Journal: Nature

Article Title: iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

doi: 10.1038/s41586-025-09803-4

Figure Lengend Snippet: (A, C) Total and (B, D) virus-specific ASCs were quantified in the liver at week 4 post-infection for (A, B) RHV and (C, D) LCMV clone 13 infection with (E, F) representative ELISpot images showing (A, B) representative and (C, D) pooled data from (A-D) n = 2 independent experiments. Mice were either untreated (WT) or splenectomized and treated with FTY720 prior to infection with treatment being maintained throughout (splX + FTY720 [D -1]). Virus localization was determined by viral transcript density for individual portal and central zones at week 1 post-infection during (G) RHV and (H) LCMV clone 13 infection from n = 10 portal and n = 10 central zone selections from a Xenium 5 K spatial transcriptomics run of n = 1 mouse liver per group. Transcript density of genes associated with oxidative phosphorylation usage were quantified in (I) portal zone hepatocyte regions and (J) immature lymphocytic clusters at week 1 post-infection. Following (K) upstream staining, (L-O) individual transcript localization was determined in the liver at week 1 post-infection. (A-D, G-J) Mean + SEM. (A-D, G-J) Two-tailed, unpaired t-tests were performed. (A) p = 0.9168, (B) p = 0.1987, (C) p = 0.0008, (D) p < 0.0001, (G) p = 0.0261, (H) p = 0.0282. Statistical significance was denoted as *=(p ≤ 0.05), **=(p ≤ 0.01), ***=(p ≤ 0.001), and ****=(p ≤ 0.0001).

Article Snippet: Fig. 5 Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts. a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection.

Techniques: Virus, Infection, Enzyme-linked Immunospot, Phospho-proteomics, Staining, Two Tailed Test

Journal: Nature

Article Title: iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

doi: 10.1038/s41586-025-09803-4

Figure Lengend Snippet: a – c , H&E staining of formalin-fixed, paraffin-embedded (FFPE) spleen ( a ) and mesenteric lymph node ( b ) sections at 4 weeks post-LCMV infection and a liver section at 4 weeks post-RHV infection ( c ). Regions of interest (ROIs) are enlarged on the right. One tissue sample from each condition was utilized for Visium HD spatial transcriptomics based on similar morphological H&E staining with limited interindividual variability for n = 4 ( a ), n = 4 ( b ) and n = 5 ( c ) mice. d – h , Visium HD spatial transcriptomic of slides shown in a – c . Individual transcript localization is shown as log 2 -scaled heat maps of 8-μm bins for Ms4a1 ( d ), H2afx ( e ), Cd3g ( f ), Ccl21a ( g ) and Xbp1 ( h ). i , Graph-based subclustering of clusters of interest from sections in a – c were manually annotated and cross-validated with ACT and PanglaoDB cell annotation databases. IFZ, interfollicular zone; SCS, subcapsular sinus. Colours indicate cell type and anatomical zones.

Article Snippet: Fig. 5 Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts. a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection.

Techniques: Staining, Formalin-fixed Paraffin-Embedded, Infection

Journal: Nature

Article Title: iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

doi: 10.1038/s41586-025-09803-4

Figure Lengend Snippet: (A-F) Visium HD spatial transcriptomic outputs at 4 weeks post-RHV infection in the liver displayed as log2-scaled heatmaps of 8 μm bins for single-parameter panels and feature sums for multiple-parameter lists. (A) Transcripts characteristic of central zone (zone 3) are shown in orange ( Glul, Cyp2e1 ) while those characteristic of portal zone (zone 1) are shown in green ( Hal, Arg1 ). (B) Co-expression of central (orange) and portal (green) zone-related transcripts shown in (A) as combined feature sums. (C) Merged expression of central (red, Glul and Cyp2e1 ) and portal (green, Hal and Arg1 ) combined transcripts. (D) Expression of various plasma cell-related transcripts as shown in blue ( Xbp1 , Derlr3 , Jchain , Irf4 ). (E) Merged expression of transcripts indicative of the central zone (red, Glul and Cyp2e1 ) and plasma cells ( Xbp1 , Derl3 , Jchain , Irf4 ). (F) Merged expression of transcripts indicative of the portal zone (green, Hal and Arg1 ) and plasma cells ( Xbp1 , Derl3 , Jchain , Irf4 ). (F) Co-expression of plasma cell-related transcripts ( Xbp1 , Derl3 , Jchain , Irf4 ) as feature sum list. (A, F) From morphologically similar H&E staining with limited interindividual variability conducted on n = 5 mice, data is shown from n = 1 mouse liver tissue with which Visium HD spatial transcriptomics was conducted.

Article Snippet: Fig. 5 Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts. a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection.

Techniques: Infection, Expressing, Clinical Proteomics, Staining

Journal: Nature

Article Title: iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

doi: 10.1038/s41586-025-09803-4

Figure Lengend Snippet: a – c , Visium HD spatial transcriptomics from liver tissue at four weeks post-RHV infection. a , Transcript feature sums of log 2 -scaled heat maps of 8-μm bins for osteopontin ( Spp1 ), Cxcl12 , type I collagen ( Col1a1 and Col1a2 ( Col1a1/2 )), Icam2 , fibronectin ( Fn1 ), Cd44 , Cxcr4 , CD138 ( Sdc1 ), LFA-1 ( Itga4 / Itgb1 ) and VLA-4 ( Itgal/Itgb7 ). b , Merged transcript localization of feature sum lists from a . c , H&E image with portal vein (blue) and central vein (yellow) ROIs (left) with associated transcript localization (right). d , Cartoon diagram representing plausible molecular factors responsible for intrahepatic plasma cell retention. LSEC, liver sinusoidal endothelial cell. Created in BioRender. Grakoui, A. (2025) https://BioRender.com/ppmu1j5 . e , f , Intrahepatic total ( e ) and E2-specific ( f ) ASCs at 4 weeks post-infection with or without acute blockade of anchoring molecules at days 26 and 27 post-infection. AMD, AMD3100; anti-V/L, anti-VLA-4 plus anti-LFA-1; anti-V/L/S, anti-VLA-4, anti-LFA-1 plus anti-SPP1. n = 2 independent experiments. Control versus anti-V/L + AMD: P = 0.0003 ( e ), P = 0.0182 ( f ). g – i , Xenium Prime 5K spatial transcriptomics on liver tissue at three weeks post-infection with upstream morphological staining ( g ), virus, vasculature and plasma cell transcript localization ( h ), and virus, GC-associated and plasma cell transcript localization in periportal regions ( i ). j , RHV RNA in serum plotted against intrahepatic E2-specific ASC frequencies at four weeks post-infection. μMT, B6.129S2-Ighmtm1Cgn/J mice lacking mature B cells; dpi, days post infection. k , l , Bulk IgH BCR sequencing at 4 weeks post-infection from n = 3 RHV-infected mice, n = 3 LCMV-infected mice and n = 1 naive mouse. SHM accrual is plotted as nucleotide divergence from germline sequences among distinct clonotypes ( k ) and IgH V–J gene pairing chord diagrams ( l ). m – o , Intrahepatic common Igkc transcript localization alongside unique Igkv gene family transcripts with upstream morphological staining ( m ) and segmented cell borders showing transcript localization of Igkc with Igkv4-51 ( n ) and Igkc with Igkv15-103 ( o ). e , f , j , Data are representative or pooled values from at least two independent experiments of at least three mice per group. e , f , k , Data are mean + s.e.m. One-way ANOVA with Tukey’s multiple comparisons test ( e , f , k ); two-tailed nonparametric Spearman correlations with Pearson’s r ( j ).

Article Snippet: Fig. 5 Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts. a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection.

Techniques: Infection, Clinical Proteomics, Control, Staining, Virus, Sequencing, Two Tailed Test

Journal: Nature

Article Title: iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

doi: 10.1038/s41586-025-09803-4

Figure Lengend Snippet: (A) Number of total IgG + ASCs in the liver at week 4 post-RHV infection following splenectomy one week prior to infection or FTY720 administration beginning at day 11 post-infection onward with representative ELISpot image. (B) Correlation between % plasma cells (CD38 low CD138 + of total B cells) and % GC B cells (CD38 low CD95 + of total B cells) in the liver at 4 weeks post-infection with RHV. (C) Representative H&E staining of FFPE sections from spleen at week 4 post-LCMV infection and liver at week 4 post-RHV infection. (D) Immunofluorescence staining of FFPE sections at 4 weeks post-RHV infection. Localization of representative Ig kappa gene families displayed as log2-scaled heatmaps of 8 μm bins at 4 weeks post-infection in (E) spleen (LCMV), (F) mesenteric lymph nodes (LCMV) and (G) liver (RHV). (H) From liver tissue at 4 weeks post-infection with RHV following FTY720 treatment, H&E (top) is shown for corresponding ROIs where Igkv19–93 localization is shown as log2-scaled heatmaps of 8 μm bins (bottom). (I) Correlation between intrahepatic CD38 low CD95 + GC B cells and RHV serum viremia at 4 weeks post-infection. (A-B) Data shown are representative or pooled values from 2-3 independent experiments of 3–9 mice per group. Visium HD spatial transcriptomics was conducted with liver tissue from n = 1 representative mouse following similar H&E morphological staining with limited interindividual variability from (C) n = 4 (spleen) and n = 4 (liver), (E) n = 4, (F) n = 4, (G) n = 5, and (H) n = 4 mice. (D) Representative image shown from immunofluorescent staining that was performed with liver tissue from n = 4 mice. (A) Mean + SEM. Statistical tests performed were (A) one-way ANOVA with Tukey’s multiple comparisons test and (B, I) two-tailed nonparametric Spearman correlation with Pearson’s r coefficient. (A) Uninfected vs. WT: p < 0.0001, WT vs. FTY720 D + 11: p = 0.9939, FTY720 D + 11 vs. Splenectomy: p = 0.9853. Statistical significance was denoted as ****=(p ≤ 0.0001).

Article Snippet: Fig. 5 Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts. a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection.

Techniques: Infection, Enzyme-linked Immunospot, Clinical Proteomics, Staining, Immunofluorescence, Two Tailed Test

Journal: Nature

Article Title: iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

doi: 10.1038/s41586-025-09803-4

Figure Lengend Snippet: a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm 2 of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection. Data are mean + s.e.m. d – g , One-way ANOVA with Tukey’s multiple comparisons test. AIH versus HBV: P = 0.0101 ( g ). h , Quantification of cell types and their direct contact partners within leukocytic aggregates from annotated spatial transcriptomics with subcellular resolution and cell segmentation during mouse RHV (orange) and human HCV (blue) infection. HSC, hepatic stellate cell. i – n , RHV-infected mouse liver ( i , k , m ) and HCV-infected human liver ( j , l , n ) tissue. Generative GC-like structures were characterized upstream staining ( i , j ) and GC-associated transcript localization ( k , l ) and colour-coded cell-type annotation with selected overlaid transcripts ( m , n ). o – t , RHV-infected mouse liver ( o , q , s ) and HCV-infected human liver ( p , r , t ) tissue. Areas of intrahepatic plasma cell residency were characterized by upstream staining ( o , p ) and plasma cell and hepatic stellate cell and fibroblast-associated transcripts ( q , r ) with colour-coded cell-type annotation ( s , t ). Based on similar morphological H&E staining with limited interindividual variability of n = 2 HCV-infected humans and n = 4 RHV-infected mice, Xenium 5K was performed on tissue from n = 2 human and n = 1 mouse livers, from which n = 1 representative tissue of each are shown in i – t .

Article Snippet: Fig. 5 Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts. a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection.

Techniques: Infection, Staining, Clinical Proteomics

Journal: Nature

Article Title: iHALT unlocks liver functionality as a surrogate secondary lymphoid organ

doi: 10.1038/s41586-025-09803-4

Figure Lengend Snippet: Xenium Prime 5 K spatial transcriptomics was performed with liver tissue obtained during human (A, B) AIH and (C, D) HBV infection. Depicted are regions of interest showing (A, C) upstream morphological staining and (B, D) expression of various color-coded transcripts and annotated cell types. Data shown from (A, B) n = 1 individual with AIH and (C, D) n = 1 individual chronically infected with HBV from which liver tissue was selected for spatial transcriptomics conducted on (A, B) n = 1 individual and (C, D) n = 2 individuals with limited interindividual variability.

Article Snippet: Fig. 5 Hepaciviral infection in mouse and human induce intrahepatic lymphoid structures with highly similar cellular composition, organizational microarchitecture and cell–cell contacts. a , b , Xenium Prime 5K spatial transcriptomics of liver tissue from healthy, AIH, HBV-infected and HCV-infected humans with upstream staining ( a ) and selected transcript localization ( b ). c – g , Quantitative analyses from spatial transcriptomics demonstrating number of leukocytic aggregates per mm of tissue ( c ), aggregate area of leukocytic aggregates ( d ), and lymphocytic cell-type proportions of B cells ( e ), CD4 + T cells ( f ) and CD8 + T cells ( g ) observed in leukocytic aggregates from individuals with AIH ( n = 1), chronic HBV ( n = 2) and chronic HCV ( n = 2) infection.

Techniques: Infection, Staining, 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: 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: Advanced Science

Article Title: Multiscale Cell–Cell Interactive Spatial Transcriptomics Analysis

doi: 10.1002/advs.202508358

Figure Lengend Snippet: Overview of MCIST workflow. Gene expression data are treated as a point cloud of cells, from which we construct a sequence of multiscale cell‐cell interaction graphs based on an affinity measure between expression profiles and k‐nearest neighbors (kNNs). These graphs give rise to an ensemble of low‐dimensional multiscale topological PCA representations of the gene expression data, each characterizing a specific combination of cell–cell connectivities. A latent space representation of the spatially resolved gene expression data is also constructed from a deep learning model to pair with the multiscale topological representation. These representations are then aligned for downstream ensemble clustering‐enabling spatial domain detection, residue‐similarity index (RSI)‐optimized trajectory inference, and differential gene expression analysis.

Article Snippet: In this study, we present the MultiScale Cell‐Cell Interactive Spatial Transcriptomics Analysis method, which unites the strengths of spatially resolved deep learning techniques with a topological representation of multi‐scale cell‐cell similarity relations.

Techniques: Gene Expression, Construct, Sequencing, Expressing, Residue