D) RNA expression of ACTA2, OXTR (lactation markers), and TP63 in basal cells of trans-male and cis-female samples (adjusted p values, MAST: ACTA2 = 8.86 × 10 −296 , OXTR = 9.59 × 10 −262 , TP63 = 1.16 × 10 −96 ). (E) Module scores of enriched pathways overlaid on the basal cell UMAP (REAC, Reactome; KEGG, Kyoto Encyclopedia of Genes and Genomes). (F) Right panel shows the enrichment of motifs among unique accessible chromatin peaks from trans-male and cis-female basal cells. Left panel shows the fraction of peaks from the corresponding cells that overlap with the motif. (G) Kernel density estimation of module scores for selected altered structural pathways in luminal-HR – cells (p values, Wilcoxon: KEGG, adherens junction = 4.13 × 10 −285 ; KEGG, focal adhesion = 1.42 × 10 −255 ; KEGG, regulation of actin cytoskeleton <1.42 × 10 −255 ). (H) Average RNA expression (top) of integrin receptors from the “KEGG: regulation of actin cytoskeleton” pathway in luminal-HR – cells (adjusted p values, MAST: ITGA2 = 4.89 × 10 −201 , ITGB8 = 6.40 × 10 −267 ) and average expression of the ITGA2 and ITGB8 ligand FN1 in fibroblast subclusters and lymphatic endothelial cells (bottom) from trans-male and cis-female samples (adjusted p values, MAST: matrix 1 = 1.66 × 10 −54 , matrix 2 = non-significant [n.s.], lipo-f = 1.32 × 10 −16 , vasc-f = n.s., lymph. EC = 3.13 × 10 −99 ). (I) Fisher exact test odds ratio (x axis) and –log 10 p value (y axis) corresponding to enrichment of each motif among the chromatin accessibility peaks for the genes of the "WikiPathways: focal adhesion pathway.” Colors indicate log 2 fold change in gene expression of transcription factors corresponding to each motif. Gray motifs represent transcription factors without differential gene expression among luminal-HR – cells. Right panel shows the fraction of genes (left) and genes annotated within the focal adhesion pathway (right) that contain a chromatin peak with an ESRRG sequence motif (cisBP ESRRG_697). (J) Spatial distribution of epithelial, stromal, immune, and endothelial cells in an example breast tissue region from cis-female (top) and trans-male (bottom) samples. (K) Ratios of stromal to epithelial cells in the epithelial neighborhood (see Figure S8 C) among regions of cis-female and transgender male tissue in CODEX microarray data (p value, Wilcoxon: 0.0052).

    Article Snippet: To study the staining patterns around epithelial cells in detail, we applied morphological operations to specifically isolate individual acini structures within the CODEX tissue microarray images ( C).

    Techniques: Expressing, RNA Expression, Sequencing, Microarray

    Androgen therapy reduces epithelial vascularization through PPARG activity (A) Microscopic images show vascularization of two ductal structures in a cis-female (left) and a trans-male (right) breast tissue in CODEX microarray data. KRT8 (green) marks luminal cells, ACTA2 (purple) marks green cells, and CD31 (red) marks endothelial cells. Arrows point out (1) larger vessels with smooth muscle layer and (2) smaller vessels without smooth muscle layer. (B) UMAP shows vasculature subclusters detected in the snRNA-seq dataset. (left, blood endothelial cells; right, lymphatic endothelial cells; upper-mid, vascular accessory cells). (C) Boxplots show the proportions of vascular subclusters in each sample of the snRNA-seq data, split by gender ID (general linearized model [GLM] fitting a Poisson, p values: vein = 6.72 × 10 −45 , capillary = 5.31 × 10 −77 , artery = 3.58 × 10 −5 , lymph. EC = 1.33 × 10 −22 , and lymph. EC 2 = 0.0071). (D) UMAP (left) shows blood endothelial cells overlaid with scVelo stream plots. The scatterplot shows the ratio of spliced (horizontal axis) and unspliced RNA molecules (vertical axis) of PPARG among vein (blue), capillary (orange), and artery (green) blood endothelial cells. Dashed diagonal indicates the steady-state ratio. Top and bottom arcs indicate the estimated kinetic parameters of PPARG induction and repression, respectively. (E) PPARG GRN module score overlaid on UMAP plot among cis-female (left) and trans-male (right) blood endothelial cells. Barplot shows GRN importance scores of the top five genes coexpressed with PPARG . (F) Volcano plot shows the average log 2 fold change and –log 10 adjusted p value for differential expression of genes within the PPARG module among the trans-male and cis-female blood endothelial cells. Purple data points indicate genes with a chromatin accessibility peak overlapping the PPARG transcription factor sequence motif (CisBP PPARG_676) match. Barplots show the fraction of all genes (left) or genes within PPARG module (right) that contain a chromatin accessibility peak overlapping the PPARG transcription factor sequence motif (purple). (G) Boxplot shows average expression of PPARG in blood endothelial cells of cis-female (purple) and trans-male (orange) samples in snRNA-seq data. (H) Heatmap shows the log 2 fold change in expression of ligand (left)-receptor (right) pairs among cell types and vascular subclusters in the trans-male and cis-female samples. Colors indicate log 2 fold change in expression, and diameter of the circle shows the percent of cis-female cells expressing the gene.

    Journal: Cell Genomics

    Article Title: The molecular consequences of androgen activity in the human breast

    doi: 10.1016/j.xgen.2023.100272

    Figure Lengend Snippet: Androgen therapy reduces epithelial vascularization through PPARG activity (A) Microscopic images show vascularization of two ductal structures in a cis-female (left) and a trans-male (right) breast tissue in CODEX microarray data. KRT8 (green) marks luminal cells, ACTA2 (purple) marks green cells, and CD31 (red) marks endothelial cells. Arrows point out (1) larger vessels with smooth muscle layer and (2) smaller vessels without smooth muscle layer. (B) UMAP shows vasculature subclusters detected in the snRNA-seq dataset. (left, blood endothelial cells; right, lymphatic endothelial cells; upper-mid, vascular accessory cells). (C) Boxplots show the proportions of vascular subclusters in each sample of the snRNA-seq data, split by gender ID (general linearized model [GLM] fitting a Poisson, p values: vein = 6.72 × 10 −45 , capillary = 5.31 × 10 −77 , artery = 3.58 × 10 −5 , lymph. EC = 1.33 × 10 −22 , and lymph. EC 2 = 0.0071). (D) UMAP (left) shows blood endothelial cells overlaid with scVelo stream plots. The scatterplot shows the ratio of spliced (horizontal axis) and unspliced RNA molecules (vertical axis) of PPARG among vein (blue), capillary (orange), and artery (green) blood endothelial cells. Dashed diagonal indicates the steady-state ratio. Top and bottom arcs indicate the estimated kinetic parameters of PPARG induction and repression, respectively. (E) PPARG GRN module score overlaid on UMAP plot among cis-female (left) and trans-male (right) blood endothelial cells. Barplot shows GRN importance scores of the top five genes coexpressed with PPARG . (F) Volcano plot shows the average log 2 fold change and –log 10 adjusted p value for differential expression of genes within the PPARG module among the trans-male and cis-female blood endothelial cells. Purple data points indicate genes with a chromatin accessibility peak overlapping the PPARG transcription factor sequence motif (CisBP PPARG_676) match. Barplots show the fraction of all genes (left) or genes within PPARG module (right) that contain a chromatin accessibility peak overlapping the PPARG transcription factor sequence motif (purple). (G) Boxplot shows average expression of PPARG in blood endothelial cells of cis-female (purple) and trans-male (orange) samples in snRNA-seq data. (H) Heatmap shows the log 2 fold change in expression of ligand (left)-receptor (right) pairs among cell types and vascular subclusters in the trans-male and cis-female samples. Colors indicate log 2 fold change in expression, and diameter of the circle shows the percent of cis-female cells expressing the gene.

    Article Snippet: To study the staining patterns around epithelial cells in detail, we applied morphological operations to specifically isolate individual acini structures within the CODEX tissue microarray images ( C).

    Techniques: Activity Assay, Microarray, Expressing, Sequencing

    Androgen therapy dominates helper T lymphocytes and reduces the presence of innate immunity (A) UMAP showing subclusters of all myeloid (left) and lymphoid (right) cells detected in the snRNA-seq data (CD8. CD8 + T cells; CD4, CD4 + T cells; T-effector, effector T cells; NK, natural killer cells; mono.DC, monocyte-derived dendritic cells; DC, dendritic cells). (B) Boxplots show the fraction of main immune cell subtypes within entire immune compartment in each sample (GLM p values, generalized linear model fitting a Poisson: CD4 = 0.00035, CD8 = 4.035 × 10 −13 , T effector = 0.045, NK = 0.00035, mono.DC = 0.017, macrophage = 0.52, monocyte = 0.055, DC = 0.0001). (C) Boxplot shows the proportion of macrophages within the periphery of epithelial cells in cis-female (purple) and trans-male (orange) tissue regions of the CODEX microarray data (p value, Wilcoxon: 0.003). (D) Kernel density estimates and boxplots show the module scores of immune-relevant Reactome pathways in macrophages of trans-male (orange) and cis-female (purple) samples (p values, Wilcoxon, class I major histocompatibility complex [MHC]-mediated antigen processing/presentation = 8.32 × 10 −17 , clathrin-mediated endocytosis = 3.64 × 10 −21 , toll-like receptor TLR1 TLR2 cascade = 3.89 × 10 −16 ). (E) Boxplots show the average RNA expression of PROS1 in basal cells of cis-female (purple) and trans-male (orange) samples (adjusted p value, MAST: 3.17 × 10 −192 ). (F) Volcano plot shows the average log 2 fold change and –log 10 adjusted p value assessing the differential expression of genes in trans-male macrophages compared with cis-female macrophages. Purple data points indicate scavenger receptors. (G) UMAP shows four immune cell staining sub-classes (macrophage, red; immune endo., green; immune main, blue; immune-epi., orange) according to the staining pattern in CODEX microarray data. Size of the data points indicates the distance to the most proximal epithelial cell. Boxplot (below) summarizes the average distance of each group of immune cells to their most proximal epithelial cell. (H) Microscopic images show staining of luminal (KRT8, green), basal (ACTA2, purple), and immune cells (CD45, red) within a trans-male (top) and a cis-female (bottom) breast tissue in CODEX microarray data. (I) Microscopic image shows IHC staining of luminal (KRT8, red), immune (CD45, green), and T-lymphocyte (CD3, purple) cells within a trans-male (top) and a cis-female (bottom) breast tissue. White cells are double-positive for CD45 and CD3. (J) Boxplot shows the ratio of immune cells (CD45 + ) expressing CD3 to those not expressing CD3 (T lymphocytes versus other immune cells) within the epithelial neighborhood of cis-female (purple) and trans-male (orange) breast tissues of IHC scan regions.

    Journal: Cell Genomics

    Article Title: The molecular consequences of androgen activity in the human breast

    doi: 10.1016/j.xgen.2023.100272

    Figure Lengend Snippet: Androgen therapy dominates helper T lymphocytes and reduces the presence of innate immunity (A) UMAP showing subclusters of all myeloid (left) and lymphoid (right) cells detected in the snRNA-seq data (CD8. CD8 + T cells; CD4, CD4 + T cells; T-effector, effector T cells; NK, natural killer cells; mono.DC, monocyte-derived dendritic cells; DC, dendritic cells). (B) Boxplots show the fraction of main immune cell subtypes within entire immune compartment in each sample (GLM p values, generalized linear model fitting a Poisson: CD4 = 0.00035, CD8 = 4.035 × 10 −13 , T effector = 0.045, NK = 0.00035, mono.DC = 0.017, macrophage = 0.52, monocyte = 0.055, DC = 0.0001). (C) Boxplot shows the proportion of macrophages within the periphery of epithelial cells in cis-female (purple) and trans-male (orange) tissue regions of the CODEX microarray data (p value, Wilcoxon: 0.003). (D) Kernel density estimates and boxplots show the module scores of immune-relevant Reactome pathways in macrophages of trans-male (orange) and cis-female (purple) samples (p values, Wilcoxon, class I major histocompatibility complex [MHC]-mediated antigen processing/presentation = 8.32 × 10 −17 , clathrin-mediated endocytosis = 3.64 × 10 −21 , toll-like receptor TLR1 TLR2 cascade = 3.89 × 10 −16 ). (E) Boxplots show the average RNA expression of PROS1 in basal cells of cis-female (purple) and trans-male (orange) samples (adjusted p value, MAST: 3.17 × 10 −192 ). (F) Volcano plot shows the average log 2 fold change and –log 10 adjusted p value assessing the differential expression of genes in trans-male macrophages compared with cis-female macrophages. Purple data points indicate scavenger receptors. (G) UMAP shows four immune cell staining sub-classes (macrophage, red; immune endo., green; immune main, blue; immune-epi., orange) according to the staining pattern in CODEX microarray data. Size of the data points indicates the distance to the most proximal epithelial cell. Boxplot (below) summarizes the average distance of each group of immune cells to their most proximal epithelial cell. (H) Microscopic images show staining of luminal (KRT8, green), basal (ACTA2, purple), and immune cells (CD45, red) within a trans-male (top) and a cis-female (bottom) breast tissue in CODEX microarray data. (I) Microscopic image shows IHC staining of luminal (KRT8, red), immune (CD45, green), and T-lymphocyte (CD3, purple) cells within a trans-male (top) and a cis-female (bottom) breast tissue. White cells are double-positive for CD45 and CD3. (J) Boxplot shows the ratio of immune cells (CD45 + ) expressing CD3 to those not expressing CD3 (T lymphocytes versus other immune cells) within the epithelial neighborhood of cis-female (purple) and trans-male (orange) breast tissues of IHC scan regions.

    Article Snippet: To study the staining patterns around epithelial cells in detail, we applied morphological operations to specifically isolate individual acini structures within the CODEX tissue microarray images ( C).

    Techniques: Derivative Assay, Microarray, RNA Expression, Expressing, Staining, Immunohistochemistry

    Testosterone induces PI3K pathway alterations with adipocytes showing distinct metabolic adaptations (A) Heatmap showing the log 2 fold change in RNA expression of PI3K activating receptors (taken from 7 cell types) that have an NR4A1 motif in an enhancer (>4 cell types). Horizontal axis shows the odds ratio (one-sided Fisher’s exact test) comparing frequency of selected genes in the pathway versus background and vertical axis shows –log 10 p value of Fisher’s exact test (FDR < 0.05 = red, p value < 0.05 = yellow, n.s. = gray (PID, Pathway Interaction Database; WP, WikiPathways). (D) Module scores for the WikiPathways "WP: insulin signaling pathway" in all cell types, split by cis-female and trans-male origin (p value in adipocytes, Wilcoxon: 7.59 × 10 −4 ). (E) Representative images of computational segmentation of lipid vacuoles (left, see ), and resulting average area of adipocyte vacuoles per IHC scan region (p value Wilcoxon: 0.00059). (F) Sample averages of AZGP1 RNA expression in each cell type in trans-male and cis-female samples (adjusted p values, MAST: adipocyte = 5.95 × 10 −12 , basal = 2.42 × 10 −70 , blood EC = 8.67 × 10 −83 , fibroblast = n.s., luminal-HR − = 4.54 × 10 −302 , luminal-HR + = 0.00, lymph. EC = 2.83 × 10 −15 , lymphoid cells = 5.46 × 10 −12 , myeloid cells = 9.23 × 10 −8 ). (G) Microscopic CODEX image of a duct stained against AR (red), AZGP1 (green), and ACTA2 (purple) in a trans-male (top) and cis-female (bottom) breast tissue region of the tissue microarray. (H) GPAM co-expression module (GRNboost2, 95 th percentile, p value, Wilcoxon: <2.22 × 10 −16 ) score in cis-female and trans-male adipocytes. (I) TCF7L2 expression in trans-male and cis-female adipocytes (adjusted p value, MAST: 3.48 × 10 −105 ). (J) Differential expression of transcription factors in trans-male compared with cis-female adipocytes. Horizontal axis shows log 2 fold change in expression and the vertical axis shows –log 10 adjusted p value. Purple data points indicate transcription factors with accessible chromatin matching the AR sequence motif (CisBP AR_689). (K) Microscopic IHC image showing staining against nuclei (DAPI, blue), adipocytes (PLIN1, green), and TCF7L2 (purple). Boxplot shows the median staining intensity of TCF7L2 among IHC scan regions of cis-female and trans-male adipocytes (p value, Wilcoxon: 0.0069). " width="100%" height="100%">

    Journal: Cell Genomics

    Article Title: The molecular consequences of androgen activity in the human breast

    doi: 10.1016/j.xgen.2023.100272

    Figure Lengend Snippet: Testosterone induces PI3K pathway alterations with adipocytes showing distinct metabolic adaptations (A) Heatmap showing the log 2 fold change in RNA expression of PI3K activating receptors (taken from "KEGG: PI3K-Akt signaling pathway") among 10 breast cell types identified. The circle diameter indicates the fraction of cis-female cells of the cell type expressing the receptor. INS, circulating insulin secreted in the pancreas. (B) Heatmap showing log 2 fold changes in RNA expression of KEGG: PI3K-Akt signaling pathway downstream transcription factors within 10 breast cell types identified. (C) Pathway enrichment of tissue wide upregulated genes (>7 cell types) that have an NR4A1 motif in an enhancer (>4 cell types). Horizontal axis shows the odds ratio (one-sided Fisher’s exact test) comparing frequency of selected genes in the pathway versus background and vertical axis shows –log 10 p value of Fisher’s exact test (FDR < 0.05 = red, p value < 0.05 = yellow, n.s. = gray (PID, Pathway Interaction Database; WP, WikiPathways). (D) Module scores for the WikiPathways "WP: insulin signaling pathway" in all cell types, split by cis-female and trans-male origin (p value in adipocytes, Wilcoxon: 7.59 × 10 −4 ). (E) Representative images of computational segmentation of lipid vacuoles (left, see ), and resulting average area of adipocyte vacuoles per IHC scan region (p value Wilcoxon: 0.00059). (F) Sample averages of AZGP1 RNA expression in each cell type in trans-male and cis-female samples (adjusted p values, MAST: adipocyte = 5.95 × 10 −12 , basal = 2.42 × 10 −70 , blood EC = 8.67 × 10 −83 , fibroblast = n.s., luminal-HR − = 4.54 × 10 −302 , luminal-HR + = 0.00, lymph. EC = 2.83 × 10 −15 , lymphoid cells = 5.46 × 10 −12 , myeloid cells = 9.23 × 10 −8 ). (G) Microscopic CODEX image of a duct stained against AR (red), AZGP1 (green), and ACTA2 (purple) in a trans-male (top) and cis-female (bottom) breast tissue region of the tissue microarray. (H) GPAM co-expression module (GRNboost2, 95 th percentile, p value, Wilcoxon: <2.22 × 10 −16 ) score in cis-female and trans-male adipocytes. (I) TCF7L2 expression in trans-male and cis-female adipocytes (adjusted p value, MAST: 3.48 × 10 −105 ). (J) Differential expression of transcription factors in trans-male compared with cis-female adipocytes. Horizontal axis shows log 2 fold change in expression and the vertical axis shows –log 10 adjusted p value. Purple data points indicate transcription factors with accessible chromatin matching the AR sequence motif (CisBP AR_689). (K) Microscopic IHC image showing staining against nuclei (DAPI, blue), adipocytes (PLIN1, green), and TCF7L2 (purple). Boxplot shows the median staining intensity of TCF7L2 among IHC scan regions of cis-female and trans-male adipocytes (p value, Wilcoxon: 0.0069).

    Article Snippet: To study the staining patterns around epithelial cells in detail, we applied morphological operations to specifically isolate individual acini structures within the CODEX tissue microarray images ( C).

    Techniques: RNA Expression, Expressing, Staining, Microarray, Sequencing