sentence stringlengths 26 1.24k | entities listlengths 0 18 | data_source stringclasses 4
values |
|---|---|---|
All research ethics committee and regulatory approvals were in place for the collection of research samples at Newcastle and for their storage at the Newcastle Dermatology Biobank (REC reference no. 19/NE/0004). | [] | Single_Cell |
Skin samples were sectioned at 15-µm thickness and the optimal tissue permeabilization time was determined as 14 min. | [] | Single_Cell |
H&E images were taken using a Zeiss AxioImager with apotome microscope (Carl Zeiss Microscopy) and Brightfield imaging (Zeiss Axiocam 105 48-color camera module) at ×20 magnification. | [] | Single_Cell |
The ZEN blue edition v.3.1 (Carl Zeiss Microscopy) software was used to acquire the H&E images following z-plane and light balance adjustment and image tile stitching. | [] | Single_Cell |
Spatial gene expression libraries were sequenced using an Illumina NovaSeq 6000 to achieve a minimum number of 50,000 read pairs per tissue covered spot. | [] | Single_Cell |
The 10x Genomics Visium data were mapped using Spaceranger v.1.3.0 using GRCh38-2020-A reference. | [] | Single_Cell |
Visium provides whole transcriptome coverage over a 55-μm diameter spot area. | [] | Single_Cell |
We therefore used the cell2location (v.0.1.3) to deconvolute the cell types predicted to be present in a given spot . | [] | Single_Cell |
We constructed a reference signature using sample as batch_key. | [] | Single_Cell |
We included our fibroblast atlas and other skin cell types from Reynolds et al. . | [
{
"end": 58,
"label": "CellType",
"start": 43,
"text": "skin cell types"
}
] | Single_Cell |
Then we performed deconvolution with the following parameters: detection_alpha = 20,N_cells_per_location = 30 andmax_epochs 50_000. | [] | Single_Cell |
Values of all other parameters were kept default. | [] | Single_Cell |
Following the cell2location tutorial, we used 5% quantile of posterior distribution (q05_cell_abundance_w_sf) as predicted cell-type abundances. | [] | Single_Cell |
Nonlesional ( n = 2; one sample nonlesional pre-treatment, one sample nonlesional post-treatment) and lesional ( n = 1; inflamed) atopic dermatitis human adult skin tissue was used to generate in situ gene expression data using the 10x Genomics Xenium in situ 5k-plex platform. | [
{
"end": 171,
"label": "Tissue",
"start": 130,
"text": "atopic dermatitis human adult skin tissue"
}
] | Single_Cell |
All research ethics committees and regulatory approvals were in place for the collection and storage of research samples at St John’s Institute of Dermatology, Guy’s Hospital, London (REC reference no. EC00/128). | [] | Single_Cell |
We used marker genes from scRNA-seq data to label fibroblast populations through manual annotation. | [
{
"end": 72,
"label": "CellType",
"start": 50,
"text": "fibroblast populations"
}
] | Single_Cell |
Cell coordinates colored by cell type were visualized using squidpy (sq.pl.spatial_scatter) . | [] | Single_Cell |
Due to the proposed role of hypoxia in myofibroblast differentiation , we included hypoxic genes for consideration in feature selection. | [] | Single_Cell |
To ensure this selection did not bias results, we repeated an scVI integration using the same methodology as for healthy fibroblasts (Supplementary Fig. 4a,b ). | [
{
"end": 132,
"label": "CellType",
"start": 113,
"text": "healthy fibroblasts"
}
] | Single_Cell |
To validate our reported fibroblast subtypes, we used two approaches. | [
{
"end": 44,
"label": "CellType",
"start": 25,
"text": "fibroblast subtypes"
}
] | Single_Cell |
First, we used skin scRNA-seq datasets not used in the original integration . | [] | Single_Cell |
Using the scPoli model to generate embeddings and transfer labels to these populations, we identified expected populations from earlier analysis (Supplementary Fig. 4c ). | [] | Single_Cell |
Second, we used Xenium data to validate the existence of the same clusters in which cell gene expression profiles are generated in situ, without tissue dissociation. | [] | Single_Cell |
All research ethics committees and regulatory approvals were in place for the collection and storage of atopic dermatitis skin samples at the St John’s Institute of Dermatology, Guy’s Hospital, London (REC reference no. EC00/128) and hidradenitis suppurativa skin samples at Newcastle Dermatology Biobank (REC reference ... | [] | Single_Cell |
Fresh-frozen OCT-embedded skin samples were sectioned at 10-µm thickness directly onto superfrost microscope slides and stored at −80 °C. | [] | Single_Cell |
Slides were air dried at room temperature for 10 min and then fixed using 4% PFA for 10 min. | [] | Single_Cell |
Next, a blocking solution of 5% normal goat serum with 0.01% Triton X-100 was applied to the tissue sections and incubated for 1 h at room temperature. | [
{
"end": 49,
"label": "Tissue",
"start": 32,
"text": "normal goat serum"
}
] | Single_Cell |
Slides were then incubated with primary antibodies overnight at 4 °C. | [] | Single_Cell |
The next day, slides were washed with 1× PBS and incubated with secondary antibodies for 1 h at room temperature. | [] | Single_Cell |
Then, 4,6-diamidino-2-phenylindole (DAPI) was used to demarcate nuclei and slides were mounted with DAKO mounting medium before applying coverslips and leaving slides to dry overnight. | [] | Single_Cell |
Skin sections were imaged using a Leica SP8 confocal microscope. | [] | Single_Cell |
We first used PAGA (as implemented in scanpy) , plotted using a threshold of 0.1 and applied to the whole dataset. | [] | Single_Cell |
In future analyses, we excluded F5: Schwann-like fibroblasts, which seemed to be distinct (Extended Data Fig. 8a ) and F_Fascia, which were observed in few diseases (Fig. 4a ). | [
{
"end": 60,
"label": "CellType",
"start": 36,
"text": "Schwann-like fibroblasts"
},
{
"end": 127,
"label": "CellType",
"start": 119,
"text": "F_Fascia"
}
] | Single_Cell |
As healing and scarring is observed on non-hair-bearing sites, we also did not include F4: hair follicle-associated fibroblasts. | [
{
"end": 61,
"label": "Tissue",
"start": 39,
"text": "non-hair-bearing sites"
},
{
"end": 127,
"label": "CellType",
"start": 91,
"text": "hair follicle-associated fibroblasts"
}
] | Single_Cell |
We generated new scVI embeddings for the lesional fibroblasts and re-calculated the k -NN graph using the top 2,000 HVGs, followed by UMAP visualization. | [
{
"end": 61,
"label": "CellType",
"start": 41,
"text": "lesional fibroblasts"
}
] | Single_Cell |
Velocity pseudotime was calculated using scvelo . | [] | Single_Cell |
We re-calculated the PAGA plot using only lesional fibroblasts, again using a threshold of 0.1. | [
{
"end": 62,
"label": "CellType",
"start": 42,
"text": "lesional fibroblasts"
}
] | Single_Cell |
For Monocle 3 (v.1.3.7) , the expression count matrix along with the corresponding cell and gene metadata from the processed anndata object in scanpy was used to create Monocle object (cell_data_set object). | [] | Single_Cell |
The cell_data_set object was then pre-processed using default settings and aligned to correct for batch effects based on the ‘dataset_id’. | [] | Single_Cell |
Dimensionality reduction was performed using ‘UMAP’ as the reduction method. | [] | Single_Cell |
Cells were clustered with a resolution of 1 × 10 − 6 and ‘UMAP’ as the reduction method. | [] | Single_Cell |
A trajectory graph was learned by adjusting parameters such as geodesic distance ratio (0.5) and minimal branch length (10) to optimize for large datasets. | [] | Single_Cell |
Finally, cells were arranged in pseudotime by manually selecting root nodes from the F2: universal population. | [
{
"end": 109,
"label": "CellType",
"start": 85,
"text": "F2: universal population"
}
] | Single_Cell |
The ordered and learned graph object was then used to plot the pseudotime trajectory plots. | [] | Single_Cell |
The RNA velocity kernel was calculated using CellRank2. | [] | Single_Cell |
We obtained post-alignment skin wound data from the authors of ref. | [] | Single_Cell |
and processed these using CellBender as previously described for skin fibroblast data. | [] | Single_Cell |
To annotate skin wound fibroblasts, we integrated the unlabeled skin wound cells with our labeled integrated skin dataset using scanVI. | [
{
"end": 34,
"label": "CellType",
"start": 12,
"text": "skin wound fibroblasts"
},
{
"end": 80,
"label": "CellType",
"start": 64,
"text": "skin wound cells"
}
] | Single_Cell |
We used the same model architecture (30 latent dimensions, two layers) and the same number of input HVGs ( n = 6,000). | [] | Single_Cell |
We selected only fibroblasts for further analysis, using the same downstream strategy as used for skin fibroblasts previously. | [
{
"end": 28,
"label": "CellType",
"start": 17,
"text": "fibroblasts"
},
{
"end": 114,
"label": "CellType",
"start": 98,
"text": "skin fibroblasts"
}
] | Single_Cell |
For cross-tissue integration, we concatenated our labeled skin data with other tissues (raw count data). | [] | Single_Cell |
HLCA data , gut atlas data and Human Endometrial Cell Atlas data were available locally. | [] | Single_Cell |
We downloaded nasal tissue data from https://ngdc.cncb.ac.cn/gsa-human/browse/HRA000772 (ref. ), | [] | Single_Cell |
heart data from https://data.humancellatlas.org/explore/projects/e9f36305-d857-44a3-93f0-df4e6007dc97 , rheumatoid arthritis from https://www.immport.org/shared/study/SDY998 (ref. ) | [] | Single_Cell |
and additional intestinal data from the Gene Expression Omnibus (GEO) under accession code GSE282122 . | [] | Single_Cell |
We used the same number of input HVGs ( n = 6,000). | [] | Single_Cell |
We integrated data in a semi-supervised manner using scANVI , where skin cell types were labeled and cells from other tissues were unlabeled. | [
{
"end": 83,
"label": "CellType",
"start": 68,
"text": "skin cell types"
},
{
"end": 125,
"label": "CellType",
"start": 101,
"text": "cells from other tissues"
}
] | Single_Cell |
We used the same scANVI hyperparameters as for wound data but with a smaller number of maximum epochs ( n = 10) due to the larger dataset size. | [] | Single_Cell |
We then calculated k -NN ( k = 30) and performed low-resolution Leiden clustering (resolution 0.1). | [] | Single_Cell |
We selected a fibroblast cluster based on canonical marker genes, which also contained the labeled skin fibroblasts. | [
{
"end": 115,
"label": "CellType",
"start": 99,
"text": "skin fibroblasts"
}
] | Single_Cell |
To annotate clusters, we labeled clusters by the majority skin fibroblast population (Fig. 6c and Extended Data Fig. 8b ) (for example if F3: FRC-like was the predominant skin fibroblast subtype, we labeled the cluster as F3: FRC-like ) . | [
{
"end": 84,
"label": "CellType",
"start": 49,
"text": "majority skin fibroblast population"
},
{
"end": 150,
"label": "CellType",
"start": 138,
"text": "F3: FRC-like"
},
{
"end": 194,
"label": "CellType",
"start": 171,
"text": "skin fibroblast subtype"
}
] | Single_Cell |
We then assessed gene expression markers for each cluster, excluding skin fibroblasts, to ensure that skin fibroblasts did not drive the gene expression signature for that cluster. | [
{
"end": 85,
"label": "CellType",
"start": 74,
"text": "fibroblasts"
},
{
"end": 85,
"label": "CellType",
"start": 69,
"text": "skin fibroblasts"
},
{
"end": 118,
"label": "CellType",
"start": 102,
"text": "skin fibroblasts"
}
] | Single_Cell |
We also plotted gene expression for each cluster by tissue using our previously reported marker genes for each cluster. | [] | Single_Cell |
To assess F3: FRC-like fibroblasts in HLCA data, we performed the same clustering strategy as previously described for skin fibroblasts and then plotted F3: FRC-like marker genes by cluster. | [
{
"end": 135,
"label": "CellType",
"start": 119,
"text": "skin fibroblasts"
},
{
"end": 34,
"label": "CellType",
"start": 10,
"text": "F3: FRC-like fibroblasts"
}
] | Single_Cell |
We used CellPhoneDB v.5 (method 2) for cell–cell communication analysis . | [] | Single_Cell |
We combined our fibroblast data with skin immune cells from our previously published scRNA-seq data from skin with more granular immune cell annotations (Reynold s et al.) . | [
{
"end": 109,
"label": "Tissue",
"start": 105,
"text": "skin"
},
{
"end": 54,
"label": "CellType",
"start": 37,
"text": "skin immune cells"
}
] | Single_Cell |
We restricted interactions to marker genes for F3: FRC-like fibroblasts and F6: inflammatory myofibroblasts. | [
{
"end": 71,
"label": "CellType",
"start": 47,
"text": "F3: FRC-like fibroblasts"
},
{
"end": 107,
"label": "CellType",
"start": 76,
"text": "F6: inflammatory myofibroblasts"
}
] | Single_Cell |
For prenatal skin and prenatal intestine, we concatenated raw count adata objects from intestine with our prenatal skin data from a previous publication . | [
{
"end": 96,
"label": "Tissue",
"start": 87,
"text": "intestine"
},
{
"end": 17,
"label": "Tissue",
"start": 4,
"text": "prenatal skin"
},
{
"end": 40,
"label": "Tissue",
"start": 22,
"text": "prenatal intestine"
}
] | Single_Cell |
We ran scVI using the same parameters as for the healthy/nonlesional integration. | [] | Single_Cell |
We used the same strategy for the adult skin and prenatal skin integration. | [] | Single_Cell |
For mouse comparisons, we downloaded the mouse steady-state atlas from https://www.fibroxplorer.com/download . | [] | Single_Cell |
We loaded the data as an adata object using pandas2ri in rpy2. | [] | Single_Cell |
We used labels for Ccl19 fibroblast from the original study. | [
{
"end": 35,
"label": "CellType",
"start": 19,
"text": "Ccl19 fibroblast"
}
] | Single_Cell |
Mapping the arterial vascular network in an intact human kidney using hierarchical phase-contrast tomography
Source paper: PMC12408821 | [
{
"end": 37,
"label": "Tissue",
"start": 12,
"text": "arterial vascular network"
},
{
"end": 63,
"label": "Tissue",
"start": 44,
"text": "intact human kidney"
}
] | Single_Cell |
The architecture of kidney vasculature is essential the organ's specialised functions, yet is challenging to structurally map in an intact human organ. | [
{
"end": 61,
"label": "Tissue",
"start": 56,
"text": "organ"
},
{
"end": 38,
"label": "Tissue",
"start": 20,
"text": "kidney vasculature"
},
{
"end": 150,
"label": "Tissue",
"start": 132,
"text": "intact human organ"
}
] | Single_Cell |
Here, we combined hierarchical phase-contrast tomography (HiP-CT) with topology network analysis to enable quantitative assessment of the intact human kidney vasculature, from the renal artery to interlobular arteries. | [
{
"end": 192,
"label": "Tissue",
"start": 180,
"text": "renal artery"
},
{
"end": 169,
"label": "Tissue",
"start": 138,
"text": "intact human kidney vasculature"
},
{
"end": 217,
"label": "Tissue",
"start": 196,
"text": "interlobular arteries"
}
] | Single_Cell |
Comparison with kidney vascular maps described for rodents revealed similar topologies to human, but human kidney vasculature possessed a significantly sharper decrease in radius from hilum to cortex, deviating from theoretically optimal flow resistance for smaller vessels. | [
{
"end": 199,
"label": "Tissue",
"start": 193,
"text": "cortex"
},
{
"end": 125,
"label": "Tissue",
"start": 101,
"text": "human kidney vasculature"
},
{
"end": 189,
"label": "Tissue",
"start": 184,
"text": "hilum"
},
{
"end": 273,
"label": "Tissue... | Single_Cell |
Structural differences in kidney hilar, medullary and cortical vasculature reflected unique functional adaptations of each zone. | [
{
"end": 38,
"label": "Tissue",
"start": 26,
"text": "kidney hilar"
},
{
"end": 49,
"label": "Tissue",
"start": 40,
"text": "medullary"
},
{
"end": 74,
"label": "Tissue",
"start": 54,
"text": "cortical vasculature"
}
] | Single_Cell |
This work represents the first time the arterial vasculature of an intact human kidney has been mapped beyond segmental arteries, potentiating novel computational models of kidney vascular flow in humans. | [
{
"end": 86,
"label": "Tissue",
"start": 40,
"text": "arterial vasculature of an intact human kidney"
},
{
"end": 128,
"label": "Tissue",
"start": 110,
"text": "segmental arteries"
}
] | Single_Cell |
Our analyses have implications for understanding how blood vessel structure collectively scales to facilitate specialised functions in human organs. | [
{
"end": 65,
"label": "Tissue",
"start": 53,
"text": "blood vessel"
},
{
"end": 147,
"label": "Tissue",
"start": 135,
"text": "human organs"
}
] | Single_Cell |
The vasculature of the kidney is highly specialised and serves multiple functions, including the delivery of oxygen and nutrients to the organ’s parenchyma, whilst also facilitating plasma ultrafiltration and solute reabsorption. | [
{
"end": 29,
"label": "Tissue",
"start": 4,
"text": "vasculature of the kidney"
},
{
"end": 155,
"label": "Tissue",
"start": 137,
"text": "organ’s parenchyma"
}
] | Single_Cell |
Despite only comprising approximately 1% of body weight, the kidney receives up to 20% of cardiac output . | [
{
"end": 67,
"label": "Tissue",
"start": 61,
"text": "kidney"
}
] | Single_Cell |
Blood enters the kidney through the renal artery, which branches from the abdominal aorta and enters the kidney hilum. | [
{
"end": 5,
"label": "Tissue",
"start": 0,
"text": "Blood"
},
{
"end": 48,
"label": "Tissue",
"start": 36,
"text": "renal artery"
},
{
"end": 23,
"label": "Tissue",
"start": 17,
"text": "kidney"
},
{
"end": 89,
"label": "Tissue",
"start": 74,
... | Single_Cell |
Once within the kidney, the renal artery divides hierarchically, first into segmental or renal feeding arteries which pass through the kidney pelvis, then branching into interlobar arteries which pass through columns between the pyramids of the kidney medulla. | [
{
"end": 40,
"label": "Tissue",
"start": 28,
"text": "renal artery"
},
{
"end": 22,
"label": "Tissue",
"start": 16,
"text": "kidney"
},
{
"end": 85,
"label": "Tissue",
"start": 76,
"text": "segmental"
},
{
"end": 111,
"label": "Tissue",
"start"... | Single_Cell |
At the distal end of the kidney columns, interlobar arteries branch into arcuate arteries that arch around the outer surface of the kidney pyramids. | [
{
"end": 39,
"label": "Tissue",
"start": 7,
"text": "distal end of the kidney columns"
},
{
"end": 60,
"label": "Tissue",
"start": 41,
"text": "interlobar arteries"
},
{
"end": 89,
"label": "Tissue",
"start": 73,
"text": "arcuate arteries"
},
{
"end": ... | Single_Cell |
From these, the interlobular vessels branch and penetrate the surrounding kidney cortex, before finally terminating at afferent arterioles . | [
{
"end": 36,
"label": "Tissue",
"start": 16,
"text": "interlobular vessels"
},
{
"end": 87,
"label": "Tissue",
"start": 74,
"text": "kidney cortex"
},
{
"end": 138,
"label": "Tissue",
"start": 119,
"text": "afferent arterioles"
}
] | Single_Cell |
This complex network perfuses specialised capillary networks, including glomerular capillaries across which plasma ultrafiltration occurs, efferent arterioles and peritubular capillaries or vasa recta, which facilitate dynamic solute exchange in the cortex and medulla, respectively. | [
{
"end": 256,
"label": "Tissue",
"start": 250,
"text": "cortex"
},
{
"end": 60,
"label": "Tissue",
"start": 42,
"text": "capillary networks"
},
{
"end": 94,
"label": "Tissue",
"start": 72,
"text": "glomerular capillaries"
},
{
"end": 158,
"label": ... | Single_Cell |
Thereafter, venous return follows the arterial supply out of the organ . | [
{
"end": 70,
"label": "Tissue",
"start": 65,
"text": "organ"
}
] | Single_Cell |
Structural and molecular changes to the kidney vasculature are a common feature of kidney pathologies, including multiple aetiologies of chronic kidney disease (CKD) and transplant rejection in both animal models and patients . | [
{
"end": 58,
"label": "Tissue",
"start": 40,
"text": "kidney vasculature"
}
] | Single_Cell |
Therefore, studying kidney vascular patterning has implications for understanding the basis of kidney function in health and disease, and aids surgical planning for tumour resection, nephrectomy and transplantation. | [] | Single_Cell |
Vascular geometries also have a central role to play in computational models that underpin the creation of digital twins, such as through the generation of synthetic data , and blood flow modelling , which are playing an increasing role in biomedical research. | [] | Single_Cell |
Vascular imaging of the kidney has advanced following technological innovations in micro-computed tomography (μCT) , magnetic resonance imaging (MRI) and μMRI , ultrasound , lightsheet microscopy and photoacoustic imaging . | [
{
"end": 30,
"label": "Tissue",
"start": 24,
"text": "kidney"
}
] | Single_Cell |
These techniques have been used to generate quantitative analyses of vascular network geometry in intact kidneys of model organisms, particularly rodents, in which kidney diameter reaches up to 12 mm . | [
{
"end": 112,
"label": "Tissue",
"start": 98,
"text": "intact kidneys"
}
] | Single_Cell |
Comparatively human kidneys, with a diameter of approximately 5 cm are far more challenging to image at high resolution whilst still intact. | [
{
"end": 27,
"label": "Tissue",
"start": 14,
"text": "human kidneys"
}
] | Single_Cell |
Corrosion casting of human kidneys has highlighted vascular heterogeneity and generated intricate 3D casts (down to 100 µm) but provides limited quantitative or accessible digitised geometries of the vascular network . | [
{
"end": 34,
"label": "Tissue",
"start": 21,
"text": "human kidneys"
}
] | Single_Cell |
Optical clearing and lightsheet microscopy have been used to quantify portions of the human kidney vascular network . | [
{
"end": 115,
"label": "Tissue",
"start": 86,
"text": "human kidney vascular network"
}
] | Single_Cell |
However, as far as the authors are aware, there is no published dataset capturing the intact vascular network of the human kidney beyond approximately six vessel divisions without physical sectioning or subsampling of the tissue . | [
{
"end": 129,
"label": "Tissue",
"start": 86,
"text": "intact vascular network of the human kidney"
},
{
"end": 171,
"label": "Tissue",
"start": 155,
"text": "vessel divisions"
}
] | Single_Cell |
MRI has been used to quantify larger vessels both in vivo and post mortem , but for large volumes of interest (VOI), lacks the resolution capable of imaging small vessels and arterioles . | [
{
"end": 185,
"label": "Tissue",
"start": 175,
"text": "arterioles"
},
{
"end": 44,
"label": "Tissue",
"start": 30,
"text": "larger vessels"
},
{
"end": 170,
"label": "Tissue",
"start": 157,
"text": "small vessels"
},
{
"end": 115,
"label": "Tissue... | Single_Cell |
µMRI can be used to image down to ~50 µm/voxel, but is limited to smaller biological samples such as rodent kidneys . | [
{
"end": 115,
"label": "Tissue",
"start": 101,
"text": "rodent kidneys"
}
] | Single_Cell |
CT and µCT have been used extensively to image and analyse rodent renal vasculature , and have also been applied to study the vasculature of ex vivo human lung and fetal kidney. | [
{
"end": 137,
"label": "Tissue",
"start": 126,
"text": "vasculature"
},
{
"end": 83,
"label": "Tissue",
"start": 59,
"text": "rodent renal vasculature"
},
{
"end": 159,
"label": "Tissue",
"start": 141,
"text": "ex vivo human lung"
},
{
"end": 176,
... | Single_Cell |
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