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: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: STK24 is elevated in LUAD epithelial cells. A UMAP showing cell types after batch correction and dimensionality reduction clustering. B Bubble plot showing STK24 expression levels across various cell types. C Violin plot showing STK24 expression in normal and tumor cells across various cell types. D , E STK24 expression levels and regional variation analysis in spatial transcriptomics. F Violin plot showing STK24 expression in normal and tumor samples in the TCGA-LUAD cohort. G Immunohistochemistry results showing STK24 staining in LUAD and normal tissue samples from the HPA database. H Independent prognostic analysis to evaluate whether the association between STK24 and tumor survival is independent of traditional clinical variables. **** P < 0.0001, *** P < 0.001, ** P < 0.01, * P < 0.05, ns P > 0.05

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Expressing, Immunohistochemistry, Staining

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Exploring the origins of STK24 Group cells through spatial transcriptomics (ST). A Schematic diagram of RCTD deconvolution and spatial trajectory analysis of spatial transcriptomics data. B – D Cell types after ST deconvolution. E , F Cell developmental trajectory and trajectory tree in ST ERS17014180. G , H Cell developmental trajectory and trajectory tree in ST ERS17014184. I , J Cell developmental trajectory and trajectory tree in ST ERS17014196. (K-M) Scatter plots showing the correlation between STK24 gene expression and developmental trajectory genes

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Gene Expression

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Interactions between STK24-positive tumor epithelial cells (STK24posEpi) and fibroblasts. A Analysis of interaction strength between STK24posEpi and various cell types. B Activated pathways in various cell communications. C Analysis of activated ligand-receptor pairs. D Schematic diagram of Heterotypic cellular network analysis and cell co-localization analysis of spatial tran-scriptomics data. E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts. I Heatmap displaying cell–cell dependency analysis in the colocated, neighboring, and extended neighboring (15-point) regions of the spatial transcriptomics data

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques:

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Communication and signal flow changes between STK24posEpi and fibroblasts in spatial transcriptomics (ST). A Schematic diagram of Cell–cell communication analysis and signal flow direction analysis of spatial transcriptomics data. B Analysis of communication intensity between STK24posEpi and fibroblasts by integrating multiple spatial transcriptomics samples. C , D Communication between STK24posEpi and fibroblasts in the PDGF signaling pathway across different spatial transcriptomics samples. E Importance of Sender, Receiver, Mediator, and Influencer in different cell types in the PDGF signaling pathway. F , G Expression and co-expression of ligand-receptor pairs related to the PDGF signaling pathway in various spatial transcriptomics samples. H Importance of Sender, Receiver, Mediator, and Influencer in different cell types in the VEGF signaling pathway. I , J Communication between STK24posEpi and fibroblasts in the VEGF signaling pathway across different spatial transcriptomics samples. K Importance of Sender, Receiver, Mediator, and Influencer in different cell types in the MIF signaling pathway. L , M Communication between STK24posEpi and fibroblasts in the MIF signaling pathway across different spatial transcriptomics samples. N , O COMMOT analysis showing the direction of MIF signal flow and expression of Senders and Receivers in various spatial transcriptomics samples

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Expressing

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Exploration of apoptosis and STK24posEpi-related pathways in spatial transcriptomics (ST). A Schematic diagram of Pathway dependency analysis of spatial transcriptomics data. B Enrichment results for the ST apoptosis pathway and comparison of differences between regions. C Heatmap displaying apoptosis-dependent cell pathways within regions in the spatial context. D , F Network diagrams showing apoptosis-dependent cell pathways in intra ( D ), juxta_5 ( E ), and para_15 ( F ) regions. G Enrichment results for the ST cell proliferation pathway and comparison of differences between the STK24 Group. H Enrichment results for the ST cell damage pathway and comparison of differences between the STK24 Group. I Comparison of ST cell cycle and DNA repair pathways between the STK24 Groups. J , K Heatmaps showing cell pathway dependency analysis for different cell types within the intra ( J ) and para_15 ( K ) regions in the spatial context. **** P < 0.0001, *** P < 0.001, ** P < 0.01, * P < 0.05, ns P > 0.05

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Comparison

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Clinical significance of STK24posEpi. A Schematic diagram of Homotypic cellular network analysis of spatial transcriptomics data. B Homotypic cell network analysis of STK24posEpi in spatial transcriptomics. C Survival analysis of STK24posEpi across multiple bulk transcriptome cohorts after Bayesian deconvolution. D Comparison of tumor-infiltrating lymphocyte scores between STK24posEpi Groups in the TCGA-LUAD cohort. E Histological slides showing differences in tumor-infiltrating lymphocytes between STK24posEpi Groups in the TCGA-LUAD cohort. F Correlation analysis of STK24posEpi and B cells in multiple bulk transcriptomes. G Differential expression of BCR signaling pathway-related genes between STK24posEpi Groups in the TCGA-LUAD cohort. H Differential expression of antigen processing and presentation pathway-related genes between STK24posEpi Groups in the TCGA-LUAD cohort. I Comparison of clinical factors between STK24posEpi Groups in the TCGA-LUAD cohort. **** P < 0.0001, *** P < 0.001, ** P < 0.01, * P < 0.05, ns P > 0.05

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Comparison, Quantitative Proteomics

Journal: Brain Tumor Pathology

Article Title: Comprehensive molecular characterization of craniopharyngiomas using whole transcriptome and spatial transcriptomics approaches

doi: 10.1007/s10014-025-00509-z

Figure Lengend Snippet: Cellular clustering and spatial localization in craniopharyngioma tissue sections using Xenium spatial transcriptomics. Left: UMAP plot display the distribution of 201,499 profiled cells from two ACP and one PCP. Cells are grouped into 13 distinct clusters based on dimensionality reduction and gene expression profiles from the Human Multi-tissue and Cancer Panel. Right: Adjacent high-resolution spatial maps for each sample depict the localization of the clusters directly on histologic sections of CP samples (top to bottom: one PCP, two ACP). Each cell cluster is color-coded consistently with the UMAP for visual correlation between transcriptional identity and tissue localization

Article Snippet: Our Xenium-based spatial transcriptomics analysis was limited to 377 genes included in the Human Multi-tissue and Cancer Panel.

Techniques: Gene Expression

Journal: Brain Tumor Pathology

Article Title: Comprehensive molecular characterization of craniopharyngiomas using whole transcriptome and spatial transcriptomics approaches

doi: 10.1007/s10014-025-00509-z

Figure Lengend Snippet: Differentially expressed genes between ACP and PCP obtained from Xenium-based spatial transcriptomics analysis. Bar plot illustrating the log2 fold change of 41 differentially expressed genes between ACP and PCP samples, with upregulated genes shown above and downregulated genes below the axis. Bar colors represent statistical significance, with a color gradient from blue (less significant) to red (highly significant) based on –log10 ( p value) ( a ). High-resolution spatial distribution maps display selected genes with significant expression differences between ACP and PCP, visualizing localization patterns of four upregulated (APCDD1, GATM, MCF2L, EPCAM) and seven downregulated (SERPINB3, CLCA2, ADAM28, SLC26A, GPRC5A, BASP1, TREM2) transcripts across tissue sections from two ACP and one PCP case. Red intensity indicates greater transcript abundance in spatial context ( b ) ( ACP adamantinomatous craniopharyngioma, PCP papillary craniopharyngioma)

Article Snippet: Our Xenium-based spatial transcriptomics analysis was limited to 377 genes included in the Human Multi-tissue and Cancer Panel.

Techniques: Expressing

Journal: Brain Tumor Pathology

Article Title: Comprehensive molecular characterization of craniopharyngiomas using whole transcriptome and spatial transcriptomics approaches

doi: 10.1007/s10014-025-00509-z

Figure Lengend Snippet: Reference-based clustering and spatial localization of brain cell populations in craniopharyngioma tissues using Xenium spatial transcriptomics. The left panel displays a UMAP plot of reference-based cluster annotation for Xenium-derived transcriptomes, generated by mapping spatial transcriptomic data from ACP and PCP to the Allen Brain Map RNA-Seq Data: Human MTG 10 × SEA-AD reference. Each color represents a distinct cell cluster identified in the tissue, revealing 24 separable clusters including perivascular macrophages (microglia-PVM), endothelial cells, astrocytes, and others. The right panels present high-resolution spatial images of whole tissue slides from PCP and ACP sections, where colored regions reflect the spatial expression and localization of these identified clusters within the tumor and adjacent brain tissue ( a ). Additional UMAP plots highlight the spatial distribution of three selected cell types: perivascular macrophages (microglia-PVM), endothelial cells, and astrocytes ( b ). Cell annotation was performed using existing brain and immune cell atlases due to the limited coverage of the gene panel, and not all clusters could be annotated with complete certainty ( ACP adamantinomatous craniopharyngioma, PCP papillary craniopharyngioma)

Article Snippet: Our Xenium-based spatial transcriptomics analysis was limited to 377 genes included in the Human Multi-tissue and Cancer Panel.

Techniques: Derivative Assay, Generated, RNA Sequencing, Expressing

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: