data dict | annotations list |
|---|---|
{
"text": "For gene expression level, we merged all counts in each organoid sample by genes using the adpbulk method and applied a natural logarithm transformation to one plus the counts. We then selected the top 500 highly variable genes and calculated PCA based on their expression. From principal components 1 and 2... | [] |
{
"text": "For the scPoli embedding level, we calculated the mean scPoli embedding for each organoid sample using the adpbulk method, followed by PCA based on the mean embedding. A linear model was then used to calculate the covariance between principal components and sample counts, stem cell source, scRNA-seq method... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 273,
"end": 277,
"text": "stem",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "To benchmark and compare different integration methods, we selected ten random samples from the dataset for validation, repeating this process ten times. Twelve integration methods, including PCA, Seurat (v.3, v.4 and v.5), scVI, scANVI, scPoli, bbknn, harmony, combat, CSS (pearson) and CSS (spearman) were... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 685,
"end": 689,
"text": "root",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "To benchmark and compare how different sample variances affect integration, we selected ten random samples from the dataset as a control and another ten random samples with the same sample variance, such as samples from the same organoid tissue type. Each group of selected samples was integrated using the ... | [] |
{
"text": "After integration, we recluster all cells in the atlas based on the scPoli integrated embedding using the Leiden method with a resolution of 10 (HEOCA and HIOCA) and 10 (HLOCA), respectively. Annotations were then assigned to each cluster using the dominant cell type per cluster. Some clusters of cells wer... | [] |
{
"text": "We randomly subset 100,000 cells from the atlas. For each cell type, we used the Wilcoxon rank-sum test to identify DEGs, selecting the top ten genes as marker genes for each cell type. We combined the selected marker genes and performed hierarchical clustering on the resulting gene set.\nSource paper: PMC... | [] |
{
"text": "The human fetal endoderm tissue atlas was downloaded . The normal endoderm tissues including the esophagus, lung, liver, intestine, stomach and pancreas were subsetted. The top 3,000 highly variable genes were subsetted for data integration. The cells in each tissue were integrated using the scPoli method ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 717,
"end": 732,
"text": "large intestine",
"labels": [
"Tissue"
]
}
},
{
"from_name": "l... |
{
"text": "For each organoid sample, the same set of variable genes used in the primary tissue atlas (adult or fetal) was chosen, and the scPoli query was executed using identical parameters to those used in the primary tissue atlas training model. The UMAP embedding was transformed using the primary tissue atlas UMA... | [] |
{
"text": "To compare and correlate cell states in primary tissue and organoid models, the miloR method was used to define and construct neighborhood graphs for each data source separately. We computed the transcriptional similarity graph for the primary tissue reference using 30 nearest neighbors and the UMAP repres... | [] |
{
"text": "For RNA velocity analysis of the HIOCA, we first excluded samples missing splicing information. We then applied scVelo to generate a UMAP representation with stream trajectory visualization. The velocity pseudotime, spanning from stem cells to enterocytes and colonocytes, has been rescaled to a range of 0 ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 230,
"end": 234,
"text": "stem",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The top 3,000 highly variable genes were subsetted for data integration. To integrate all the cells, we applied the scPoli method with the same parameters used in the HEOCA atlas integration. The scPoli model was saved for the downstream comparison.\nSource paper: PMC12081310"
} | [] |
{
"text": "Lung organoid single-cell data curated from different studies was subsetted on top 3,000 highly variable genes for integration. We applied scPoli to learn 30-dimensional latent representations of the cells, and 10-dimensional latent representations of the samples using a neural network with 2 hidden layers... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 0,
"end": 4,
"text": "Lung",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "The scRNA-seq data from both duodenum fetal and adult primary tissues were obtained from two research papers . We focused on epithelial cells and subsetted them for analysis. The top 3,000 highly variable genes were subsetted for data integration. To integrate all the cells, we applied the scPoli method wi... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 125,
"end": 141,
"text": "epithelial cells",
"labels": [
"Cell"
]
}
},
{
"from_name": "la... |
{
"text": "To identify the heterogeneity of intestinal organoid stem cells and enterocytes, cells from the HIOCA were subsetted. Integration was performed using the CSS method based on 1,000 highly variable genes across all cells. Leiden clustering with a resolution of 0.1 was applied to identify subclusters. The Wil... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 53,
"end": 57,
"text": "stem",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The scRNA-seq data from both duodenum fetal and adult primary tissues were obtained from two research papers . The top 3,000 highly variable genes were subsetted for data integration. To integrate all the cells, we applied the scPoli method with the same parameters used in the HEOCA atlas integration. The ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 29,
"end": 37,
"text": "duodenum",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "Samples of scRNA-seq raw reads were mapped to the human genome, and counts of the matrix were obtained. The same set of variable genes used in HEOCA was chosen, and the scPoli query was executed using identical parameters to those used in the HEOCA training model. The UMAP embedding was transformed using t... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 21,
"end": 24,
"text": "raw",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "For each cell in the organoid protocols, organoid perturbation, and disease samples, a matched HEOCA cell was reconstructed using the top ten k NN in HEOCA. The mean expression of these ten neighbors was calculated to represent the expression profile of the matched sample reference in HEOCA. In addition, t... | [] |
{
"text": "To compare expression levels of the samples, the above-mentioned matched sample reference in HEOCA was identified. The expression difference per gene for each cell pair was calculated based on the log-normalized expression values. For each gene, the variance over the calculated expression difference per ce... | [] |
{
"text": "scRNA-seq reads for each sample were mapped to the human genome, and gene counts were generated using CellRanger. These counts served as input for sc2heoca, with default settings used to map all samples to HEOCA. During mapping, each cell was annotated with a level 2 cell type, and the distance to HEOCA wa... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 396,
"end": 399,
"text": "raw",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "The scIBD database was downloaded, and samples from healthy individuals, and patients with colitis, Crohn’s disease and ulcerative colitis were extracted. Only epithelial cells were selected for downstream analysis. Pseudo-bulk gene expression was calculated for each individual, and the DEGs identified in ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 160,
"end": 176,
"text": "epithelial cells",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "The disease sample analysis is similar to the sample incorporation step. The raw count matrices were downloaded from the original papers. The same set of variable genes used in HEOCA was chosen, and the scPoli query was executed using identical parameters to those used in the HEOCA training model. The UMAP... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 77,
"end": 80,
"text": "raw",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "In the analysis of DEGs between colon cancer organoid colonocytes and bronchial COPD organoid basal cells, we performed separate subsetting for all colonocytes and basal cells. For each dataset, we isolated the top 3,000 highly variable genes. We then integrated these subsets of cells using the bbknn metho... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 32,
"end": 37,
"text": "colon",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "We used D2C to score the expression levels of 2,395 drug target signatures in single cells from the human organoid cell atlas and the human lung cell atlas. D2C scores were scaled to mitigate scale differences between different datasets in the atlas. We used the R package scDECAF (v.0.99.0) to select drug ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 140,
"end": 144,
"text": "lung",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\nSource paper: PMC12081310"
} | [] |
{
"text": "Online content\nSource paper: PMC12081310"
} | [] |
{
"text": "Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-0... | [] |
{
"text": "PMC12116388\nSource paper: PMC12116388"
} | [] |
{
"text": "Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of C-labelled glucose metabolism\nSource paper: PMC12116388"
} | [] |
{
"text": "Abstract\nSource paper: PMC12116388"
} | [] |
{
"text": "Transcriptomic studies have attempted to classify glioblastoma (GB) into subtypes that predict survival and have different therapeutic vulnerabilities . Here we identified three metabolic subtypes: glycolytic, oxidative and a mix of glycolytic and oxidative, using mass spectrometry imaging of rapidly excis... | [] |
{
"text": "Main\nSource paper: PMC12116388"
} | [] |
{
"text": "GB is the most common primary adult brain cancer . Transcriptomic analyses have attempted to classify GB into subtypes that could predict treatment response , and a recent study that used a pathway-based classification defined metabolism-associated subtypes with distinct therapeutic vulnerabilities. These ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 36,
"end": 41,
"text": "brain",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "An important question is the extent to which the metabolism displayed by tumour cells in vivo is cell-intrinsic and how much is defined by the tumour microenvironment (TME) . We addressed this question by using mass spectrometry imaging (MSI) of rapidly excised tumour sections from patients with GB who wer... | [] |
{
"text": "Results\nSource paper: PMC12116388"
} | [] |
{
"text": "We infused three patients, two with GB and a third with an adenocarcinoma metastasis, with [U- C]glucose and performed MSI on rapidly excised tumour tissue that was dissected during tumour debulking surgery (Fig. 1a ). We sampled 16 regions from two patients with GB and seven regions from the patient with ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 373,
"end": 379,
"text": "cortex",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "Tissue sampling began 90 min after the start of infusion , during which time the plasma glucose fractional enrichment reached a steady state (Fig. 1c ). Fractional labelling of lactate, an end product of the glycolytic pathway, and of [ C 2 ]glutamate, which is labelled via α-ketoglutarate in the tricarbox... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 1307,
"end": 1312,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The metastatic adenocarcinoma occupied small, discrete areas surrounded by gliotic and normal-appearing brain parenchyma (Fig. 1g ) and therefore provided apparently normal brain tissue for comparative analysis. We segmented the mass spectrometry (MS) images using seven C-labelled metabolites from glycolys... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 110,
"end": 120,
"text": "parenchyma",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label"... |
{
"text": "Next, we assessed glycolytic activity and TCA cycle activity in the tumours and in normal-appearing brain. [U- C]lactate signals were significantly lower in GB1 than in GB2 and the metastasis, whereas the [ C 2 ]glutamate signals in GB1 were significantly higher than in the metastasis (Fig. 1h ), reflectin... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 100,
"end": 105,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "We segmented the GB MS images using the same seven C-labelled metabolites from glycolysis and the TCA cycle. We reasoned that the activity of these two pathways could result in four cellular states; however, the MSI spectra did not contain a high glycolytic, high TCA cycle phenotype, and images were better... | [] |
{
"text": "To exclude perfusion and hypoxia as explanations for the observed metabolic heterogeneity, we assessed cellular energy status from measurements of the ATP, ADP and PCr concentrations. All tumour regions had ATP/ADP and PCr/ATP ratios comparable to those in normal brain (Fig. 2d ). ADP was more abundant in ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 264,
"end": 269,
"text": "brain",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "The three metabolic states and normal brain showed similar redox status, as assessed from measurements of the ascorbic acid to dehydroascorbic acid (AsA:DHA) and reduced to oxidized glutathione (GSH:GSSG) ratios, reflecting the NADPH/NADP ratio , and the [U- C]lactate/[U- C]pyruvate ratios, reflecting the ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 38,
"end": 43,
"text": "brain",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "Next, we analysed contiguous sections by imaging mass cytometry (IMC) for the presence of immune cells, blood vessels and proliferating cells. We defined five immune phenotypes: CD3 CD45 CD4 (helper T cells) ; CD3 CD45 CD8 (cytotoxic T cells) ; CD3 CD45 CD8 GZMB (activated T cells) ; CD45 GZMB (natural kil... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 339,
"end": 350,
"text": "macrophages",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",... |
{
"text": "Tumour areas identified by a strong malignant signal in the spatial transcriptomics data showed the three metabolic states in spatially coherent areas on co-registered MS images (Fig. 2g–i ), whereas regions identified as containing immune cells were predominantly glycolytic and those containing neurons we... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 297,
"end": 304,
"text": "neurons",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "The bioenergetic status and microenvironment of the three metabolic states indicate that differences in metabolic activity are unlikely to have been influenced by differences in tissue perfusion, the presence of necrosis, differences in cell proliferation or the presence of immune cell infiltrates, but rat... | [] |
{
"text": "Despite there being no significant correlation between metabolic phenotype and blood vessel density, we nevertheless investigated a possible relationship between metabolic phenotype and proximity to the vasculature. We selected large vessels (Collagen I and αSMA ) (Extended Data Fig. 7a ), co-registered th... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 85,
"end": 91,
"text": "vessel",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "To confirm that the metabolic phenotypes are tumour-cell-intrinsic and not a consequence of differences in the TME, we derived 30 primary cell lines from 26 patients with GB (two patients had two cell lines derived from multi-regional tumour sampling). These were grown as neurospheres with [U- C]glucose be... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 138,
"end": 148,
"text": "cell lines",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Next, we looked at neurospheres derived from multi-regional sampling of the same tumour to determine whether the neurospheres captured the regional metabolic heterogeneity observed in the tumour samples. GTP2 Med (medial tumour) and GTP2 Lat (lateral tumour) formed similar-sized neurospheres but showed dif... | [] |
{
"text": "To test the robustness of the tumour-cell-intrinsic metabolic phenotype, we grew a subset of neurospheres, representative of the highly glycolytic to the more oxidative phenotypes, under normoxic and hypoxic conditions (0.5% O 2 ) and compared their transcriptomes. Despite prolonged exposure to hypoxia (16... | [] |
{
"text": "To further test the cell-intrinsic nature of the metabolic phenotypes, we implanted A11, AT8, S2 and AT5 into the brains of athymic rats and found that the cells retained the metabolic phenotypes that were observed when they were grown as neurospheres. AT8 and S2 formed more glycolytic xenografts, as demon... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 114,
"end": 120,
"text": "brains",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "The concentrations of unlabelled serine, threonine, glutamine and glutamate were significantly higher in oxidative regions in the patient tumour sections (ANOVA, Tukey’s P < 0.05), and in the neurospheres, the concentrations of unlabelled leucine/isoleucine, glutamine, glutamate, histidine and phenylalanin... | [] |
{
"text": "Discussion\nSource paper: PMC12116388"
} | [] |
{
"text": "The extent to which tumour metabolism is driven by cell-intrinsic mechanisms or microenvironmental pressures is an open question in tumour biology . To address this question, we measured the metabolic activity of GB within its native microenvironment using MS imaging of isotope labelling in rapidly quenche... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 505,
"end": 510,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "Using a targeted approach, we identified distinct glycolytic and oxidative metabolic phenotypes. Although recent reports have identified metabolic phenotypes from a transcriptomic analysis , we describe here the classification of metabolic phenotypes based on measurements of metabolic activity in patient t... | [] |
{
"text": "We observed no change in lactate and glutamate labelling with distance from the blood vessels. This appears to be inconsistent with a study in an orthotopically implanted glioma cell line model (U87MG) in immunocompromised mice that showed high mitochondrial activity in cells adjacent to vessels and increa... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 178,
"end": 187,
"text": "cell line",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "The cell-intrinsic nature of the metabolic phenotypes was confirmed using neurospheres grown in vitro, which reproduced the metabolic phenotypes observed in the patients and were preserved following their orthotopic implantation in rats. Exposing the neurospheres to chronic hypoxia did not lead to signific... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 516,
"end": 520,
"text": "lung",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "A recent study in mouse models of leukaemia, pancreatic, lung and colon cancer showed that these tumours suppress TCA cycle activity relative to normal tissue. By contrast, the glutamate labelling observed here in the GB tumours was similar to that in normal-appearing brain. Similar observations of substan... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 269,
"end": 274,
"text": "brain",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "Metabolic phenotypes with distinct therapeutic vulnerabilities have been identified in several cancers , including GB , in which GB cells with an oxidative phenotype were shown to be more sensitive to inhibitors of mitochondrial complex I and to radiation treatment. We have shown previously that S2 cells a... | [] |
{
"text": "Cells displaying these distinct metabolic phenotypes occupy territories that are sufficiently large to be imaged clinically using techniques such as hyperpolarized C magnetic resonance imaging (MRI) and deuterium metabolic imaging . These metabolic phenotypes show a correlation with treatment responsivenes... | [] |
{
"text": "We have demonstrated here high-resolution imaging of isotope labelling of cellular metabolites in a human tumour in vivo. In conjunction with studies on patient-derived neurospheres and orthotopically implanted xenografts, we have demonstrated the presence of different metabolic phenotypes within GB that a... | [] |
{
"text": "Methods\nSource paper: PMC12116388"
} | [] |
{
"text": "Three male patients from Addenbrooke’s Hospital, Cambridge, were infused with [U- C]glucose. The selection criteria included first clinical presentation, MRI consistent with GB and no significant co-morbidities.\nSource paper: PMC12116388"
} | [] |
{
"text": "Following induction of anaesthesia, a pyrogen-free 5% solution of [U- C]glucose in sterile saline (Merck) was administered as a bolus of 8 g over 10 min followed by 8 g h continuous infusion, as described previously for patients with GB and several other tumour types . Arterial blood was collected by a per... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 279,
"end": 284,
"text": "blood",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The study was approved by the Central Cambridge Research Ethics Committee and was compliant with the Health Research Authority. The study adhered to the principles of the Declaration of Helsinki and the Guidelines for Good Clinical Practice. Participants did not receive financial compensation and gave info... | [] |
{
"text": "Frozen tumour samples were embedded in a hydroxypropyl methylcellulose/polyvinylpyrrolidone hydrogel , and 10 µm-thick cryo-sections were obtained. Sections were thaw-mounted onto Superfrost microscope slides for desorption electrospray ionization (DESI) and IMC experiments (Thermo Scientific), while secti... | [] |
{
"text": "DESI-MSI analysis was performed on a Q-Exactive mass spectrometer (Thermo Scientific) equipped with an automated 2D-DESI ion source (Prosolia). The spectrometer was used with a home-made Swagelok DESI sprayer and a mixture of 95% methanol, 5% water delivered at a flow rate of 1.5 µl min and nebulized with ... | [] |
{
"text": "MALDI-MSI analysis was performed using a RapifleX Tissuetyper instrument (Bruker Daltonik) operated in negative ion detection mode. 9-Aminoacridine, prepared in an 80:20 methanol-to-water ratio, was used as a matrix and spray-deposited using an automated spray system (M3-Sprayer, HTX technologies). Mass sp... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 510,
"end": 513,
"text": "Raw",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "DESI and MALDI data and images were normalized to the total ion current to compensate for signal variation during the course of the experiments, and acquisition parameters and data processing were identical for human tissue, neurospheres embedded in Matrigel and xenograft sections.\nSource paper: PMC121163... | [] |
{
"text": "Snap-frozen tumour samples were homogenized with 5 µl mg of 2 M perchloric acid. The extract was centrifuged at 13,000 g for 15 min, and the pH of the supernatant was adjusted to 7.0 using 2 M KOH. Extracts were lyophilized and dissolved in 550 µl deuterium oxide containing methylenediphosphonic acid at 10... | [] |
{
"text": "Arterial blood collected during intra-operative infusion was centrifuged at 2,000 g and 4 °C for 20 min to collect the plasma, which was snap-frozen and stored at −80 °C. Samples were thawed on wet ice and aliquots diluted 50-fold with cold methanol:acetonitrile:water (50:30:20) in chilled tubes, vortexed ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 291,
"end": 296,
"text": "tubes",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "The HILIC–HRMS system consisted of a Shimadzu Nexera X2 UHPLC and Sciex 6600 Triple TOF mass spectrometer, using a SeQuant ZIC-pHILIC 5 µm 150 × 2.1 mm column (with a ZIC-pHILIC 20 × 2.1 mm guard column) at 45 °C. The liquid chromatography gradient started at 80% acetonitrile and 20% 20 mM ammonium carbona... | [] |
{
"text": "Data were acquired using Sciex Analyst TF and processed using Sciex MultiQuant software. Extracted ion chromatograms were generated from the theoretical m / z ± 20 ppm. Peak integration was reviewed manually, and the peak area of each metabolite and isotope was exported.\nSource paper: PMC12116388"
} | [] |
{
"text": "Primary human cell lines were derived at Addenbrooke’s Hospital, Cambridge, UK, as described previously . Tissue collection was approved by a Regional Ethics Committee (REC18/EE/0283) and was compliant with the UK Human Tissue Act 2004. Resected tissue samples were washed with Hanks’ balanced salt solution... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 14,
"end": 24,
"text": "cell lines",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "For neurosphere formation, cells were seeded in Ultra Low Attachment 96-well plates (Corning) at a density of 10,000 cells per well. Sphere diameter was monitored using an IncuCyte microscope (Sartorius). Spheres were embedded in 150 μl Matrigel (Corning) domes in 24-well plates (Corning). The domes were c... | [] |
{
"text": "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. The medium was then replaced with either fresh medium or medium containing the following... | [] |
{
"text": "Neurospheres in Matrigel domes were grown at 37 °C, with one plate incubated in atmospheric O 2 and the other grown in 0.5% O 2 , 0.5 Pa (Avatar, XCellbio). 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 th... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 848,
"end": 851,
"text": "raw",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "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. Athymic, female nude rats that were at least 9 weeks old were implanted orthot... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 702,
"end": 706,
"text": "head",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "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. ). 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 de... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 18,
"end": 27,
"text": "cell line",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "The 10× Genomics Visium platform was used and analysed with the Space Ranger pipeline. Downstream analyses were conducted in R using the Seurat package . Samples were processed individually using the SCTransform() function. Spots were filtered based on standard quality control thresholds (for example, mito... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 469,
"end": 474,
"text": "joint",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "To annotate TME spots, we performed two deconvolution steps with robust cell type decomposition , using previously published reference cell annotations . 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 d... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 507,
"end": 518,
"text": "macrophages",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",... |
{
"text": "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. Immediately neighbouring pixels (≤8) were identified for each MSI pixel, and across five iterations, for each pixel that h... | [] |
{
"text": "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. The values for each of the seven C-labelled metabolites used for metabolic clustering were standardised into z -scores (by... | [] |
{
"text": "Antibodies used for immunohistochemistry are described in Supplementary Table 5 . Antibodies were tagged using the Fluidigm Maxpar Antibody Labelling Kit. 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 casei... | [] |
{
"text": "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 ). Images were analysed using Aperio image-viewing software and HALO (v.3.6.4134.137).\nSource paper: ... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 19,
"end": 25,
"text": "brains",
"labels": [
"Tissue"
]
}
}
]
}
] |
{
"text": "HALO (v.3.6.4134.137) and HighPlex FL (v.4.1.3) modules were used for automated image analysis. 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, 3.5... | [] |
{
"text": "Five cellular phenotypes were identified: CD4 helper T cells (CD45 CD4 CD3 ); cytotoxic T cells (CD3 CD45 CD8A ); activated cytotoxic T cells (CD3 CD45 CD8A GZMB ); natural killer cells and neutrophils (CD45 GZMB ); and macrophages and microglia (CD68 ). A random forest classifier was used to distinguish v... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 220,
"end": 231,
"text": "macrophages",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",... |
{
"text": "Sample size for human tumour collection was determined intra-operatively based on patient and tumour factors (for example, proximity to eloquent brain). A minimum of six samples were collected for each tumour region. For xenograft and neurosphere studies, a minimum of three independent biological replicate... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 358,
"end": 367,
"text": "cell line",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\nSource paper: PMC12116388"
} | [] |
{
"text": "PMC12133578\nSource paper: PMC12133578"
} | [] |
{
"text": "Immune–epithelial–stromal networks define the cellular ecosystem of the small intestine in celiac disease\nSource paper: PMC12133578"
} | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 78,
"end": 87,
"text": "intestine",
"labels": [
"Tissue"
]
}
},
{
"from_name": "label",
... |
{
"text": "Abstract\nSource paper: PMC12133578"
} | [] |
{
"text": "The immune–epithelial–stromal interactions underpinning intestinal damage in celiac disease (CD) are incompletely understood. To address this, we performed single-cell transcriptomics (RNA sequencing; 86,442 immune, parenchymal and epithelial cells; 35 participants) and spatial transcriptomics (20 particip... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 666,
"end": 677,
"text": "lymphocytes",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",... |
{
"text": "Main\nSource paper: PMC12133578"
} | [] |
{
"text": "Celiac disease (CD) is a common gastrointestinal disorder affecting 1–2% of European and North American populations, in which small intestinal inflammation and damage are driven by aberrant adaptive immune responses to gluten . The only treatment is a lifelong gluten-free diet (GFD). There is an unmet ther... | [] |
{
"text": "A strong genetic component drives CD, dominated by HLA-DQ2 and HLA-DQ8 (ref. ), with association studies identifying over 40 non-HLA genomic loci, implicating over 100 candidate genes and a role for immunoregulatory mechanisms . Murine models implicate viral infection as a trigger of loss of tolerance driv... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 72,
"end": 75,
"text": "ref",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "CD pathophysiology is multifactorial with several cell types implicated . Dietary gluten is deamidated by tissue transglutaminase 2, and deamidated gluten peptides presented via HLA-DQ2/HLA-DQ8 to CD4 T cells . Gluten-specific CD4 T cells possess a distinct type 1 helper T (T H 1)/follicular helper T (T FH... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 363,
"end": 369,
"text": "B cell",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Gluten-specific T cells are necessary but not sufficient to generate mucosal damage . The mechanisms by which this response leads to tissue architectural change are incompletely understood. Intraepithelial lymphocytes (IELs), mainly CD8 T IELs, are highly enriched in CD, likely driven by epithelial and mye... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 206,
"end": 217,
"text": "lymphocytes",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",... |
{
"text": "While novel treatments are under development , recent therapeutic trials targeting gluten degradation, gluten-specific CD4 T cell tolerance and IL-15 have been unsuccessful . However, therapies including tissue transglutaminase inhibitors and inducers of immune tolerance have shown promise .\nSource paper:... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 123,
"end": 129,
"text": "T cell",
"labels": [
"Cell"
]
}
}
]
}
] |
{
"text": "Single-cell transcriptomics have redefined cellular landscapes in the gastrointestinal tract , offering insights into CD immunopathology . Recent studies have sought to understand the cellular basis of CD using mass cytometry, including studies of refractory CD , gluten-specific T cells , and mucosal and c... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 280,
"end": 287,
"text": "T cells",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Here, we combined single-cell and spatial transcriptomics to define the network of intestinal immune, epithelial and parenchymal cell populations in adults and children with CD. Our description of spatially localized immune–parenchymal interactions driving inflammation and remodeling of the mucosa, and wit... | [
{
"result": [
{
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 337,
"end": 343,
"text": "T cell",
"labels": [
"Cell"
]
}
},
{
"from_name": "label",
... |
{
"text": "Results\nSource paper: PMC12133578"
} | [] |
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