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We contrasted cell-type proportions between groups (tumour vs. background or LUAD vs. LUSC) using a Wilcoxon rank-sum test.
|
[] |
Single_Cell
|
Finally, we corrected for multiple testing using a two-sided Bonferroni correction independently for each group analysed.
|
[] |
Single_Cell
|
Source paper: PMC11116453
The association between the relative cell-type abundance for each immune cell type was evaluated on the Pearson’s product-moment correlation coefficients.
|
[
{
"end": 110,
"label": "CellType",
"start": 94,
"text": "immune cell type"
}
] |
Single_Cell
|
Source paper: PMC11116453
To test consistency in cell-type annotation performed separately in tumour and B/H, we performed reference-query mapping from tumour to B/H using scArches .
|
[
{
"end": 102,
"label": "Tissue",
"start": 96,
"text": "tumour"
},
{
"end": 110,
"label": "Tissue",
"start": 107,
"text": "B/H"
},
{
"end": 160,
"label": "Tissue",
"start": 154,
"text": "tumour"
},
{
"end": 167,
"label": "Tissue",
"start": 164,
"text": "B/H"
}
] |
Single_Cell
|
For the 828,191 immune cells (464,952 in tumour and 363,239 in B/H) identified through our separate annotations, we selected a common set of 10,000 HVGs.
|
[
{
"end": 28,
"label": "CellType",
"start": 16,
"text": "immune cells"
},
{
"end": 47,
"label": "Tissue",
"start": 41,
"text": "tumour"
},
{
"end": 66,
"label": "Tissue",
"start": 63,
"text": "B/H"
}
] |
Single_Cell
|
We first built an scVI model and trained it on the tumour dataset using broad cell types for reference, and applied scHPL method (provided in the scArches package, parameters set to use KNN classifier, 100 neighbours and with PCA dimensionality reduction) to obtain the hierarchy for the tumour cell types.
|
[
{
"end": 88,
"label": "CellType",
"start": 72,
"text": "broad cell types"
},
{
"end": 305,
"label": "CellType",
"start": 288,
"text": "tumour cell types"
}
] |
Single_Cell
|
We then applied the B/H dataset to the pretrained reference model for a query, and predicted B/H broad cell types based on tumour hierarchy (probability threshold set as 0.2).
|
[
{
"end": 113,
"label": "CellType",
"start": 93,
"text": "B/H broad cell types"
}
] |
Single_Cell
|
Finally, we compared the predicted cell types with our separate annotations in B/H using a heatmap to visualise the confusion matrix.
|
[
{
"end": 45,
"label": "CellType",
"start": 25,
"text": "predicted cell types"
},
{
"end": 82,
"label": "Tissue",
"start": 79,
"text": "B/H"
}
] |
Single_Cell
|
Source paper: PMC11116453
We initially identified a putative long list of cell–cell interactions differentially observed in the tumour environment by inferring statistically significant ligand–receptor pairs, and their corresponding cell types, using CellPhoneDB .
|
[
{
"end": 245,
"label": "CellType",
"start": 235,
"text": "cell types"
}
] |
Single_Cell
|
We treated the tumour (LUAD or LUSC), background, and healthy scRNA-seq profiles as independent datasets and ran CellPhoneDB separately.
|
[] |
Single_Cell
|
To reduce the impact of randomness in the way CellPhoneDB samples from input datasets, we required that any ligand–receptor pair of interest from the CellPhoneDB database be expressed in at least 30% of cells in a particular cell-type cluster of interest.
|
[
{
"end": 208,
"label": "CellType",
"start": 203,
"text": "cells"
}
] |
Single_Cell
|
The final ligand–receptor lists were further filtered by requiring that the mean log(1 + expression) of the ligand–receptor pair be greater than 1.0, and the Bonferroni-adjusted P value be less than 0.01.
|
[] |
Single_Cell
|
From these filtered long lists, ligand–receptor pairs and corresponding cell types relevant to the tumour data are identified.
|
[
{
"end": 82,
"label": "CellType",
"start": 72,
"text": "cell types"
}
] |
Single_Cell
|
Source paper: PMC11116453
When evaluating the ligand–receptor lists calculated with CellPhoneDB, we did not run on the complete datasets due to the difficulty in scaling up the CellPhoneDB statistical permutation tests to scRNA-seq with more than 10 cells.
|
[
{
"end": 257,
"label": "CellType",
"start": 252,
"text": "cells"
}
] |
Single_Cell
|
Instead, we separately stratified the tumour, healthy and background datasets such that the proportion of cell types, patients, and samples in the reduced 50% of the data recapitulated the proportions in the full dataset.
|
[
{
"end": 116,
"label": "CellType",
"start": 106,
"text": "cell types"
},
{
"end": 139,
"label": "Tissue",
"start": 132,
"text": "samples"
}
] |
Single_Cell
|
Source paper: PMC11116453
Differentiation expression analysis (DEA) was performed for AT2 cells, anti-inflammatory macrophages and alveolar macrophages using a pseudo-bulk approach to compare tumour versus background.
|
[
{
"end": 153,
"label": "CellType",
"start": 133,
"text": "alveolar macrophages"
},
{
"end": 97,
"label": "CellType",
"start": 88,
"text": "AT2 cells"
},
{
"end": 128,
"label": "CellType",
"start": 99,
"text": "anti-inflammatory macrophages"
},
{
"end": 200,
"label": "Tissue",
"start": 194,
"text": "tumour"
},
{
"end": 218,
"label": "Tissue",
"start": 208,
"text": "background"
}
] |
Single_Cell
|
Pseudobulks were built for each patient by summing raw gene counts across all cells in each cell type investigated.
|
[
{
"end": 83,
"label": "CellType",
"start": 78,
"text": "cells"
},
{
"end": 101,
"label": "CellType",
"start": 92,
"text": "cell type"
}
] |
Single_Cell
|
The patients 1 and 4 were not included in the analysis as their cancer subtype and stage were not known at the time of analysis.
|
[] |
Single_Cell
|
Since there were differences in the cell count between datasets we downsampled the biggest cluster to the size of the smaller.
|
[] |
Single_Cell
|
The downsampling routine was repeated 100 times, such that 100 new datasets were created that match the smaller dataset.
|
[] |
Single_Cell
|
DEA was performed using sample-level pseudobulks and a Pythonic version of the DESeq2 pipeline (py_DESeq2), including the patient information as co-variate .
|
[] |
Single_Cell
|
The median adjusted p value by Benjamini–Hochberg procedure and median log2FC for each differentially expressed gene (DEG) was calculated across 100 iterations.
|
[] |
Single_Cell
|
We verified the robustness of this choice of 100 iterations by visualising the variability of the median p value across iterations, in order to assess its stability (Supplementary Fig. 6C ).
|
[] |
Single_Cell
|
DEGs were filtered with median(padj)≤0.05 and |median(logFC)|≥1.
|
[] |
Single_Cell
|
Prior to performing overrepresentation analysis, the genes that were commonly upregulated in more than 50% of the contrasts were removed (DNAJB1, HSPA1A, HSPA1B, HSPB1, HSPE1, IGHA1, IGKC, IGLC2).
|
[] |
Single_Cell
|
DEGs were used to perform gene ontology (GO) overrepresentation using the clusterProfiler package .
|
[] |
Single_Cell
|
To define STAB1 + Mɸ and AMɸ gene signatures, we compared DEA results and intersected the genes significantly upregulated by STAB1 + Mɸ (or AMɸ) compared to the other Mɸ populations in tumour.
|
[
{
"end": 20,
"label": "CellType",
"start": 10,
"text": "STAB1 + Mɸ"
},
{
"end": 28,
"label": "CellType",
"start": 25,
"text": "AMɸ"
},
{
"end": 135,
"label": "CellType",
"start": 125,
"text": "STAB1 + Mɸ"
},
{
"end": 143,
"label": "CellType",
"start": 140,
"text": "AMɸ"
},
{
"end": 169,
"label": "CellType",
"start": 167,
"text": "Mɸ"
},
{
"end": 191,
"label": "Tissue",
"start": 185,
"text": "tumour"
}
] |
Single_Cell
|
Source paper: PMC11116453
To analyse myeloid cell trajectory in tumour dataset, we recomputed a neighbourhood graph from the same 15-dimensional harmonised PCA space as above, but only within myeloid cell populations.
|
[
{
"end": 51,
"label": "CellType",
"start": 39,
"text": "myeloid cell"
},
{
"end": 206,
"label": "CellType",
"start": 194,
"text": "myeloid cell"
}
] |
Single_Cell
|
We next applied PAGA within the Scanpy package to the neighbourhood graph.
|
[] |
Single_Cell
|
In parallel, we computed the diffusion map and its force-directed layout for visualisation using the Pegasus package .
|
[] |
Single_Cell
|
We finally overlaid the PAGA network with the diffusion map using the scVelo package.
|
[] |
Single_Cell
|
We repeated the same analysis workflow but on non-immune cells in the tumour dataset.
|
[
{
"end": 62,
"label": "CellType",
"start": 46,
"text": "non-immune cells"
}
] |
Single_Cell
|
Source paper: PMC11116453
We applied the CopyKAT package to the single-cell RNA-seq data to obtain copy number calls.
|
[] |
Single_Cell
|
The Copykat pipeline was extended to obtain confident copy number calls per cell, per chromosome arm, beyond the hierarchical clustering the standard pipeline produces.
|
[
{
"end": 80,
"label": "CellType",
"start": 76,
"text": "cell"
}
] |
Single_Cell
|
Source paper: PMC11116453
Per cell copy number calls were obtained as follows: first, the regular CopyKAT (v1.0.5) pipeline was run on the unmodified UMI counts of a particular patient/environment (i.e., tumour or background) combination with default parameters, except for norm.cell.names.
|
[
{
"end": 36,
"label": "CellType",
"start": 32,
"text": "cell"
}
] |
Single_Cell
|
The norm.cell.names parameter allows for specifying which cells are used as confident diploid normals during expression normalisation.
|
[
{
"end": 63,
"label": "CellType",
"start": 58,
"text": "cells"
}
] |
Single_Cell
|
CopyKAT was set to use all cells labelled as cDC2 dendritic cells, as they are available in great numbers across all patients and an initial inspection of their expression profiles revealed no systematic copy number alterations.
|
[
{
"end": 65,
"label": "CellType",
"start": 45,
"text": "cDC2 dendritic cells"
},
{
"end": 32,
"label": "CellType",
"start": 27,
"text": "cells"
}
] |
Single_Cell
|
Source paper: PMC11116453
After CopyKAT has completed, a calling step was applied that is aimed to call whole chromosome arm alterations in individual cells.
|
[
{
"end": 158,
"label": "CellType",
"start": 142,
"text": "individual cells"
}
] |
Single_Cell
|
We reasoned that, on a chromosome arm basis, the distribution of binned-and-normalised expression from CopyKAT should be significantly different (higher or lower) than the distribution of the same bins in all confidently diploid cells.
|
[
{
"end": 234,
"label": "CellType",
"start": 221,
"text": "diploid cells"
}
] |
Single_Cell
|
For each chromosome arm, we model the distribution of all data bins from the confidently diploid cells as a normal distribution.
|
[
{
"end": 102,
"label": "CellType",
"start": 89,
"text": "diploid cells"
}
] |
Single_Cell
|
Each bin on that same chromosome arm from a candidate aneuploid cell is then tested against that distribution.
|
[
{
"end": 68,
"label": "CellType",
"start": 54,
"text": "aneuploid cell"
}
] |
Single_Cell
|
Finally, when more than 50% of bins across that chromosome arm are significant, the arm is marked as altered in that cell.
|
[] |
Single_Cell
|
Source paper: PMC11116453
The above-described procedure yields a conservative true/false call per cell, per chromosome arm without directly distinguishing between gains and losses.
|
[
{
"end": 104,
"label": "CellType",
"start": 100,
"text": "cell"
}
] |
Single_Cell
|
To obtain a profile with gains and losses as is shown in Fig. 4A , we discretise the values for each bin in each cell: If the arm is altered and the expression value of the bin is negative: −1, if the arm is altered and the expression value is positive: +1, if the arm is unaltered: 0.
|
[
{
"end": 117,
"label": "CellType",
"start": 113,
"text": "cell"
}
] |
Single_Cell
|
The discretized values are then finally summed per bin across all cells of a particular cell type and divided by the number of cells of that cell type to obtain the fraction of cells with an alteration as shown in Fig. 4A .
|
[
{
"end": 71,
"label": "CellType",
"start": 66,
"text": "cells"
},
{
"end": 97,
"label": "CellType",
"start": 88,
"text": "cell type"
},
{
"end": 150,
"label": "CellType",
"start": 141,
"text": "cell type"
}
] |
Single_Cell
|
Source paper: PMC11116453
Tissues were frozen in dry-ice-cooled isopentane and stored in air-tight tissue cryovials at −80 °C.
|
[
{
"end": 35,
"label": "Tissue",
"start": 28,
"text": "Tissues"
}
] |
Single_Cell
|
The tissues were embedded in an optimal cutting temperature compound (OCT) and cryosectioned in a pre-cooled cryostat at 10 μm thickness on SuperFrost slides.
|
[
{
"end": 11,
"label": "Tissue",
"start": 4,
"text": "tissues"
}
] |
Single_Cell
|
On the day of the experiment, slides were thawed at room temperature for less than 5 min, then immersed in a fixation solution (4% PFA in PBS) for 20 min.
|
[] |
Single_Cell
|
After three washes with PBS, each section was permeabilized with freshly prepared 0.2% Triton-X100 (Sigma Aldrich) for 10 min at room temperatures, followed by three washes in PBS.
|
[
{
"end": 41,
"label": "Tissue",
"start": 34,
"text": "section"
}
] |
Single_Cell
|
Unspecific binding was blocked by incubating the sections in PBS + 2.5% BSA for 1 h at room temperature.
|
[
{
"end": 57,
"label": "Tissue",
"start": 49,
"text": "sections"
}
] |
Single_Cell
|
Following two washes in PBS, sections were incubated with recombinant rabbit anti-CD68 (Abcam ab213363, 1:50) and mouse anti-STAB1 (Santa Cruz Biotechnology sc-293254, 10 µg/ml) in PBS + 0.5% BSA overnight at 4 °C.
|
[
{
"end": 37,
"label": "Tissue",
"start": 29,
"text": "sections"
}
] |
Single_Cell
|
Primary antibodies were removed and sections washed three times with PBS, then incubated with the appropriate secondary antibodies (goat anti-rabbit AlexaFluor 594 and goat anti-mouse AlexaFluor 488 Abcam) 1:500 in PBS + 0.5% BSA for 2 h at room temperature, protected from light.
|
[
{
"end": 44,
"label": "Tissue",
"start": 36,
"text": "sections"
}
] |
Single_Cell
|
Two confocal immunohistochemistry z-stacks each for tumour and background tissue from three patients were analysed.
|
[
{
"end": 58,
"label": "Tissue",
"start": 52,
"text": "tumour"
},
{
"end": 80,
"label": "Tissue",
"start": 63,
"text": "background tissue"
}
] |
Single_Cell
|
Using Fiji (ImageJ) software, the STAB1+ and CD68+ areas were segmented by automatic thresholding and quantified in each image of the z-stack.
|
[] |
Single_Cell
|
Source paper: PMC11116453
To assess the levels of cholesterol and neutral lipids we further stained tumour and background tissue sections with BODIPY™ 493/503 (Invitrogen).
|
[
{
"end": 108,
"label": "Tissue",
"start": 102,
"text": "tumour"
},
{
"end": 139,
"label": "Tissue",
"start": 113,
"text": "background tissue sections"
}
] |
Single_Cell
|
After three washes in PBS, sections were incubated with a 10 µg/ml solution of BODIPY™ 493/503 in PBS (1:100 from a stock 1 mg/ml solution in DMSO) for 15 min at room temperature.
|
[
{
"end": 35,
"label": "Tissue",
"start": 27,
"text": "sections"
}
] |
Single_Cell
|
Following four washes in PBS, sections were incubated for 90 s with TrueVIEW (Vector Laboratories), washed by immersing in PBS for 5 min, then tap-dried and mounted in VECTASHIELD Vibrance™ Antifade.
|
[
{
"end": 38,
"label": "Tissue",
"start": 30,
"text": "sections"
}
] |
Single_Cell
|
Sections were imaged using a Zeiss LSM 710 confocal microscope at ×20 (Plan-Apochromat ×20/0.8 M27) and ×63 (Plan-Apochromat ×63/1.40 Oil DIC M27) magnification.
|
[
{
"end": 8,
"label": "Tissue",
"start": 0,
"text": "Sections"
}
] |
Single_Cell
|
Tile scans were set to cover an area of 3541 × 3542 microns for all sections.
|
[
{
"end": 76,
"label": "Tissue",
"start": 68,
"text": "sections"
}
] |
Single_Cell
|
ImageJ was used to remove background BODIPY signals and calculate the area covered by the thresholded BODIPY on the stitched images.
|
[] |
Single_Cell
|
To compare the area covered by BODIPY in tumour and background, we used a paired t test at a patient level, after confirming the normal distribution of the data using a Shapiro–Wilk test.
|
[
{
"end": 47,
"label": "Tissue",
"start": 41,
"text": "tumour"
},
{
"end": 62,
"label": "Tissue",
"start": 52,
"text": "background"
}
] |
Single_Cell
|
Source paper: PMC11116453
To investigate the oncofetal reprogramming of myeloid cells in NSCLC, we took advantage of a published scRNA-seq dataset of foetal lung myeloid cells and the published “MoMac-VERSE” .
|
[
{
"end": 87,
"label": "CellType",
"start": 74,
"text": "myeloid cells"
},
{
"end": 177,
"label": "CellType",
"start": 152,
"text": "foetal lung myeloid cells"
}
] |
Single_Cell
|
The expression of the “STAB1 signature genes” and of the “AMɸ signature genes” across lung foetal myeloid cells was determined using the AddModuleScore function in Seurat v4.3.
|
[
{
"end": 111,
"label": "CellType",
"start": 86,
"text": "lung foetal myeloid cells"
}
] |
Single_Cell
|
To combine foetal lung and adult lung tumour-infiltrating myeloid cells, we isolated the myeloid cells from our tumour and background datasets and integrated those with the aforementioned foetal lung myeloid dataset using the Pegasus package, following the following workflow: (i) remove rarely expressed genes (less than 10 cells), normalisation and log1p transformation, (ii) robust and highly-variable gene selection, (iii) PCA with optimal PC number determined by random matrix theory (resulting in 75 PCs), (iv) batch effect correction using Harmony , and (v) Leiden clustering on neighbourhood graph.
|
[
{
"end": 22,
"label": "CellType",
"start": 11,
"text": "foetal lung"
},
{
"end": 71,
"label": "CellType",
"start": 27,
"text": "adult lung tumour-infiltrating myeloid cells"
},
{
"end": 102,
"label": "CellType",
"start": 89,
"text": "myeloid cells"
}
] |
Single_Cell
|
The dendrogram was built by estimating the correlation distance between cell types on the harmonised PC embedding space, under complete linkage criterion of hierarchical clustering.
|
[
{
"end": 82,
"label": "CellType",
"start": 72,
"text": "cell types"
}
] |
Single_Cell
|
The UMAP was computed to obtain a 2D summary of the harmonised PC space.
|
[] |
Single_Cell
|
Source paper: PMC11116453
Tissues were frozen in dry-ice-cooled isopentane and stored in air-tight tissue cryovials at −80 °C.
|
[
{
"end": 35,
"label": "Tissue",
"start": 28,
"text": "Tissues"
}
] |
Single_Cell
|
Prior to undertaking any spatial transcriptomics protocol, the tissues were embedded in OCT compound and tested for RNA quality with an Agilent BioAnalyser.
|
[
{
"end": 70,
"label": "Tissue",
"start": 63,
"text": "tissues"
}
] |
Single_Cell
|
Tissues with RNA integrity (RIN) values > 7 were cryosectioned in a pre-cooled cryostat at 10 μm thickness.
|
[
{
"end": 7,
"label": "Tissue",
"start": 0,
"text": "Tissues"
}
] |
Single_Cell
|
Two consecutive sections were cryosectioned at 10 μm thickness in a pre-cooled cryostat and transferred to the four 6.5 mm × 6.5 mm capture areas of the gene expression slide.
|
[
{
"end": 24,
"label": "Tissue",
"start": 16,
"text": "sections"
}
] |
Single_Cell
|
Slides were fixed in methanol for 30 min prior to staining with H&E and then imaged using the Nanozoomer slide scanner.
|
[] |
Single_Cell
|
The tissues underwent permeabilization for 24 min.
|
[
{
"end": 11,
"label": "Tissue",
"start": 4,
"text": "tissues"
}
] |
Single_Cell
|
Reverse transcription and second strand synthesis was performed on the slide with cDNA quantification using qRT-PCR using KAPA SYBR FAST-qPCR kit (KAPA Biosystems) and analysed on the QuantStudio (ThermoFisher).
|
[] |
Single_Cell
|
Following library construction, these were quantified and pooled at 2.25 nM concentration.
|
[] |
Single_Cell
|
Pooled libraries from each slide were sequenced on NovaSeq SP (Illumina) using 150 base pair paired-end dual-indexed set-up to obtain a sequencing depth of ~50,000 reads as per 10x Genomics recommendations.
|
[] |
Single_Cell
|
The sequencing libraries were then processed by SpaceRanger (version 1.1.0) on the reference GRCh38 human reference genome to estimate gene expression on spots.
|
[] |
Single_Cell
|
Source paper: PMC11116453
We used cell2location to deconvolute the cellular composition of each capture area (spot).
|
[] |
Single_Cell
|
As our scRNA-seq cells were annotated independently for tumour and the combined B/H datasets, we applied the deconvolution model separately as well, using tumour annotation to infer spatial cell composition of tumour sections, and background annotations for background datasets.
|
[
{
"end": 225,
"label": "Tissue",
"start": 210,
"text": "tumour sections"
},
{
"end": 22,
"label": "CellType",
"start": 7,
"text": "scRNA-seq cells"
}
] |
Single_Cell
|
Only spots with total UMI counts above 800 were used in downstream analysis.
|
[] |
Single_Cell
|
Source paper: PMC11116453
The cell-type abundance in tumour and background sections were computed by summing up the q05 cell abundance, as estimated by cell2location, across spots that passed QC.
|
[
{
"end": 61,
"label": "Tissue",
"start": 55,
"text": "tumour"
},
{
"end": 85,
"label": "Tissue",
"start": 66,
"text": "background sections"
}
] |
Single_Cell
|
Cell-type composition was computed by normalising each cell type’s abundance with the total abundance of all cell types.
|
[
{
"end": 119,
"label": "CellType",
"start": 109,
"text": "cell types"
}
] |
Single_Cell
|
We compared cell-type composition between tumour and background with Wilcoxon signed-rank test, followed by Bonferroni correction.
|
[
{
"end": 21,
"label": "CellType",
"start": 12,
"text": "cell-type"
},
{
"end": 48,
"label": "Tissue",
"start": 42,
"text": "tumour"
},
{
"end": 63,
"label": "Tissue",
"start": 53,
"text": "background"
}
] |
Single_Cell
|
Source paper: PMC11116453
On tumour sections, we estimated the correlation distance on cell-type composition across valid spots, applied hierarchical clustering with complete linkage, and visualised the results as a dendrogram.
|
[
{
"end": 46,
"label": "Tissue",
"start": 31,
"text": "tumour sections"
},
{
"end": 98,
"label": "CellType",
"start": 89,
"text": "cell-type"
}
] |
Single_Cell
|
In addition, we applied non-negative matrix factorisation analysis to the q05 estimation of cell-type abundance with eight factors.
|
[] |
Single_Cell
|
Source paper: PMC11116453
To study the expression of ligand–receptor pairs on the 10X Visium, we first binarised the expression of each gene in the LR pairs in the spots that passed QC.
|
[] |
Single_Cell
|
We considered a gene being expressed in a spot if its cell2location estimated abundance were higher than the median counts for that gene in the corresponding section.
|
[
{
"end": 165,
"label": "Tissue",
"start": 158,
"text": "section"
}
] |
Single_Cell
|
We counted spots where both genes in each LR pair were either co-expressed or not, in tumour and background sections from the same patient, and subsequently, applied the χ test on the contingency table.
|
[
{
"end": 92,
"label": "Tissue",
"start": 86,
"text": "tumour"
},
{
"end": 116,
"label": "Tissue",
"start": 97,
"text": "background sections"
}
] |
Single_Cell
|
To correct for multiple comparisons, we adjusted the P value using a conservative Bonferroni correction for all the LRs enriched in tumours in the cellphoneDB analysis (309 * 8 patients).
|
[
{
"end": 139,
"label": "Tissue",
"start": 132,
"text": "tumours"
}
] |
Single_Cell
|
LRs were considered significantly enriched in tumour if the Bonferroni-adjusted P value was lower than 0.05 in at least four patients.
|
[
{
"end": 52,
"label": "Tissue",
"start": 46,
"text": "tumour"
}
] |
Single_Cell
|
Source paper: PMC11116453
5 μm thick sections were generated from NSCLC FFPE tumour blocks.
|
[
{
"end": 47,
"label": "Tissue",
"start": 39,
"text": "sections"
},
{
"end": 92,
"label": "Tissue",
"start": 68,
"text": "NSCLC FFPE tumour blocks"
}
] |
Single_Cell
|
An antibody cocktail was prepared with optimal dilutions of each of the following conjugated antibodies: anti-human Stabilin-1 antibody (clone #840449, catalogue #MAB3825, R&D systems) was conjugated to a custom oligo barcode according to instructions in Akoya Biosciences’ antibody conjugation kit (Conjugation kit, #7000009; Akoya) while human CD68 (clone #KP1, catalogue #4550113, Akoya) and human PanCK (clone AE-1/AE-3, catalogue #4150020, Akoya) were obtained directly pre-conjugated to oligo barcodes from Akoya Biosciences.
|
[] |
Single_Cell
|
Complementary oligo-conjugated fluorophore reporters were obtained from Akoya Biosciences.
|
[] |
Single_Cell
|
Tissue multiplexed immunofluorescence staining and image acquisition were performed according to Akoya Phenocycler-Fusion user guide (PD-000011 Rev. A., Akoya).
|
[] |
Single_Cell
|
OME-TIFF files were generated and processed for image analysis.
|
[] |
Single_Cell
|
Source paper: PMC11116453
Analysis of the multiplexed immunofluorescence images (generated from Akoya Phenocycler-Fusion platform) was performed using Visiopharm (version 2023.09.3.15043 × 64) on the entire tissue area.
|
[] |
Single_Cell
|
Briefly, cell segmentation (including both nuclear and cytoplasmic segmentation) was first performed using Visiopharm’s “Cell Detection, AI (Fluorescence)” (version 2023.09.3.15043 × 64) with its default parameters.
|
[] |
Single_Cell
|
After cell segmentation, Visiopharm’s “Phenoplex Guided Workflow” was used.
|
[] |
Single_Cell
|
DAPI (nucleus), CD68 (cell body) and STAB1 (cell body) variables were selected and manually thresholded to define positive and negative cells for each marker and generate a co-occurrence matrix.
|
[
{
"end": 122,
"label": "CellType",
"start": 114,
"text": "positive"
},
{
"end": 141,
"label": "CellType",
"start": 127,
"text": "negative cells"
}
] |
Single_Cell
|
Macrophages were defined as [DAPI + , CD68 + ] while STAB1+ macrophages were defined as [DAPI + , CD68 + , STAB1 + ].
|
[
{
"end": 11,
"label": "CellType",
"start": 0,
"text": "Macrophages"
},
{
"end": 71,
"label": "CellType",
"start": 53,
"text": "STAB1+ macrophages"
}
] |
Single_Cell
|
Source paper: PMC11116453
Mapping the arterial vascular network in an intact human kidney using hierarchical phase-contrast tomography
Source paper: PMC12408821
The architecture of kidney vasculature is essential the organ's specialised functions, yet is challenging to structurally map in an intact human organ.
|
[
{
"end": 65,
"label": "Tissue",
"start": 40,
"text": "arterial vascular network"
},
{
"end": 91,
"label": "Tissue",
"start": 72,
"text": "intact human kidney"
},
{
"end": 225,
"label": "Tissue",
"start": 220,
"text": "organ"
},
{
"end": 202,
"label": "Tissue",
"start": 184,
"text": "kidney vasculature"
},
{
"end": 314,
"label": "Tissue",
"start": 296,
"text": "intact human organ"
}
] |
Single_Cell
|
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