Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: The framework comprises two modules: Development and Retrieval. a, The engine development has two stages: model training and index generation. Stage 1 trains a prediction model composed of an image encoder, attention map, and decoder using TCGA data from 32 tumor types. The input to the image encoder is H&E whole-slide images, and the decoder output includes a binary indication of gene mutation functions in cancer progression and average expression of pathway gene sets. Stage 2 indexes patch vectors encoded from the whole slide images from the NCI Lab of Pathology, TCGA, CPTAC, and spatial transcriptomics studies. b, Two retrieval applications. For a query H&E image patch or region, the Image-to-Image retrieval returns similar image patches and associated whole slides from the indexed database. If users select images with spatial transcriptomics (ST) data, HERE returns images that closely match the query and the corresponding gene expression profiles from ST detection spots. The Transcriptomics-to-Image retrieval finds H&E slides with paired ST data where the query gene has high expression levels in ST detection spots associated with specific image feature clusters from those slides.

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Mutagenesis, Expressing, Gene Expression

Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: a , An example query image from a tumor with hypoxia indicated by anti-Pimonidazole staining . The right side shows examples of matched image patches (green squares) within an H&E slide with spatial transcriptomics (ST) data. b , Transcriptomics heatmap of ST detection spots from the top three ST profiles, each representing a distinct cancer type. Expression values are variance-stabilizing transformed values relative to levels in all ST spots and sequencing depth per slide . Only genes with expression values greater than 5 in at least three spots across all match patches are shown, and genes are ranked by mean values across all spots. Within each cancer type, columns (spots) are organized by hierarchical clustering with correlation distances. Only spots with expression values greater than 5 in at least three genes are shown. c , Glycolysis gene set enrichment. Along the x-axis, all genes are ranked from high to low by mean expression value (lower y-axis) among all ST detection spots returned by the image query. Members of the glycolysis hallmark gene set are indicated by horizontal blue lines in the middle of the plot. The top y-axis plots the glycolysis enrichment score at each gene rank. The p-value was computed through the one-sided permutation test with 1000 randomizations. d , Gene sets with higher enrichment scores than hypoxia.

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Staining, Expressing, Transformation Assay, Sequencing

Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: a , Screenshot of the 3D UMAP of patch encoding vectors from H&E images paired with spatial transcriptomics. The full interactive UMAP is available at https://hereapp.ccr.cancer.gov/ST_CONCH_umap3d.html . Some image patch clusters or local regions comprise image patches from mostly one or two ST profiles (circle highlights), typical of batch effects. b , Statistical links between gene expression and image feature clusters. For each ST cluster, we counted the number of genes with FDR < 0.05 (Benjamini-Hochberg corrected from the two-sided Wilcoxon rank sum test). The histogram of gene count values across image clusters from all ST profiles is shown. The total number of clusters evaluated was 1,039, and the total number of genes was 11,137. c , Gene set enrichment analysis. For Cohen’s d profile for each ST cluster, we performed gene set enrichment analysis. The X-axis presents the fraction of ST clusters above which a GO_BP term is enriched (GSEA q-value < 0.05). The left Y-axis presents the fraction of GO_BP terms enriched above the threshold on the X-axis for real and randomly permuted data. The right Y-axis presents the False Discovery Rate, computed as the (Random GO_BP term fraction) / (Real GO_BP term fraction). d , The H&E image from the human lung tumor region shown in , which has high expression of C1R , C1S , and SERPING1 . e , In-vitro growth of B16F10 cancer cells in culture, measured by XTT assay. Dots and error bars represent mean and standard deviations (n = 3 cell culture replicates). Metabolic activity is measured as optical density at 492 nm (read) divided by the value at 620 nm (reference)

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Gene Expression, Expressing, In Vitro, XTT Assay, Cell Culture, Activity Assay

Journal: bioRxiv

Article Title: Web engine for tumor pathology image retrievals on massive scales

doi: 10.1101/2025.10.25.684566

Figure Lengend Snippet: a , Associations between gene expression and image features. Hierarchical clustering was applied to Spatial transcriptomics (ST) data from a human lung tumor to organize image patches around ST detection spots into eight clusters (left panel; each cluster is a different color). For a given query gene (e.g., C1R and SERPING1, center panel) and each image cluster (for example, cluster #2 (blue) in left panel), the expression difference among ST detection spots within the image cluster region and spots outside the cluster region is quantified using the Cohen’s d value (right panel). Testing each of all possible query genes against each of every ST profile cluster will generate the result matrix (bottom panel). b , Complement Activation gene set enrichment. Along the x-axis, all genes are ranked from high to low by Cohen’s d values (bottom Y-axis) computed for cluster #2 of the ST profile in panel a. Members of the “complement activation” pathway from Gene Ontology biological processes (GO_BP) are indicated by horizontal blue lines in the middle of the plot. The top y-axis plots “complement activation” enrichment scores at each gene rank. The P -value is computed through the one-sided permutation test (1000 randomizations). c , Top 20 GO_BP terms associated with image features. For each term, the Y-axis presents the fraction of ST profile clusters whose Cohen’s d gene scores are significantly enriched (False Discovery Rate < 0.05). Multiple GO_BP terms related to similar biological processes are grouped with the y-axis presenting mean values across all merged terms. The star marks the complement activation pathway discussed in the main text. d , Cohen’s d heatmap of complement activation genes. Columns are labeled with each ST profile’s cancer type and the cluster index. e , In-vivo effects of Serping1 overexpression on tumor volume. Left panel: B16-mhgp100 cells with Serping1 and vector-only overexpression were inoculated subcutaneously into mice treated by immune checkpoint blockade. Right panel: The tumor sizes on day 28, the day before the first tumor reached an endpoint (tumor volume ≥ 2000 mm or length ≥ 2 cm). Box plots are shown as in . Group values were compared through the two-sided Wilcoxon rank-sum test. f , Serping1 overexpression in tumors extended survival. B16-mhgp100 cells express immunogenic antigen hgp100, while the B16F10 cell line is less immunogenic. On the Y-axis, the fraction of mice with endpoint-free survival is plotted against days since tumor inoculation (X-axis). The two-sided log-rank test compared group survival differences.

Article Snippet: Spatial transcriptomics (ST) data paired with H&E images also makes it possible to implement a Transcriptomics-to-Image module, which returns image features associated with the high expression level of a query gene name (e.g., SERPING1 ) ( ).

Techniques: Gene Expression, Expressing, Activation Assay, Labeling, In Vivo, Over Expression, Plasmid Preparation

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: Nature Communications

Article Title: Spatial single-cell atlas reveals regional variations in healthy and diseased human lung

doi: 10.1038/s41467-025-65704-0

Figure Lengend Snippet: A Experimental outline including the location of sample collection from donated healthy human lungs, and the methods used for the mRNA-based cell type mapping. 1—trachea, 2—proximal bronchi, 3a—bottom part of upper left lobe, 3b—top part of upper left lobe, 3c—bottom part of lower left lobe. HybISS Hybridization-based in situ sequencing, SCRINSHOT Single-Cell Resolution IN Situ Hybridization On Tissues, RRST RNA-Rescue Spatial Transcriptomics. B UMAP of cells after leiden clustering profiled with HybISS, colored by the assigned cell type (35 cell types presented). Gen general, adv adventitial, nan not annotated. C Heatmap of relative abundance of clustered cell types between locations demonstrating their frequency across the profiled regions. Similarity in cell type composition between three distal lung regions can be assessed by hierarchical clustering dendrogram on the left side. D Representative histological images from four biological replicates of an analysed trachea (donor 4) and a distal lung (donor 1) biopsies with hematoxyllin and eosin (H&E) staining (up) coupled to the maps of cell types identified by HybISS on top of nuclei (DAPI, white) in the same sections (down). Spots represent detected transcripts, colored according to the corresponding cell type of the cell they were assigned to. Colors as in Fig. 1B. Dashed lines indicate the approximate borders of histologic compartments. SMG submucosal gland, aw airway, alv alveolar region, bv blood vessel. Scale bar 200 µm. E Cell type neighborhood enrichment graph representing cell types as nodes, and edges indicating a positive neighborhood enrichment (>2) between cell types across the profiled sections. Suggested neighborhoods are shown as bubbles. Node colors as in Fig. 1B. Source data are provided as a Source Data file.

Article Snippet: HybISS Hybridization-based in situ sequencing, SCRINSHOT Single-Cell Resolution IN Situ Hybridization On Tissues, RRST RNA-Rescue Spatial Transcriptomics.

Techniques: Hybridization, In Situ, Sequencing, In Situ Hybridization, Staining

Journal: Journal of Translational Medicine

Article Title: Spatial omics in 3D culture model systems: decoding cellular positioning mechanisms and microenvironmental dynamics

doi: 10.1186/s12967-025-07390-6

Figure Lengend Snippet: Typical workflow for studying the tumor microenvironment via spatial omics. Tissue or 3D culture samples are sectioned (fresh-frozen or FFPE) and analyzed through either imaging-based assays or capture-based arrays. Imaging-based techniques (upper panel) rely on fluorescent probe hybridization or in situ sequencing to directly visualize transcripts within tissue sections. Typical steps include probe hybridization to target RNA, iterative imaging cycles using high-resolution microscopy, image alignment/registration, and computational spot detection coupled with cell segmentation to generate single-cell or subcellular expression profiles. Capture-based techniques (lower panel) employ spatially barcoded oligonucleotide arrays or beads to capture RNA molecules released from tissue sections. mRNA hybridizes to spatially barcoded primers containing unique molecular identifiers (UMIs), which are reverse-transcribed into cDNA and converted into sequencing libraries. Sequencing reads retain positional barcodes, enabling reconstruction of transcriptome-wide gene expression profiles at a resolution defined by spot or bead size. This figure was drawn using Biorender

Article Snippet: 2024 , HiFi-Slide spatial RNA sequencing , BBB assembloid (PSC-derived brain + vasculature) , Achieved 3D spatial transcriptomics on a PSC-derived brain–blood-vessel assembloid; identified 12 spatial domains and colocalized cell types (e.g. ECs with GABAergic neurons, SMCs with glutamatergic neurons), recapitulating neurovascular arrangement. , [ ] .

Techniques: Imaging, Hybridization, In Situ, Sequencing, Microscopy, Expressing, Reverse Transcription, Transcriptome Wide Gene Expression

Journal: Journal of Translational Medicine

Article Title: Spatial omics in 3D culture model systems: decoding cellular positioning mechanisms and microenvironmental dynamics

doi: 10.1186/s12967-025-07390-6

Figure Lengend Snippet: Applications of spatial omics in organoids. ( A ) Workflow of the LOSRT technique, including organoid generation, lamination, fixation, and sequencing. ( B ) ssDNA imaging was performed on laminated lung organoids, followed by spatially resolved cell-type annotation and classification. The spatial distributions of alveolar type II cells (SFTPC-positive) and macrophages (F4/80-positive) were mapped and subsequently validated via immunofluorescence staining of cryosectioned organoid slices. ( C ) ssDNA imaging was performed on laminated liver organoids, followed by spatially resolved cell‑type annotation and classification. The spatial distributions of hepatocytes (CK18‑positive) and macrophages (CD68‑positive) were mapped and subsequently validated via immunofluorescence staining of cryosectioned organoid slices. These images are reproduced with the permission of Refs . ( D ) Differentiation workflow for human embryonic organoids (HEMOs). HEMOs were harvested at defined developmental stages for 10x Chromium single-cell RNA sequencing and 10x Visium spatial transcriptomics, enabling the identification of distinct cellular niches. These images are reproduced with the permission of Refs . ( E ) Spatial omics analysis of neural organoids under optogenetic stimulation. (a, b) Schematic of the optogenetic stimulation protocol for SHH and its integration with spatial transcriptomic readouts. (c) H&E-stained image of hiPSC-derived neural organoid sections transferred onto a 10x Visium slide, with a spatial subset of Visium spots centered on the SHH-induced region. Scale bar = 500 μm. (d) Optogenetic stimulation pattern applied to neural organoids. (e) Spatial distance distributions of Visium spots in control (dark) versus SHH-induced (light) organoids. Scale bar = 100 μm. These images are reproduced with the permission of Refs . ( F ) Spatial Proteomic Atlas of Human Retinal Organoids. (a, b) Schematic of the 4i workflow and downstream analysis, applied across the developmental time course of retinal organoid maturation. (c) Representative 4i dataset image showing Hoechst-stained nuclei in a section of a 39‐week-old retinal organoid. (d-f) Example pixel‐based clustering results for the same 39-week organoid section, illustrating distinct proteomic domains. (g) Overview schematic for the integration of spatial proteomic data with complementary transcriptomic datasets. These images are reproduced with the permission of Refs

Article Snippet: 2024 , HiFi-Slide spatial RNA sequencing , BBB assembloid (PSC-derived brain + vasculature) , Achieved 3D spatial transcriptomics on a PSC-derived brain–blood-vessel assembloid; identified 12 spatial domains and colocalized cell types (e.g. ECs with GABAergic neurons, SMCs with glutamatergic neurons), recapitulating neurovascular arrangement. , [ ] .

Techniques: Sequencing, Imaging, Immunofluorescence, Staining, RNA Sequencing, Derivative Assay, Control

Journal: Clinical and Translational Medicine

Article Title: Novel cancer‐associated secretory cells and IL‐1β + macrophages as key players in early lung adenocarcinoma progression in female never‐smokers

doi: 10.1002/ctm2.70433

Figure Lengend Snippet: Cancer‐associated secretory (CAS) cells originated from alveolar type 2 (AT2) cells. (A) Principal component analysis (PCA) plot of alveolar type 1 (AT1), AT2 and CAS cells in the solid component of tumour (S) and ground‐glass component of tumour (GG) regions, with lines representing the inferred trajectories. Each dot represents a single cell and is coloured according to cell type. Lines indicate inferred trajectories, estimated using Slingshot. (B) PCA plots of single‐cell transcriptomes, with cells (dots) coloured by region (GG vs. S) (top) and patient (bottom). (C) Pseudotime analysis depicting the gene expression dynamics of surfactant protein A1 (SFTPA1) (AT2 marker), advanced glycation end‐product specific receptor (AGER) (AT1 marker), secretoglobin family 3A member 2 (SCGB3A2) (CAS marker) and carcinoembryonic antigen‐related cell adhesion molecule 6 (CEACAM6) (CAS marker) along the inferred trajectory. The black line and points represent lineage 1 (AT2 to AT1), while the red line and points represent lineage 2 (AT2 to CAS). (D) Violin plots showing the expression levels of carcinoembryonic antigen‐related cell adhesion molecule 5 (CEACAM5), CEACAM6 and serine peptidase inhibitor Kazal type 1 (SPINK1) across different samples in CAS cell types from single‐cell RNA sequencing (scRNA‐seq). (E) Box plots displaying normalised expression levels of SFTPA1, SCGB3A2, CEACAM5, CEACAM6 and SPINK1 across different components (N, GG and S, n = 7, respectively) in whole‐transcriptome sequencing analysis. The Kruskal–Wallis test was performed. (F) Box plots showing the normalised expression levels of SFTPA1, SCGB3A2, CEACAM5, CEACAM6 and SPINK1 across normal (N, n = 23) and cancer (C, n = 34) tissues from a study by Zhang et al. (2020). Wilcox statistical significance is indicated by p ‐values. PSN, part‐solid nodule; SCGB3A1, secretoglobin family 3A member 1.

Article Snippet: Analyses included whole‐exome sequencing (WES) and whole‐transcriptome sequencing (WTS) ( n = 7), single‐cell RNA sequencing ( n = 4) and spatial transcriptomics ( n = 1, SMC‐19) (Figure ).

Techniques: Gene Expression, Marker, Expressing, RNA Sequencing, Sequencing

Journal: bioRxiv

Article Title: A non-canonical top-down pathway regulating relapse to opioid

doi: 10.1101/2025.11.27.691060

Figure Lengend Snippet: (A) Schematic showing the sequencing chip of stereo-seq technology. (B) Visualization of the spatial transcriptome of the coronal brain slice containing RSG region. Scale bars, 300 μm. (C) Clustering analysis of RSG cells visualized by Uniform manifold approximation and projection (UMAP) dimensional reduction. (D) Spatial distribution of different clusters of glutamatergic and GABAergic neurons in RSG. (E) Dotplot showing the Cckbr mRNA expression in different clusters of RSG glutamatergic and GABAergic neurons. (F) Representative image showing the expression of Cckbr protein in RSG. Scale bars, 100 μm. (G) Normalized fluorescence intensity of Cckbr protein across the different layers of RSG. (H) Area under curve of the fluorescence intensity of Cckbr protein in different layers of RSG ( n = 5). One-way ANOVA (F (3, 16) = 72.32, p < 0.0001) followed by Tukey’s post hoc test, **** p < 0.0001. (I) Left: representative images showing the expression of Cckbr protein in RSG layer 5 of rats in Saline SA and Heroin SA groups. Scale bars, 50 μm. Right: average expression level of Cckbr protein in RSG layer 5 of Saline SA ( n = 3) vs Heroin SA ( n = 3) rats. Mann-Whitney test, * p < 0.05. (J) Recognition and separation of different layers in RSG. Scale bars, 200 μm. (K) Heatmap showing the differential IEGs expression in RSG layer 2/3, layer 5 and layer 6. Multiple Mann-Whitney test followed by False Discovery Rate (FDR) post test, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs L2/3, #### p < 0.0001 vs L6. (L) Left: schematic of the viral strategy for chemogenetic inhibition of ZI neurons and the representative image showing the hM4Di expression in ZI. Scale bars, 100 μm. Right: number of responses of rats in EGFP control group ( n = 10) vs hM4Di group ( n = 9). Two-way ANOVA (F (1,34) = 1.635, p = 0.2096) followed by Sidak’s post hoc test, * p < 0.05. (M) Left: representative images showing the expression of TH and c-fos (top) or Gad and c-fos (bottom) in ZI of rats in ABB group and ABA group. Scale bars, 100 μm. Right: number of c-fos-positive cells in TH + or Gad + neurons in ZI of ABB group ( n = 4) vs ABA group ( n = 5). Two-way ANOVA (F (1,14) = 25.68, p < 0.001) followed by Sidak’s post hoc test, **** p < 0.0001, ns, no significant difference. (N) Left: representative image showing the co-localization of Cckbr and the mCherry-labeled ZI-projecting neurons in RSG layer 5. Scale bars, 50 μm. Right: percentage of Cckbr + and Cckbr - cells in mCherry + neurons in RSG layer 5 ( n = 3). (O) Left: representative images showing the expression of mCherry and c-fos in RSG layer 5 of rats in ABB group and ABA group. Scale bars, 50 μm. Right: number of mCherry + c-fos + neurons in RSG layer 5 of ABB group ( n = 5) vs ABA group ( n = 3) and percentage of Fos + and Fos - nuclei in mCherry + cells in RSG layer 5 in ABA group. Unpaired t test, ** p < 0.01, **** p < 0.0001. (P) Top: representative images showing the co-localization of Vgat , Vglut2 and mCherry in ZI, and percentage of mCherry-positive cells in Vglut2 + and Vgat + neurons in ZI ( n = 4). Unpaired t test, **** p < 0.0001. Bottom: representative images showing the co-localization of Vgat , mCherry and Fos in ZI after context-induced relapse, and percentage of Fos + and Fos - nuclei in Vgat + mCherry + cells in ZI after context-induced relaspe ( n = 4). Scale bars, 100 μm and 50 μm. Unpaired t test, **** p < 0.0001. (Q) Schematic showing the training and perfusion schedule, the viral strategy for Cckbr knockout and chemogenetic activation of RSG glutamatergic neurons and the representative image of RSG axon terminals in ZI. Scale bars, 500 μm. (R) Left: representative images showing the c-fos expression in ZI adjacent to the axon terminals of RSG glutamatergic neurons in rats of control, Cckbr knockdown and Cckbr knockdown with hM3Dq groups after context-induced relapse. Scale bars, 50 μm. Right: number of c-fos-positive neurons in ZI of rats in control ( n = 4), Cckbr knockdown ( n = 5) and Cckbr knockdown with hM3Dq ( n = 6) groups after context-induced relapse. One-way ANOVA (F( 2, 12) = 12.30, p < 0.01) followed by Tukey’s post hoc test, ** p < 0.01, ns, no significant difference. (S) Schematic of the viral strategy for chemogenetic inhibition of RSG Glu-Cckbr -ZI GABA circuit. (T) Number of responses in mCherry control group ( n = 8) and hM4Di group ( n = 9) during test 1 with clozapine injection (i.p.). Two-way ANOVA (F (1,30) = 6.145, p < 0.05) followed by Sidak’s post hoc test, * p < 0.05. (U) Number of responses in mCherry control group ( n = 8) and hM4Di group ( n = 9) during test 2 with vehicle injection. Two-way RM ANOVA (F (1,30) = 0.3091, p = 0.5824) followed by Sidak’s post hoc test. ns, no significant difference.

Article Snippet: The spatial transcriptome sequencing was conducted by Novogene Co. Ltd (Beijing, China).

Techniques: Sequencing, Slice Preparation, Expressing, Fluorescence, Saline, MANN-WHITNEY, Inhibition, Control, Labeling, Knock-Out, Activation Assay, Knockdown, Injection