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spatial metabolomics  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc spatial metabolomics
    Spatial Metabolomics, 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+metabolomics/metabolomics+spatial/pmc12374702-15-2-0
    Average 86 stars, based on 1 article reviews
    spatial metabolomics - by Bioz Stars, 2026-09
    86/100 stars

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    Related Articles

    In Situ:

    Article Title: A perioperative study of Safusidenib in patients with IDH1 -mutated glioma
    Article Snippet: Spatial transcriptomics using in situ digital sequencing and spatial metabolomics on sections pre- and post-safusidenib treatment. .. Spatial transcriptomics using in situ digital sequencing and spatial metabolomics on sections pre- and post-safusidenib treatment. ..

    Sequencing:

    Article Title: A perioperative study of Safusidenib in patients with IDH1 -mutated glioma
    Article Snippet: Spatial transcriptomics using in situ digital sequencing and spatial metabolomics on sections pre- and post-safusidenib treatment. .. Spatial transcriptomics using in situ digital sequencing and spatial metabolomics on sections pre- and post-safusidenib treatment. ..

    Spatial Transcriptomics:

    Article Title: Spatial omics shed light on the tumour organisation of glioblastoma.
    Article Snippet: .. Glioblastoma Tumour microenvironment Spatial omics Spatial transcriptomics Spatial metabolomics A B S T R A C T The glioblastoma tumour microenvironment is characterised by immense heterogeneity, with malignant and non-malignant cells that interact in a complex ecosystem. ..

    Article Title: Spatial metabolomics in head and neck tumors: a review
    Article Snippet: .. They have conducted Spatial metabolomics (SM), Spatial Lipidomics (SL) and Spatial Transcriptomics (ST) analysis on frozen cancer tissue sections from patients with gastric adenocarcinoma, and depicted spatially-resolved maps of metabolites, lipids, and gene expression patterns both in tumor cells and normal cells. ..

    Gene Expression:

    Article Title: Metabolic Reprogramming Shapes the Progression and Therapeutic Landscape of Ovarian Cancer
    Article Snippet: Single-cell RNA Sequencing (scRNA-seq) , Transcriptome profiling at single-cell resolution , Identifies intratumoral metabolic heterogeneity , Dissecting metabolic programs of tumor and immune cells , [ ] . .. Spatial Transcriptomics / Spatial Metabolomics , Spatial mapping of gene expression or metabolite distribution , Preserves tissue architecture; detects metabolic gradients , Defining hypoxia/metabolism-associated tumor regions , [ , ] . .. Genome-scale Metabolic Modeling , Computational prediction of metabolic vulnerabilities based on multi-omics data , Integrates genomics, transcriptomics, metabolomics , Identifying targetable metabolic pathways and simulating drug response , [ , ] .

    Article Title: Spatial metabolomics in head and neck tumors: a review
    Article Snippet: .. They have conducted Spatial metabolomics (SM), Spatial Lipidomics (SL) and Spatial Transcriptomics (ST) analysis on frozen cancer tissue sections from patients with gastric adenocarcinoma, and depicted spatially-resolved maps of metabolites, lipids, and gene expression patterns both in tumor cells and normal cells. ..



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    The design and workflow of this study. This study mainly contained four parts. First, 68 patients with unilateral or bilateral DFUs were treated with TTT surgery, and samples at six key time points were collected for the study. Second, label-free proteomic analysis of immunomodulatory proteins and regulatory pathways ( N =30, TTT 0 , TTT 3 , TTT 7 , TTT 14 , TTT 21 , and TTT 35 ) was performed. Third, skin tissue samples from three key time points were collected for spatial <t>metabolomics</t> to validate the immunological metabolites ( N =7, TTT 0 , TTT 14, and TTT 21 ). Next, the specific immune biomarkers were validated in the clinical laboratory (TTT 0 , TTT 3 , TTT 7 , TTT 14 , TTT 21 , and TTT 35 ). In addition, transcriptomics analysis for skin tissue samples was applied to screen out differential genes and signal pathways in regulating the immune response and wound healing ( N =3, TTT 0 , TTT 14 , and TTT 21 ). Finally, the integration of data from multiomic and clinical immune biomarker analyses was applied to investigate possible immune responses. N, the number of patients; TTT 0 , 1 day before the TTT external fixator was fixed; TTT 3 , 3 days after the TTT external fixator was fixed; TTT 7 , 7 days after the TTT external fixator was fixed; TTT 14 , the day at which the upward movement was completed; TTT 21 , the day at which the downward transfer was completed; TTT 35 , the second day after the TTT external fixator was removed. DFU, diabetic foot ulceration; TTT, tibial cortex transverse transport.
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    Image Search Results


    Spatial metabolomics profiled shows that TZP reduced glycolysis in the colon cancer tissues. A) Illustration of colonic and peritumoral tissues used for spatial metabolomic datasets. B) Examples of the microscopy‐mass spectrometry imaging (MSI) overlay. MSI images of D‐glucose (m/z = 249.1235) and H&E images of CRC tissue sections. Scale bar: 50 µm C) Heatmap of glycolysis‐related metabolites across all experimental animals of both groups. Red represented higher levels and blue, lower levels. D) Region‐specific MS images of CRC tissue sections. E) Violin plotting to show data distribution and its probability density; the thin black line extending from it, the 95% confidence interval; the black horizontal line in the middle, the median; and the outer shape, the distribution density of the data ( n = 3). Statistical significance was determined by unpaired t‐test and p values were adjusted with the t unpaired test comparisons test.

    Journal: Advanced Science

    Article Title: The Novel Dual GIP and GLP‐1 Receptor Agonist Tirzepatide Attenuates Colon Cancer Development by Regulating Glucose Metabolism

    doi: 10.1002/advs.202411980

    Figure Lengend Snippet: Spatial metabolomics profiled shows that TZP reduced glycolysis in the colon cancer tissues. A) Illustration of colonic and peritumoral tissues used for spatial metabolomic datasets. B) Examples of the microscopy‐mass spectrometry imaging (MSI) overlay. MSI images of D‐glucose (m/z = 249.1235) and H&E images of CRC tissue sections. Scale bar: 50 µm C) Heatmap of glycolysis‐related metabolites across all experimental animals of both groups. Red represented higher levels and blue, lower levels. D) Region‐specific MS images of CRC tissue sections. E) Violin plotting to show data distribution and its probability density; the thin black line extending from it, the 95% confidence interval; the black horizontal line in the middle, the median; and the outer shape, the distribution density of the data ( n = 3). Statistical significance was determined by unpaired t‐test and p values were adjusted with the t unpaired test comparisons test.

    Article Snippet: Spatially resolved metabolomics profiling was conducted at MetWare Biological Science and Technology Co., Ltd. (Wuhan, China).

    Techniques: Microscopy, Mass Spectrometry, Imaging

    The design and workflow of this study. This study mainly contained four parts. First, 68 patients with unilateral or bilateral DFUs were treated with TTT surgery, and samples at six key time points were collected for the study. Second, label-free proteomic analysis of immunomodulatory proteins and regulatory pathways ( N =30, TTT 0 , TTT 3 , TTT 7 , TTT 14 , TTT 21 , and TTT 35 ) was performed. Third, skin tissue samples from three key time points were collected for spatial metabolomics to validate the immunological metabolites ( N =7, TTT 0 , TTT 14, and TTT 21 ). Next, the specific immune biomarkers were validated in the clinical laboratory (TTT 0 , TTT 3 , TTT 7 , TTT 14 , TTT 21 , and TTT 35 ). In addition, transcriptomics analysis for skin tissue samples was applied to screen out differential genes and signal pathways in regulating the immune response and wound healing ( N =3, TTT 0 , TTT 14 , and TTT 21 ). Finally, the integration of data from multiomic and clinical immune biomarker analyses was applied to investigate possible immune responses. N, the number of patients; TTT 0 , 1 day before the TTT external fixator was fixed; TTT 3 , 3 days after the TTT external fixator was fixed; TTT 7 , 7 days after the TTT external fixator was fixed; TTT 14 , the day at which the upward movement was completed; TTT 21 , the day at which the downward transfer was completed; TTT 35 , the second day after the TTT external fixator was removed. DFU, diabetic foot ulceration; TTT, tibial cortex transverse transport.

    Journal: International Journal of Surgery (London, England)

    Article Title: Tibial cortex transverse transport surgery improves wound healing in patients with severe type 2 DFUs by activating a systemic immune response: a cross-sectional study

    doi: 10.1097/JS9.0000000000001897

    Figure Lengend Snippet: The design and workflow of this study. This study mainly contained four parts. First, 68 patients with unilateral or bilateral DFUs were treated with TTT surgery, and samples at six key time points were collected for the study. Second, label-free proteomic analysis of immunomodulatory proteins and regulatory pathways ( N =30, TTT 0 , TTT 3 , TTT 7 , TTT 14 , TTT 21 , and TTT 35 ) was performed. Third, skin tissue samples from three key time points were collected for spatial metabolomics to validate the immunological metabolites ( N =7, TTT 0 , TTT 14, and TTT 21 ). Next, the specific immune biomarkers were validated in the clinical laboratory (TTT 0 , TTT 3 , TTT 7 , TTT 14 , TTT 21 , and TTT 35 ). In addition, transcriptomics analysis for skin tissue samples was applied to screen out differential genes and signal pathways in regulating the immune response and wound healing ( N =3, TTT 0 , TTT 14 , and TTT 21 ). Finally, the integration of data from multiomic and clinical immune biomarker analyses was applied to investigate possible immune responses. N, the number of patients; TTT 0 , 1 day before the TTT external fixator was fixed; TTT 3 , 3 days after the TTT external fixator was fixed; TTT 7 , 7 days after the TTT external fixator was fixed; TTT 14 , the day at which the upward movement was completed; TTT 21 , the day at which the downward transfer was completed; TTT 35 , the second day after the TTT external fixator was removed. DFU, diabetic foot ulceration; TTT, tibial cortex transverse transport.

    Article Snippet: The analytical platform used for proteomic analysis was from Dashuo Biotech Co. Ltd (Dalian, China), the spatially resolved metabolomics platform was from MetWare Co. Ltd (Wuhan, China), and the transcriptomics platform was from Kangxiang Biotechnology Co. Ltd (Shanghai, China).

    Techniques: Biomarker Assay

    Spatially resolved metabolomics analysis of DFU skin tissues. (A) Crucial processing of skin tissue samples for spatial metabolomics analysis, including H&E staining of skin tissue sections, DHB matrix spraying of frozen sections, and metabolite spatial distribution with Gaussian smoothing. (B and C) OPLS-DA of spatial metabolomics datasets. (D–F) Representative mass spectra of DFU skin tissue (TTT 0 , TTT 14 , and TTT 21 ). (G–I) The bar diagram shows the expression levels of differentially abundant metabolites in glycerophospholipid, fatty acid, and glycerophospholipid metabolism pathways at TTT 14 and TTT 21 compared with TTT 0 . A total of eight differentially abundant metabolites were identified in TTT 0 , TTT 14 , and TTT 21 , and their change trends were statistically analyzed. (J–L) Bar plot of KEGG pathway enrichment analysis for the differentially abundant metabolites. The glycerophospholipid metabolism pathway was validated in DFU skin tissues. ** P value <0.005; * P value <0.05. DFU, diabetic foot ulceration; KEGG, Kyoto Encyclopedia of Genes and Genomes; TTT, tibial cortex transverse transport.

    Journal: International Journal of Surgery (London, England)

    Article Title: Tibial cortex transverse transport surgery improves wound healing in patients with severe type 2 DFUs by activating a systemic immune response: a cross-sectional study

    doi: 10.1097/JS9.0000000000001897

    Figure Lengend Snippet: Spatially resolved metabolomics analysis of DFU skin tissues. (A) Crucial processing of skin tissue samples for spatial metabolomics analysis, including H&E staining of skin tissue sections, DHB matrix spraying of frozen sections, and metabolite spatial distribution with Gaussian smoothing. (B and C) OPLS-DA of spatial metabolomics datasets. (D–F) Representative mass spectra of DFU skin tissue (TTT 0 , TTT 14 , and TTT 21 ). (G–I) The bar diagram shows the expression levels of differentially abundant metabolites in glycerophospholipid, fatty acid, and glycerophospholipid metabolism pathways at TTT 14 and TTT 21 compared with TTT 0 . A total of eight differentially abundant metabolites were identified in TTT 0 , TTT 14 , and TTT 21 , and their change trends were statistically analyzed. (J–L) Bar plot of KEGG pathway enrichment analysis for the differentially abundant metabolites. The glycerophospholipid metabolism pathway was validated in DFU skin tissues. ** P value <0.005; * P value <0.05. DFU, diabetic foot ulceration; KEGG, Kyoto Encyclopedia of Genes and Genomes; TTT, tibial cortex transverse transport.

    Article Snippet: The analytical platform used for proteomic analysis was from Dashuo Biotech Co. Ltd (Dalian, China), the spatially resolved metabolomics platform was from MetWare Co. Ltd (Wuhan, China), and the transcriptomics platform was from Kangxiang Biotechnology Co. Ltd (Shanghai, China).

    Techniques: Staining, Expressing

    Spatial expression images of differentially abundant metabolites in DFU tissue sections based on spatially resolved metabolomics data (intensity in color scale is relative value). (A) Expression and spatial distributions of theobromine, levocarnitine propionate, canthaxanthin A, and triacetin at TTT 14 compared with those at TTT 0 . (B) Expression and spatial distributions of elemonic acid and camelliagenin at TTT 21 compared with those at TTT 0 . (C) Decreased concentrations of L-palmitoylcarnitine and stearoylcarnitine in DFU tissues from TTT 14 and TTT 21 patients compared with those of TTT 0 patients. * P value <0.05, ** P value <0.01. DFU, diabetic foot ulceration; TTT, tibial cortex transverse transport.

    Journal: International Journal of Surgery (London, England)

    Article Title: Tibial cortex transverse transport surgery improves wound healing in patients with severe type 2 DFUs by activating a systemic immune response: a cross-sectional study

    doi: 10.1097/JS9.0000000000001897

    Figure Lengend Snippet: Spatial expression images of differentially abundant metabolites in DFU tissue sections based on spatially resolved metabolomics data (intensity in color scale is relative value). (A) Expression and spatial distributions of theobromine, levocarnitine propionate, canthaxanthin A, and triacetin at TTT 14 compared with those at TTT 0 . (B) Expression and spatial distributions of elemonic acid and camelliagenin at TTT 21 compared with those at TTT 0 . (C) Decreased concentrations of L-palmitoylcarnitine and stearoylcarnitine in DFU tissues from TTT 14 and TTT 21 patients compared with those of TTT 0 patients. * P value <0.05, ** P value <0.01. DFU, diabetic foot ulceration; TTT, tibial cortex transverse transport.

    Article Snippet: The analytical platform used for proteomic analysis was from Dashuo Biotech Co. Ltd (Dalian, China), the spatially resolved metabolomics platform was from MetWare Co. Ltd (Wuhan, China), and the transcriptomics platform was from Kangxiang Biotechnology Co. Ltd (Shanghai, China).

    Techniques: Expressing