Journal: bioRxiv

Article Title: fastCNV: Fast and accurate copy number variation prediction from High-Definition Spatial Transcriptomics and scRNA-Seq Data

doi: 10.1101/2025.10.22.683855

Figure Lengend Snippet:

Article Snippet: Notably, fastCNV enables, for the first time, the analysis of CNVs from the Visium HD spatial transcriptomics technology.

Techniques:

Journal: bioRxiv

Article Title: fastCNV: Fast and accurate copy number variation prediction from High-Definition Spatial Transcriptomics and scRNA-Seq Data

doi: 10.1101/2025.10.22.683855

Figure Lengend Snippet: Illustration of subclonal heterogeneity in Spatial Transcriptomics colon cancer data. Analysis of the SN048_A121573_Rep1 colon cancer Visium ST sample from Valdeolivas et al series. (a) H&E coloration of the tissue slide. (b) Spots of the Visium ST slide are colored according to the histological annotation (tumor: red, non-tumor: blue, immune cells: green). (c) Pangenomic heatmap showing the (spots x genomic regions) matrix of fastCNV denoised CNV scores, with spots ordered according to CNV clusters (1 to 3). The histological annotations per spot are given on the right. (d) Heatmap displaying discretized CNV events by chromosome arm (copy gain: red, copy loss: blue, diploid status: white) across CNV clusters after curation. (e) Phylogenetic tree based on CNV clusters 1 to 3. Branches are annotated using curated CNV events. Spots of the Visium ST slide are colored according to (f) CNV clusters (1: red, 2: green, 3: blue) and (g) pangenomic CNV fraction (low: white, high: blue).

Article Snippet: Notably, fastCNV enables, for the first time, the analysis of CNVs from the Visium HD spatial transcriptomics technology.

Techniques:

Journal: bioRxiv

Article Title: fastCNV: Fast and accurate copy number variation prediction from High-Definition Spatial Transcriptomics and scRNA-Seq Data

doi: 10.1101/2025.10.22.683855

Figure Lengend Snippet: Illustration of subclonal heterogenetity prior to CNV curation on colon cancer ST sample. Analysis of the SN048_A121573_Rep1 colon cancer Visium ST sample from Valdeolivas et al series. (a) Heatmap displaying discretized CNV events per chromosome arm (copy gain: red, copy loss: blue, diploid status: white) across CNV clusters (1 to 3) before curation. (b) Phylogenetic tree based on CNV clusters 1 to 3, with branches annotated using discretized chromosome arm level CNV events, before curation.

Article Snippet: Notably, fastCNV enables, for the first time, the analysis of CNVs from the Visium HD spatial transcriptomics technology.

Techniques:

Journal: bioRxiv

Article Title: fastCNV: Fast and accurate copy number variation prediction from High-Definition Spatial Transcriptomics and scRNA-Seq Data

doi: 10.1101/2025.10.22.683855

Figure Lengend Snippet: Illustration of subclonal heterogeneity in Visium HD Breast cancer data Analysis of the Visium_HD_FF_Human_Breast_Cancer Visium HD sample from the 10x Genomics database. (a) Spots of the Visium HD slide are colored according to tumor status (tumor: blue, non-tumor: pink). (b) Pangenomic heatmap showing the (spots x genomic regions) matrix of fastCNV denoised CNV scores, with spots ordered according to CNV clusters (1 to 6). The histological annotations per spot are given on the right. (c) Boxplots showing the pangenomic CNV fraction per CNV cluster. (d) Spots of the Visium HD slide are colored according to the CNV clusters (1 to 6). (e) H&E-stained slide, with contours focusing on regions related to distinct histologies. Spots of the Visium HD slide are colored according to (f) the pangenomic CNV fraction and (g) the CNV status for chromosome 11 arm q. (h) Phylogenetic tree based on CNV clusters 1 to 6. Branches are annotated using curated CNV events at the chromosome arm level.

Article Snippet: Notably, fastCNV enables, for the first time, the analysis of CNVs from the Visium HD spatial transcriptomics technology.

Techniques: Staining

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

Journal: mBio

Article Title: A spatial transcriptomic atlas of the host response to oropharyngeal candidiasis

doi: 10.1128/mbio.00849-25

Figure Lengend Snippet: Major cell type annotation. Spatially resolved transcriptome ( A ) and t-SNE plots ( B ) showing representation of five major cell types in the tongue tissue. ( C ) Cell Chat software revealed the number and strength of inferred cellular signaling interactions from the spatial transcriptomics data.

Article Snippet: To analyze the microenvironment during OPC, we employed the 10× Genomics Visium spatial transcriptomics technology on frozen tissue sections ( n = 4) from tongues of normal and C. albicans -infected Balb/c mice at 60 h of OPC (hereby referred to as day 2, for ease of representation).

Techniques: Software

Journal: Cancer Discovery

Article Title: TREM2-Expressing Multinucleated Giant Macrophages Are a Biomarker of Good Prognosis in Head and Neck Squamous Cell Carcinoma

doi: 10.1158/2159-8290.CD-24-0018

Figure Lengend Snippet: Spatial transcriptomics reveals a unique MGC signature. A, Low magnification of a representative HES section of a carcinoma selected for spatial transcriptomic analysis. Inset, high magnification of an MGC. B, Same HES section with the overlay of the spots analyzed by Visium technology. One spot is covering a single MGC. C, Low magnification of a representative HES of an MGC High carcinoma from a patient in the GR cohort. D, Overlay of the seven cell populations analyzed by unsupervised clustering. E, Same HES section showing pathologist annotations of the tumor area (blue) and the MGC (yellow). F, Low magnification of a representative HES of an MGC Low carcinoma from a patient in the GR cohort. G, Overlay of the seven cell populations analyzed by unsupervised clustering. H, Same HES section showing pathologist annotations of the tumor area (blue). I, Projection of the Visium spots onto a UMAP space; spots from nine different patients. J, Histograms showing the number of spots capturing the different cell types in MGC High and MGC Low carcinomas. K, Volcano plot showing MGC RNA signature extracted from the DEG analysis between supervised MGC spots and all the other non-MGC spots. L, Volcano plot showing the DEG between MGC High and MGC Low tumors from patients in TCGA cohort ( n = 108 patients). M, GSEA plot showing the enrichment of the Visium MGC signature in MGC High vs. MGC Low patients classified from TCGA. The green line represents the running enrichment score for the MGC signature, with the peak indicating maximum enrichment. The normalized enrichment score (NES) is 3.24, and the P value is 0.000522, demonstrating significant enrichment of the MGC signature in MGC High patients. The barcode plot shows the positions of the MGC signature genes within the ranked list of genes from the bulk RNA sequencing data, with a higher density of genes towards the left, indicating higher enrichment.

Article Snippet: We exploited Visium spatial transcriptomic technology (10x Genomics) to analyze the single giant cell transcriptomes of MGC, on formalin-fixed, paraffin-embedded (FFPE) HNSCC tumor sections ( and ).

Techniques: RNA Sequencing