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Heterogeneity of tumor histomorphology and tumor metabolic phenotype. (A) Haematoxylin and eosin (H&E) stained image of Lewis lung cancer. (B) Left: Image of tumor tissue spatial segmentation through automatic pixel labeling using t -distributed <t>stochastic</t> <t>neighbor</t> <t>embedding</t> ( t <t>-SNE).</t> Right: t -SNE 3D plot visualizing the distribution of four clusters. (C) Mass spectra of the four microregions in A. (D) Mass spectrometry images of representive metabolites in four types of cancers. The area drawn by the red and blue line refers to the cancerous area and paracancerous area, respectively; TCA: tricarboxylic acid; CHK: choline kinase.
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Heterogeneity of tumor histomorphology and tumor metabolic phenotype. (A) Haematoxylin and eosin (H&E) stained image of Lewis lung cancer. (B) Left: Image of tumor tissue spatial segmentation through automatic pixel labeling using t -distributed <t>stochastic</t> <t>neighbor</t> <t>embedding</t> ( t <t>-SNE).</t> Right: t -SNE 3D plot visualizing the distribution of four clusters. (C) Mass spectra of the four microregions in A. (D) Mass spectrometry images of representive metabolites in four types of cancers. The area drawn by the red and blue line refers to the cancerous area and paracancerous area, respectively; TCA: tricarboxylic acid; CHK: choline kinase.
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Heterogeneity of tumor histomorphology and tumor metabolic phenotype. (A) Haematoxylin and eosin (H&E) stained image of Lewis lung cancer. (B) Left: Image of tumor tissue spatial segmentation through automatic pixel labeling using t -distributed <t>stochastic</t> <t>neighbor</t> <t>embedding</t> ( t <t>-SNE).</t> Right: t -SNE 3D plot visualizing the distribution of four clusters. (C) Mass spectra of the four microregions in A. (D) Mass spectrometry images of representive metabolites in four types of cancers. The area drawn by the red and blue line refers to the cancerous area and paracancerous area, respectively; TCA: tricarboxylic acid; CHK: choline kinase.
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Heterogeneity of tumor histomorphology and tumor metabolic phenotype. (A) Haematoxylin and eosin (H&E) stained image of Lewis lung cancer. (B) Left: Image of tumor tissue spatial segmentation through automatic pixel labeling using t -distributed <t>stochastic</t> <t>neighbor</t> <t>embedding</t> ( t <t>-SNE).</t> Right: t -SNE 3D plot visualizing the distribution of four clusters. (C) Mass spectra of the four microregions in A. (D) Mass spectrometry images of representive metabolites in four types of cancers. The area drawn by the red and blue line refers to the cancerous area and paracancerous area, respectively; TCA: tricarboxylic acid; CHK: choline kinase.
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KEY RESOURCES TABLE
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Image Search Results


Heterogeneity of tumor histomorphology and tumor metabolic phenotype. (A) Haematoxylin and eosin (H&E) stained image of Lewis lung cancer. (B) Left: Image of tumor tissue spatial segmentation through automatic pixel labeling using t -distributed stochastic neighbor embedding ( t -SNE). Right: t -SNE 3D plot visualizing the distribution of four clusters. (C) Mass spectra of the four microregions in A. (D) Mass spectrometry images of representive metabolites in four types of cancers. The area drawn by the red and blue line refers to the cancerous area and paracancerous area, respectively; TCA: tricarboxylic acid; CHK: choline kinase.

Journal: Journal of Pharmaceutical Analysis

Article Title: Discovering metabolic vulnerability using spatially resolved metabolomics for antitumor small molecule-drug conjugates development as a precise cancer therapy strategy

doi: 10.1016/j.jpha.2023.02.010

Figure Lengend Snippet: Heterogeneity of tumor histomorphology and tumor metabolic phenotype. (A) Haematoxylin and eosin (H&E) stained image of Lewis lung cancer. (B) Left: Image of tumor tissue spatial segmentation through automatic pixel labeling using t -distributed stochastic neighbor embedding ( t -SNE). Right: t -SNE 3D plot visualizing the distribution of four clusters. (C) Mass spectra of the four microregions in A. (D) Mass spectrometry images of representive metabolites in four types of cancers. The area drawn by the red and blue line refers to the cancerous area and paracancerous area, respectively; TCA: tricarboxylic acid; CHK: choline kinase.

Article Snippet: The t -distributed stochastic neighbor embedding ( t -SNE) spatial segmentation was performed using MATLAB 2018a (MathWorks, Natick, MA, USA) with self-written code ( ).

Techniques: Staining, Labeling, Mass Spectrometry

KEY RESOURCES TABLE

Journal: Neuron

Article Title: Patient-Tailored, Connectivity-Based Forecasts of Spreading Brain Atrophy

doi: 10.1016/j.neuron.2019.08.037

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: ​ REAGENT or RESOURCE SOURCE IDENTIFIER Deposited Data Brainnetome atlas Fan et al., 2016 http://www.brainnetome.org/ Software and Algorithms MATLAB Mathworks RRID:SCR_001622; https://www.mathworks.com SPM The Wellcome Centre For Human Neuroimaging https://www.fil.ion.ucl.ac.uk/spm/ FSL FMRIB; ( Smith et al., 2004 ) https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ CAT12 toolbox http://www.neuro.uni-jena.de/cat/ R Open source https://www.r-project.org/ Brain Connectivity Toolbox Rubinov and Sporns, 2010 https://sites.google.com/site/bctnet/ custom MATLAB code https://github.com/jbrown81/connect_metrics ; https://github.com/jbrown81/neuron_2019 Connectome Workbench https://www.humanconnectome.org/software/connectome-workbench Networkx https://networkx.github.io/ matplotlib https://matplotlib.org/ t-SNE van der Maaten and Hinton, 2008 https://www.mathworks.com/help/stats/tsne.html Open in a separate window KEY RESOURCES TABLE Patient-tailored “epicenters” identified Connectivity-based model can predict longitudinal spread of atrophy Generalized additive model shows nonlinear spatial and temporal progression

Techniques: Software