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: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Comparative spatial multi-omics analysis of acute myeloid leukemia patients’ bone marrow and extramedullary tissues (A) Schematic representation of the study workflow. Paired bone marrow (BM) samples (BM1 and BM2) and extramedullary (EM) samples (EM1, from skin; and EM2, from lymph node) from 2 newly diagnosed patients with acute myeloid leukemia (AML) (PT1 and PT2) were fixed in formalin and embedded in paraffin (FFPE) and then sectioned for use in Visium assays (v1 and v2), and Opal multiplex fluorescent immunohistochemistry (mfIHC). The Visium spatial transcriptomics (ST) results were validated using GeoMx digital spatial profiling (DSP) with tissue microarrays (TMAs) of samples from 3 newly diagnosed patients with AML (PT3, PT4, and PT5). An additional 4 AML bone marrow samples that performed the Visium gene and protein expression assay are used as a validational cohort (PT6, PT7, PT8, and PT9). Image created with BioRender ( https://biorender.com ). (B) Uniform manifold approximation and projection (UMAP) plot showing our reference map consisted of 79,029 cells collected from 9 healthy BM donors and 7 patients with AML with diploid cytogenetics to match the patient cytogenetic profiles, and included both newly generated scRNA data and previous works. This map consisted of 21 cell types, including T cells (CD4 + and CD8 + naive, effector, and memory T cells, T regulatory [Treg] cells, and unconventional T cells), other immune cells (Natural killer [NK] cells, B cells and plasma cells), hematopoietic progenitors (Hematopoietic stem cells [HSCs], common lymphoid progenitors [CLPs], granulocyte-monocyte progenitors [GMPs]), myeloid cells (megakaryocytes/platelets, monocytes, early and late erythroid cells, conventional and plasmacytoid dendritic cells) and leukemic (AML) cell populations. (C) Immunohistochemical staining of CD11c, MPO, and CD3e on BM1 sections that were used for histopathological annotation. The scale bar for the main tissue panels represents 1 mm. The scale bar for the zoomed-in panels, corresponding to the boxed regions, represents 100 μm. (D) Unsupervised clustering and pathology annotation for the projected spatial map of BM1, revealing 3 distinct regions with an adjusted rand index (ARI) of 0.46. (E) Spatial deconvolution of BM1 tissue, showing erythroid and AML cell populations, with CD11c immunohistochemistry (IHC) overlaid on an image of hematoxylin and eosin (H&E)-staining. The dotted red lines represent regions enriched for the erythroid cell population; dotted black lines, regions enriched for the AML cell population; and solid lines, regions that overlapped with other tissue sections. (F) Heatmap of Z score normalized canonical markers in pathology annotations, with matching unsupervised cluster distributions represented as a pie chart. HBB, HBD, HBA2, GATA1/2 are erythroid genes and S100A12, FCGR3A, CD14, MS4A7, and , CD33 are monocyte/leukemic genes. (G) Representative overlay of Visium H&E staining with Opal mfIHC and the generated spot-level data for CD33, CD71, CXCL12, CXCR4, CD68, and IL-6. Boxes illustrate magnified regions showing concordance between transcript-level (Visium) and protein-level (Opal) signals at the spot level. (H) Phenotype staining on near-adjacent tissue sections for markers of leukemic (CD33), monocytic (CD68), and erythroid (CD71) populations. DAPI was used as a nuclear counterstain. The spatial distribution of these markers corroborates ST-based spot deconvolution. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (I) Box and spatial plots of mfIHC staining intensities for phenotypic markers across ST-defined clusters in BM1, highlighting the enrichment of leukemic and monocytic populations in cluster 3 and that of erythroid populations in cluster 2 at BM1. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). ns, not significant. ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Biomarker Discovery, Multiplex Assay, Immunohistochemistry, Expressing, Generated, Clinical Proteomics, Immunohistochemical staining, Staining

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Spatial multi-omics profiling identifies leukemic infiltration and tissue composition in extramedullary acute myeloid leukemia samples (A) Unsupervised clustering of the extramedullary sample EM1 into 3 spatial clusters (left) compared against the pathology-based annotation (right; indicating a composition of leukemia, dermis, epidermis, and gland). The adjusted rand index (ARI; 0.51) reflects moderate agreement between the clusters and pathology annotations. (B) Spatial deconvolution scores obtained using the SpaCET algorithm show EM1’s malignant cell distribution overlaid on the hematoxylin and eosin (H&E) image. (C) Heatmap of canonical marker expression in EM1 regions, validating transcriptional segregation and matching pathologist-defined regions. Markers of leukemic populations and dermis regions show shared expression profiles. Unsupervised cluster overlap is represented as pie charts, with pathology annotation. (D) Phenotypic staining (Opal multiplex immunofluorescent) on near-adjacent sections validating the spatial distribution of CD33 (malignant cells), CD68, and CD71, which is consistent with the Visium malignant signature (spot-level) and CD68 and CD71 expression patterns. Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels).

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Biomarker Discovery, Marker, Expressing, Staining, Multiplex Assay

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Inflammatory microenvironment analysis reveals region-specific signatures in bone marrow and extramedullary tissues from patients with acute myeloid leukemia (A) Distribution of spatial inflammation classes in BM1 and EM1, based on composite inflammation scores from inflammation-related hallmark pathways (Inflammatory response, IL6/JAK/STAT3 signaling, TNF-α/NF-κB signaling, IFN-γ response, IFN-α response, Complement, IL2/STAT5 signaling). Classes were defined using Jenks' natural breaks optimization. (B) Mean activity comparison of individual inflammatory related pathways in spots with high-inflammatory activity revealed the highest activity of IFN-γ response in EM tissue. Complement pathway activity is higher in BM1 when compared with the EM1 inflammatory niche. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (C) Boxplots of inflammation scores across the 3 clusters in BM1 (left) and EM1 (right). Each cluster displays significantly different levels of inflammatory activity; leukemia-enriched cluster 3 in BM1 and cluster 1 in EM1 have higher inflammation scores. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (D) IL-6 staining (Opal multiplex fluorescent immunohistochemistry [mfIHC]) in whole-slide images (left) of BM1 (top) and EM1 (bottom) and corresponding magnified regions (center), aligned with Visium spot-level composite inflammation score (right). Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (E) Scatterplots showing the correlation of IL-6 protein staining intensity (mfIHC IL-6) with the composite inflammation score in BM1 (left) and EM1 (right). IL-6 levels are higher in high-inflammation regions in both BM1 and EM1. (F) Dot plot showing the localization of T cell subtypes (exhausted, CD8 + dysfunction, senescence, regulatory T cells [Treg]) based on inflammation class in BM1 and EM1.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Activity Assay, Comparison, Staining, Multiplex Assay, Immunohistochemistry

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Chemokine signaling through the CXCL12-CXCR4 axis is linked to inflammatory niches and trans-differentiation in acute myeloid leukemia (A) Spatial and chord diagrams of the strength of interactions among acute myeloid leukemia (AML) cells, granulocyte-monocyte progenitors (GMP), and monocytes through the CXCL12-CXCR4 axis, as predicted by CellChat. (B) Boxplots of the expression levels of CXCL12 and CXCR4 in BM1, stratified by inflammation class (top), and corresponding spot-level expression maps (bottom) for the bone marrow sample BM1. Red spots indicate higher expression levels. (C and D) Whole-slide images of Opal multiplex fluorescent immunohistochemistry (mfIHC; left) for CXCR4 (turquoise) and CXCL12 (magenta) overlaid with DAPI (blue), alongside magnified Opal regions and Visium-based gene expression maps (right) in BM1 (C) and the extramedullary sample EM1 (D). Scale bars: 1 mm (whole-slide panels) and 100 μm (selected region panels). (E) Scatterplot shows the positive correlation of the PI3K/Akt/mTOR pathway score with the combined CXCL12-CXCR4 co-expression score (R = 0.50, p < 2.2e-16). Colors denote inflammation class. (F) Relationship between CXCR4 expression and inflammation score in EM1 (R = 0.19, p < 2.2e-16). Spatial maps show the distribution of CXCR4 expression. (G) Boxplots comparing CXCR4 protein signal intensity (mfIHC) across inflammation classes in BM1 (left) and EM1 (right). Spot-level images illustrate higher CXCR4 signal intensities in high-inflammation areas. (H) Sections 1 and 2 represent adjacent serial sections of the same EM1 biopsy embedded on a single Visium capture area. Visium ST visualization of PI3K/Akt/mTOR pathway (left) and trans -differentiation pathway (right) activity in these EM1 sections, revealing elevated pathway scores in high-inflammation and leukemic regions.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques: Expressing, Multiplex Assay, Immunohistochemistry, Gene Expression, Activity Assay

Journal: iScience

Article Title: Integrative spatial multi-omics reveal niche-specific inflammatory signaling and differentiation hierarchies in AML

doi: 10.1016/j.isci.2025.114289

Figure Lengend Snippet: Bone proximity analysis reveals the spatial distribution of acute myeloid leukemia cells in different differentiation states (A) Representative spatial map of SpatialTime calculated distances from trabeculae overlaid with hematoxylin and eosin (H&E) image. (B) Boxplots show deconvolution scores of primitive-like, granulocyte-monocyte progenitor (GMP)-like, and committed-like acute myeloid leukemia (AML) cells relative to their distance from bone in Visium data. ∗ p < 0.05, ∗∗∗∗ p < 0.0001, Wilcoxon rank-sum test. (C) GeoMx analysis of AML deconvolution in bone marrow regions from 3 patients with AML (PT3, PT4, PT5). D, distal (dark red); P, proximal (dark blue); B, bone (white). Stacked bar plots represent cell type deconvolution within distal and proximal regions. Scale bars: 250 μm. (D) Line graphs show proportions of primitive-like and GMP-like cells relative to distance from bone.

Article Snippet: Visium Spatial Transcriptomics Data , This paper , GEO: GSE279576.

Techniques:

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: Advanced Science

Article Title: Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury

doi: 10.1002/advs.202503691

Figure Lengend Snippet: Single‐cell and spatial transcriptome landscape of healthy and fibrotic kidneys after unilateral ischemia‐reperfusion injury (UIRI). a) Schematic representation of single‐cell RNA sequencing (scRNA‐seq) and spatial transcriptomics (ST) of kidneys from the sham and 10‐day UIRI mice, graphically designed with Biorender ( https://www.biorender.com/ ). b) t‐SNE plot illustrating the intricate cellular diversity in fibrotic kidneys, demonstrating distinct clusters representing glomerular endothelial cells (GEC), podocytes (Podo), mesangial cells (Mesa), Bowman's capsule epithelium (BC), proximal tubules (PT), descending limbs of Henle (DLOH), ascending limbs of Henle (ALOH), distal tubules (DT), principal cells (PC), intercalated cells (IC), fibroblasts (Fib), smooth muscle cells (SMC), extraglomerular endothelial cells (EGEC), monocytes (Mono), dendritic cells (DC), macrophages (Mϕ), plasmacytoid dendritic cells (pDC), proliferating mononuclear lineage (Prolif mono_L), and neutrophils (Neu), B cells (B), T cells (T), proliferating T cells (prolif T), and natural killer cells (NK). These cell types were further categorized into four major compartments: Glomerular, Renal, Interstitium, and Immune, as indicated by color grouping in the plot. c) Bubble plot illustrating the relative proportions of major kidney cell types in sham and UIRI samples. Each dot represents the proportion of a given cell type in a specific sample group, with dot size corresponding to its relative proportion. d) A comprehensive heatmap depicting the unique marker gene signature of major renal cell types. e) UMAP plot illustrating the inferred renal cell region distribution based on integrated spatial transcriptomics data from normal (Sham) and UIRI 10D mouse kidneys, generated using the 10x Genomics Visium platform. The identified regions include glomerular cells (Glom), distinct segments of the proximal tubule (PTS1, PTS1S2, PTS2), injured proximal tubules (InjPT), ascending limbs of Henle in cortex (ALOH(C)), distal tubules (DT), connecting tubules and collecting ducts (CNT_CD), cells at the corticomedullary junction (CMJ), fibrogenic niche regions (Niche1, Niche2), the inner stripe of the outer medulla (IOM), inner medulla (IM), renal capsule (RC), and perirenal tissue (Perirenal). f) Spatial maps illustrating the anatomical distribution of renal cell regions in Sham and UIRI 10D mouse kidneys. Region colors correspond to the classifications defined in panel (e). g) Bubble plot illustrating the relative proportions of major renal cell regions in spatial transcriptomics data from sham and UIRI 10D mouse kidneys. h) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in spatial transcriptomics data. Dot color indicates the average gene expression level within each region, while dot size represents the proportion of spatial spots expressing the gene. i) Schematic diagram of nephron segmentation by cell types. j) Comparison of kidney anatomical regions and spatial transcriptomic clusters, showing clusters in kidney tissue (top) and the corresponding Visium H&E‐stained section (bottom). k) Renal tissue structure alterations at the corticomedullary junction (CMJ) in UIRI samples, showing the formation of two distinct fibrogenic niches, Niche1 and Niche2. l) A heatmap showing the deconvolution scores of cell type compositions across different regions in Visium spatial transcriptomics data, obtained using the RCTD method. m) Spatial FeaturePlots of RCTD‐derived cell type scores in the sham (top) and UIRI (bottom) groups, with paired panels sharing a common legend.

Article Snippet: For the preparation of sections for Visium Spatial Transcriptomics sequencing, samples were equilibrated at −18 °C and a 10 μm thick section was cut onto the active sequencing area (6 mm x 6 mm) of a spatial barcoded slide.

Techniques: RNA Sequencing, Marker, Generated, Expressing, Gene Expression, Comparison, Staining, Derivative Assay

Journal: Advanced Science

Article Title: Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury

doi: 10.1002/advs.202503691

Figure Lengend Snippet: High‐resolution spatial transcriptomics and immunostaining reveal the TNC‐enriched fibroblast‐macrophage niche organization in fibrotic kidneys. a) Schematic diagram of the Visium HD workflow applied to kidney tissues from sham and UIRI model mice. b) UMAP visualization of integrated Visium HD spatial transcriptomics data from control mice (obtained from the 10x Genomics public dataset) and UIRI mice (this study), processed using canonical correlation analysis (CCA). This dimensionality reduction visualization reveals distinct clusters representing various renal parenchymal and stromal cell populations, including: Glomerulus, Vasculature, PTS1, PTS2, PTS1S2, InjPT, ascending limbs of Henle in cortex [ALOH(Cortex)], distal tubule and connecting tubule (DT_CNT), connecting tubule and collecting duct (CNT_CD), collecting duct in cortex [CD(Cortex)], PTS3, injured PTS3 (InjPTS3), Fibrogenic Niche, Vasa recta, loop of Henle in outer medulla [LOH(IOM)], collecting duct in outer medulla [CD(IOM)], collecting duct in inner medulla [CD(IM)], thin ascending limbs of Henle in inner medulla [tALOH(IM)], renal capsule (RC), Perirenal Fibrous tissue, and Perirenal Adipose tissue. c) Bubble plot comparing the regional distribution in Control versus UIRI 10d kidneys (Visium HD). d) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in Visium HD data. e) Spatial maps generated using Visium HD illustrate the inferred anatomical distribution of renal cell regions in kidney tissues from Control and UIRI mice. f) Spatial Feature Plots of Visium HD data showing the spatial distribution of selected renal cell types in controls (top) and UIRI mice (bottom), based on cell‐type deconvolution using RCTD. g) A heatmap showing the correlation between NMF factors and cell‐type deconvolution scores in standard Visium spatial transcriptomics data. h) Spatial distribution of gene scores associated with the NMF factors most correlated with the fibrogenic niche, along with the contribution of key genes to each factor. i) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in standard Visium. j) A heatmap showing the correlation between NMF factors and cell type deconvolution scores in Visium HD spatial transcriptomics data. k) Spatial distribution of NMF factors (NMF3 and NMF11) associated with the fibrogenic niche in Visium HD data, along with their corresponding high‐contributing genes. l) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in Visium HD datasets. m) Immunofluorescence staining demonstrates colocalization of TNC with macrophages (F4/80⁺) in the CMJ interstitial region. From top to bottom: an overview merged image (Merge), followed by magnified views of TNC, Vimentin, and F4/80 staining in the same region, and an enlarged merged image (Enlarged Merge) at the bottom.

Article Snippet: For the preparation of sections for Visium Spatial Transcriptomics sequencing, samples were equilibrated at −18 °C and a 10 μm thick section was cut onto the active sequencing area (6 mm x 6 mm) of a spatial barcoded slide.

Techniques: Immunostaining, Control, Expressing, Marker, Generated, Immunofluorescence, Staining

Journal: Advanced Science

Article Title: Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury

doi: 10.1002/advs.202503691

Figure Lengend Snippet: TLR4 knockout in macrophages attenuates renal inflammation and renal fibrosis in vivo. a) The diagram shows the experimental protocol. Bone marrow chimera models were established by transplanting the WT bone marrow to WT mice, or TLR4 KO bone marrow to WT mice. Mice were irradiated at a single dose of 1100 Rads and then underwent bone marrow transplantation. After 8 weeks of successful transplantation, a unilateral ischemia‐reperfusion (UIRI) model was established. b) PCR‐based identification of kidney genotypes in the recipient mice of bone marrow transplantation models using TLR4 mutation site primers and wild‐type site primers, respectively. c,d) Graphic presentations show serum creatinine (Scr) (c) and blood urea nitrogen (BUN) (d) levels in different groups as indicated at 11 days after IRI. * p < 0.05 versus WT‐WT (n = 4–6). e,f) Western blot analyses show renal expression of TLR4, p‐P65, and P65 in different groups as indicated. Representative Western blot (e) and quantitative data (f) are shown. * p < 0.05 versus WT‐WT (n = 4–6). g) Representative micrographs show renal expression and co‐localization of TLR4 and F4/80 by immunofluorescence staining in different groups as indicated. The areas between the dashed lines represent the corticomedullary junction of the kidney. h,i) Western blot analyses show renal expression of MR, Arg‐1, iNOS, TNF‐α, and CCL2 in different groups as indicated. Representative Western blot (h) and quantitative data (i) are shown. * p < 0.05 versus WT‐WT (n = 4–6). j,k) Western blot analyses show renal expression of TNC, FN, and α‐SMA in different groups as indicated. Representative Western blot (j) and quantitative data (k) are shown. * p < 0.05 versus WT‐WT (n = 4–6). l) A schematic diagram shows a crucial role of TNC in organizing the proinflammatory and profibrotic niche. By integrating single‐cell RNA sequencing and spatial transcriptomics, we unveil TNC as a central organizer of the proinflammatory and profibrotic niche in kidney fibrosis. TNC promotes macrophage activation through TLR4/NF‐κB signaling, leading to macrophage activation, proliferation, and cytokine production.

Article Snippet: For the preparation of sections for Visium Spatial Transcriptomics sequencing, samples were equilibrated at −18 °C and a 10 μm thick section was cut onto the active sequencing area (6 mm x 6 mm) of a spatial barcoded slide.

Techniques: Knock-Out, In Vivo, Irradiation, Transplantation Assay, Mutagenesis, Western Blot, Expressing, Immunofluorescence, Staining, RNA Sequencing, Activation Assay

Journal: bioRxiv

Article Title: A Foundational Generative Model for Cross-platform Unified Enhancement of Spatial Transcriptomics

doi: 10.64898/2025.12.23.696267

Figure Lengend Snippet: a , Model overview. FOCUS is a diffusion-based generative model that leverages ST and H&E encoders , pretrained on large-scale, cross-tissue data to extract robust multimodal features. It integrates multimodal conditions as inputs, including under-refined ST maps, paired H&E images with cell segmentation masks, scRNA-seq references, and spatial gene co-expression matrices. Each challenge is addressed through tailored modules, with a cross-challenge coordination strategy enabling module interaction for coherent improvement across challenges. b , Large-scale, cross-platform multimodal dataset. In total, we assemble 6,876 paired ST-H&E images (corresponding to over 1.7 million patches) with matched cell segmentation masks, referenced scRNA-seq from public resources (over 5.8 million scRNA-seq cell profiles; Table S1), and precomputed spatial gene co-expression matrices. The data collection spans ten ST platforms, including eight sequencing-based (Visium (probe-based and polyA-based), Visium Cytassist, VisiumHD, Spatial Transcriptomics, Stereo-seq, BMK S1000, and Open-ST) and two imaging-based (Xenium and CosMx) platforms, and two species (human and mouse), comprising 17 normal and 17 cancer tissues, with whole-transcriptome profiles available for both species. c, Benchmarking and validation across challenges and downstream tasks, including spatial domain characterization, cell-cell communications, and cell-cell co-localization.

Article Snippet: FOCUS consistently performed the best in all comparisons, with SSIM gains of 0.80 and 0.28, and RMSE reductions of 0.20 and 0.18 (all P < 0.001) on Visium (probe) and Spatial Transcriptomics, respectively, highlighting real-world utility.

Techniques: Diffusion-based Assay, Expressing, Sequencing, Imaging, Biomarker Discovery

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: Nature Communications

Article Title: Regional and cell specific bioactivity of injectable extracellular matrix biomaterials in myocardial infarction

doi: 10.1038/s41467-025-65351-5

Figure Lengend Snippet: a MI was induced followed by an intramyocardial injection of ECM hydrogel or saline 7 days post-MI. Hearts were then harvested for either snRNAseq or spatial transcriptomics 7 days post-injection (14 days post-MI). Figure created in BioRender, and is licensed under CC BY 4.0 ( https://biorender.com/r54nzgf ). Sample size: n = 2 ECM hydrogel replicates, 7658 spots. b Myocardium (green) was labeled with anti-alpha-actinin antibody alongside fluorescently tagged ECM hydrogel (light blue) with nuclei stained with DAPI (blue). c The adjacent cryosection was used for spatial transcriptomics via 10X Visium, where the infarct-containing ECM hydrogel (red) was found to cluster separately from the infarct alone (cyan). d The top upregulated differentially expressed genes defining the ECM hydrogel zone (red) were found to be immune and vascularly dominating genes compared to the downregulated genes impacting the infarct zone (cyan). e , f All differentially expressed genes in the ECM hydrogel zone (red) and infarct only zone were subjected to GO enrichment. Significance was determined via nonparametric Wilcoxon rank-sum tests with a Benjamini–Hochberg FDR adjustment to determine gene lists ( d ), and via Kolmogoro-Smirnov tests and permutation testing, with Benjamin-Hochberg FDR adjustment ( e , f ). Source data are provided as a Source Data file. ECM extracellular matrix, Neg negative, reg regulation, Pop population, Prolif proliferation, FC fold change.

Article Snippet: Odd slices were frozen in TissueTek OCT TM and sectioned into 10 μm thick slices and placed onto a 10X Visium Spatial Transcriptomics Slide or a regular histology slide.

Techniques: Injection, Saline, Labeling, Staining

Journal: Nature Communications

Article Title: Regional and cell specific bioactivity of injectable extracellular matrix biomaterials in myocardial infarction

doi: 10.1038/s41467-025-65351-5

Figure Lengend Snippet: a MI is induced followed by an intramyocardial injection of ECM hydrogel or saline 8 weeks post-MI. Hearts are then harvested for either snRNAseq or spatial transcriptomics 7 days post-injection. Figure created in BioRender, and is licensed under CC BY 4.0 ( https://biorender.com/r54nzgf ). Sample size: n = 3 ECM hydrogel replicates, 9594 spots. b Myocardium (green) was labeled with an anti-alpha-actinin antibody alongside fluorescently tagged ECM hydrogel (light blue). c An adjacent cryosection to the immunofluorescence image in ( b ) was used for spatial transcriptomics via 10X Visium, where the infarct containing ECM hydrogel (red) was found to cluster separately from the normal infarct zone (cyan). d Top differentially expressed genes for both ECM within infarct (red) and infarct alone (cyan) are shown. e, f A comparison of the two zones reflects the ECM hydrogel activates fibroblasts and is responsible for further vascular development, as demonstrated through GO enrichment. Significance was determined via nonparametric Wilcoxon rank-sum tests with a Benjamini–Hochberg FDR adjustment to determine gene lists ( d ), and via Kolmogoro–Smirnov tests and permutation testing, with Benjamin–Hochberg FDR adjustment ( e, f ). Source data are provided as a Source Data file. ECM extracellular matrix, Neg negative, reg regulation, Pop population, Prolif proliferation, FC fold change.

Article Snippet: Odd slices were frozen in TissueTek OCT TM and sectioned into 10 μm thick slices and placed onto a 10X Visium Spatial Transcriptomics Slide or a regular histology slide.

Techniques: Injection, Saline, Labeling, Immunofluorescence, Comparison

Journal: Nature Communications

Article Title: Regional and cell specific bioactivity of injectable extracellular matrix biomaterials in myocardial infarction

doi: 10.1038/s41467-025-65351-5

Figure Lengend Snippet: a The top upregulated differentially expressed genes defining the ECM hydrogel zone (red) with integrated subacute and chronic Visium were found to be immune, fibroblast, and vascularly dominating genes compared to the downregulated genes impacting the infarct zone (cyan). Sample size: n = 2 for subacute ECM hydrogel (7658 spots); n = 3 for chronic ECM hydrogel (9594 spots) b The ECM zones in both subacute and chronic models of MI have higher expression of the matrix specific genes relative to the infarct zone. c –f Macrophages ( c ), endothelial cells ( d ), cardiomyocytes ( e ), and fibroblasts ( f ) treated with ECM hydrogel in subacute and chronic MI were subsetted, reclustered, and compared with respect to MI timepoint. Sample size: n = 2 subacute ECM hydrogel (downsampled to 3000 cells), n = 2 chronic ECM hydrogel (downsampled to 3000 cells). Top differentially expressed genes were displayed via Volcano Plot, and the differentially expressed genes were subjected to GO enrichment. g Comparison of transcriptomic findings between subacute and chronic MI. Significance was determined via nonparametric Wilcoxon rank-sum tests with a Benjamini–Hochberg FDR adjustment to determine gene lists ( a, c – f ), and via Kolmogorov–Smirnov tests and permutation testing, with Benjamini–Hochberg FDR adjustment ( c – f ). Source data are provided as a Source Data file. ECM extracellular matrix, neg negative, vasc vascular, pos positive, reg regulation, pop population, prolif proliferation.

Article Snippet: Odd slices were frozen in TissueTek OCT TM and sectioned into 10 μm thick slices and placed onto a 10X Visium Spatial Transcriptomics Slide or a regular histology slide.

Techniques: Expressing, Comparison

Journal: STAR Protocols

Article Title: Protocol to evaluate mouse brain spatial cell type-resolved transcriptomic discoveries using 10× Visium spatial transcriptomics and FLEX scRNA-seq

doi: 10.1016/j.xpro.2025.104277

Figure Lengend Snippet: Web summary metrics generated from 10× Genomics SpaceRanger Web summaries generated from 10× Genomics Space Ranger pipeline after receiving raw data for P0 mouse tissue in Visium spatial transcriptomics step. The summary page will provide detailed information regarding data quality including “Fraction Reads in Spots Under Tissue”. To determine localization of diffused RNA and confirm that RNA is “leaking” from tissue section, rerun Space Ranger on all spots in the Visium capture area. If Fraction Reads in Spots Under Tissue is below 50%, optimization is required. (A) Unsuccessful reads in spots are most likely due to over permeabilization when releasing RNA. (B) Successful processing of P0 tissue with 10× Visium.

Article Snippet: Permeabilization time is determined with Visium Spatial Tissue Optimization (protocol CG000238) which needs to be performed before starting Spatial Transcriptomics Tissue Processing.

Techniques: Generated