Journal: The Journal of Pathology

Article Title: Spatial transcriptomics exploration of the primary neuroblastoma microenvironment in archived FFPE samples unveils novel paracrine interactions

doi: 10.1002/path.6457

Figure Lengend Snippet: Spatial transcriptomic profiling of six tumor sections obtained from two NB patients. (A) The Visium spatial transcriptomics platform was used to profile three tumors (two sections each) from two NB patients (NB1 and NB2). Both patients received prior chemotherapy (NB1Post and NB2Post) and for NB1 we also profiled pretherapy tumor materials (NB1Pre). Main genomic alterations indicated. Created in BioRender. Van den Eynden, J. (2025) https://BioRender.com/q78y390 . (B) Hematoxylin and eosin (H&E) staining of the six tumor sections that were used in this study. (C) Clustering and annotation of seven main spatial clusters across the six samples. Cluster annotations were based on the most representative cell type, as predicted from marker gene expression, enrichment analyses, and similarities to single‐cell data. See supplementary material, Figures and for details. (D) Dot plots showing relative expression (colors) and proportional expression in the spots (sizes) of the top five representative genes for each cluster, as indicated. Genes derived from the leading edges from the GSEA shown in supplementary material, Figure . (E) UMAP plots showing the main clusters corresponding to each tumor (left), the CNV score, which is representative for the overall copy number variability (center), and the expected proportion of NE cells, as predicted from a deconvolution analysis using the NBAtlas as a reference (right). NE, neuroendocrine cells; CAF, cancer‐associated fibroblasts; Schwann, Schwann cells; Macro, macrophages; Endo, endothelial cells; Plasma, plasma cells; AC‐like, adrenocortical‐like.

Article Snippet: To better understand the cellular composition of the NB tumor microenvironment (TME) and the specific spatial interactions between these previously identified cell types and states, we performed whole‐transcriptome spatial profiling of six sections from three FFPE‐archived NB tumors using the Visium Spatial Gene Expression platform (10x Genomics, Pleasanton, CA, USA).

Techniques: Staining, Marker, Gene Expression, Expressing, Derivative Assay, Clinical Proteomics

Journal: Pathology, research and practice

Article Title: Spatial transcriptome analysis of lung squamous cell carcinoma arising from interstitial pneumonia provides insights into tumor heterogeneity.

doi: 10.1016/j.prp.2024.155805

Figure Lengend Snippet: Fig. 1. Spatial transcriptome analysis of lung squamous cell carcinoma (LUSC) tissue. (A) Schematic workflow of spatial transcriptome analysis. (B) Results of spatial transcriptome analysis of an LUSC tissue specimen from a patient with interstitial pneumonia. Hematoxylin and eosin staining (left), gene expression profiles of 2104 barcoded spots (middle), and Uniform Manifold Approximation and Projection (UMAP) visualization (right) are presented. Whole tissues were classified via graph-based clustering. (C) Histological image of cancerous areas, colored orange (left), and an image representing the two clusters identified as representative of LUSC (right). (D) The top seven upregulated pathways in cluster 2 compared to cluster 5 (left), and the top nine pathways upregulated in cluster 5 compared to cluster 2 (right). Only pathways where the false discovery rate (FDR) was < 1.0e-5 are presented.

Article Snippet: Among them, tissue from one patient was subjected to spatial transcriptome analysis (Visium; 10x Genomics, Pleasanton, CA, USA).

Techniques: Staining, Gene Expression

Journal: Pathology, research and practice

Article Title: Spatial transcriptome analysis of lung squamous cell carcinoma arising from interstitial pneumonia provides insights into tumor heterogeneity.

doi: 10.1016/j.prp.2024.155805

Figure Lengend Snippet: Fig. 6. Analysis of tumor-associated macrophages with spatial transcriptome data. (A) Clusters of LUSC (clusters 2, 5) and clusters histologically including inflammatory cells in the tumor microenvironment (clusters 3, 4, 7) in the transcriptome data. (B) Bubble heatmap showing the expression levels of the macrophage marker genes in clusters 3, 4, and 7. (C) Bubble heatmap showing the expression levels of the M2 macrophage marker genes in clusters 3, 4, and 7. (D) Bubble heatmap showing the expression levels of the vascular endothelial cell marker genes in clusters 3, 4, and 7.

Article Snippet: Among them, tissue from one patient was subjected to spatial transcriptome analysis (Visium; 10x Genomics, Pleasanton, CA, USA).

Techniques: Expressing, Marker

Journal: bioRxiv

Article Title: Development of an Integrated Single-Cell and Spatial Transcriptomics Atlas of Healthy Human Skin Focusing on the Pilosebaceous Unit

doi: 10.1101/2025.09.09.675235

Figure Lengend Snippet: Healthy human skin scRNA-seq datasets were collected and curated. Datasets were divided into PSU-containing and PSU-free samples. PSU-containing datasets underwent standardized reanalysis and processing, and integration performance was benchmarked. The most suitable tool was used to integrate these datasets into the HSCA core, followed by cell type annotation. Through transfer learning, 21 additional PSU-free datasets were incorporated, resulting in the HSCA extended (160 subjects, 177 samples, 110 cell types, >800,000 cells). Gene marker signatures were validated and refined using Visium HD spatial transcriptomics. Downstream analyses included the identification of novel and rare cell types, functional enrichment, and cell–cell communication analysis.

Article Snippet: To validate the spatial organization of the PSU defined in our core atlas and to assess additional relevant cell types, we generated two 10X Visium HD spatial transcriptomics sections (8 μm spot diameter) derived from healthy facial skin of a 48-year-old White female donor ( ).

Techniques: Marker, Functional Assay

Journal: bioRxiv

Article Title: Development of an Integrated Single-Cell and Spatial Transcriptomics Atlas of Healthy Human Skin Focusing on the Pilosebaceous Unit

doi: 10.1101/2025.09.09.675235

Figure Lengend Snippet: ( a , b ) Two 10X Genomics Visium HD spatial transcriptomic sections (8 µm spot diameter) derived from healthy facial skin of a 48-year-old White female donor (temporal region). Spots were annotated with marker gene expression, and the derived cell types are overlaid on the H&E sections. The bottom-right inset of each panel displays the number of detected genes per spot (maximum 3,683 in D1 and 3,199 in D2). Bar = 250 µm. Abbreviations: see Supplementary Table 3.

Article Snippet: To validate the spatial organization of the PSU defined in our core atlas and to assess additional relevant cell types, we generated two 10X Visium HD spatial transcriptomics sections (8 μm spot diameter) derived from healthy facial skin of a 48-year-old White female donor ( ).

Techniques: Derivative Assay, Marker, Gene Expression

Journal: bioRxiv

Article Title: Development of an Integrated Single-Cell and Spatial Transcriptomics Atlas of Healthy Human Skin Focusing on the Pilosebaceous Unit

doi: 10.1101/2025.09.09.675235

Figure Lengend Snippet: (a) Illustrative schematic of hair bulb anatomy. (b) Visium HD spots corresponding to the hair bulb overlaid on the tissue section. ( c ) Spatial feature plot of Dermal papilla markers. ( d ) Dot plot showing marker gene expression across major bulb cell types. ( e ) Catagen hair follicle section (D2) highlighting cell clustering. ( f ) Violin plots of gene expression in the catagen follicle cluster, reflecting hair-cycle-specific transcriptional dynamics. ( g ) Heatmap of spatial ligand-receptor crosstalk between follicular compartments inferred by CellChat. Bar = 8 µm. Abbreviations: see Supplementary Table 3.

Article Snippet: To validate the spatial organization of the PSU defined in our core atlas and to assess additional relevant cell types, we generated two 10X Visium HD spatial transcriptomics sections (8 μm spot diameter) derived from healthy facial skin of a 48-year-old White female donor ( ).

Techniques: Marker, Gene Expression

Journal: bioRxiv

Article Title: Development of an Integrated Single-Cell and Spatial Transcriptomics Atlas of Healthy Human Skin Focusing on the Pilosebaceous Unit

doi: 10.1101/2025.09.09.675235

Figure Lengend Snippet: ( a ) UMAP of the HSCA core restricted to 8,572 cells from lower follicular compartments. ( b ) RCTD deconvolution of Visium HD data (from ) using the HSCA core, showing concordant cell type gene signatures. ( c , d ) Violin plots of marker gene expression for the SHG in the HSCA core (c) and in Visium HD (d). ( e ) PHATE embedding of sebaceous gland cells illustrating differentiation trajectories. ( f ) Pie chart summarizing the relative abundance of sebocyte maturation stages in the HSCA core. ( g ) Pie chart showing dataset origin of sebaceous cells across maturation stages. ( h ) Violin plots of PTN and C1QTNF12 expression in sebaceous progenitors and the JZ in the HSCA core. ( i ) Independent spatial validation of PTN and C1QTNF12 expression in Visium HD sections. Abbreviations: see Supplementary Table 3.

Article Snippet: To validate the spatial organization of the PSU defined in our core atlas and to assess additional relevant cell types, we generated two 10X Visium HD spatial transcriptomics sections (8 μm spot diameter) derived from healthy facial skin of a 48-year-old White female donor ( ).

Techniques: Marker, Gene Expression, Expressing, Biomarker Discovery

Journal: bioRxiv

Article Title: Development of an Integrated Single-Cell and Spatial Transcriptomics Atlas of Healthy Human Skin Focusing on the Pilosebaceous Unit

doi: 10.1101/2025.09.09.675235

Figure Lengend Snippet: (a) Feature plot of CCER2 expression highlighting the Merkel cell cluster in the HSCA core. (b) Gene signature of the cluster, including the characteristic KRT20 marker for Merkel cells. (c) Functional enrichment analysis of the Merkel cell gene signature, visualized as a dot plot. ( d , e ) Spatial visualization of CCER2 expression in the bulge region of hair follicles in Visium HD sections.

Article Snippet: To validate the spatial organization of the PSU defined in our core atlas and to assess additional relevant cell types, we generated two 10X Visium HD spatial transcriptomics sections (8 μm spot diameter) derived from healthy facial skin of a 48-year-old White female donor ( ).

Techniques: Expressing, Marker, Functional Assay

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Acquisition of paired breast cancer spatial transcriptomics datasets and histology images from 10x Visium and Xenium. B. Co-registration of Visium and Xenium histology slides into a common coordinate system. The green box highlights the overlapping region retained between the two technologies. C. Rasterization of gene counts onto a uniform grid matched to Visium spot resolution, followed by extraction of the overlapping tissue region. Expression is visualized as patches. D. Training of deep learning models to predict per-patch gene expression from histology image patches. E. Performance evaluation on held-out replicates, comparison across technologies, and ablation experiments of inputs.

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.

Techniques: Extraction, Expressing, Gene Expression, Comparison

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Histogram showing the distribution of Pearson correlation coefficients for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. Results are computed on the held-out test set and represent the average performance across five independently trained models. B. Scatterplot comparing the Pearson correlation coefficients of predictions from Visium and Xenium data. The gray dotted line denotes x=y, and select genes corresponding to (C) labeled. C. Representative examples of ground truth and predicted gene expression for HDC , ANKRD30A , AHSP , and GZMK in both the Visium and Xenium datasets. Predicted gene expressions are visualized for the full dataset, while the performance metrics (PCC and normalized rMSE) are computed from the held-out test set only.

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.

Techniques: Gene Expression, Labeling

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram showing the distribution of normalized rMSE for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean rMSE, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the normalized rMSE of predictions from Visium and Xenium data, based on the test set and averaged over five models. The gray dotted line denotes x=y.

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.

Techniques: Gene Expression

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram of Pearson correlation coefficients for gene expression predictions using Visium and Xenium data with the Visium image. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. B. Scatterplot comparing PCC values from Visium and Xenium data with the Visium image on the test set, averaged across five models. The gray dotted line denotes x=y. C. Histogram of PCC values for predictions using Visium and Xenium data with the Xenium image. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. D. Scatterplot comparing PCC values from Visium and Xenium data with the Xenium image on the test set, averaged across five models. The gray dotted line denotes x=y. E. Scatterplot comparing PCC values between Xenium, an increasing amount of sparsity in the Xenium dataset, and the Visium results on the test and replicate 2 Xenium data. The dotted line indicates the dataset used, and error bars represent the standard error across five runs. The histogram below denotes the total number of genes used to calculate the mean PCC. F. Scatterplot comparing PCC values between Xenium, an increasing amount of Poisson noise in the Xenium dataset, and the Visium results on the test and replicate 2 Xenium data. The dotted line indicates the dataset used, and error bars represent the standard error across five runs. G. Scatterplot comparing PCC values between Visium, various imputation methods on the Visium dataset, and the Xenium results on the test and replicate 2 Xenium data. The dotted line indicates the dataset used, and error bars represent the standard error across five runs.

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.

Techniques: Gene Expression

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Scatterplots of normalized rMSE for models trained on varied molecular inputs, evaluated on the held-out test set and averaged across five independent runs, using (A) the Visium histology image and (B) the Xenium histology image. The gray dotted line denotes x=y.

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.

Techniques:

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Violin plots of the per-patch fraction of zero counts in Visium and Xenium molecular data. The shape of each violin reflects the density of values along the y-axis, and the overlaid boxplot indicates the median and the 25th and 75th percentiles.

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.

Techniques:

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram showing the distribution of Pearson correlation coefficients for gene expression predictions using Visium data with the Visium and Xenium images. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the Pearson correlation coefficients of predictions from Visium data with the Visium and Xenium images, based on the test set and averaged over five models. The gray dotted line denotes x=y. C. Histogram showing the distribution of Pearson correlation coefficients for gene expression predictions using the Xenium data with the Visium and Xenium image. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. D. Scatterplot comparing the Pearson correlation coefficients of predictions from Xenium data with the Visium and Xenium image, based on the test set and averaged over five models. The gray dotted line denotes x=y. E. Scatterplot of mean Pearson correlation coefficients on both the test set and the Replicate 2 Xenium section, comparing the Xenium, Xenium images with increasing Gaussian blur, and Visium results (all applied with the same blur levels). The dotted line indicates the dataset used, and error bars represent the standard error of the mean across five independent model runs. F. Grad-CAM heatmaps for two select genes: CD4 (T-cell marker) and PDGFRA (fibroblast marker).

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.

Techniques: Gene Expression, Marker

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Scatterplots of normalized RMSE for models trained on varied image inputs, evaluated on the held-out test set and averaged across five independent runs, using (A) the Visium molecular data and (B) the Xenium molecular data. The gray dotted line denotes x=y.

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.

Techniques:

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram showing the distribution of Pearson correlation for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean rMSE, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the Pearson correlation of predictions from Visium and Xenium data, based on the test set and averaged over five models. The gray dotted line denotes x=y. C. Histogram showing the distribution of normalized rMSE for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean rMSE, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the normalized rMSE of predictions from Visium and Xenium data, based on the test set and averaged over five models. The gray dotted line denotes x=y.

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.

Techniques: Gene Expression