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tissue sections  (Qiagen)


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    Structured Review

    Qiagen tissue sections
    Tissue Sections, supplied by Qiagen, used in various techniques. Bioz Stars score: 99/100, based on 4531 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/tissue+sectioning/RNeasy+FFPE+Kit/pmc13160414-83-4-12
    Average 99 stars, based on 4531 article reviews
    tissue sections - by Bioz Stars, 2026-10
    99/100 stars

    Images

    Related Articles

    RNA Extraction:

    Article Title: A Spatial Atlas of Muscle-Invasive Bladder Cancer Reveals Lineage-Specific Vulnerabilities and Immune Architecture
    Article Snippet: 766 ST library preparation and sequencing 767 Spatial transcriptomics (ST) analysis on FFPE slides were performed with the Visium spatial 768 technology from 10x Genomics. .. Two to three consecutive tissue sections of 5-μm thickness were 769 collected for RNA extraction with the Qiagen RNeasy FFPE Kit to assess the RNA quality of the 770 tissue. ..

    Article Title: Nanosystems as selective vehicles
    Article Snippet: .. To do this, we performed RNA extraction from 5 paraffin sections of 14 microns each using a specific extraction kit for paraffinized samples (RNeasy FFPE kit, QIAGEN). .. In order to know the concentration of it, the Nanodrop equipment (Nanodrop 2000C, Thermoscientific) was used.

    Formalin-fixed Paraffin-Embedded:

    Article Title: A Spatial Atlas of Muscle-Invasive Bladder Cancer Reveals Lineage-Specific Vulnerabilities and Immune Architecture
    Article Snippet: 766 ST library preparation and sequencing 767 Spatial transcriptomics (ST) analysis on FFPE slides were performed with the Visium spatial 768 technology from 10x Genomics. .. Two to three consecutive tissue sections of 5-μm thickness were 769 collected for RNA extraction with the Qiagen RNeasy FFPE Kit to assess the RNA quality of the 770 tissue. ..

    Article Title: Comprehensive analysis of six MED15::TFE3 renal cell carcinomas including two novel solid MED15::TFE3 renal cell carcinomas, highlighting their morphologic, immunohistochemical and molecular differences from their cystic counterparts.
    Article Snippet: .. RNA was extracted using the RNeasy FFPE Kit (Qiagen, Valencia, CA) and quantified using a Qubit fluorometric assay (Thermo Fisher Scientific, FosterCity, CA) adjusted for the percentage of fragments greater than 100 bp using the TapeStation system (Agilent). .. 300 ng RNA was subjected to library prep using the KAPA stranded RNA-Seq Kit with RiboErase (Kapa Biosystems), followed by quantitation using the KAPA library quantification kit (Kapa Biosystems).

    Article Title: Spatial transcriptomic profiling of decalcified murine musculoskeletal samples via Xenium Prime 5K
    Article Snippet: .. RNA was isolated from tissue sections using the RNeasy FFPE isolation kit (Qiagen; 73504). ..

    Article Title: Spatial Proximity Between PD-L1(+) Tumor-Associated Macrophages and CD8(+) T Cells Influences Response to Atezolizumab Plus Bevacizumab in Hepatocellular Carcinoma.
    Article Snippet: .. Total RNA was extracted using the RNeasy FFPE Kit (QIAGEN, Hilden, Germany). .. RNA quantity and quality were assessed using a Qubit 3.0 Fluorometer with the RNA HS Assay (Thermo Fisher Scientific, Waltham, MA, USA), and RNA integrity was evaluated using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA).

    Article Title: Microarray Gene Expression Analysis of Lesional Skin in Canine Vesicular Cutaneous Lupus Erythematosus (VCLE).
    Article Snippet: Background: Vesicular cutaneous lupus erythematosus (VCLE) is a rare autoimmune disease in dogs and is considered the canine counterpart of human subacute cutaneous lupus erythematosus (SCLE).. However, the molecular mechanisms underlying VCLE remain incompletely defined.. Objective/Hypothesis: To characterise gene expression changes in lesional skin from dogs with VCLE and identify immune pathways involved in disease pathogenesis.

    Article Title: Chimeric antigen receptor T cell therapy
    Article Snippet: .. Gene Expression Analysis and Immunosign RNA was extracted from frozen or fixed biopsies using QIAGEN RNeasy kit or QIAGEN RNeasy FFPE extraction kit, respectively. ..

    Article Title: Preanalytical Determinants of DNA and RNA Quality in FFPE Tissues: Practical Recommendations for Molecular Testing.
    Article Snippet: FFPE tissue is essential for molecular testing, but nucleic acid quality is affected by preanalytical factors.. Using porcine models, we systematically examined how formalin concentration, fixation duration, cold ischemia time, and tissue size affect DNA and RNA quality.. Fresh liver was trimmed to 2, 10, or 50 mm and fixed in 10% or 20% neutral‐buffered formalin (NBF) for 1, 3, or 7 days.

    Article Title: Nanosystems as selective vehicles
    Article Snippet: .. To do this, we performed RNA extraction from 5 paraffin sections of 14 microns each using a specific extraction kit for paraffinized samples (RNeasy FFPE kit, QIAGEN). .. In order to know the concentration of it, the Nanodrop equipment (Nanodrop 2000C, Thermoscientific) was used.

    Isolation:

    Article Title: Spatial transcriptomic profiling of decalcified murine musculoskeletal samples via Xenium Prime 5K
    Article Snippet: .. RNA was isolated from tissue sections using the RNeasy FFPE isolation kit (Qiagen; 73504). ..

    Article Title: Microarray Gene Expression Analysis of Lesional Skin in Canine Vesicular Cutaneous Lupus Erythematosus (VCLE).
    Article Snippet: Background: Vesicular cutaneous lupus erythematosus (VCLE) is a rare autoimmune disease in dogs and is considered the canine counterpart of human subacute cutaneous lupus erythematosus (SCLE).. However, the molecular mechanisms underlying VCLE remain incompletely defined.. Objective/Hypothesis: To characterise gene expression changes in lesional skin from dogs with VCLE and identify immune pathways involved in disease pathogenesis.

    Gene Expression:

    Article Title: Chimeric antigen receptor T cell therapy
    Article Snippet: .. Gene Expression Analysis and Immunosign RNA was extracted from frozen or fixed biopsies using QIAGEN RNeasy kit or QIAGEN RNeasy FFPE extraction kit, respectively. ..

    Extraction:

    Article Title: Chimeric antigen receptor T cell therapy
    Article Snippet: .. Gene Expression Analysis and Immunosign RNA was extracted from frozen or fixed biopsies using QIAGEN RNeasy kit or QIAGEN RNeasy FFPE extraction kit, respectively. ..

    Article Title: Nanosystems as selective vehicles
    Article Snippet: .. To do this, we performed RNA extraction from 5 paraffin sections of 14 microns each using a specific extraction kit for paraffinized samples (RNeasy FFPE kit, QIAGEN). .. In order to know the concentration of it, the Nanodrop equipment (Nanodrop 2000C, Thermoscientific) was used.

    Concentration Assay:

    Article Title: Preanalytical Determinants of DNA and RNA Quality in FFPE Tissues: Practical Recommendations for Molecular Testing.
    Article Snippet: FFPE tissue is essential for molecular testing, but nucleic acid quality is affected by preanalytical factors.. Using porcine models, we systematically examined how formalin concentration, fixation duration, cold ischemia time, and tissue size affect DNA and RNA quality.. Fresh liver was trimmed to 2, 10, or 50 mm and fixed in 10% or 20% neutral‐buffered formalin (NBF) for 1, 3, or 7 days.



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    Image Search Results


    A . Workflow for predicting spatial gene-expression programs directly from histology images using SQUALL. B . Benchmarking of virtual biomarker prediction on internal and external Xenium5K sections. SQUALL was benchmarked against ST-Net, iSTAR, EGN, EGNv2, and Path2Space on three Xenium5K sections. Performance was evaluated using Pearson correlation on ( Left ) per tissue sections and ( Right ) overall correlations. HCC: hepatocellular carcinoma; OC: ovarian carcinoma; CC: cervical carcinoma. Box plots: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; statistical test: two-sided Mann-Whitney U test. C . Pairwise scatterplots comparing per-gene Pearson correlations between SQUALL (x-axis, one panel per method) and each of six competing methods. D . Bar plot showing reverse ranking results of prediction performance across biomarker subsets. Prediction performance was inverse ranked within each section by average Pearson correlation, with higher rank indicates better performance. E . Representative virtual biomarker predictions. Left to right: original histology images (20× magnification) and virtually profiled expression of selected genes from all competing methods and SQUALL. Rows show ground truth and predictions for MET (HCC, internal), IFGR1 (OC, internal), and STAT1 (CC, external). Scale bar, 1 mm. F . Schematic of SQUALL applied to virtual biomarker profiling directly using cohort-level histology whole-slides images. G . Bar plot showing GSEA results based on SQUALL-virtually profiled bulk gene expression in the TCGA-LIHC cohort. Genes were ranked by coefficients from a multivariable Cox regression model adjusted for age and stage. Pathways with NES > 0 are associated with poorer prognosis, whereas pathways with NES < 0 are associated with better prognosis. NES, normalized enrichment score. H . Spatial plot of representative annotated tumor regions from Chiara et al. Scale bar, 1 mm. I . Ranking plot of hazard ratios for SQUALL-virtually profiled gene expression within tumor regions of the TCGA-CESC cohort. Hazard ratios were estimated using multivariable Cox proportional hazards models adjusted for age and stage. Representative genes are shown, including DNA double-strand break response (blue), DNA repair (orange), innate immune/complement regulation (green), and adaptive immune response (violet) genes. Solid dots indicate genes with FDR < 0.1. J . Ridge plot of gene set enrichment analysis based on SQUALL virtually profiled tumor region biomarkers on TCGA-CESC cohort. NES, normalized enrichment score. K . Hazard ratios for clinical variables and SQUALL-predicted CD8 + T cell signatures in the TCGA-CESC cohort, estimated using univariate Cox proportional hazards models. Dots indicate hazard ratios and bars indicate 95% confidence interval. Statistical significance was assessed using a one-sided Wald test. L . Representative examples of predicted CD8 + T cell infiltration in the TCGA-CESC cohort. Long-term survivor with predicted intertumoral CD8 + T cells (red dashed area) ( Left ). Short-term survivor with CD8 + cells mainly at the tumor margin ( Right ). Scale bar, 5 mm. Statistical significance: * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001; n.s., not significant.

    Journal: bioRxiv

    Article Title: Integrating Histology with Spatial Molecular Programs Using a Multimodal Foundation Model

    doi: 10.64898/2026.06.01.729028

    Figure Lengend Snippet: A . Workflow for predicting spatial gene-expression programs directly from histology images using SQUALL. B . Benchmarking of virtual biomarker prediction on internal and external Xenium5K sections. SQUALL was benchmarked against ST-Net, iSTAR, EGN, EGNv2, and Path2Space on three Xenium5K sections. Performance was evaluated using Pearson correlation on ( Left ) per tissue sections and ( Right ) overall correlations. HCC: hepatocellular carcinoma; OC: ovarian carcinoma; CC: cervical carcinoma. Box plots: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; statistical test: two-sided Mann-Whitney U test. C . Pairwise scatterplots comparing per-gene Pearson correlations between SQUALL (x-axis, one panel per method) and each of six competing methods. D . Bar plot showing reverse ranking results of prediction performance across biomarker subsets. Prediction performance was inverse ranked within each section by average Pearson correlation, with higher rank indicates better performance. E . Representative virtual biomarker predictions. Left to right: original histology images (20× magnification) and virtually profiled expression of selected genes from all competing methods and SQUALL. Rows show ground truth and predictions for MET (HCC, internal), IFGR1 (OC, internal), and STAT1 (CC, external). Scale bar, 1 mm. F . Schematic of SQUALL applied to virtual biomarker profiling directly using cohort-level histology whole-slides images. G . Bar plot showing GSEA results based on SQUALL-virtually profiled bulk gene expression in the TCGA-LIHC cohort. Genes were ranked by coefficients from a multivariable Cox regression model adjusted for age and stage. Pathways with NES > 0 are associated with poorer prognosis, whereas pathways with NES < 0 are associated with better prognosis. NES, normalized enrichment score. H . Spatial plot of representative annotated tumor regions from Chiara et al. Scale bar, 1 mm. I . Ranking plot of hazard ratios for SQUALL-virtually profiled gene expression within tumor regions of the TCGA-CESC cohort. Hazard ratios were estimated using multivariable Cox proportional hazards models adjusted for age and stage. Representative genes are shown, including DNA double-strand break response (blue), DNA repair (orange), innate immune/complement regulation (green), and adaptive immune response (violet) genes. Solid dots indicate genes with FDR < 0.1. J . Ridge plot of gene set enrichment analysis based on SQUALL virtually profiled tumor region biomarkers on TCGA-CESC cohort. NES, normalized enrichment score. K . Hazard ratios for clinical variables and SQUALL-predicted CD8 + T cell signatures in the TCGA-CESC cohort, estimated using univariate Cox proportional hazards models. Dots indicate hazard ratios and bars indicate 95% confidence interval. Statistical significance was assessed using a one-sided Wald test. L . Representative examples of predicted CD8 + T cell infiltration in the TCGA-CESC cohort. Long-term survivor with predicted intertumoral CD8 + T cells (red dashed area) ( Left ). Short-term survivor with CD8 + cells mainly at the tumor margin ( Right ). Scale bar, 5 mm. Statistical significance: * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001; n.s., not significant.

    Article Snippet: Three Xenium5K tissue sections were used for benchmarking ( Table S31 ): one from the SPATCH cohort (hepatocellular carcinoma) and two public datasets from 10x Genomics (ovarian cancer: https://www.10xgenomics.com/cn/datasets/xenium-prime-ffpe-human-ovarian-cancer ; cervical cancer: https://www.10xgenomics.com/cn/datasets/xenium-prime-ffpe-human-cervical-cancer ).

    Techniques: Gene Expression, Biomarker Discovery, MANN-WHITNEY, Expressing

    A-C . Quantitative comparison of SQUALL to iSTAR, ST-Net, EGN, Hist2ST, DeepPT, and Path2Space for virtual biomarker profiling using three Xenium5K sections. Pairwise scatterplots comparing per-gene Pearson correlations between SQUALL (x-axis, one panel per method) and each of six competing methods on one internal hepatocellular carcinoma section ( A ), one external ovarian carcinoma section ( B ), and one external cervical carcinoma section ( C ). D-E . Box plots showing predicted biomarkers expression. Cervical cancer-related differential genes and transcription factors ( D ). T cell, CD4 + T cell, and CD8 + T cell signatures ( E, Left ). CD8 + T effector and exhaustion cell signatures ( E, Right ). Expression was normalized and ranked per tile within tissue section; higher ranks indicate higher predicted expression levels. F . Box plots comparing SQUALL-predicted T cell-related biomarker signature expression between tumor and non-tumor regions. Box plots ( D-F ): center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; statistical test: two-sided Mann-Whitney U test. Statistical significance: * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001; n.s., not significant.

    Journal: bioRxiv

    Article Title: Integrating Histology with Spatial Molecular Programs Using a Multimodal Foundation Model

    doi: 10.64898/2026.06.01.729028

    Figure Lengend Snippet: A-C . Quantitative comparison of SQUALL to iSTAR, ST-Net, EGN, Hist2ST, DeepPT, and Path2Space for virtual biomarker profiling using three Xenium5K sections. Pairwise scatterplots comparing per-gene Pearson correlations between SQUALL (x-axis, one panel per method) and each of six competing methods on one internal hepatocellular carcinoma section ( A ), one external ovarian carcinoma section ( B ), and one external cervical carcinoma section ( C ). D-E . Box plots showing predicted biomarkers expression. Cervical cancer-related differential genes and transcription factors ( D ). T cell, CD4 + T cell, and CD8 + T cell signatures ( E, Left ). CD8 + T effector and exhaustion cell signatures ( E, Right ). Expression was normalized and ranked per tile within tissue section; higher ranks indicate higher predicted expression levels. F . Box plots comparing SQUALL-predicted T cell-related biomarker signature expression between tumor and non-tumor regions. Box plots ( D-F ): center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; statistical test: two-sided Mann-Whitney U test. Statistical significance: * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001; n.s., not significant.

    Article Snippet: Three Xenium5K tissue sections were used for benchmarking ( Table S31 ): one from the SPATCH cohort (hepatocellular carcinoma) and two public datasets from 10x Genomics (ovarian cancer: https://www.10xgenomics.com/cn/datasets/xenium-prime-ffpe-human-ovarian-cancer ; cervical cancer: https://www.10xgenomics.com/cn/datasets/xenium-prime-ffpe-human-cervical-cancer ).

    Techniques: Comparison, Biomarker Discovery, Expressing, MANN-WHITNEY