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Lung cancer is the second most frequently diagnosed cancer and the leading cause of cancer-related mortality worldwide. | [] | PMC11116453 | Single_Cell |
Tumour ecosystems feature diverse immune cell types. | [
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"label": "CellType",
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"text": "immune cell types"
}
] | PMC11116453 | Single_Cell |
Myeloid cells, in particular, are prevalent and have a well-established role in promoting the disease. | [
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"label": "CellType",
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"text": "Myeloid cells"
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] | PMC11116453 | 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. | [] | PMC11116453 | 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. | [
{
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"text": "T cells"
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"text": "anti-inflammatory macrophages"
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"text": "NK cells"
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{
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"label"... | PMC11116453 | 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. | [] | PMC11116453 | 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. | [
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"text": "macrophages"
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"label": "Tissue",
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"text": "tumours"
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] | PMC11116453 | 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. | [
{
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"label": "CellType",
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"text": "tumour-associated macrophages"
}
] | PMC11116453 | Single_Cell |
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 . | [] | PMC11116453 | 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) . | [] | PMC11116453 | 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. | [
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"label": "CellType",
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"text": "macrophages"
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{
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"label": "CellType",
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"text": "Mɸ"
}
] | PMC11116453 | 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 . | [
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"text": "tumour-associated Mɸ"
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{
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"label": "CellType",
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"text": "TAMs"
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] | PMC11116453 | 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. | [
{
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"label": "CellType",
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"text": "Mɸ"
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] | PMC11116453 | Single_Cell |
Within NSCLC, adenocarcinoma (LUAD) is the most common histological subtype, followed by squamous-cell carcinoma (LUSC). | [] | PMC11116453 | 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",
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"text": "lung lobe"
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] | PMC11116453 | 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 . | [] | PMC11116453 | Single_Cell |
A number of studies have employed single-cell technologies to explore transcriptional changes in NSCLC . | [] | PMC11116453 | 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 . | [
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"text": "tumour-infiltrating myeloid cells"
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"... | PMC11116453 | 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 . | [
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"text": "metastatic tumours"
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"s... | PMC11116453 | 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. | [
{
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"label": "Tissue",
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"text": "tumour"
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{
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"label": "Tissue",
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"text": "adjacent non-tumour tissue"
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] | PMC11116453 | 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. | [] | PMC11116453 | 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. | [] | PMC11116453 | Single_Cell |
Therefore, impeding our ability to fully explore the tumour microenvironment (TME) and the complexity of cell–cell interactions therein. | [] | PMC11116453 | Single_Cell |
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. | [
{
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"text": "tumour"
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{
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"label": "Tissue",
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"text": "adjacent non-tumour tissue"
}
] | PMC11116453 | 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. | [
{
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"label": "CellType",
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"text": "Mɸ"
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{
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"label": "CellType",
"start": 138,
"text": "Mɸ"
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] | PMC11116453 | Single_Cell |
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 ). | [
{
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"label": "Tissue",
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"text": "lung tissue resections"
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] | PMC11116453 | 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. | [
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"text": "erythroid cells"
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"label": "Tissue",
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"text": "matched normal non-tumorigenic tissue"
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{
"end": 114,... | PMC11116453 | 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 ). | [
{
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"label": "Tissue",
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"text": "tumour"
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{
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"label": "Tissue",
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"text": "background tissue sections"
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] | PMC11116453 | Single_Cell |
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). | [
{
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"text": "tumour"
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"label": "Tissue",
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"text": "background"
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{
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"label": "Tissue"... | PMC11116453 | 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”). | [
{
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"text": "tumour"
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{
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"text": "B/H"
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] | PMC11116453 | 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 ). | [
{
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"label": "CellType",
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"text": "mast cells"
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"text": "macrophages"
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{
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"label": "CellType",
"start": 75,
"text": "monocytes"
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{
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"label": "CellType"... | PMC11116453 | Single_Cell |
We did not detect neutrophilic granulocytes, most probably due to their sensitivity to degradation after collection and in particular to the freezing-thawing cycle. | [
{
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"label": "CellType",
"start": 18,
"text": "neutrophilic granulocytes"
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] | PMC11116453 | 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 ). | [] | PMC11116453 | Single_Cell |
These cells were found within the tumour and exhibited similarities to previously described cancer-associated macrophage-like cells (CAMLs) . | [
{
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{
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"text": "CAMLs"
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] | PMC11116453 | Single_Cell |
CAMLs represent a distinct population of large myeloid cells with concomitant epithelial tumour protein expression . | [
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"label": "CellType",
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"text": "CAMLs"
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{
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"label": "CellType",
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"text": "large myeloid cells"
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] | PMC11116453 | Single_Cell |
These unique cells have been observed in blood samples of patients with various malignancies, including NSCLC . | [
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"text": "unique cells"
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{
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"label": "Tissue",
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"text": "blood samples"
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] | PMC11116453 | Single_Cell |
The abundance of CAMLs exhibits a direct correlation with response to therapeutic interventions, highlighting their functional significance . | [
{
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"label": "CellType",
"start": 17,
"text": "CAMLs"
}
] | PMC11116453 | Single_Cell |
Even after further subclustering, CAMLs maintained their distinct dual myeloid-epithelial signature (Supplementary Fig. 1D ). | [] | PMC11116453 | 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",
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"text": "macrophage"
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{
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"text": "CAMLs"
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{
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"label": "CellType",
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"text": "tumour cell"
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] | PMC11116453 | Single_Cell |
All clusters included cells from multiple patients, with the cluster size ranging from 2520 to 124,459 cells (Supplementary Fig. 1F, G ). | [] | PMC11116453 | 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 ). | [] | PMC11116453 | Single_Cell |
The composition of the immune and non-immune compartment was markedly different between the tumour and background. | [
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"text": "tumour"
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{
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"text": "background"
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] | PMC11116453 | 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 ). | [
{
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"text": "fibroblasts"
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{
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"text": "lymphatic endothelial cells"
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{
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"label": ... | PMC11116453 | 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 ). | [
{
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{
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{
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"text": "transitioning epithelial cells"
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... | PMC11116453 | 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. | [
{
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"text": "mutant tumour"
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"label... | PMC11116453 | 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 . | [
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"text": "alveolar type I"
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{
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"text": "basal cells"
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] | PMC11116453 | Single_Cell |
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 ). | [
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"label": "Tissue",
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"text": "tumour samples"
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{
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"label... | PMC11116453 | 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 (S | [
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"label": "Tissue",
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"text": "tumour"
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{
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"text": "background tissue"
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] | PMC11116453 | Single_Cell |
upplementary Fig. 2D, E , Supplementary Data 4 and 5 , Supplementary Fig. 3 , and Supplementary Notes ). | [] | PMC11116453 | 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 ). | [
{
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"label": "CellType",
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"text": "NK cells"
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{
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"label": "CellType",
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"text": "DCs"
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{
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"text": "monocytes"
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{
"end": 217,
"label": "CellType",
"s... | PMC11116453 | Single_Cell |
This is consistent with the monocytic o | [] | PMC11116453 | Single_Cell |
rigin of mo-DC2s under inflammatory conditions . | [
{
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"label": "CellType",
"start": 9,
"text": "mo-DC2s"
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] | PMC11116453 | Single_Cell |
Similarly, we found an expansion of B cells expressing LYZ and TNF , and depletion of NKB cells (Fig. 1I and Supplementary Notes ). | [
{
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"label": "CellType",
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"text": "B cells"
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{
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"label": "CellType",
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"text": "NKB cells"
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] | PMC11116453 | 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 ). | [
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"label": "Tissue",
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"text": "regulatory T cells"
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{
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"label": "CellTy... | PMC11116453 | 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 | [
{
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{
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{
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"label": "CellType",
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"text": "T cells"
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] | PMC11116453 | Single_Cell |
(Fig. 1I and Supplementary Data 6 ). | [] | PMC11116453 | 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",
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"text": "T cells"
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] | PMC11116453 | 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. | [
{
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"text": "anti-inflammatory Mɸ"
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"label": "CellType",
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"text": "cycling anti-inflammatory Mɸ"
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{
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... | PMC11116453 | Single_Cell |
Interestingly, we found a strong negative correlation between the frequency of STAB1 + Mɸ/ | [
{
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"label": "CellType",
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"text": "STAB1 + Mɸ"
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] | PMC11116453 | Single_Cell |
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 ). | [
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"text": "Mɸ"
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{
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"label": "CellType",
"start": 104,... | PMC11116453 | 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 . | [
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"text": "NK cells"
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] | PMC11116453 | Single_Cell |
LUAD and LUSC have very different prognoses and are often considered as different clinical entities . | [] | PMC11116453 | 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. | [
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"label": "CellType",
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{
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"label": "Tissue",
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"text": "CD235- samples"
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] | PMC11116453 | 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 ). | [] | PMC11116453 | 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. | [
{
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"text": "immune"
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{
"end": 96,
"label": "CellType",
"start": 80,
"text": "non-immune cells"
}
] | PMC11116453 | Single_Cell |
While LUAD and LUSC shared similar cellular compositions, the observed clinical distinctions may arise from varying intercellular interactions. | [] | PMC11116453 | Single_Cell |
Therefore, we examined whether different cell–cell interaction networks were employed within the TME in LUAD versus LUSC. | [] | PMC11116453 | 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 . | [
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"text": "corresponding cell types"
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{
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"text": "background"
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] | PMC11116453 | 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. | [
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{
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"end": 134,
"label": "C... | PMC11116453 | Single_Cell |
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. | [] | PMC11116453 | 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 ). | [] | PMC11116453 | 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 ). | [] | PMC11116453 | 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"
}
] | PMC11116453 | Single_Cell |
LILRBs (leucocyte Ig-like receptors) are emerging as potential targets for next-generation immunotherapeutics as their blocking can potentiate immune responses . | [] | PMC11116453 | 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 . | [] | PMC11116453 | 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"
}
] | PMC11116453 | 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. | [] | PMC11116453 | Single_Cell |
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": 153,
"label": "CellType",
"start": 140,
"text": "myeloid cells"
}
] | PMC11116453 | 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"
}
] | PMC11116453 | 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"
}
] | PMC11116453 | 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": "... | PMC11116453 | Single_Cell |
TLS are usually correlated with the longer relapse-free survival in NSCLC . | [
{
"end": 3,
"label": "Tissue",
"start": 0,
"text": "TLS"
}
] | PMC11116453 | Single_Cell |
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. | [] | PMC11116453 | 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. | [] | PMC11116453 | Single_Cell |
To achieve this, we considered how the scRNA-seq-identified cell types are spatially arranged on tissue sections. | [
{
"end": 112,
"label": "Tissue",
"start": 97,
"text": "tissue sections"
}
] | PMC11116453 | 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": "T... | PMC11116453 | 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"
}
] | PMC11116453 | 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"... | PMC11116453 | 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”). | [] | PMC11116453 | Single_Cell |
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": 56,
"label": "Tissue",
"start": 41,
"text": "tissue sections"
},
{
"end": 139,
"label": "Tissue",
"start": 133,
"text": "tumour"
},
{
"end": 161,
"label": "Tissue",
"start": 144,
"text": "background tissue"
}
] | PMC11116453 | 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"
}
] | PMC11116453 | 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": 94,
"label": "Tissue",
"start": 86,
"text": "sections"
},
{
"end": 104,
"label": "Tissue",
"start": 98,
"text": "tumour"
},
{
"end": 122,
"label": "Tissue",
"start": 112,
"text": "background"
}
] | PMC11116453 | 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",
... | PMC11116453 | 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": 112,
"label": "Tissue",
"start": 106,
"text": "tumour"
},
{
"end": 126,
"label": "Tissue",
"start": 116,
"text": "background"
}
] | PMC11116453 | 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... | PMC11116453 | 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. | [] | PMC11116453 | Single_Cell |
In the case of scRNA-seq, variations in cell digestion sensitivity can lead to differential representation of cell types. | [] | PMC11116453 | 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"
}
] | PMC11116453 | Single_Cell |
Next, we examined the spatial co-localisation of the L–Rs identified by cellphoneDB. | [] | PMC11116453 | 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. | [] | PMC11116453 | Single_Cell |
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