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557 scrna seq single cell rna sequencing 558 strna seq spatial transcriptomics rna sequencing 559 ici immune cell infiltration 560 tams tumor  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc 557 scrna seq single cell rna sequencing 558 strna seq spatial transcriptomics rna sequencing 559 ici immune cell infiltration 560 tams tumor
    557 Scrna Seq Single Cell Rna Sequencing 558 Strna Seq Spatial Transcriptomics Rna Sequencing 559 Ici Immune Cell Infiltration 560 Tams Tumor, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/spatial+transcriptome+sequencing/ai+cell+immune/pm41345972-345-23-30
    Average 86 stars, based on 1 article reviews
    557 scrna seq single cell rna sequencing 558 strna seq spatial transcriptomics rna sequencing 559 ici immune cell infiltration 560 tams tumor - by Bioz Stars, 2026-09
    86/100 stars

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    Related Articles

    Biomarker Discovery:

    Article Title: Bu Yang Huan Wu Decoction controls synovitis via HIF-1/VEGF signaling for osteoarthritis therapy
    Article Snippet: .. Immune Cell AI (ICA) integrates multi-omics data (e.g., scRNA-seq, epigenetics, spatial transcriptomics) with a context-aware deep learning framework, enabling higher-resolution deconvolution of immune cell subsets (e.g., exhausted T cells, M2 macrophages) in complex tissues. ..

    Spatial Transcriptomics:

    Article Title: Bu Yang Huan Wu Decoction controls synovitis via HIF-1/VEGF signaling for osteoarthritis therapy
    Article Snippet: .. Immune Cell AI (ICA) integrates multi-omics data (e.g., scRNA-seq, epigenetics, spatial transcriptomics) with a context-aware deep learning framework, enabling higher-resolution deconvolution of immune cell subsets (e.g., exhausted T cells, M2 macrophages) in complex tissues. ..

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    Single Cell:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    RNA Sequencing:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    Saline:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    Quantitative RT-PCR:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    Reverse Transcription:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    Polymerase Chain Reaction:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    Western Blot:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    CCK-8 Assay:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..

    Colony Assay:

    Article Title: Integrated bulk and single-cell transcriptomics to develop an efferocytosis-related prognostic model for lung adenocarcinoma and validate the key gene LDHA.
    Article Snippet: .. 551 Abbreviations: 552 LUAD Lung adenocarcinoma 553 ERGs Efferocytosis-related genes 554 DEGs Differentially expressed genes 555 KM Kaplan-Meier 556 ROC Receiver operating characteristic 557 scRNA-seq Single-cell RNA sequencing 558 stRNA-seq Spatial transcriptomics RNA sequencing 559 ICI Immune cell infiltration 560 TAMs Tumor-associated macrophages 561 CAFs Cancer-associated fibroblasts 562 ECM Extracellular matrix 563 LC Lung cancer 564 NSCLC Non-small cell lung cancer 565 GSEA Gene Set Enrichment Analysis 566 GO Gene Ontology 567 KEGG Kyoto Encyclopedia of Genes and Genomes 568 GSVA Gene Set Variation Analysis 569 AUC Area under the curve 570 OS Overall survival 571 TMB Tumor Mutational Burden 572 TIDE Tumor Immune Dysfunction and Exclusion 573 IPS Immunophenoscore 574 CSC Cancer stem cell 575 FBS Fetal bovine serum 576 PBS Phosphate-buffered saline 577 CM conditioned medium 578 qRT-PCR Quantitative reverse transcription PCR 579 WB Western bloting 580 CCK-8 Cell Counting Kit-8 581 CFA Colony formation assay 582 AR TIC LE IN PR ES S ERDEGs Efferocytosis-related differentially expressed genes 583 DCA Decision curve analyses 584 NK Natural killer 585 RNAss RNA-based stemness score 586 ICIs Immune checkpoint inhibitors 587 Acknowledgments: The authors sincerely appreciate the providers of the open-access datasets, which were 588 essential for conducting this study. ..



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    Spatial Transcriptomics Inc spatial transcriptomics sequencing
    Investigations on SGMS2—related Cellular and Molecular Interactions in Hepatocellular Carcinoma. a Western blotting analysis of SGMS2 expression in THP—1 cells and differentiated macrophages. Each experiment was independently repeated three times. b , c Apoptosis levels of Huh7 tumor cells co—cultured with control macrophages and SGMS2—overexpressing macrophages were detected by flow cytometry (FCM). d Expression of SGMS2 in spatial <t>transcriptomics</t> <t>sequencing</t> data. e Abundance estimation of the CD56dimCD16highNR4A3high NK cell population by single—sample gene—set enrichment analysis (ssGSEA). f Multiplex immunofluorescence (mIF) images of SGMS2, CD68, CD16, CD56, and NR4A3 markers in 6 human HCC tissue samples. “Zoom macrophage” indicates the aggregation area of SGMS2—positive macrophages, and “Zoom NK cell” represents the CD56dimCD16highNR4A3high NK cells. The scale bar is 50 um or 20 um. g Scatter plots showing the density of CD56dimCD16highNR4A3high NK cells between patients with high and low infiltration of SGMS2—positive macrophages. Statistical analysis was performed using the Mann—Whitney U test. h Pearson correlation analysis of the density of CD56dimCD16highNR4A3high NK cells and the density of SGMS2—positive macrophages. i Kaplan—Meier analysis of OS, RFS, and early RFS in HCC patients with different infiltration densities of SGMS2—positive macrophages and CD56dimCD16highNR4A3high NK cells. Survival distributions were compared using the log—rank test. Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, *** P < 0.001; ns indicates no significant difference
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    (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.

    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

    Investigations on SGMS2—related Cellular and Molecular Interactions in Hepatocellular Carcinoma. a Western blotting analysis of SGMS2 expression in THP—1 cells and differentiated macrophages. Each experiment was independently repeated three times. b , c Apoptosis levels of Huh7 tumor cells co—cultured with control macrophages and SGMS2—overexpressing macrophages were detected by flow cytometry (FCM). d Expression of SGMS2 in spatial transcriptomics sequencing data. e Abundance estimation of the CD56dimCD16highNR4A3high NK cell population by single—sample gene—set enrichment analysis (ssGSEA). f Multiplex immunofluorescence (mIF) images of SGMS2, CD68, CD16, CD56, and NR4A3 markers in 6 human HCC tissue samples. “Zoom macrophage” indicates the aggregation area of SGMS2—positive macrophages, and “Zoom NK cell” represents the CD56dimCD16highNR4A3high NK cells. The scale bar is 50 um or 20 um. g Scatter plots showing the density of CD56dimCD16highNR4A3high NK cells between patients with high and low infiltration of SGMS2—positive macrophages. Statistical analysis was performed using the Mann—Whitney U test. h Pearson correlation analysis of the density of CD56dimCD16highNR4A3high NK cells and the density of SGMS2—positive macrophages. i Kaplan—Meier analysis of OS, RFS, and early RFS in HCC patients with different infiltration densities of SGMS2—positive macrophages and CD56dimCD16highNR4A3high NK cells. Survival distributions were compared using the log—rank test. Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, *** P < 0.001; ns indicates no significant difference

    Journal: Journal of Translational Medicine

    Article Title: SGMS2+ macrophages enhance NR4A3hi NK cell infiltration to improve prognosis and PD-1 treatment efficacy in hepatocellular carcinoma

    doi: 10.1186/s12967-025-07040-x

    Figure Lengend Snippet: Investigations on SGMS2—related Cellular and Molecular Interactions in Hepatocellular Carcinoma. a Western blotting analysis of SGMS2 expression in THP—1 cells and differentiated macrophages. Each experiment was independently repeated three times. b , c Apoptosis levels of Huh7 tumor cells co—cultured with control macrophages and SGMS2—overexpressing macrophages were detected by flow cytometry (FCM). d Expression of SGMS2 in spatial transcriptomics sequencing data. e Abundance estimation of the CD56dimCD16highNR4A3high NK cell population by single—sample gene—set enrichment analysis (ssGSEA). f Multiplex immunofluorescence (mIF) images of SGMS2, CD68, CD16, CD56, and NR4A3 markers in 6 human HCC tissue samples. “Zoom macrophage” indicates the aggregation area of SGMS2—positive macrophages, and “Zoom NK cell” represents the CD56dimCD16highNR4A3high NK cells. The scale bar is 50 um or 20 um. g Scatter plots showing the density of CD56dimCD16highNR4A3high NK cells between patients with high and low infiltration of SGMS2—positive macrophages. Statistical analysis was performed using the Mann—Whitney U test. h Pearson correlation analysis of the density of CD56dimCD16highNR4A3high NK cells and the density of SGMS2—positive macrophages. i Kaplan—Meier analysis of OS, RFS, and early RFS in HCC patients with different infiltration densities of SGMS2—positive macrophages and CD56dimCD16highNR4A3high NK cells. Survival distributions were compared using the log—rank test. Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, *** P < 0.001; ns indicates no significant difference

    Article Snippet: Spatial transcriptomics sequencing data were obtained from http://lifeome.net/supp/livercancer-st/data.htm and analyzed using Seurat in R. Subsequently, SCTtransform normalization was performed.

    Techniques: Western Blot, Expressing, Cell Culture, Control, Flow Cytometry, Sequencing, Multiplex Assay, Immunofluorescence, MANN-WHITNEY