Journal: Science immunology

Article Title: Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems

doi: 10.1126/sciimmunol.abq7991

Figure Lengend Snippet: (A) Schematic of spatial transcriptomics study workflow. Table S1 contains metadata for each sample. (B) Schematic of skin, representative hematoxylin-eosin (H&E) image and corresponding ST plot (left-to-right). Scale bar = 440μm (C) UMAP visualization of 3,815 spots colored by cluster obtained from healthy skin samples (N=3, n=5). (D) Composition plots displaying relative abundance of each cluster by sample. Note up to two samples (labeled S) were collected from each Healthy Volunteer (HV). Replicate arrays are labeled “R” along the X axis. (E) Integration with a publicly-sourced single cell RNA-seq data set (dataset 1) with a representative ST spatial feature plot. See Figure S4 for UMAP of annotated cell type clusters. SMC=smooth muscle cell. Scale bar = 520μm (F) Multimodal intersection analysis (MIA) of overlap between data from datasets 1 and 2 and our ST-generated clusters. A sample hypergeometric distribution of keratinocyte cluster from dataset 1 and our epidermis cluster (cluster 6). MIA enrichment heatmaps of non-immune cell types in dataset 1 (G) and dataset 2 (H) and ST clusters from healthy skin. The X axis denotes the scRNA seq-identified cell types while the Y axis represents the ST-generated clusters. Differentiated keratinocytes (Diff KC) lymphatic endothelium (LE), proliferating keratinocytes (Prolif KC), vascular endothelium (VE), keratinocyte (KC). (I) MIA heatmap showing the enrichment of scRNA-seq-identified adipose- cell types from Hildreth et al. within pooled healthy skin ST clusters. (J) KEGG pathway analysis of the adipose cluster (cluster 2).

Article Snippet: Spatial Transcriptomics (ST) faithfully maps gene expression in healthy human skin We developed a human skin specific protocol for 10X Genomics Visium platform and performed ST on 25 skin samples collected from 3 healthy controls and 11 patients with PsO/PsA ( , figs. S1, A to C , S14A and table S1 ).

Techniques: Labeling, RNA Sequencing, Generated

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Overview of the single-cell and spatial data generated from TB-diseased and control lungs. (A) Schematic showing the experimental flow for the isolation of cells from human lung tissues, generation of single-cell libraries using Seq-Well S 3 . Four TB-negative and nine TB-positive lung samples were processed through scRNA-seq. Shown adjacent to the process flow is a low-dimensional embedding (UMAP) of the 19,632 cells passing quality control annotated with high-level cell types (middle) or detailed cell subtype (right). (B) 10x Visium platform workflow for spatial transcriptomics profiling on FFPE samples from TB-diseased lung resections. 21 of these samples come from current TB patients with detectable M.tb ; 9 came from post-TB patient, where bacteria are no longer detected in BAL TB culture after infection. Samples contain either granulomas, iBALTs, or lung LNs, representing different pathological states.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Generated, Control, Isolation, Bacteria, Infection

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Spatial transcriptomics on TB-infected human lung samples and single-cell deconvolution. (A) H&E staining on all 30 lung samples from patients previously infected with TB. Scale bars: 800 μm. Identical images for pid_0037, pid_177, pid_0186, pid_187, pid_0192, pid_199, pid_0209, and pid_304. (B) Examples of manual annotation on granuloma structures on H&E staining images. Scale bars: 800 μm.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Infection, Staining

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Single-cell transcriptomic reveals heterogeneity within neutrophil populations with disease-specific difference. (A) Neutrophil ( n = 2,963) subclustering reveals three subclusters (left), also colored by patient ID (middle) and disease condition (right). (B) Volcano plot of differential gene expression results of each neutrophil subcluster compared with the rest. Y axis shows −log10 (BH-adjusted P value); x axis shows log2 fold change between cells in subcluster and outside the subcluster. (C) Heatmap of subtype top 10 differentially expressed (DE) genes in each of the neutrophil subcluster. (D) Expression of marker genes in neutrophil subclusters by disease conditions. (E) Fisher’s exact test on abundance of detailed neutrophil subclusters between TB conditions. Statistical annotations: fold-change >2 (ΔΔ). (F) Cell2loc imputed neutrophil abundance distribution on the Visium dataset grouped by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance at each Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. ****: P < 0.0001.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Gene Expression, Expressing, Marker, MANN-WHITNEY

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Single-cell transcriptomic reveals heterogeneity within monocyte and macrophage populations with disease-specific difference. (A) Monocyte/macrophage ( n = 8,318) subclustering reveals 10 subclusters (left), also colored by patient ID (middle) and disease condition (right). (B) Heatmap of subtype top 10 DE genes in each of the monocyte/macrophage subcluster. (C) Expression of marker genes in monocyte/macrophage subclusters by disease conditions. (D) Two-sided Fisher’s exact test on abundance of detailed macrophage (left) and monocyte (right) subclusters between TB conditions. Holm’s method was applied to adjust P values for multiple-testing correction. Statistical annotations: P value < 0.05 (*), P value < 0.01 (**), P value < 0.001 (***), fold-change >1 (Δ), fold-change >2 (ΔΔ), and fold-change <1 (∇). (E) Cell2loc imputed macrophage (left) and monocyte (right) abundance distribution on the Visium dataset grouped by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance at each Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. Statistical annotations: P value < 0.0001 (****). (F) Similar to E, but grouped by TB status and HIV status.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Expressing, Marker, MANN-WHITNEY

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Deconvolution of bulk human LN dataset and fibroblast in spatial and single-cell dataset. (A) Dot plot showing distribution of cell type proportion from deconvolution results on each bulk RNA-seq human LN TB granuloma sample, separated by cell type and colored by TB conditions. Only cell types with significant difference between TB conditions are shown. Two-sided T test with Bonferroni correction was used to compare the means. Statistical annotations: P value < 0.05 (*) and P value < 0.01 (**). (B) Cell2loc imputed fibroblast abundance distribution on the Visium dataset group by TB and granuloma status (Materials and methods). The 5% quantile of the estimated posterior distribution of cell abundance per Visium spot is displayed, representing the value of cell abundance that the model has high confidence in. Two-sided Mann–Whitney U test without correction were used for statistical testing. P value < 0.0001 (****); P value > 0.05 (ns). (C) Same as B, but grouped by HIV and TB status. (D) Bar plot of patient distribution in each fibroblast subcluster. (E) UMAP embedding of fibroblasts colored by HIV status of the sample.

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: RNA Sequencing, MANN-WHITNEY

Journal: The Journal of Experimental Medicine

Article Title: Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

doi: 10.1084/jem.20251067

Figure Lengend Snippet: Spatial transcriptomics analysis on post- and current TB lung resections. (A) Heatmap showing the expression of human TB-myofibroblast gene signature and SPP1 + CHI3L1 + macrophage markers on selective tissue slides from patients who are post-TB (top) or current TB (bottom), alongside paired H&E staining (these H&E stains are also shown in together with those other samples used for spatial transcriptomics not shown here). (B) Distribution of human TB-myofibroblast signature expression on the spatial cohort. HIV statuses are shown in different shades of blue for positive or negative. Two-sided Mann–Whitney U test without correction was used for statistical testing. Statistical annotation: P value < 0.0001 (****). (C) Distribution of SPP1 + CHI3L1 + macrophage markers and human TB-myofibroblast signature on the spatial data across all Visium spots. Left two panels: Manual segmentation of the granuloma structure was done to allow separation of the Visium slide into three different regions: in granuloma, on granuloma border (cuff), and outside of granuloma (Materials and methods). Right two panels: The same as left panels with the exception that “on border” = True means on granuloma cuff and False means the rest. Two-sided Mann–Whitney U test without correction was used for statistical testing. Statistical annotation: P value < 0.0001 (****). (D) Correlation between human TB-myofibroblast signature and all macrophage subpopulations’ markers. Each circle represents a Visium sample. Boxplot of the Pearson’s r distribution is shown for each macrophage subtype. Mann–Whitney U test without correction were used for statistical testing. Statistical annotation: P value < 0.0001 (****). (E) Spatially informed ligand–receptor (L–R) analysis using LIANA+ on Visium samples. Examples are shown where SPP1(L)–CD44(R) interactions are being nominated as top L–R pairs. H&E overlaid with pathology annotation for granuloma structures are shown next to heatmap of L–R interaction scores, which are calculated at each Visium spot using spatially weighted Cosine similarity (Materials and methods).

Article Snippet: Spatial transcriptomics (Visium) samples: A section of lung was cut and transferred to 10% buffered formalin to fix for 24 h, then transferred to 70% ethanol until wax embedding.

Techniques: Expressing, Staining, MANN-WHITNEY

Journal: Nature Communications

Article Title: GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations

doi: 10.1038/s41467-023-44017-0

Figure Lengend Snippet: a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial transcriptomics data based on the annotation of the human dorsolateral prefrontal cortex (DLPFC) section 151673. b Spot clustering performance on the raw data and the imputed data by CoSTCo, DTD, FIST ( λ = 0 or 0.01), and GNTD ( λ = 0 or 0.1) at different ranks in the simulated spatial transcriptomics data with 40% or 80% zero inflation rate. c Visualization of the spatial domains detected by spot clustering on the raw data and the imputed data of the simulated spatial transcriptomics data with 40% and 80% zero inflation rates. The imputed data with the best rank by each tensor decomposition method was used in the visualization. d Spatially variable genes detection comparison. The plot shows the percentage of correctly detected spatially variable genes by the AUC thresholds of the recovered highly expressed spots in the more sparse simulated spatial transcriptomics data with 80% zero inflation rate. e Spatial patterns visualization of three example genes by their expression in the ground-truth data, raw data, and the imputation data of the simulated spatial transcriptomics data with 80% zero inflation rate. Note that in ( d ) and ( e ), a higher AUC indicates a better consistency between the imputed or raw expressions and the ground-truth expression over the spots for the gene. Source data for ( b ) and ( d ) are provided as a Source Data file.

Article Snippet: These methods range from lower resolution Spatial Transcriptomics (ST) (commercialized as 10x Genomics Visium ), to higher resolution Slide-seq , or even sub-cellular resolution technologies such as high-definition spatial transcriptomics (HDST) and Spatio-temporal enhanced resolution omics-sequencing (Stereo-seq) .

Techniques: Comparison, Expressing

Journal: Frontiers in Reproductive Health

Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

doi: 10.3389/frph.2025.1747902

Figure Lengend Snippet: Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

Techniques:

Journal: Frontiers in Reproductive Health

Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

doi: 10.3389/frph.2025.1747902

Figure Lengend Snippet: Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

Techniques: Expressing

Journal: Frontiers in Reproductive Health

Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

doi: 10.3389/frph.2025.1747902

Figure Lengend Snippet: High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

Techniques: Expressing, Membrane

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: SpaIM comprises an ST autoencoder and an ST generator. Both the ST autoencoder and the ST generator are built on the multilayer recursive style transfer (ReST) layers.

Article Snippet: Spatial transcriptomics (ST) autoencoder The ST autoencoder (Fig. ) comprises multilayer ReST encoders.

Techniques:

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: a Benchmarking results on the NanoString CosMx spatial transcriptomics dataset (Lung5–rep3), using evaluation metrics including structural similarity index measure (SSIM) and Jaccard similarity (JS). Data are presented as mean values ± 95% confidence intervals across predicted genes ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} n = 2,038). b Spatial visualization of cell types in the whole slide. c Spatial visualization of cell types in specific field of views (FOVs).

Article Snippet: Spatial transcriptomics (ST) autoencoder The ST autoencoder (Fig. ) comprises multilayer ReST encoders.

Techniques:

Journal: Nature Communications

Article Title: SpaIM: single-cell spatial transcriptomics imputation via style transfer

doi: 10.1038/s41467-025-63185-9

Figure Lengend Snippet: a Benchmarking results on the NanoString CosMx spatial transcriptomics dataset (Lung5–rep3), using evaluation metrics including structural similarity index measure (SSIM) and Jaccard similarity (JS). Data are presented as mean values ± 95% confidence intervals across predicted genes ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document} n = 2,038). b Spatial visualization of cell types in the whole slide. c Spatial visualization of cell types in specific field of views (FOVs).

Article Snippet: Spatial transcriptomics (ST) generator A similar architecture (Fig. ) is used to generate ST data from SC data.

Techniques: