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To compare these factors to each other, we computed the Pearson correlation between the gene score vectors for each pair of factors.
|
[] |
Single_Cell
|
We then identified factors with a pairwise correlation that was greater than the 95% confidence threshold with at least two other factors, which yielded 31 scHPF factors from across the six major immune lineages.
|
[
{
"end": 211,
"label": "CellType",
"start": 190,
"text": "major immune lineages"
}
] |
Single_Cell
|
Finally, we performed hierarchical clustering of the Pearson correlation matrix for these 31 factors ( seaborn.clustermap using Euclidean distances) to identify modules containing factors with similar gene signatures that originated from different, lineage-specific scHPF models (Extended Data Fig. 4 ).
|
[] |
Single_Cell
|
Modules of genes were further interrogated by average gene expression and validated in specific immune subsets using pseudobulk GEX DE and ADT DE as described above.
|
[
{
"end": 110,
"label": "CellType",
"start": 87,
"text": "specific immune subsets"
}
] |
Single_Cell
|
Source paper: PMC12396968
To detect shifts in the subset composition of specific lineages across the age groups and CMV serostatus (Supplementary Table 14 ), we performed generalized linear modeling by fitting a statsmodels.
|
[] |
Single_Cell
|
GLM model for each tissue subset, considering sex, sequencing chemistry, CMV serostatus and processing site as additional covariates.
|
[] |
Single_Cell
|
Donors with fewer than 50 cells for a particular tissue-group–lineage combination or tissue-group–lineage combinations with fewer than four suitable donors were excluded from analysis.
|
[
{
"end": 31,
"label": "CellType",
"start": 26,
"text": "cells"
}
] |
Single_Cell
|
The estimated coefficients were used to calculate a covariate-aware log 2 (FC) for visualization.
|
[] |
Single_Cell
|
We used the statsmodels.multipletests function to adjust P values for multiple comparisons (Benjamini–Hochberg method), and subset–tissue combinations with adjusted P < 0.05 were considered significantly changing across age.
|
[] |
Single_Cell
|
Source paper: PMC12396968
To depict age-associated effects on the immune system, we visualized the similarity of trending DEGs by age on immune subsets across tissues using t -distributed stochastic neighbor embedding ( t -SNE) (Fig. 5d ).
|
[
{
"end": 81,
"label": "Tissue",
"start": 68,
"text": "immune system"
},
{
"end": 153,
"label": "CellType",
"start": 139,
"text": "immune subsets"
},
{
"end": 168,
"label": "Tissue",
"start": 161,
"text": "tissues"
}
] |
Single_Cell
|
We first calculated trending DEG (unadjusted P < 0.05; <40 or >40 years old) pairwise similarities by summing the intersection of positively regulated genes (log 2 (FC) > 0.1) and negatively regulated genes (log 2 (FC) < −0.1), divided by the overall union of both.
|
[] |
Single_Cell
|
The similarity or distance (1 − similarity) was applied to cell types containing more than 70 DEGs (unadjusted P < 0.05; mean log-normalized expression, >0.05) and present in at least 3 donors per tissue and age group.
|
[
{
"end": 69,
"label": "CellType",
"start": 59,
"text": "cell types"
},
{
"end": 203,
"label": "Tissue",
"start": 197,
"text": "tissue"
}
] |
Single_Cell
|
The similarity levels of cell types and tissues with more than 200 DEGs were further clustered using the Ward.
|
[
{
"end": 35,
"label": "CellType",
"start": 25,
"text": "cell types"
},
{
"end": 47,
"label": "Tissue",
"start": 40,
"text": "tissues"
}
] |
Single_Cell
|
D2 method and projected into a distance-based t -SNE illustration.
|
[] |
Single_Cell
|
Source paper: PMC12396968
To investigate the effect of age on specific genes within each immune subset, we plotted genes that were significant in at least one tissue (adjusted P < 0.05) and within the top 50 significant genes.
|
[] |
Single_Cell
|
Although our power to detect age effects by DE was limited, genes that were significantly DE in one subset were often trending in the same direction across multiple tissue groups.
|
[
{
"end": 179,
"label": "Tissue",
"start": 165,
"text": "tissue groups."
}
] |
Single_Cell
|
To assess the effect of age on surface protein expression (Supplementary Table 17 ), we used landmark-registered protein expression data (by MMoCHi, see above) to account for donor-to-donor batch effects in ADT staining quality.
|
[] |
Single_Cell
|
Although landmark registration preserves the separation between positive-expressing and negative-expressing cells for thresholding, this non-parametric normalization can obscure changes in overall expression intensity between samples.
|
[
{
"end": 83,
"label": "CellType",
"start": 64,
"text": "positive-expressing"
},
{
"end": 113,
"label": "CellType",
"start": 88,
"text": "negative-expressing cells"
}
] |
Single_Cell
|
Therefore, we focused on shifts in percent positivity for a marker in each tissue subset.
|
[
{
"end": 88,
"label": "Tissue",
"start": 75,
"text": "tissue subset"
}
] |
Single_Cell
|
We performed automatic thresholding by MMoCHi, followed by manual adjustment (as described above), on the landmark-registered expression of all ADTs corresponding to a DEG by age.
|
[] |
Single_Cell
|
The percentage of cells with expression of a given ADT above the positive threshold was calculated for each donor–tissue-group–subset combination.
|
[
{
"end": 23,
"label": "CellType",
"start": 18,
"text": "cells"
}
] |
Single_Cell
|
Donors with fewer than 50 cells for a particular tissue-group–lineage combination or tissue-group–lineage combinations with fewer than four suitable donors were excluded from analysis.
|
[
{
"end": 31,
"label": "CellType",
"start": 26,
"text": "cells"
}
] |
Single_Cell
|
The percent positivity was used as the response variable in the same linear regression model used to detect shifts in composition across age groups.
|
[] |
Single_Cell
|
We adjusted P values for multiple comparisons as above, and ADTs with adjusted P < 0.05 were considered significant.
|
[] |
Single_Cell
|
Source paper: PMC12396968
We constructed donor-level scHPF models for each major immune lineage with uniform representation of cells from each donor to identify age-associated gene signatures, as described above.
|
[
{
"end": 134,
"label": "CellType",
"start": 129,
"text": "cells"
},
{
"end": 97,
"label": "CellType",
"start": 77,
"text": "major immune lineage"
}
] |
Single_Cell
|
For each scHPF model, we performed LMM to account for covariates and identify age associations.
|
[] |
Single_Cell
|
Each LMM contained six categorical covariates as fixed effects: age group, sequencing chemistry, sex, processing site and CMV serostatus.
|
[] |
Single_Cell
|
We also considered three tissue types: mucosal (BAL, lung parenchyma, JLP, JEL), LNs (ILN, MLN and LLN) and blood-rich, including blood, BM and spleen), which required us to select one category (blood-rich) as a held-out variable.
|
[
{
"end": 135,
"label": "Tissue",
"start": 130,
"text": "blood"
},
{
"end": 150,
"label": "Tissue",
"start": 144,
"text": "spleen"
},
{
"end": 68,
"label": "Tissue",
"start": 53,
"text": "lung parenchyma"
},
{
"end": 37,
"label": "Tissue",
"start": 25,
"text": "tissue types"
},
{
"end": 46,
"label": "Tissue",
"start": 39,
"text": "mucosal"
},
{
"end": 51,
"label": "Tissue",
"start": 48,
"text": "BAL"
},
{
"end": 73,
"label": "Tissue",
"start": 70,
"text": "JLP"
},
{
"end": 78,
"label": "Tissue",
"start": 75,
"text": "JEL"
},
{
"end": 84,
"label": "Tissue",
"start": 81,
"text": "LNs"
},
{
"end": 89,
"label": "Tissue",
"start": 86,
"text": "ILN"
},
{
"end": 94,
"label": "Tissue",
"start": 91,
"text": "MLN"
},
{
"end": 102,
"label": "Tissue",
"start": 99,
"text": "LLN"
},
{
"end": 118,
"label": "Tissue",
"start": 108,
"text": "blood-rich"
},
{
"end": 139,
"label": "Tissue",
"start": 137,
"text": "BM"
},
{
"end": 205,
"label": "Tissue",
"start": 195,
"text": "blood-rich"
}
] |
Single_Cell
|
Thus, we have two categorical variables for tissue, which effectively represent mucosal versus blood-rich and LN versus blood-rich.
|
[
{
"end": 50,
"label": "Tissue",
"start": 44,
"text": "tissue"
},
{
"end": 87,
"label": "Tissue",
"start": 80,
"text": "mucosal"
},
{
"end": 105,
"label": "Tissue",
"start": 95,
"text": "blood-rich"
},
{
"end": 112,
"label": "Tissue",
"start": 110,
"text": "LN"
},
{
"end": 130,
"label": "Tissue",
"start": 120,
"text": "blood-rich"
}
] |
Single_Cell
|
We encoded donor identity as a random effect.
|
[] |
Single_Cell
|
LMM coefficients and P values were computed for each factor in a given scHPF model using the cell scores as response variables by fitting a statsmodels.
|
[] |
Single_Cell
|
MixedLM model and using the statsmodels.multipletests function to adjust P values for multiple comparisons (Benjamini–Hochberg method).
|
[] |
Single_Cell
|
Source paper: PMC12396968
To cross-validate age-associated scHPF factors in other datasets, we further analyzed a bone marrow atlas containing 36 age-annotated donors with good B cell representation for a B cell aging factor, a lung atlas containing 29 age-annotated donors with good macrophage and CD8 T cell coverage and PBMC data from the Sound Life cohort (age 25–65 years, n = 96) from the Human Immune Health Atlas (Figs. 5 and 6 and Extended Data Fig. 5 ).
|
[
{
"end": 296,
"label": "CellType",
"start": 286,
"text": "macrophage"
},
{
"end": 185,
"label": "CellType",
"start": 179,
"text": "B cell"
},
{
"end": 311,
"label": "CellType",
"start": 301,
"text": "CD8 T cell"
}
] |
Single_Cell
|
Using the published cell-type annotations from each atlas, we extracted the appropriate scRNA-seq profiles and projected them into the corresponding donor-level scHPF models generated from the data reported here using the scHPF project function.
|
[] |
Single_Cell
|
This resulted in cell scores for cells from the external data sets for the same factors that were generated from this data set, allowing us to compare the average cell scores for young versus older donors from the external data.
|
[
{
"end": 38,
"label": "CellType",
"start": 33,
"text": "cells"
}
] |
Single_Cell
|
As an orthogonal approach, we also performed pseudobulk DE analysis between older and younger donors (using an age cutoff of 40 years) from the external data sets, ranked the genes by FC and used GSEA to analyze the statistical enrichment of age-associated factors among young versus old donors (Supplementary Table 19 ).
|
[] |
Single_Cell
|
We used the top 200 genes (ranked by scHPF gene score) in each age-associated factor as gene sets for GSEA.
|
[] |
Single_Cell
|
Source paper: PMC12396968
For the DE analysis described in Fig. 7 , we subsetted the MrVI sample embeddings to each tissue group and modeled the predicted ε in MrVI by a linear model adjusting for covariates in sex, CMV serostatus and age group as fixed effects, considering sequencing chemistry and processing site as site covariates in MrVI.
|
[
{
"end": 130,
"label": "Tissue",
"start": 118,
"text": "tissue group"
}
] |
Single_Cell
|
A ridge regression parameter of 0.1, owing to collinearity of cofactors, was added.
|
[] |
Single_Cell
|
This decomposition of ε was performed for every single cell.
|
[
{
"end": 59,
"label": "CellType",
"start": 48,
"text": "single cell"
}
] |
Single_Cell
|
This yields an estimated effect in Z -space for each covariate.
|
[] |
Single_Cell
|
The effect vector was added to the mean cell embedding in Z- space, and DEGs were computed based on the modified and mean embedding for each cell.
|
[
{
"end": 145,
"label": "CellType",
"start": 141,
"text": "cell"
}
] |
Single_Cell
|
For downstream analysis, this matrix of estimated log(FC) for each cell and gene was further processed for each immune subset.
|
[
{
"end": 71,
"label": "CellType",
"start": 67,
"text": "cell"
},
{
"end": 125,
"label": "CellType",
"start": 112,
"text": "immune subset"
}
] |
Single_Cell
|
First, all cells that were represented only in fewer than three samples were filtered out .
|
[
{
"end": 16,
"label": "CellType",
"start": 11,
"text": "cells"
}
] |
Single_Cell
|
Second, for each cell type, we excluded genes with less than 0.01 raw average expression or an estimated log(FC) across age groups with a 95th percentile below 0.1, retaining only genes that might be affected by age in a group of cells.
|
[
{
"end": 26,
"label": "CellType",
"start": 16,
"text": " cell type"
},
{
"end": 235,
"label": "CellType",
"start": 230,
"text": "cells"
}
] |
Single_Cell
|
To dissect predicted gene effects into modules, neighborhood smoothing was performed using 15 nearest neighbors in U- space and multiplying two times the normalized affinity matrix by the predicted gene effects.
|
[] |
Single_Cell
|
Spectral co-clustering was performed with four gene clusters and four cell clusters, with mini-batch enabled using sklearn.cluster.
|
[] |
Single_Cell
|
SpectralCoclustering .
|
[] |
Single_Cell
|
Marker genes for each module were identified by averaging the predicted log(FC) across all cells from the corresponding cell module, and the top 50 genes for each module were identified.
|
[] |
Single_Cell
|
We used decoupleR-py to compute a module score of log(FC) scores using weighted means of the signs of those marker genes (Supplementary Table 23 ).
|
[] |
Single_Cell
|
Source paper: PMC12396968
For the lung, we isolated a gene module in CD4 T cells that contained T RM cells.
|
[
{
"end": 40,
"label": "Tissue",
"start": 36,
"text": "lung"
},
{
"end": 82,
"label": "CellType",
"start": 71,
"text": "CD4 T cells"
},
{
"end": 108,
"label": "CellType",
"start": 98,
"text": "T RM cells"
}
] |
Single_Cell
|
To detect similar cells in other tissues, we computed the best cutoff for the module score to identify cells in a specific cell module based on Youden’s J statistic, computed the module score for all cells from other tissues as described above and applied the same cutoff to all other tissues as the tissue of interest.
|
[
{
"end": 23,
"label": "CellType",
"start": 10,
"text": "similar cells"
},
{
"end": 40,
"label": "Tissue",
"start": 33,
"text": "tissues"
},
{
"end": 108,
"label": "CellType",
"start": 103,
"text": "cells"
},
{
"end": 205,
"label": "CellType",
"start": 200,
"text": "cells"
},
{
"end": 224,
"label": "Tissue",
"start": 211,
"text": "other tissues"
},
{
"end": 292,
"label": "Tissue",
"start": 279,
"text": "other tissues"
},
{
"end": 306,
"label": "Tissue",
"start": 300,
"text": "tissue"
}
] |
Single_Cell
|
Given that the gut contained T RM cells with a T H 17 phenotype and all other tissues had no module-positive cells, we selected all cells with a MrVI predicted negative log 2 (FC) of IL17A below −0.05.
|
[
{
"end": 18,
"label": "Tissue",
"start": 15,
"text": "gut"
},
{
"end": 39,
"label": "CellType",
"start": 29,
"text": "T RM cells"
},
{
"end": 85,
"label": "Tissue",
"start": 78,
"text": "tissues"
},
{
"end": 114,
"label": "CellType",
"start": 93,
"text": "module-positive cells"
},
{
"end": 137,
"label": "CellType",
"start": 132,
"text": "cells"
}
] |
Single_Cell
|
To confirm our findings on a per-gene level, we selected module-positive cells and used pseudobulk estimates of DE using dreamlet (Supplementary Table 24 ).
|
[
{
"end": 79,
"label": "CellType",
"start": 57,
"text": "module-positive cells "
}
] |
Single_Cell
|
Samples with fewer than 5 module-positive cells or 1,000 total counts and genes with fewer than 3 total counts were removed.
|
[
{
"end": 47,
"label": "CellType",
"start": 26,
"text": "module-positive cells"
}
] |
Single_Cell
|
Aging DE was performed using the contrasts method, as described above.
|
[] |
Single_Cell
|
Genes within a shared functional group were manually selected from the MrVI signature for visualization.
|
[] |
Single_Cell
|
Pseudobulk DE analysis was performed on the classifier-predicted cells in other tissues using the same settings as above in this cell subset.
|
[
{
"end": 70,
"label": "CellType",
"start": 65,
"text": "cells"
},
{
"end": 87,
"label": "Tissue",
"start": 74,
"text": "other tissues"
},
{
"end": 140,
"label": "CellType",
"start": 129,
"text": "cell subset"
}
] |
Single_Cell
|
Enrichment of module or selected marker genes in the pseudobulk DE analysis was performed using GSEA implemented in decoupler.run_gsea (Supplementary Table 23 ).
|
[] |
Single_Cell
|
Source paper: PMC12396968
TCR or BCR data were then transferred into the corresponding AnnData object.
|
[] |
Single_Cell
|
Cells without receptor data or that presented more than one receptor were discarded from further immunoreceptor analysis.
|
[] |
Single_Cell
|
For T cell analysis, cells annotated as MAIT cells or ɣδ T cells were also discarded.
|
[
{
"end": 26,
"label": "CellType",
"start": 21,
"text": "cells"
},
{
"end": 50,
"label": "CellType",
"start": 40,
"text": "MAIT cells"
},
{
"end": 64,
"label": "CellType",
"start": 53,
"text": " ɣδ T cells"
}
] |
Single_Cell
|
Clonality of the different populations was calculated as 1 − Pielou’s evenness index, varying from zero (more diverse) to one (less diverse), with the Pielou’s evenness corresponding H s / H max , where H s is the Shannon entropy of sample s and H max = log 2 C , where C is the number of unique clonotypes in s .
|
[] |
Single_Cell
|
All clonality scores were calculated on a subsample of 100 cells for each donor, cell type, tissue or cell type and tissue.
|
[
{
"end": 64,
"label": "CellType",
"start": 59,
"text": "cells"
},
{
"end": 90,
"label": "CellType",
"start": 81,
"text": "cell type"
},
{
"end": 111,
"label": "CellType",
"start": 102,
"text": "cell type"
},
{
"end": 98,
"label": "Tissue",
"start": 92,
"text": "tissue"
},
{
"end": 122,
"label": "Tissue",
"start": 116,
"text": "tissue"
}
] |
Single_Cell
|
To detect shifts in the BCR isotype composition of specific B cell lineages across the age groups (Supplementary Table 20 ), we performed generalized linear modeling by fitting a statsmodels.
|
[
{
"end": 75,
"label": "CellType",
"start": 51,
"text": "specific B cell lineages"
}
] |
Single_Cell
|
GLM model for each tissue subset, considering sex, sequencing chemistry, CMV serostatus and processing site as additional covariates.
|
[] |
Single_Cell
|
Donors with fewer than 50 cells for a particular tissue-group–lineage combination or tissue-group–lineage combinations with fewer than for suitable donors were excluded from analysis.
|
[
{
"end": 31,
"label": "CellType",
"start": 26,
"text": "cells"
}
] |
Single_Cell
|
The estimated coefficients were used to calculate a covariate-aware log 2 (FC) for visualization.
|
[] |
Single_Cell
|
We used the statsmodels.multipletests function to adjust P values for multiple comparisons (Benjamini–Hochberg method), and subset–tissue combinations with adjusted P < 0.05 were considered significantly changing across age.
|
[] |
Single_Cell
|
Source paper: PMC12396968
Lung cancer is the second most frequently diagnosed cancer and the leading cause of cancer-related mortality worldwide.
|
[] |
Single_Cell
|
Tumour ecosystems feature diverse immune cell types.
|
[
{
"end": 51,
"label": "CellType",
"start": 34,
"text": "immune cell types"
}
] |
Single_Cell
|
Myeloid cells, in particular, are prevalent and have a well-established role in promoting the disease.
|
[
{
"end": 13,
"label": "CellType",
"start": 0,
"text": "Myeloid cells"
}
] |
Single_Cell
|
In our study, we profile approximately 900,000 cells from 25 treatment-naive patients with adenocarcinoma and squamous-cell carcinoma by single-cell and spatial transcriptomics.
|
[
{
"end": 52,
"label": "CellType",
"start": 47,
"text": "cells"
}
] |
Single_Cell
|
We note an inverse relationship between anti-inflammatory macrophages and NK cells/T cells, and with reduced NK cell cytotoxicity within the tumour.
|
[
{
"end": 90,
"label": "CellType",
"start": 83,
"text": "T cells"
},
{
"end": 69,
"label": "CellType",
"start": 40,
"text": "anti-inflammatory macrophages"
},
{
"end": 82,
"label": "CellType",
"start": 74,
"text": "NK cells"
},
{
"end": 147,
"label": "Tissue",
"start": 141,
"text": "tumour"
}
] |
Single_Cell
|
While we observe a similar cell type composition in both adenocarcinoma and squamous-cell carcinoma, we detect significant differences in the co-expression of various immune checkpoint inhibitors.
|
[] |
Single_Cell
|
Moreover, we reveal evidence of a transcriptional “reprogramming” of macrophages in tumours, shifting them towards cholesterol export and adopting a foetal-like transcriptional signature which promotes iron efflux.
|
[
{
"end": 80,
"label": "CellType",
"start": 69,
"text": "macrophages"
},
{
"end": 91,
"label": "Tissue",
"start": 84,
"text": "tumours"
}
] |
Single_Cell
|
Our multi-omic resource offers a high-resolution molecular map of tumour-associated macrophages, enhancing our understanding of their role within the tumour microenvironment.
|
[
{
"end": 95,
"label": "CellType",
"start": 66,
"text": "tumour-associated macrophages"
}
] |
Single_Cell
|
Source paper: PMC11116453
Lung cancer is the second most commonly diagnosed cancer and the first cause of cancer death worldwide , with a 5-year survival of ~6% in patients with the most advanced stages .
|
[] |
Single_Cell
|
Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer (~85% of total cases), followed by small-cell lung cancer (15% of total cases) .
|
[] |
Single_Cell
|
Lung cancer is a complex disease in which the tumour microenvironment plays a critical role and macrophages (Mɸ) are intimately involved in the progression of the disease.
|
[
{
"end": 107,
"label": "CellType",
"start": 96,
"text": "macrophages"
},
{
"end": 111,
"label": "CellType",
"start": 109,
"text": "Mɸ"
}
] |
Single_Cell
|
In particular, tumour-associated Mɸ (TAMs) can exhibit a dual role, contributing to tumour promotion by suppressing the immune response, facilitating angiogenesis, and aiding in tissue remodelling, but also tumour suppression by promoting inflammation and engaging in cytotoxic activity against cancer cells .
|
[
{
"end": 35,
"label": "CellType",
"start": 15,
"text": "tumour-associated Mɸ"
},
{
"end": 41,
"label": "CellType",
"start": 37,
"text": "TAMs"
},
{
"end": 307,
"label": "CellType",
"start": 295,
"text": "cancer cells"
}
] |
Single_Cell
|
The intricate interplay between lung cancer and Mɸ highlights the importance of understanding their dynamic relationship in order to develop more effective therapeutic strategies.
|
[
{
"end": 50,
"label": "CellType",
"start": 48,
"text": "Mɸ"
}
] |
Single_Cell
|
Source paper: PMC11116453
Within NSCLC, adenocarcinoma (LUAD) is the most common histological subtype, followed by squamous-cell carcinoma (LUSC).
|
[] |
Single_Cell
|
Lobectomy (i.e., the anatomical resection of a lung lobe) is currently the gold standard for the treatment of early stages of NSCLC (stage I/II), while patients with unresectable stage III or metastatic stage IV NSCLC are treated with a combination of chemotherapy and neoadjuvant targeting vascular endothelial growth factor (VEGF) or immune checkpoint inhibitors (ICIs) like PD1, PDL1 and CTLA4.
|
[
{
"end": 56,
"label": "Tissue",
"start": 47,
"text": "lung lobe"
}
] |
Single_Cell
|
Advancements made in the last decade in uncovering predictive biomarkers have paved the way for novel therapeutic prospects in the fields of targeted therapy and immunotherapy on the basis of tumour histology and PDL1 expression .
|
[] |
Single_Cell
|
Source paper: PMC11116453
A number of studies have employed single-cell technologies to explore transcriptional changes in NSCLC .
|
[] |
Single_Cell
|
They have extensively examined the lung tumour microenvironment revealing diverse T-cell functions linked to patient prognosis, relevance of diversity of B cells in NSCLC for anti-tumour therapy, multiple states of tumour-infiltrating myeloid cells, proposing them as a new target in immunotherapy, as well as the association of tissue-resident neutrophils with anti-PDL1 therapy failure .
|
[
{
"end": 88,
"label": "CellType",
"start": 82,
"text": "T-cell"
},
{
"end": 161,
"label": "CellType",
"start": 154,
"text": "B cells"
},
{
"end": 248,
"label": "CellType",
"start": 215,
"text": "tumour-infiltrating myeloid cells"
},
{
"end": 356,
"label": "CellType",
"start": 329,
"text": "tissue-resident neutrophils"
}
] |
Single_Cell
|
They further unveiled tumour heterogeneity and cellular changes in advanced and metastatic tumours as well as tumour therapy-induced transition of cancer cells to a primitive cell state .
|
[
{
"end": 28,
"label": "Tissue",
"start": 22,
"text": "tumour"
},
{
"end": 75,
"label": "Tissue",
"start": 67,
"text": "advanced"
},
{
"end": 98,
"label": "Tissue",
"start": 80,
"text": "metastatic tumours"
},
{
"end": 116,
"label": "Tissue",
"start": 110,
"text": "tumour"
},
{
"end": 159,
"label": "CellType",
"start": 147,
"text": "cancer cells"
}
] |
Single_Cell
|
In many of these studies, a limited number of cells was analysed per patient, and often there was no systematic collection of patient-matched non-tumour tissue, thus restricting dissection of the biological heterogeneity within tumour and adjacent non-tumour tissue.
|
[
{
"end": 51,
"label": "CellType",
"start": 46,
"text": "cells"
},
{
"end": 159,
"label": "Tissue",
"start": 142,
"text": "non-tumour tissue"
},
{
"end": 234,
"label": "Tissue",
"start": 228,
"text": "tumour"
},
{
"end": 265,
"label": "Tissue",
"start": 239,
"text": "adjacent non-tumour tissue"
}
] |
Single_Cell
|
Additionally, with some exceptions , LUAD and LUSC were considered as a single entity thus hindering the investigation of specific hallmarks of the two cancer types which are radically distinct both at the molecular and pathological level.
|
[] |
Single_Cell
|
While single-cell RNA-seq (scRNA-seq) can identify cell types and their states at high resolution within tissues, it lacks the capability to pinpoint their spatial distribution or capture the local cell–cell interactions as well as ligands and receptors that mediate these interactions.
|
[
{
"end": 61,
"label": "CellType",
"start": 51,
"text": "cell types"
},
{
"end": 112,
"label": "Tissue",
"start": 105,
"text": "tissues"
}
] |
Single_Cell
|
Therefore, impeding our ability to fully explore the tumour microenvironment (TME) and the complexity of cell–cell interactions therein.
|
[] |
Single_Cell
|
Source paper: PMC11116453
To overcome above mentioned limitations, we combined scRNA-seq data from nearly 900,000 cells from 25 treatment-naive patients with LUAD or LUSC and spatial transcriptomics from eight patients to investigate the differences in cellular organisation in tumour and adjacent non-tumour tissue.
|
[
{
"end": 121,
"label": "CellType",
"start": 116,
"text": "cells"
},
{
"end": 286,
"label": "Tissue",
"start": 280,
"text": "tumour"
},
{
"end": 317,
"label": "Tissue",
"start": 291,
"text": "adjacent non-tumour tissue"
}
] |
Single_Cell
|
We further examined Mɸ populations and molecular changes they undergo in the tumour environment, some of which resemble those observed in Mɸ during human foetal development.
|
[
{
"end": 22,
"label": "CellType",
"start": 20,
"text": "Mɸ"
},
{
"end": 140,
"label": "CellType",
"start": 138,
"text": "Mɸ"
}
] |
Single_Cell
|
Source paper: PMC11116453
To determine the heterogeneity of immune and non-immune cellular states and their spatial landscape in LUAD and LUSC, we collected lung tissue resections from 25 treatment-naive patients with either LUAD ( n = 13), LUSC ( n = 8) or undetermined lung cancer (LC, n = 4), and two healthy deceased donors (Fig. 1A, B and Supplementary Data 1 ).
|
[
{
"end": 181,
"label": "Tissue",
"start": 159,
"text": "lung tissue resections"
}
] |
Single_Cell
|
We collected both tumour and matched normal non-tumorigenic tissue (i.e., background), isolated CD45+ immune cells (Supplementary Fig. 1A ) as well as tumour and other non-immune populations (using CD235a column to deplete erythroid cells), and performed scRNA-seq.
|
[
{
"end": 238,
"label": "CellType",
"start": 223,
"text": "erythroid cells"
},
{
"end": 24,
"label": "Tissue",
"start": 18,
"text": "tumour"
},
{
"end": 66,
"label": "Tissue",
"start": 29,
"text": "matched normal non-tumorigenic tissue"
},
{
"end": 114,
"label": "CellType",
"start": 96,
"text": "CD45+ immune cells"
},
{
"end": 157,
"label": "CellType",
"start": 151,
"text": "tumour"
},
{
"end": 190,
"label": "CellType",
"start": 168,
"text": "non-immune populations"
},
{
"end": 84,
"label": "Tissue",
"start": 74,
"text": "background"
}
] |
Single_Cell
|
In addition, tumour and background tissue sections from eight patients (of the aforementioned 25) were processed for spatial transcriptomics using the 10x Genomics Visium platform ( n = 36 sections in total) (Fig. 1A and Supplementary Data 1 ).
|
[
{
"end": 19,
"label": "Tissue",
"start": 13,
"text": "tumour"
},
{
"end": 50,
"label": "Tissue",
"start": 24,
"text": "background tissue sections"
}
] |
Single_Cell
|
Source paper: PMC11116453
Following quality control (QC) on the scRNA-seq dataset, we identified 895,806 high-quality cells in total, of which 503,549 were from tumour and 392,257 from combined background and healthy tissue (from here on referred to as B/H).
|
[
{
"end": 125,
"label": "CellType",
"start": 107,
"text": "high-quality cells"
},
{
"end": 169,
"label": "Tissue",
"start": 163,
"text": "tumour"
},
{
"end": 206,
"label": "Tissue",
"start": 196,
"text": "background"
},
{
"end": 225,
"label": "Tissue",
"start": 211,
"text": "healthy tissue"
},
{
"end": 258,
"label": "Tissue",
"start": 255,
"text": "B/H"
}
] |
Single_Cell
|
After performing normalisation and log1p transformation, highly-variable gene selection, dimensionality reduction, batch correction, and Leiden clustering, cells originating from tumour and B/H were separately annotated into distinct broad cell types and visualised via Uniform Manifold Approximation and Projection (UMAP) (Fig. 1C , Supplementary Fig. 1B, C , and “Methods”).
|
[
{
"end": 185,
"label": "Tissue",
"start": 179,
"text": "tumour"
},
{
"end": 161,
"label": "CellType",
"start": 156,
"text": "cells"
},
{
"end": 193,
"label": "Tissue",
"start": 190,
"text": "B/H"
},
{
"end": 249,
"label": "CellType",
"start": 240,
"text": "cell type"
}
] |
Single_Cell
|
We identified clusters of myeloid cells with transcriptional signatures of monocytes, macrophages, dendritic cells (DCs), as well as mast cells, natural killer (NK) cells, T cells, B cells and non-immune cells (Fig. 1C, D ).
|
[
{
"end": 143,
"label": "CellType",
"start": 133,
"text": "mast cells"
},
{
"end": 97,
"label": "CellType",
"start": 86,
"text": "macrophages"
},
{
"end": 84,
"label": "CellType",
"start": 75,
"text": "monocytes"
},
{
"end": 114,
"label": "CellType",
"start": 99,
"text": "dendritic cells"
},
{
"end": 188,
"label": "CellType",
"start": 181,
"text": "B cells"
},
{
"end": 179,
"label": "CellType",
"start": 172,
"text": "T cells"
},
{
"end": 39,
"label": "CellType",
"start": 26,
"text": "myeloid cells"
},
{
"end": 119,
"label": "CellType",
"start": 116,
"text": "DCs"
},
{
"end": 170,
"label": "CellType",
"start": 145,
"text": "natural killer (NK) cells"
},
{
"end": 209,
"label": "CellType",
"start": 193,
"text": "non-immune cells"
}
] |
Single_Cell
|
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