PMCID
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Black, clear-bottomed 96-well plates (Corning) pre-coated with ECM (Merck) were seeded at 5,000 cells per well and incubated at 37 °C in 200 μl of Neurobasal medium supplemented with growth factors, as described above.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The medium was then replaced with either fresh medium or medium containing the following drug concentrations: AZD2014, 1 μM; gefitinib, 1 μM; imatinib, 10 μM. The cells were incubated for 72 h before re-imaging using the IncuCyte microscope, and cell numbers were determined using the Sartorius cell confluency module.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
At the end of the incubation, the medium was aspirated, and 100 μl of PBS containing 3 μM propidium iodide was added to each well to measure necrotic cell death.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The plates were imaged using the IncuCyte 10× cell-by-cell module using the red channel.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The experiment was repeated three times with six technical replicates for each drug treatment.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Neurospheres in Matrigel domes were grown at 37 °C, with one plate incubated in atmospheric O2 and the other grown in 0.5% O2, 0.5 Pa (Avatar, XCellbio).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Medium was refreshed every 48 h, and on day 10 it was removed, the plates placed on ice and the wells washed with 1 ml of ice-cold PBS, followed by the addition of 1 ml Corning Cell Recovery Solution.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The plates were incubated at 4 °C for 1 h and then washed four times with ice-cold PBS.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
RNA was isolated from cell pellets using a QIAshredder spin column (Qiagen) and AllPrep DNA/RNA Mini Kit (Qiagen), quantified using a Qubit fluorometer (Thermo Fisher Scientific) and quality tested using an Agilent 4200 TapeStation.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
For library preparation, the Illumina TruSeq Stranded mRNA Kit was used, and single-read sequencing was performed on a HiSeq 4000 machine (Illumina).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Quality control of raw sequence data was carried out using FastQC (v.0.11.8).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Some reads were trimmed to remove adaptor content using Trimmomatic (v.0.39).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Reads were aligned to GRCh38 Ensembl release 102 using STAR (v.2.7.7a) and alignment quality control was carried out using Picard tools (v.2.25.1).
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Quantification was carried out using Salmon (v.1.6.0) against a reference transcriptome for the same genome release.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Differential gene expression analysis was carried out in R (v.4.2.2) using the DESeq2 package (v.1.38.3) with default parameters.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Multiple testing correction of P values was carried out using the Benjamini–Hochberg method.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Genes were determined to be differentially expressed at an adjusted P value of 0.05.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Gene set enrichment analysis was carried out using clusterProfiler (v.4.6.0).
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Experiments were performed under the authority of a Home Office project licence (PP5634271) and approved by an Animal Welfare and Ethical Review Body at the Cancer Research UK (CRUK) Cambridge Institute, University of Cambridge.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Athymic, female nude rats that were at least 9 weeks old were implanted orthotopically with the primary GB lines at passage 10.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Animals were anaesthetized using 2% isoflurane (Isoflo, Abbott Laboratories) in O2/air (25/75%, vol/vol, 2 l min) with 5 mg kg Carpofen (Zoetis) and 0.3 mg ml buprenorphine hydrochloride (Alstoe) subcutaneous analgesia.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Body temperature was maintained using a heating pad.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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A stereotactic surgical frame (Kopf) was used to secure the animal’s head.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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A midline incision was made followed by a 1 mm burr hole anterior and to the right of the bregma.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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A total of 1 × 10 cells were injected in 5 µl of Neurobasal medium at a depth of 4 mm.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The burr hole was closed with bone wax (Ethicon) and skin with 6/0 vicryl (Ethicon).
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Tumour growth was monitored using T2-weighted MRI.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Specifically, animals were anaesthetized using 2% isoflurane (Isoflo, Abbott Laboratories) in O2/air (25/75%, vol/vol, 2 l min) and placed supine inside a 7T magnet (Agilent), and T2-weighted MRI was used to monitor tumour growth.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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A 72 mm H volume coil was placed around the animal’s head, and breathing rate and temperature were monitored with a small animal instruments monitoring system (SAII).
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Axial H T2-weighted images were acquired using a fast spin-echo sequence with an echo time of 50 ms, pulse repetition time of 1,500 ms, flip angle of 60–90° and a slice thickness of 2.0 mm, field-of-view of 40 mm × 40 mm and 128 × 128 or 256 × 256 data points.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Three animals per cell line were administered with [U-C]glucose as a bolus at 0.4 mg g, followed by continuous infusion of 0.012 mg g min at 300 µl h for 120 min (ref. ).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The brains were snap-frozen in liquid nitrogen, cryo-sectioned at a thickness of 10 µm and analysed with DESI-MSI and MALDI-MSI, as described above.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The 10× Genomics Visium platform was used and analysed with the Space Ranger pipeline.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Downstream analyses were conducted in R using the Seurat package.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Samples were processed individually using the SCTransform() function.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Spots were filtered based on standard quality control thresholds (for example, mitochondrial gene percentage of >20%, nCount_Spatial of <1,000 and nFeature_Spatial of <1,000).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Data were re-corrected across samples using PrepSCTFindMarkers() for joint numerical analyses.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Gene set scoring was performed using the Hallmark gene sets and the UCELL package, excluding mitochondrial genes from the oxidative phosphorylation score calculations.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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To annotate spatial spots, genes from Hallmark gene sets of interest were combined and subjected to k-means clustering (k = 3).
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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To annotate TME spots, we performed two deconvolution steps with robust cell type decomposition, using previously published reference cell annotations.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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First, a balanced normal reference was sampled from the non-neoplastic cells, combined with the neoplastic cells and used as input for robust cell type decomposition.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The assignment was further confirmed by histological evaluation.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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To label TME niches, we further deconvolved TME signals into oligodendrocytes, astrocytes, vascular cells, immune cells (macrophages) and neurons.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Given that each spatial spot contained multiple cells, we normalized the deconvolution weights to a maximum of one, providing a relative abundance.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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To mitigate over-assignment to sorted TME cell types, we included unsorted cell types (neoplastic + oligodendrocyte precursor cell with high transcriptomic similarity) in this deconvolution step as a ‘block’ effect.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Joint feature plots (tumour and TME) were generated using a custom modification of the SpatialFeaturePlotBlend() source function.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The original repository is available at https://github.com/george-hall-ucl/SpatialFeaturePlotBlend.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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For the TME population-level quantification, for each spot, the weights of all deconvolved populations were normalized to sum to one, thereby representing the relative contribution of each cell population within that specific spot.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Subsequently, these normalized weights were plotted across the three metabolic states.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Total ion count-normalized MSI data were extracted using the SCiLS Lab API (v.2022b; Bruker Daltonik), and metabolic labels were spatially smoothed to provide coherent spatial regions.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Immediately neighbouring pixels (≤8) were identified for each MSI pixel, and across five iterations, for each pixel that had at least two neighbours and for which the majority of neighbours were assigned a different label, the label was replaced with the majority label.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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An image was created using spatial coordinates, in which pixels were coloured using the first three principal components as RGB channels.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Landmarks were identified between this MSI dimensionality reduction image and the H&E image of the corresponding tissue section, and subsequently between the H&E image and the corresponding H&E image of the contiguous tissue section used for spatial transcriptomic analysis.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
These landmarks were then used to map the MSI coordinates onto the spatial transcriptomic coordinate space using an affine transform.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
MSI labels were finally transferred to spots on the spatial transcriptomic image using the k-nearest neighbours algorithm (k = 3).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Regions were defined using k-means clustering and fitted using the Kmeans function in the amap R package and performed independently on the neurosphere, human and metastases datasets.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The values for each of the seven C-labelled metabolites used for metabolic clustering were standardised into z-scores (by subtracting the metabolite’s mean and dividing by the metabolite’s standard deviation).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Each pixel was then assigned to three or four groups, using k-means clustering with Manhattan distances.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Antibodies used for immunohistochemistry are described in Supplementary Table 5.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Antibodies were tagged using the Fluidigm Maxpar Antibody Labelling Kit.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Slides were fixed with 4% paraformaldehyde in PBS for 10 min, washed three times in PBS, permeabilized using a 1:1,000 dilution of Triton X-100 in casein solution, washed another three times in PBS and then blocked for 30 min with casein solution (Thermo Fisher).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Antibodies were diluted in casein solution, and the slides were incubated overnight at 4 °C.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The slide was then washed three times in PBS, and nuclei were stained with DNA intercalator-iridium (Fluidigm) at a dilution of 1:400 in PBS for 30 min.
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PMC12116388
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Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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The slide was washed three times in PBS, 30 s in deionized water and then dried at room temperature.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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A region for IMC analysis was selected using consecutive H&E-stained sections and the DESI-MSI data.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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IMC analysis was performed using a Hyperion Instrument (Fluidigm Corporation) with an ablation energy of 4 db and an ablation frequency of 200 Hz.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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IMC images were produced using MCD viewer (v.1.0; Fluidigm), and analysis was performed using HALO (Indica Labs).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Tumour-bearing rat brains were snap-frozen in liquid nitrogen and sectioned at 6 μm thickness for immunohistochemistry analysis using Leica’s Polymer Refine Kit (antibodies listed in Supplementary Table 6).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Images were analysed using Aperio image-viewing software and HALO (v.3.6.4134.137).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
HALO (v.3.6.4134.137) and HighPlex FL (v.4.1.3) modules were used for automated image analysis.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Optical densities for weakly, moderately and strongly stained cells used for the automated quantitative analysis of scanned sections were as follows: Ki67 – (nuclear) 7, 40.7522, 54.385, p53 – (nuclear) 1.8, 3.5929, 5.1327, CC3 – (cytoplasm) 1.9427, 4.646, 6.1947, Vimentin – (cytoplasm) 13.3343, 25.7522, 41.2035, CD3 – (nuclear) 2.2832, 32.6875, 63, GZMB – (nuclear) 0.6956, 0.8673, 1.6106, CD4 – (cytoplasm) 3.9823, 6.7699, 10.354, CD8A – (nuclear) 1.6327, 18.7679, 26, CD68 – (cytoplasm) 10.4106, 45.9027, 78.6903, CD45 – (cytoplasm) 2, 5.3363, 7.115, ASMA – (cytoplasm) 2.8, 12.1327, 18.6239, CD31 – (cytoplasm) 2.274, 4.5841, 6.876, panCK – (cytoplasm) 1.836, 2.8673, 3.9823, Collagen I – (cytoplasm) 28.293, 104.7301, 179.58.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Five cellular phenotypes were identified: CD4 helper T cells (CD45CD4CD3); cytotoxic T cells (CD3CD45CD8A); activated cytotoxic T cells (CD3CD45CD8AGZMB); natural killer cells and neutrophils (CD45GZMB); and macrophages and microglia (CD68).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
A random forest classifier was used to distinguish vessels and non-vessels within the images.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Large vessels were positive for Collagen I and CD31.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Annotations were created manually on several images, and these were used to train the classifier.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
The five cellular phenotypes were plotted spatially, three 60 µm bands travelling out from the vessels were defined and the total number of cells displaying these cellular phenotypes in each band was determined.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Sample size for human tumour collection was determined intra-operatively based on patient and tumour factors (for example, proximity to eloquent brain).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
A minimum of six samples were collected for each tumour region.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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For xenograft and neurosphere studies, a minimum of three independent biological replicates were used (three different rats; three different cell line passages with a minimum of six technical replicates).
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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No randomization or blinding was used, and all data were included in the analysis.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Statistical analyses were performed using GraphPad Prism (v.10.0.3), R (v.4.3.0) and SCiLS.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Bioinformatics analyses were performed in R. Statistical tests were two-sided unless stated otherwise.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
|
Student’s t-tests and one-way ANOVA with Tukey’s multiplicity correction were used to test the equality of means between two or more groups, respectively.
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PMC12116388
|
Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of <sup>13</sup>C-labelled glucose metabolism.
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Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
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PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
We describe a mechanism of tumorigenesis mediated by kinase-dead BRAF in the presence of oncogenic RAS.
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PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
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We show that drugs that selectively inhibit BRAF drive RAS-dependent BRAF binding to CRAF, CRAF activation, and MEK–ERK signaling.
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PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
This does not occur when oncogenic BRAF is inhibited, demonstrating that BRAF inhibition per se does not drive pathway activation; it only occurs when BRAF is inhibited in the presence of oncogenic RAS.
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PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
Kinase-dead BRAF mimics the effects of the BRAF-selective drugs and kinase-dead Braf and oncogenic Ras cooperate to induce melanoma in mice.
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PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
Our data reveal another paradigm of BRAF-mediated signaling that promotes tumor progression.
|
PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
They highlight the importance of understanding pathway signaling in clinical practice and of genotyping tumors prior to administering BRAF-selective drugs, to identify patients who are likely to respond and also to identify patients who may experience adverse effects.
|
PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
The RAS–ERK (extracellular-signal regulated protein kinase) MAPK (mitogen-activated protein kinase) signaling pathway regulates cell responses to environmental cues (Marshall, 1995) and plays an important role in human cancer (Gray-Schopfer et al., 2007).
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PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
The pathway comprises the RAS small guanine-nucleotide binding protein and the protein kinases RAF, MEK (mitogen and extracellular-regulated protein kinase kinase), and ERK.
|
PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
RAS is attached to the inner face of the plasma membrane and is activated downstream of growth factor, cytokine, and hormone receptors.
|
PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
Active RAS recruits RAF to the membrane for activation through a complex process involving changes in phosphorylation and binding to other enzymes and scaffold proteins (Kolch, 2000).
|
PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
RAF phosphorylates and activates MEK, which phosphorylates and activates ERK.
|
PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
|
The complexity of this pathway is increased by the multiplicity of its components.
|
PMC2872605
|
Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
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There are three RAS (HRAS, NRAS, and KRAS), three RAF (ARAF, BRAF, and CRAF), two MEK (MEK1 and MEK2), and two ERK (ERK1 and ERK2) genes that encode proteins with nonredundant functions.
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PMC2872605
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Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
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Furthermore, the pathway is not linear.
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PMC2872605
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Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
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BRAF binds to and activates CRAF in a RAS-dependent manner that appears to require CRAF transphosphorylation by BRAF (Garnett et al., 2005; Rushworth et al., 2006; Weber et al., 2001), providing subtle pathway regulation that is not fully understood.
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PMC2872605
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Kinase-dead BRAF and oncogenic RAS cooperate to drive tumor progression through CRAF.
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ERK phosphorylates many substrates and the duration and intensity of its activity affects how cells respond to extracellular signals (Marshall, 1995).
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