Journal: eLife

Article Title: Chronic hyperactivation of midbrain dopamine neurons causes preferential dopamine neuron degeneration

doi: 10.7554/eLife.98775

Figure Lengend Snippet: ( A ) DAT IRES cre animals that received clozapine- N -oxide (CNO) (CNO alone, n=2 mice) or were injected with AAV-hM3Dq-mCherry and received vehicle (GqVeh, n=3 mice) or CNO (GqCNO, n=3 mice) were treated for 1 week before brains were flash-frozen for spatial transcriptomic analysis. ( B ) Image of midbrain and striatal sections stained with TH (green), NeuN (purple), and DAPI (blue) shows discs assigned to regions of interest. Inset shows a disc containing two TH+ cell bodies. ( C ) Expression of dopaminergic and striatal genes is confined to expected spatial regions. Expression of genes involved in DA metabolism decreases with chronic CNO. 2–49 discs were compiled per ventral tegmental area (VTA), and 1–7 discs were compiled per SN. 357–560 capture areas were compiled per CP. The thalamus was selected as a midbrain control region, while white matter tracts (white) and the lateral septal complex (LSX) were used as striatal controls. ( D ) Principal components analysis of midbrain regions (top) and the caudate putamen (bottom) for GqCNO, GqVeh, and CNO alone groups. ( E ) The deep learning NEUROeSTIMator model was used to predict neural activity of GqCNO, GqVEH, and CNO alone within the SN and VTA from Visium spatial transcriptomics. Groups were compared using the Kolmogorov-Smirnov (KS) test (see Methods). ( F ) Hits were used for Enrichr pathway analysis if significant in both GqCNO vs CNO alone and GqCNO vs GqVeh comparisons. Gene rankings for hit analysis were established using fold change score (FCS) and signal-to-noise score (SNS). ( G ) Volcano plots comparing GqCNO vs GqVeh in the SN, VTA, and CP. Genes highlighted are also significantly altered when comparing GqCNO vs CNO alone. Scale bars indicate 500 µm. Data indicate mean ± SEM. *p<0.05, **p<0.01, ***p<0.001 by one-way ANOVA followed by Holm-Sidak post hoc test.

Article Snippet: Spatial transcriptomics were acquired with Visium spatial gene expression kits (10x Genomics).

Techniques: Injection, Staining, Expressing, Control, Activity Assay

Journal: Gut

Article Title: Targeting Treg–fibroblast interaction to enhance immunotherapy in steatotic liver disease-related hepatocellular carcinoma

doi: 10.1136/gutjnl-2025-335084

Figure Lengend Snippet: Spatial architecture of SLD-HCC and non-SLD-HCC. ( A ) Data analysis workflow for Visium spatial transcriptomics (ST). ( B ) UMAP plot illustrating the distribution of ST data after Harmony integration. n=126 099 spots from n=7 SLD-HCCs and n=5 non-SLD-HCCs. ( C ) UMAP plots showing gene signature scorings for major immune and non-immune subsets from all Visium data. ( D ) Representative H&E images on FFPE tissues comprising tumours, non-tumours and margin areas from patients with SLD-HCC and non-SLD-HCC (left); spatial deconvolution using SpaCET, indicating tumour and non-tumour regions (right). Box, field of view (FOV)=6.5 x 6.5 mm. ( E ) Spatially aware clustering using Banksy, showing the different domains within each tissue sample. ( F ) Heatmap showing relative gene signature scoring used to estimate the cellular composition within each domain. DC, dendritic cell; FFPE, formalin-fixed, paraffin-embedded; NK, natural killer; scRNA seq, single-cell RNA sequencing; SLD-HCC, steatotic liver disease-related hepatocellular carcinoma; Treg, regulatory T cell; UMAP, uniform manifold approximation and projection.

Article Snippet: Spatial RNA library preparation was performed using the Visium Human Transcriptome Probe Kit V.2 (10x Genomics, California, USA).

Techniques: Formalin-fixed Paraffin-Embedded, RNA Sequencing

Journal: Gut

Article Title: Targeting Treg–fibroblast interaction to enhance immunotherapy in steatotic liver disease-related hepatocellular carcinoma

doi: 10.1136/gutjnl-2025-335084

Figure Lengend Snippet: Cellular interaction network in SLD-HCC and non-SLD-HCC. ( A ) Representative immunofluorescence (IF) image matched with ST data generated from CosMx unsupervised clustering. Each cluster is colour-coded. PanCK, pan-cytokeratin. Each FOV=0.7 x 0.9mm. ( B ) UMAP plot illustrating all clusters from CosMx with n=152 214 total cells, n=50 FOVs from two SLD-HCCs and two non-SLD-HCCs. ( C ) Heatmap showing relative expression levels of selected genes representing each CosMx cluster. ( D ) Bar graphs showing the absolute number (top) and proportion within total cells (bottom) of each identified cell type across the two SLD-HCC and two non-SLD-HCC tissue samples. ( E ) Neighbourhood enrichment scores showing interaction strength between Tregs and other cell types from CosMx ST data. Two-sided p values calculated by pairwise Mann-Whitney test. ( F ) Neighbourhood enrichment scores showing interaction strength between Tregs and fibroblasts at margin domains from deconvoluted Visium ST data (n=7 SLD-HCCs and n=5 non-SLD-HCCs). Two-sided p values calculated by pairwise Mann-Whitney test. ( G ) Representative IF images of margin areas from SLD-HCC and non-SLD-HCC tissues stained for CD4, FoxP3 (Treg) and αSMA (fibroblast). DAPI was used for nuclear staining. Scale bar denotes 20 µm. ( H ) Comparison of mean number of Tregs between SLD-HCC and non-SLD-HCC, quantified from three to five randomly selected FOVs per tissue at tumour margin. ( I ) The distance between Treg and nearest fibroblast in the same FOVs from ( H ) was compared between SLD-HCCs and non-SLD-HCCs. ( F, H and I ) Boxplots show median and the whiskers represent minimum and maximum values with the box edges showing the first and third quartiles. ( H and I ) mIF data was obtained from six SLD-HCCs and six non-SLD-HCCs, and analysis was performed using Mann-Whitney U test. Graphs show mean±SEM. CAFs, cancer-associated fibroblasts; DAPI, 4',6-diamidino-2-phenylindole; DC, dendritic cell; EC, endothelial cell; FOVs, fields of views; LSEC, Liver sinusoidal endothelial cells; mIF, multiplex immunofluorescence; NK, natural killer; pDC, plasmacytoid dendritic cell; SLD-HCC, steatotic liver disease-related hepatocellular carcinoma; ST, spatial transcriptomic; Treg, regulatory T cell; UMAP, uniform manifold approximation and projection.

Article Snippet: Spatial RNA library preparation was performed using the Visium Human Transcriptome Probe Kit V.2 (10x Genomics, California, USA).

Techniques: Immunofluorescence, Generated, Expressing, MANN-WHITNEY, Staining, Comparison, Multiplex Assay

Journal: Gut

Article Title: Targeting Treg–fibroblast interaction to enhance immunotherapy in steatotic liver disease-related hepatocellular carcinoma

doi: 10.1136/gutjnl-2025-335084

Figure Lengend Snippet: Ligand–receptor interactomes in SLD-HCC. ( A ) Heatmap showing relative COMMOT scores of enriched L–R pathways from CosMx data. ( B ) Heatmap showing relative expression levels of L–R pairs, determined by NICHES analysis. A representative Visium map highlighting the tumour margin domains was shown (upper right). Specific enriched L–R pairs from clusters enriched at the tumour margin domains were shown (boxed, bottom right). ( C ) Representative images from Visium data showing tumour fraction scoring, tissue segmentation into tumour, margin and non-tumour regions as well as relative expression of TNFSF14-TNFRSF14 in SLD-HCCs versus non-SLD-HCCs. ( D ) COMMOT analysis on Visium data showing distinct TNFSF14-TNFRSF14 strength and directionality in SLD-HCCs versus non-SLD-HCCs. Gene expression intensity is marked by size and directionality by the pointed end of the arrows. Tumour (T) and non-tumour (NT) regions are separated by dashed red lines. ( E ) Representative IF images showing expression of TNFSF14-TNFRSF14 at tumour margins in SLD-HCCs and non-SLD-HCCs. Scale bar denotes 100 µm. ( F ) COMMOT scores comparing the strength of the TNFSF14-TNFRSF14 interaction between SLD-HCCs (n=7) versus non-SLD-HCCs (n=5) on Visium data. Treg–CAFs and CAFs–Treg interactions from margin domains were analysed. ( G ) COMMOT scores comparing the strength of the TNFSF14-TNFRSF14 interaction between three responders and five non-responders to immunotherapy in SLD-HCC (n=5) versus non-SLD-HCC (n=3). Treg–CAFs and CAFs–Treg interactions from margin domains were analysed. ( B–D ) Visium FOV=6.5 x 6.5 mm. ( F and G ) Boxplots show median and the whiskers represent minimum and maximum values with the box edges showing the first and third quartiles. P value determined by two-tailed Mann-Whitney test. CAFs, cancer-associated fibroblasts; COMMOT, COMMunication analysis by Optimal Transport; DAPI, 4',6-diamidino-2-phenylindole;FOV, fields of view; IF, immunofluorescence; L–R, ligand–receptor; NA, not applicable; NICHES, Niche Interactions and Communication Heterogeneity in Extracellular Signaling; SLD-HCC, steatotic liver disease-related hepatocellular carcinoma; TNFSF14, tumour necrosis factor superfamily member 14; TNFRSF14, tumour necrosis factor receptor superfamily member 14; Treg, regulatory T cell.

Article Snippet: Spatial RNA library preparation was performed using the Visium Human Transcriptome Probe Kit V.2 (10x Genomics, California, USA).

Techniques: Expressing, Gene Expression, Two Tailed Test, MANN-WHITNEY, Immunofluorescence

Journal: Cancer Medicine

Article Title: TMEM106A as a Macrophage‐Associated Biomarker of Prognosis in IDH ‐Wildtype Glioma: Integrative Multi‐Omics and Spatial Analyses

doi: 10.1002/cam4.71454

Figure Lengend Snippet: Spatial Distribution of TMEM106A in the Ivy Glioblastoma Atlas. (A) Box plots depicting TMEM106A expression across five histologic subregions: Leading edge ( n = 19), infiltrating tumor ( n = 24), cellular tumor ( n = 30), microvascular proliferation ( n = 24), and pseudopalisading cells around necrosis ( n = 25). (B) A summary table displays the mean and confidence intervals for each region, highlighting the progressive increase in TMEM106A from the outer rim to the necrotic center. (C) H&E‐stained section from a human GBM sample featuring multiple tissue cores overlaid with Visium spots. (D) Pseudocolor map of TMEM106A expression at each spot (scale: Yellow = low, red = high). The asterisk indicated the necrotic area. (E) The Nomogram incorporates TMEM106A expression, gender, chemotherapy, age, Karnofsky performance score, tumor grade, and radiotherapy.

Article Snippet: For spatially resolved tumor analyses, we incorporated the Ivy Glioblastoma Atlas ( https://glioblastoma.alleninstitute.org/ ) and a 10x Genomics Visium Glioblastoma dataset ( https://www.10xgenomics.com/datasets/human‐glioblastoma‐whole‐transcriptome‐analysis‐1‐standard‐1‐2‐0 ).

Techniques: Expressing, Staining