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We did not detect neutrophilic granulocytes, most probably due to their sensitivity to degradation after collection and in particular to the freezing-thawing cycle.
|
[
{
"end": 43,
"label": "CellType",
"start": 18,
"text": "neutrophilic granulocytes"
}
] |
Single_Cell
|
Finally, we identified a cluster characterised by the co-expression of myeloid ( LYZ, CD68, CD14, MRC1 ) and epithelial genes ( KRT19, EPCAM ) (Fig. 1D–F ).
|
[] |
Single_Cell
|
These cells were found within the tumour and exhibited similarities to previously described cancer-associated macrophage-like cells (CAMLs) .
|
[
{
"end": 11,
"label": "CellType",
"start": 6,
"text": "cells"
},
{
"end": 40,
"label": "Tissue",
"start": 34,
"text": "tumour"
},
{
"end": 131,
"label": "CellType",
"start": 92,
"text": "cancer-associated macrophage-like cells"
},
{
"end": 138,
"label": "CellType",
"start": 133,
"text": "CAMLs"
}
] |
Single_Cell
|
CAMLs represent a distinct population of large myeloid cells with concomitant epithelial tumour protein expression .
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "CAMLs"
},
{
"end": 60,
"label": "CellType",
"start": 41,
"text": "large myeloid cells"
}
] |
Single_Cell
|
These unique cells have been observed in blood samples of patients with various malignancies, including NSCLC .
|
[
{
"end": 18,
"label": "CellType",
"start": 6,
"text": "unique cells"
},
{
"end": 54,
"label": "Tissue",
"start": 41,
"text": "blood samples"
}
] |
Single_Cell
|
The abundance of CAMLs exhibits a direct correlation with response to therapeutic interventions, highlighting their functional significance .
|
[
{
"end": 22,
"label": "CellType",
"start": 17,
"text": "CAMLs"
}
] |
Single_Cell
|
Even after further subclustering, CAMLs maintained their distinct dual myeloid-epithelial signature (Supplementary Fig. 1D ).
|
[] |
Single_Cell
|
It is noteworthy that doublet detection software Scrublet assigned a low doublet score to CAMLs, suggesting their expression profile is unlikely to be explained as a combined signature arising from the coincidental sequencing of a tumour cell and a macrophage (Supplementary Fig. 1E ).
|
[
{
"end": 259,
"label": "CellType",
"start": 249,
"text": "macrophage"
},
{
"end": 95,
"label": "CellType",
"start": 90,
"text": "CAMLs"
},
{
"end": 242,
"label": "CellType",
"start": 231,
"text": "tumour cell"
}
] |
Single_Cell
|
All clusters included cells from multiple patients, with the cluster size ranging from 2520 to 124,459 cells (Supplementary Fig. 1F, G ).
|
[
{
"end": 27,
"label": "CellType",
"start": 22,
"text": "cells"
}
] |
Single_Cell
|
Furthermore, we conducted reference-query mapping using scArches to confirm the consistency of our annotations in the tumour and B/H dataset (Supplementary Fig. 2A–C and Supplementary Notes ).
|
[] |
Single_Cell
|
Source paper: PMC11116453
The composition of the immune and non-immune compartment was markedly different between the tumour and background.
|
[
{
"end": 126,
"label": "Tissue",
"start": 120,
"text": "tumour"
},
{
"end": 141,
"label": "Tissue",
"start": 131,
"text": "background"
}
] |
Single_Cell
|
In the tumour, we detected fibroblasts and a decrease in the fraction of lymphatic endothelial cells (LECs) ( P adj = 0.0025, Fig. 1G and Supplementary Data 2 ).
|
[
{
"end": 38,
"label": "CellType",
"start": 27,
"text": "fibroblasts"
},
{
"end": 13,
"label": "Tissue",
"start": 7,
"text": "tumour"
},
{
"end": 100,
"label": "CellType",
"start": 73,
"text": "lymphatic endothelial cells"
},
{
"end": 106,
"label": "CellType",
"start": 102,
"text": "LECs"
}
] |
Single_Cell
|
Furthermore, the population of epithelial cells showed higher diversity, with the presence of alveolar type II (AT2), atypical epithelial cells which downregulated epithelial markers ( KRT19 , EPCAM , CDH1 ), transitioning epithelial cells which upregulated myeloid markers ( LYZ ), and cycling epithelial cells in tumour tissues (Fig. 1G , Supplementary Notes , and Supplementary Fig. 2D, E ).
|
[
{
"end": 47,
"label": "CellType",
"start": 31,
"text": "epithelial cells"
},
{
"end": 143,
"label": "CellType",
"start": 118,
"text": "atypical epithelial cells"
},
{
"end": 239,
"label": "CellType",
"start": 209,
"text": "transitioning epithelial cells"
},
{
"end": 311,
"label": "CellType",
"start": 287,
"text": "cycling epithelial cells"
},
{
"end": 329,
"label": "Tissue",
"start": 315,
"text": "tumour tissues"
},
{
"end": 110,
"label": "CellType",
"start": 94,
"text": "alveolar type II"
},
{
"end": 115,
"label": "CellType",
"start": 112,
"text": "AT2"
}
] |
Single_Cell
|
These differences are in agreement with the fact that in tumour specimens, epithelial cells are likely to be a mixture of mutant tumour and non-mutant normal cells, and suggest that neoplastic transformation leads to further diversity of cell states.
|
[
{
"end": 91,
"label": "CellType",
"start": 75,
"text": "epithelial cells"
},
{
"end": 73,
"label": "Tissue",
"start": 57,
"text": "tumour specimens"
},
{
"end": 135,
"label": "CellType",
"start": 122,
"text": "mutant tumour"
},
{
"end": 163,
"label": "CellType",
"start": 140,
"text": "non-mutant normal cells"
}
] |
Single_Cell
|
We did not detect alveolar type I (AT1) or basal cells, possibly due to their loss during dissociations, as previously reported by others .
|
[
{
"end": 33,
"label": "CellType",
"start": 18,
"text": "alveolar type I"
},
{
"end": 38,
"label": "CellType",
"start": 35,
"text": "AT1"
},
{
"end": 54,
"label": "CellType",
"start": 43,
"text": "basal cells"
}
] |
Single_Cell
|
Source paper: PMC11116453
As previously reported, the proportion of monocytes and immature myeloid cells was significantly reduced in tumour samples compared to background ( P adj = 0.022 and P adj = 0.00001, respectively) , while DCs and B cells were overall expanded ( P adj = 0.0023 and P adj = 0.0044, respectively; Fig. 1H and Supplementary Data 3 ).
|
[
{
"end": 79,
"label": "CellType",
"start": 70,
"text": "monocytes"
},
{
"end": 248,
"label": "CellType",
"start": 241,
"text": "B cells"
},
{
"end": 106,
"label": "CellType",
"start": 84,
"text": "immature myeloid cells"
},
{
"end": 150,
"label": "Tissue",
"start": 136,
"text": "tumour samples"
},
{
"end": 173,
"label": "Tissue",
"start": 163,
"text": "background"
},
{
"end": 236,
"label": "CellType",
"start": 233,
"text": "DCs"
}
] |
Single_Cell
|
To get further insight into the cellular composition of tumour versus background tissue, we subclustered each of the broad clusters and identified 46 cell types/states (Supplementary Fig. 2D, E , Supplementary Data 4 and 5 , Supplementary Fig. 3 , and Supplementary Notes ).
|
[
{
"end": 62,
"label": "Tissue",
"start": 56,
"text": "tumour"
},
{
"end": 87,
"label": "Tissue",
"start": 70,
"text": "background tissue"
}
] |
Single_Cell
|
In the tumour, we found that a significantly higher proportion of NK cells had a lower cytotoxicity phenotype ( Supplementary Notes ), and that the significant majority of DCs were derived from monocytes (i.e., mo-DC2), ( Supplementary Notes ) compared to background ( P adj = 0.00002 and P adj = 0.00002, respectively, Fig. 1I and Supplementary Data 6 ).
|
[
{
"end": 203,
"label": "CellType",
"start": 194,
"text": "monocytes"
},
{
"end": 74,
"label": "CellType",
"start": 66,
"text": "NK cells"
},
{
"end": 175,
"label": "CellType",
"start": 172,
"text": "DCs"
},
{
"end": 217,
"label": "CellType",
"start": 211,
"text": "mo-DC2"
}
] |
Single_Cell
|
This is consistent with the monocytic origin of mo-DC2s under inflammatory conditions .
|
[
{
"end": 55,
"label": "CellType",
"start": 48,
"text": "mo-DC2s"
}
] |
Single_Cell
|
Similarly, we found an expansion of B cells expressing LYZ and TNF , and depletion of NKB cells (Fig. 1I and Supplementary Notes ).
|
[
{
"end": 43,
"label": "CellType",
"start": 36,
"text": "B cells"
},
{
"end": 95,
"label": "CellType",
"start": 86,
"text": "NKB cells"
}
] |
Single_Cell
|
Among T cells, tumour samples showed an accumulation of regulatory T cells (Tregs), known to hinder the immune surveillance of tumours (Fig. 1I ).
|
[
{
"end": 13,
"label": "CellType",
"start": 6,
"text": "T cells"
},
{
"end": 29,
"label": "Tissue",
"start": 15,
"text": "tumour samples"
},
{
"end": 74,
"label": "CellType",
"start": 56,
"text": "regulatory T cells"
},
{
"end": 81,
"label": "CellType",
"start": 76,
"text": "Tregs"
},
{
"end": 134,
"label": "Tissue",
"start": 127,
"text": "tumours"
}
] |
Single_Cell
|
Conversely, there was a reduction of exhausted cytotoxic T cells ( P adj = 0.00002) in the tumour and absence of T cells, which have been associated with survival in NSCLC (Fig. 1I and Supplementary Data 6 ).
|
[
{
"end": 120,
"label": "CellType",
"start": 113,
"text": "T cells"
},
{
"end": 64,
"label": "CellType",
"start": 37,
"text": "exhausted cytotoxic T cells"
},
{
"end": 97,
"label": "Tissue",
"start": 91,
"text": "tumour"
}
] |
Single_Cell
|
T cells are capable of recognising and lysing diverse ranges of cancer cells, and thus have been suggested for a role in pan-cancer immunotherapy .
|
[
{
"end": 7,
"label": "CellType",
"start": 0,
"text": "T cells"
},
{
"end": 76,
"label": "CellType",
"start": 64,
"text": "cancer cells"
}
] |
Single_Cell
|
Finally, we saw an increase in heterogeneity and proportion of anti-inflammatory Mɸ (AIMɸ), with a subset of cycling anti-inflammatory Mɸ, STAB1 + Mɸ (Fig. 1I ) and CAMLs (Fig. 1H ) being abundantly present in tumour tissue.
|
[
{
"end": 83,
"label": "CellType",
"start": 63,
"text": "anti-inflammatory Mɸ"
},
{
"end": 89,
"label": "CellType",
"start": 85,
"text": "AIMɸ"
},
{
"end": 137,
"label": "CellType",
"start": 109,
"text": "cycling anti-inflammatory Mɸ"
},
{
"end": 149,
"label": "CellType",
"start": 139,
"text": "STAB1 + Mɸ"
},
{
"end": 170,
"label": "CellType",
"start": 165,
"text": "CAMLs"
},
{
"end": 223,
"label": "Tissue",
"start": 210,
"text": "tumour tissue"
}
] |
Single_Cell
|
Interestingly, we found a strong negative correlation between the frequency of STAB1 + Mɸ/AIMɸ and T/NK cells across patients, highlighting the key role of Mɸ in restraining the infiltration of cytotoxic cells in the lung tumour tissue (Fig. 2A ).
|
[
{
"end": 89,
"label": "CellType",
"start": 79,
"text": "STAB1 + Mɸ"
},
{
"end": 94,
"label": "CellType",
"start": 90,
"text": "AIMɸ"
},
{
"end": 109,
"label": "CellType",
"start": 99,
"text": "T/NK cells"
},
{
"end": 158,
"label": "CellType",
"start": 156,
"text": "Mɸ"
},
{
"end": 209,
"label": "CellType",
"start": 194,
"text": "cytotoxic cells"
},
{
"end": 235,
"label": "Tissue",
"start": 217,
"text": "lung tumour tissue"
}
] |
Single_Cell
|
This is in line with a recent work describing that monocyte-derived Mɸ in human NSCLC acquire an immunosuppressive phenotype and restrain the infiltration of NK cells .
|
[
{
"end": 70,
"label": "CellType",
"start": 51,
"text": "monocyte-derived Mɸ"
},
{
"end": 166,
"label": "CellType",
"start": 158,
"text": "NK cells"
}
] |
Single_Cell
|
Source paper: PMC11116453
LUAD and LUSC have very different prognoses and are often considered as different clinical entities .
|
[] |
Single_Cell
|
To examine if differences in clinical features stem from distinct cellular composition, we compared the frequency of immune and non-immune cell subsets within CD235- samples from LUAD versus LUSC patients.
|
[
{
"end": 123,
"label": "CellType",
"start": 117,
"text": "immune"
},
{
"end": 151,
"label": "CellType",
"start": 128,
"text": "non-immune cell subsets"
},
{
"end": 173,
"label": "Tissue",
"start": 159,
"text": "CD235- samples"
}
] |
Single_Cell
|
We observed minor differences in cell frequency that did not reach statistical significance after P value correction (Supplementary Fig. 4A and Supplementary Data 7 and 8 ).
|
[] |
Single_Cell
|
Furthermore, there was no clear association between the frequency of immune and non-immune cells observed in patients and the cancer subtype, cancer stage or sex (Supplementary Fig. 4B, C ), suggesting that the TME composition is rather similar in LUAD and LUSC.
|
[
{
"end": 75,
"label": "CellType",
"start": 69,
"text": "immune"
},
{
"end": 96,
"label": "CellType",
"start": 80,
"text": "non-immune cells"
}
] |
Single_Cell
|
While LUAD and LUSC shared similar cellular compositions, the observed clinical distinctions may arise from varying intercellular interactions.
|
[] |
Single_Cell
|
Therefore, we examined whether different cell–cell interaction networks were employed within the TME in LUAD versus LUSC.
|
[] |
Single_Cell
|
To this end, we identified a putative list of cell–cell interactions exclusively observed in each tumour type environment by inferring statistically significant ligand–receptor pairs (L–Rs) that were not detected in background or healthy and their corresponding cell types, using CellPhoneDB .
|
[
{
"end": 272,
"label": "CellType",
"start": 248,
"text": "corresponding cell types"
},
{
"end": 226,
"label": "Tissue",
"start": 216,
"text": "background"
},
{
"end": 237,
"label": "Tissue",
"start": 230,
"text": "healthy"
}
] |
Single_Cell
|
Although the two tumour subtypes showed a similar interaction network that mostly involved interactions between non-immune cells, AIMɸ and T cells (Fig. 2B ), there were also some notable differences.
|
[
{
"end": 146,
"label": "CellType",
"start": 139,
"text": "T cells"
},
{
"end": 32,
"label": "Tissue",
"start": 17,
"text": "tumour subtypes"
},
{
"end": 128,
"label": "CellType",
"start": 112,
"text": "non-immune cells"
},
{
"end": 134,
"label": "CellType",
"start": 130,
"text": "AIMɸ"
}
] |
Single_Cell
|
Source paper: PMC11116453
First, we identified overall a higher number of L–Rs in the LUAD dataset (Supplementary Fig. 4D and Supplementary Data 9 – 12 ), which was not driven by a difference in the number of cells in the LUAD ( n = 105,749 cells) vs LUSC ( n = 230,066 cells) dataset.
|
[] |
Single_Cell
|
Secondly, several pairs of immune checkpoint inhibitors (ICI) and their respective inhibitory molecules were differentially co-expressed in LUAD versus LUSC (Fig. 2C, D ).
|
[] |
Single_Cell
|
For example, LGALS9-HAVCR2 (TIM3), NECTIN2-CD226 (DNAM1) and NECTIN2/NECTIN3-TIGIT were frequently identified in LUAD, and the putative ICI CD96-NECTIN1 was found preferentially in LUSC (Fig. 2C, D ).
|
[] |
Single_Cell
|
In contrast, CD80/CD86-CTLA4 and HLAF-LILRB1/2 were found in both tumour subtypes (Fig. 2C, D ).
|
[
{
"end": 81,
"label": "Tissue",
"start": 66,
"text": "tumour subtypes"
}
] |
Single_Cell
|
LILRBs (leucocyte Ig-like receptors) are emerging as potential targets for next-generation immunotherapeutics as their blocking can potentiate immune responses .
|
[] |
Single_Cell
|
The most commonly used immunotherapies for lung cancer block the interaction between PD1 and PDL1, and recent clinical trials suggested that anti-CTLA4 and anti-PD1 combination therapy improved the survival of patients independent of tumour PD1 expression .
|
[] |
Single_Cell
|
Within our dataset, we did not observe PD1-PDL1 interactions in either of the tumour subtypes (Fig. 2C, D ).
|
[
{
"end": 93,
"label": "Tissue",
"start": 78,
"text": "tumour subtypes"
}
] |
Single_Cell
|
Our initial analysis suggests that other ICIs (such as CTLA4, TIGIT, LILRB1/2 and TIM3) might be promising targets in the treatment of NSCLC.
|
[] |
Single_Cell
|
Source paper: PMC11116453
Of the significant L–Rs detected in both LUAD and LUSC we noted several pairs involved in angiogenic signalling in different populations of myeloid cells such as VEGFA/B-FLT1, VEGFA-KDR and VEGFA-NRP1/2 .
|
[
{
"end": 181,
"label": "CellType",
"start": 168,
"text": "myeloid cells"
}
] |
Single_Cell
|
Although VEGFA and VEGFB were found to be expressed in both LUAD and LUSC, their receptors were more frequently found in LUAD, especially in fibroblasts (Fig. 2E and Supplementary Fig. 4E ).
|
[
{
"end": 152,
"label": "CellType",
"start": 141,
"text": "fibroblasts"
}
] |
Single_Cell
|
Similarly, we observed significant expression of EGFR ligands signalling in AT2 and cycling epithelial cells, such as EGFR-EREG , EGFR-AREG , EGFR-HBEGF and EGFR-MIF , although MIF expression was found more frequently in cells from LUSC (Fig. 2F and Supplementary Fig. 4F ).
|
[
{
"end": 79,
"label": "CellType",
"start": 76,
"text": "AT2"
},
{
"end": 108,
"label": "CellType",
"start": 84,
"text": "cycling epithelial cells"
},
{
"end": 236,
"label": "CellType",
"start": 221,
"text": "cells from LUSC"
}
] |
Single_Cell
|
Finally, we observed key co-stimulatory signals required to support lymphoid cell activation, such as CD40-CD40LG , CD2-CD58 , CD28-CD86 , CCL21-CCR7 , and TNFRSF13B/C-TNFSF13B ( TACI/BAFFR-BAFF ) (Supplementary Fig. 4G ), which are often associated with the presence of ectopic lymphoid organs mainly consisting of B cells, T cells, and DCs i.e., tertiary lymphoid structures (TLS).
|
[
{
"end": 323,
"label": "CellType",
"start": 316,
"text": "B cells"
},
{
"end": 332,
"label": "CellType",
"start": 325,
"text": "T cells"
},
{
"end": 294,
"label": "Tissue",
"start": 271,
"text": "ectopic lymphoid organs"
},
{
"end": 341,
"label": "CellType",
"start": 338,
"text": "DCs"
},
{
"end": 376,
"label": "Tissue",
"start": 348,
"text": "tertiary lymphoid structures"
},
{
"end": 381,
"label": "Tissue",
"start": 378,
"text": "TLS"
}
] |
Single_Cell
|
TLS are usually correlated with the longer relapse-free survival in NSCLC .
|
[
{
"end": 3,
"label": "Tissue",
"start": 0,
"text": "TLS"
}
] |
Single_Cell
|
Source paper: PMC11116453
The significant L–Rs and their interacting cell types were calculated based on the co-expression of genes in different cell-type clusters from the scRNA-seq dataset using CellPhoneDB.
|
[
{
"end": 81,
"label": "CellType",
"start": 71,
"text": "cell types"
}
] |
Single_Cell
|
However, in order to discern biologically significant interactions, it is essential to ascertain whether the cell types identified as interacting are indeed physically co-located.
|
[
{
"end": 119,
"label": "CellType",
"start": 109,
"text": "cell types"
}
] |
Single_Cell
|
To achieve this, we considered how the scRNA-seq-identified cell types are spatially arranged on tissue sections.
|
[
{
"end": 70,
"label": "CellType",
"start": 60,
"text": "cell types"
},
{
"end": 112,
"label": "Tissue",
"start": 97,
"text": "tissue sections"
}
] |
Single_Cell
|
We applied an integrative approach which combines the scRNA-seq of the tumour and background samples with the spatial transcriptomic (STx) profile of the fresh frozen tumour and background tissue sections.
|
[
{
"end": 77,
"label": "Tissue",
"start": 71,
"text": "tumour"
},
{
"end": 100,
"label": "Tissue",
"start": 82,
"text": "background samples"
},
{
"end": 173,
"label": "Tissue",
"start": 154,
"text": "fresh frozen tumour"
},
{
"end": 204,
"label": "Tissue",
"start": 178,
"text": "background tissue sections"
}
] |
Single_Cell
|
We performed 10× Visium on two consecutive, 10-µm sections, from eight patients, seven of which matched the samples used for the scRNA-seq.
|
[
{
"end": 58,
"label": "Tissue",
"start": 50,
"text": "sections"
}
] |
Single_Cell
|
We analysed 36 sections in total ( n tumour = 20, n background = 16) with an average UMI count of 6894/spot in tumour and 3350/spot in the background.
|
[
{
"end": 23,
"label": "Tissue",
"start": 15,
"text": "sections"
},
{
"end": 117,
"label": "Tissue",
"start": 111,
"text": "tumour"
},
{
"end": 149,
"label": "Tissue",
"start": 139,
"text": "background"
},
{
"end": 43,
"label": "Tissue",
"start": 37,
"text": "tumour"
},
{
"end": 62,
"label": "Tissue",
"start": 52,
"text": "background"
}
] |
Single_Cell
|
Next, we used cell2location and cell-type specific expression profiles from our scRNA-seq dataset to deconvolute cell-type abundances on the tissue (Fig. 3A , see “Methods”).
|
[
{
"end": 147,
"label": "Tissue",
"start": 141,
"text": "tissue"
}
] |
Single_Cell
|
Source paper: PMC11116453
Once the cell types were resolved on the tissue sections, we examined the frequency of different cell types across all sections from tumour and background tissue.
|
[
{
"end": 47,
"label": "CellType",
"start": 37,
"text": "cell types"
},
{
"end": 84,
"label": "Tissue",
"start": 69,
"text": "tissue sections"
},
{
"end": 135,
"label": "CellType",
"start": 125,
"text": "cell types"
},
{
"end": 167,
"label": "Tissue",
"start": 161,
"text": "tumour"
},
{
"end": 189,
"label": "Tissue",
"start": 172,
"text": "background tissue"
}
] |
Single_Cell
|
The cell-type abundance in tumour and background were computed by summing up the posterior 5% quantile (q05) value of estimated cell abundance by cell2location, across spots that passed QC (“Methods”).
|
[
{
"end": 33,
"label": "Tissue",
"start": 27,
"text": "tumour"
},
{
"end": 48,
"label": "Tissue",
"start": 38,
"text": "background"
}
] |
Single_Cell
|
Our analysis confirmed that the differences in the frequency of cell types across all sections in tumour versus background was in line with the results obtained in the scRNA-seq data (Fig. 3B ).
|
[
{
"end": 74,
"label": "CellType",
"start": 64,
"text": "cell types"
},
{
"end": 94,
"label": "Tissue",
"start": 86,
"text": "sections"
},
{
"end": 104,
"label": "Tissue",
"start": 98,
"text": "tumour"
},
{
"end": 122,
"label": "Tissue",
"start": 112,
"text": "background"
}
] |
Single_Cell
|
For example, in tumours we found an increase in the proportion of B cells ( P adj = 0.0372) and cycling AT2 cells ( P adj = 0.0147) compared to the background tissue, and a decrease in the proportion of immature cells ( P adj = 0.0012), NK cells ( P adj = 0.0012), and LECs ( P adj = 0.00077, Supplementary Data 13 and 14 ).
|
[
{
"end": 73,
"label": "CellType",
"start": 66,
"text": "B cells"
},
{
"end": 23,
"label": "Tissue",
"start": 16,
"text": "tumours"
},
{
"end": 113,
"label": "CellType",
"start": 96,
"text": "cycling AT2 cells"
},
{
"end": 165,
"label": "Tissue",
"start": 148,
"text": "background tissue"
},
{
"end": 217,
"label": "CellType",
"start": 203,
"text": "immature cells"
},
{
"end": 245,
"label": "CellType",
"start": 237,
"text": "NK cells"
},
{
"end": 273,
"label": "CellType",
"start": 269,
"text": "LECs"
}
] |
Single_Cell
|
However, the proportions of other cell types estimated from the scRNA-seq data or the STx data within the tumour or background showed some discrepancies (Supplementary Fig. 4H, I ).
|
[
{
"end": 44,
"label": "CellType",
"start": 34,
"text": "cell types"
},
{
"end": 112,
"label": "Tissue",
"start": 106,
"text": "tumour"
},
{
"end": 126,
"label": "Tissue",
"start": 116,
"text": "background"
}
] |
Single_Cell
|
This was particularly evident within the non-immune populations, where STx estimated higher proportions of LECs, activated adventitial fibroblasts and cycling subsets, compared to scRNA-seq.
|
[
{
"end": 63,
"label": "CellType",
"start": 41,
"text": "non-immune populations"
},
{
"end": 146,
"label": "CellType",
"start": 113,
"text": "activated adventitial fibroblasts"
},
{
"end": 111,
"label": "CellType",
"start": 107,
"text": "LECs"
},
{
"end": 166,
"label": "CellType",
"start": 151,
"text": "cycling subsets"
}
] |
Single_Cell
|
Disparities in cell proportions between different methodologies were previously shown by others , underscoring the potential influence of distinct sampling biases inherent to scRNA-seq and STx techniques like Visium.
|
[] |
Single_Cell
|
In the case of scRNA-seq, variations in cell digestion sensitivity can lead to differential representation of cell types.
|
[
{
"end": 120,
"label": "CellType",
"start": 110,
"text": "cell types"
}
] |
Single_Cell
|
Meanwhile, with Visium, discrepancies might arise from variations in the location of tumour resections as well as differences in sample sizes compared to scRNA-seq studies.
|
[
{
"end": 102,
"label": "Tissue",
"start": 85,
"text": "tumour resections"
}
] |
Single_Cell
|
Nevertheless, the overall concordance in the results obtained by scRNA-seq and Visium suggests that our spatial “map” of different cell types faithfully represents their distribution in the tissue.
|
[
{
"end": 196,
"label": "Tissue",
"start": 190,
"text": "tissue"
}
] |
Single_Cell
|
Source paper: PMC11116453
Next, we examined the spatial co-localisation of the L–Rs identified by cellphoneDB.
|
[] |
Single_Cell
|
The L–Rs were considered to co-localise if both genes were expressed in the same spot and above median value for the given genes across the section spots.
|
[] |
Single_Cell
|
We then compared the frequency of spots in which L–R genes were colocalising versus non-colocalising in the matched tumour versus background sections, using a χ test (“Methods”).
|
[
{
"end": 122,
"label": "Tissue",
"start": 116,
"text": "tumour"
},
{
"end": 149,
"label": "Tissue",
"start": 130,
"text": "background sections"
}
] |
Single_Cell
|
Due to the low number of tissue blocks collected from LUSC and LUAD patients (N LUSC = 3, N LUAD = 5), the statistical power was not sufficient to perform a comparative analysis between spatial localisation of LUAD/LUSC-specific L–Rs.
|
[] |
Single_Cell
|
Nevertheless, we confirmed that several of the aforementioned tumour-specific L–Rs colocalized significantly more in tumour than in background sections, including NRP1-VEGFA and the ICIs NECTIN2-TIGIT , LGALS9-HAVCR2 , and CD96-NECTIN1 (Fig. 3C–E and Supplementary Data 15 ).
|
[
{
"end": 123,
"label": "Tissue",
"start": 117,
"text": "tumour"
},
{
"end": 151,
"label": "Tissue",
"start": 132,
"text": "background sections"
}
] |
Single_Cell
|
Consistent with the cellphoneDB results, we found no significant colocalization of PD1-PDL1 in the tumour sections.
|
[
{
"end": 114,
"label": "Tissue",
"start": 99,
"text": "tumour sections"
}
] |
Single_Cell
|
Source paper: PMC11116453
Tumour samples obtained from surgical resection contain both malignant and residual normal epithelial cells.
|
[
{
"end": 42,
"label": "Tissue",
"start": 28,
"text": "Tumour samples"
},
{
"end": 98,
"label": "CellType",
"start": 89,
"text": "malignant"
},
{
"end": 135,
"label": "CellType",
"start": 103,
"text": "residual normal epithelial cells"
}
] |
Single_Cell
|
A significant challenge in scRNA-seq of human tumours lies in the differentiation of cancer cells from non-malignant counterparts.
|
[
{
"end": 53,
"label": "Tissue",
"start": 40,
"text": "human tumours"
},
{
"end": 97,
"label": "CellType",
"start": 85,
"text": "cancer cells"
},
{
"end": 129,
"label": "CellType",
"start": 103,
"text": "non-malignant counterparts"
}
] |
Single_Cell
|
Therefore, we applied Copynumber Karyotyping of Tumors (CopyKAT ) to discern genome-wide aneuploidy within individual cells.
|
[
{
"end": 123,
"label": "CellType",
"start": 118,
"text": "cells"
}
] |
Single_Cell
|
The principle driving the computation of DNA copy number events from scRNA-seq data is rooted in the notion that the expression levels of neighbouring genes can provide valuable information to infer genomic copy numbers within that specific genomic segment.
|
[] |
Single_Cell
|
Since aneuploidy is common in human cancers, cells with genome-wide CNAs are considered as tumour cells.
|
[
{
"end": 50,
"label": "CellType",
"start": 45,
"text": "cells"
},
{
"end": 103,
"label": "CellType",
"start": 91,
"text": "tumour cells"
}
] |
Single_Cell
|
Source paper: PMC11116453
Analysis using CopyKAT revealed extensive, patient-specific CNAs in tumour tissue (Fig. 4A and Supplementary Fig. 5A ) but not in the background.
|
[
{
"end": 109,
"label": "Tissue",
"start": 96,
"text": "tumour tissue"
}
] |
Single_Cell
|
Within individual tumour samples, the CNAs were detected in AT2 and cycling AT2 cells, and in some patients these genetic alterations were shared between AT2/cycling AT2 cells and atypical epithelial cells, suggesting a close lineage relationship between different epithelial subpopulations (Fig. 4A and Supplementary Fig. 5A ).
|
[
{
"end": 32,
"label": "Tissue",
"start": 18,
"text": "tumour samples"
},
{
"end": 63,
"label": "CellType",
"start": 60,
"text": "AT2"
},
{
"end": 85,
"label": "CellType",
"start": 68,
"text": "cycling AT2 cells"
},
{
"end": 157,
"label": "CellType",
"start": 154,
"text": "AT2"
},
{
"end": 175,
"label": "CellType",
"start": 158,
"text": "cycling AT2 cells"
},
{
"end": 205,
"label": "CellType",
"start": 180,
"text": "atypical epithelial cells"
},
{
"end": 290,
"label": "CellType",
"start": 265,
"text": "epithelial subpopulations"
}
] |
Single_Cell
|
We confirmed this finding by inferring the trajectory of non-blood cell populations in tumour using Partition-Based Graph Abstraction (PAGA) .
|
[
{
"end": 83,
"label": "CellType",
"start": 57,
"text": "non-blood cell populations"
},
{
"end": 93,
"label": "Tissue",
"start": 87,
"text": "tumour"
}
] |
Single_Cell
|
PAGA showed differentiation continuity between AT2 cells, cycling AT2/epithelial cells, and atypical epithelial cells on one side and ciliated epithelial cells and transitioning epithelial cells on the other (Fig. 4B ).
|
[
{
"end": 86,
"label": "CellType",
"start": 70,
"text": "epithelial cells"
},
{
"end": 56,
"label": "CellType",
"start": 47,
"text": "AT2 cells"
},
{
"end": 69,
"label": "CellType",
"start": 58,
"text": "cycling AT2"
},
{
"end": 117,
"label": "CellType",
"start": 92,
"text": "atypical epithelial cells"
},
{
"end": 159,
"label": "CellType",
"start": 134,
"text": "ciliated epithelial cells"
},
{
"end": 194,
"label": "CellType",
"start": 164,
"text": "transitioning epithelial cells"
}
] |
Single_Cell
|
Furthermore, blinded histological evaluation confirmed the overlap between pathologist-defined tumour sites and AT2 and cycling AT2 cells predicted by cell2location, suggesting their tumour cells status (Fig. 4C ).
|
[
{
"end": 115,
"label": "CellType",
"start": 112,
"text": "AT2"
},
{
"end": 107,
"label": "Tissue",
"start": 95,
"text": "tumour sites"
},
{
"end": 137,
"label": "CellType",
"start": 120,
"text": "cycling AT2 cells"
}
] |
Single_Cell
|
Less overlap was observed for atypical epithelial cells (Fig. 4C ).
|
[
{
"end": 55,
"label": "CellType",
"start": 30,
"text": "atypical epithelial cells"
}
] |
Single_Cell
|
The differential expression analysis (DEA) of AT2 cells from tumours compared to background showed upregulation of genes involved in hypoxia, TP53 pathways, and metabolic rewiring in tumours.
|
[
{
"end": 55,
"label": "CellType",
"start": 46,
"text": "AT2 cells"
},
{
"end": 68,
"label": "Tissue",
"start": 61,
"text": "tumours"
},
{
"end": 91,
"label": "Tissue",
"start": 81,
"text": "background"
},
{
"end": 190,
"label": "Tissue",
"start": 183,
"text": "tumours"
}
] |
Single_Cell
|
AT2 cells in tumour-upregulated genes involved both in glycolysis and oxidative phosphorylation (Fig. 4D and Supplementary Data 16 ).
|
[
{
"end": 9,
"label": "CellType",
"start": 0,
"text": "AT2 cells"
}
] |
Single_Cell
|
While the importance of glycolysis in tumour cells is well-established , it was recently reported that human NSCLC use glucose and lactate to fuel the tricarboxylic acid (TCA) cycle .
|
[
{
"end": 50,
"label": "Tissue",
"start": 38,
"text": "tumour cells"
}
] |
Single_Cell
|
In addition, the tumour AT2 cells were noted to express more LYPD3 compared to background AT2 cells (log2FC = 2.04, P adj = 0.039, Supplementary Data 16 ), an adhesion protein which has previously been connected to poor prognosis in NSCLC and is currently being targeted in preclinical and clinical studies .
|
[
{
"end": 33,
"label": "CellType",
"start": 17,
"text": "tumour AT2 cells"
},
{
"end": 99,
"label": "CellType",
"start": 79,
"text": "background AT2 cells"
}
] |
Single_Cell
|
Source paper: PMC11116453
Interestingly, the population of CAMLs also showed substantial CNAs that were similar to those of AT2 cells and cycling AT2 cells from the same patient (Fig. 4A, E and Supplementary Fig. 5A, B ).
|
[
{
"end": 66,
"label": "CellType",
"start": 61,
"text": "CAMLs"
},
{
"end": 135,
"label": "CellType",
"start": 126,
"text": "AT2 cells"
},
{
"end": 157,
"label": "CellType",
"start": 140,
"text": "cycling AT2 cells"
}
] |
Single_Cell
|
To measure the difference of the distribution of genomic gain and loss between cell types in a statistically robust manner, we calculated the Kullback–Leibler (KL) divergence (Fig. 4F and Supplementary Fig. 5C ).
|
[
{
"end": 89,
"label": "CellType",
"start": 79,
"text": "cell types"
}
] |
Single_Cell
|
CAMLs had KL divergence values comparable to CNA-harbouring tumour cells, thus confirming the similarity of their CNA profiles (Fig. 4F and Supplementary Fig. 5C ).
|
[
{
"end": 5,
"label": "CellType",
"start": 0,
"text": "CAMLs"
},
{
"end": 72,
"label": "CellType",
"start": 45,
"text": "CNA-harbouring tumour cells"
}
] |
Single_Cell
|
As CAMLs co-expressed a wide array of myeloid genes as well as typical epithelial genes (Fig. 1D–F and Supplementary Fig. 1D ), had a low doublet score and shared the same CNA signature as tumour cells, we hypothesised that these cells might represent a subset of Mɸ tightly attached to a cancer cell.
|
[
{
"end": 8,
"label": "CellType",
"start": 3,
"text": "CAMLs"
},
{
"end": 201,
"label": "CellType",
"start": 189,
"text": "tumour cells"
},
{
"end": 235,
"label": "CellType",
"start": 230,
"text": "cells"
},
{
"end": 266,
"label": "CellType",
"start": 264,
"text": "Mɸ"
},
{
"end": 300,
"label": "CellType",
"start": 289,
"text": "cancer cell"
}
] |
Single_Cell
|
It is possible that these Mɸ were undergoing phagocytosis or fusion.
|
[
{
"end": 28,
"label": "CellType",
"start": 26,
"text": "Mɸ"
}
] |
Single_Cell
|
Source paper: PMC11116453
CAMLs have been previously isolated from peripheral blood of cancer patients and described to facilitate circulating tumour cells seeding of distant metastases .
|
[
{
"end": 85,
"label": "Tissue",
"start": 69,
"text": "peripheral blood"
},
{
"end": 33,
"label": "CellType",
"start": 28,
"text": "CAMLs"
},
{
"end": 157,
"label": "CellType",
"start": 133,
"text": "circulating tumour cells"
}
] |
Single_Cell
|
Our analysis suggested that CAMLs can also be isolated from tumour tissue.
|
[
{
"end": 33,
"label": "CellType",
"start": 28,
"text": "CAMLs"
},
{
"end": 73,
"label": "Tissue",
"start": 60,
"text": "tumour tissue"
}
] |
Single_Cell
|
To validate that CAMLs are in physical proximity to tumour cells in situ we examined our STx sections.
|
[
{
"end": 22,
"label": "CellType",
"start": 17,
"text": "CAMLs"
},
{
"end": 64,
"label": "CellType",
"start": 52,
"text": "tumour cells"
},
{
"end": 101,
"label": "Tissue",
"start": 89,
"text": "STx sections"
}
] |
Single_Cell
|
We calculated across all sections (8 patients, n sections = 20) the Pearson correlation between the relative abundance of the cell types that reside in the same spot and are therefore co-localised.
|
[
{
"end": 33,
"label": "Tissue",
"start": 25,
"text": "sections"
},
{
"end": 136,
"label": "CellType",
"start": 126,
"text": "cell types"
}
] |
Single_Cell
|
Our analysis showed that CAMLs indeed co-localised with AT2 cells (Fig. 4G, H ).
|
[
{
"end": 30,
"label": "CellType",
"start": 25,
"text": "CAMLs"
},
{
"end": 65,
"label": "CellType",
"start": 56,
"text": "AT2 cells"
}
] |
Single_Cell
|
We confirmed this finding using non-negative matrix factorisation (NMF) on the absolute cell-type abundances estimated by cell2location that defined factors of co-occurring cell states (Fig. 4I ).
|
[] |
Single_Cell
|
Source paper: PMC11116453
To determine the specific Mɸ population from which CAMLs likely originate, we employed PAGA to elucidate the differentiation path of the myeloid cell population in our tumour dataset (Supplementary Fig. 5D ).
|
[
{
"end": 67,
"label": "CellType",
"start": 54,
"text": "Mɸ population"
},
{
"end": 84,
"label": "CellType",
"start": 79,
"text": "CAMLs"
},
{
"end": 188,
"label": "CellType",
"start": 165,
"text": "myeloid cell population"
}
] |
Single_Cell
|
The analysis revealed continuity of the differentiation transitions between diverse populations of myeloid cells .
|
[
{
"end": 112,
"label": "CellType",
"start": 99,
"text": "myeloid cells"
}
] |
Single_Cell
|
Within the PAGA trajectory, alveolar Mɸ (AMɸ) and AIMɸ showed high PAGA connectivity indicating their high transcriptional similarity.
|
[
{
"end": 39,
"label": "CellType",
"start": 28,
"text": "alveolar Mɸ"
},
{
"end": 44,
"label": "CellType",
"start": 41,
"text": "AMɸ"
},
{
"end": 54,
"label": "CellType",
"start": 50,
"text": "AIMɸ"
}
] |
Single_Cell
|
Both AIMɸ and AMɸ showed the strongest connectivity on the PAGA trajectory with STAB1 + Mɸ which, in turn, were linked with CAMLs.
|
[
{
"end": 9,
"label": "CellType",
"start": 5,
"text": "AIMɸ"
},
{
"end": 17,
"label": "CellType",
"start": 14,
"text": "AMɸ"
},
{
"end": 90,
"label": "CellType",
"start": 80,
"text": "STAB1 + Mɸ"
},
{
"end": 129,
"label": "CellType",
"start": 124,
"text": "CAMLs"
}
] |
Single_Cell
|
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