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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",
"start": 258,
"text": "smaller vessels"
}
] |
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",
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"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
|
Source paper: PMC12408821
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": 57,
"label": "Tissue",
"start": 32,
"text": "vasculature of the kidney"
},
{
"end": 183,
"label": "Tissue",
"start": 165,
"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,
"text": "abdominal aorta"
},
{
"end": 117,
"label": "Tissue",
"start": 105,
"text": "kidney hilum"
}
] |
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"
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{
"end": 22,
"label": "Tissue",
"start": 16,
"text": "kidney"
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{
"end": 85,
"label": "Tissue",
"start": 76,
"text": "segmental"
},
{
"end": 111,
"label": "Tissue",
"start": 89,
"text": "renal feeding arteries"
},
{
"end": 148,
"label": "Tissue",
"start": 135,
"text": "kidney pelvis"
},
{
"end": 189,
"label": "Tissue",
"start": 170,
"text": "interlobar arteries"
},
{
"end": 259,
"label": "Tissue",
"start": 209,
"text": "columns between the pyramids of the kidney medulla"
}
] |
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": 147,
"label": "Tissue",
"start": 111,
"text": "outer surface of the kidney pyramids"
}
] |
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"
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{
"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"
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{
"end": 60,
"label": "Tissue",
"start": 42,
"text": "capillary networks"
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{
"end": 94,
"label": "Tissue",
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"text": "glomerular capillaries"
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{
"end": 158,
"label": "Tissue",
"start": 139,
"text": "efferent arterioles"
},
{
"end": 186,
"label": "Tissue",
"start": 163,
"text": "peritubular capillaries"
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{
"end": 200,
"label": "Tissue",
"start": 190,
"text": "vasa recta"
},
{
"end": 268,
"label": "Tissue",
"start": 261,
"text": "medulla"
}
] |
Single_Cell
|
Thereafter, venous return follows the arterial supply out of the organ .
|
[
{
"end": 70,
"label": "Tissue",
"start": 65,
"text": "organ"
}
] |
Single_Cell
|
Source paper: PMC12408821
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": 86,
"label": "Tissue",
"start": 68,
"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
|
Source paper: PMC12408821
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": 58,
"label": "Tissue",
"start": 52,
"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",
"start": 84,
"text": "large volumes of interest (VOI)"
}
] |
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,
"label": "Tissue",
"start": 164,
"text": "fetal kidney"
}
] |
Single_Cell
|
However, no detailed segmentation and quantitative analysis of vascular networks in the human kidney have been performed down to the level of arterioles, because of a lack of available imaging data.
|
[
{
"end": 152,
"label": "Tissue",
"start": 142,
"text": "arterioles"
},
{
"end": 100,
"label": "Tissue",
"start": 63,
"text": "vascular networks in the human kidney"
}
] |
Single_Cell
|
Source paper: PMC12408821
Due to this limitation, analysis of human kidney vascular networks is often focused on the first three, large branches of the arterial tree , or limited to subregions within the network .
|
[
{
"end": 94,
"label": "Tissue",
"start": 64,
"text": "human kidney vascular networks"
},
{
"end": 167,
"label": "Tissue",
"start": 132,
"text": "large branches of the arterial tree"
},
{
"end": 213,
"label": "Tissue",
"start": 184,
"text": "subregions within the network"
}
] |
Single_Cell
|
Where multiscale modelling has been performed, parameters from rodent kidneys are assumed to be representative of human kidney vascular networks .
|
[
{
"end": 77,
"label": "Tissue",
"start": 63,
"text": "rodent kidneys"
},
{
"end": 144,
"label": "Tissue",
"start": 114,
"text": "human kidney vascular networks"
}
] |
Single_Cell
|
However, semi-quantitative studies of human kidney vascular casts have shown large anatomical variation in segmental artery patterning , whilst smaller vessels such as the arcuate arteries, interlobular arteries and afferent or efferent arterioles have not been assessed quantitatively at the organ scale.
|
[
{
"end": 298,
"label": "Tissue",
"start": 293,
"text": "organ"
},
{
"end": 159,
"label": "Tissue",
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"text": "smaller vessels"
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{
"end": 188,
"label": "Tissue",
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"text": "arcuate arteries"
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{
"end": 211,
"label": "Tissue",
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"text": "interlobular arteries"
},
{
"end": 224,
"label": "Tissue",
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"text": "afferent"
},
{
"end": 247,
"label": "Tissue",
"start": 228,
"text": "efferent arterioles"
}
] |
Single_Cell
|
Source paper: PMC12408821
One imaging modality that could address the challenge of imaging intact organ vascular networks is synchrotron phase-contrast tomography.
|
[
{
"end": 123,
"label": "Tissue",
"start": 93,
"text": "intact organ vascular networks"
}
] |
Single_Cell
|
Hierarchical phase-contrast tomography (HiP-CT) is a technique which leverages the European Synchrotron Radiation Facility’s (ESRF) Extremely Brilliant Source (EBS), a high-energy fourth-generation synchrotron source, to image intact human organs.
|
[
{
"end": 246,
"label": "Tissue",
"start": 227,
"text": "intact human organs"
}
] |
Single_Cell
|
By utilising the high spatial coherence of the ESRF-EBS and the long beamlines available at ESRF, the development of HiP-CT has allowed the scaling of synchrotron phase-contrast tomography to sample sizes up to and including intact human organs.
|
[
{
"end": 244,
"label": "Tissue",
"start": 225,
"text": "intact human organs"
}
] |
Single_Cell
|
Datasets created with HiP-CT are hierarchically nested three-dimensional (3D) volumes at multiple resolutions, with exceptional soft tissue contrast spanning from small VOI to the whole intact organ (Fig. 1A ).
|
[
{
"end": 172,
"label": "Tissue",
"start": 163,
"text": "small VOI"
},
{
"end": 198,
"label": "Tissue",
"start": 180,
"text": "whole intact organ"
}
] |
Single_Cell
|
As an example of HiP-CT’s potential, we have previously profiled human glomerular morphology and number across cubic centimetres of intact human kidney .
|
[
{
"end": 151,
"label": "Tissue",
"start": 132,
"text": "intact human kidney"
}
] |
Single_Cell
|
However, the soft tissue contrast achievable with HiP-CT, coupled with its high spatial resolution, potentiates the visualisation and quantification of vascular networks across whole human organs, and could address the limitations of current imaging technologies used to map kidney vascular architecture.
|
[
{
"end": 24,
"label": "Tissue",
"start": 13,
"text": "soft tissue"
},
{
"end": 169,
"label": "Tissue",
"start": 152,
"text": "vascular networks"
},
{
"end": 195,
"label": "Tissue",
"start": 177,
"text": "whole human organs"
}
] |
Single_Cell
|
Source paper: PMC12408821
Here, we demonstrate how the arterial network of an intact human kidney can be extracted and quantified across multiple length scales using HiP-CT without use of a vascular contrast agent.
|
[
{
"end": 99,
"label": "Tissue",
"start": 57,
"text": "arterial network of an intact human kidney"
}
] |
Single_Cell
|
Our pipeline utilises the benefits of HiP-CT, such as validation of segmentation using multiscale data, whilst also providing solutions for the technical challenges associated with HiP-CT, for example, the collapse of large vessels.
|
[
{
"end": 231,
"label": "Tissue",
"start": 218,
"text": "large vessels"
}
] |
Single_Cell
|
Within the human kidney, we delineated the extent and morphology of the vasculature, from the renal artery down to the interlobular arteries.
|
[
{
"end": 106,
"label": "Tissue",
"start": 94,
"text": "renal artery"
},
{
"end": 83,
"label": "Tissue",
"start": 72,
"text": "vasculature"
},
{
"end": 23,
"label": "Tissue",
"start": 11,
"text": "human kidney"
},
{
"end": 140,
"label": "Tissue",
"start": 119,
"text": "interlobular arteries"
}
] |
Single_Cell
|
In doing so, we were able to quantify heterogeneity in vascular architecture within the context of ordering schemes describing morphological network branching.
|
[] |
Single_Cell
|
We also demonstrate how the multiscale nature of HiP-CT allows estimation of the vascular network between the interlobular arteries and afferent arterioles in smaller VOIs, which we describe as 'local' scans,within the still-intact kidney.
|
[
{
"end": 131,
"label": "Tissue",
"start": 110,
"text": "interlobular arteries"
},
{
"end": 155,
"label": "Tissue",
"start": 136,
"text": "afferent arterioles"
},
{
"end": 238,
"label": "Tissue",
"start": 219,
"text": "still-intact kidney"
},
{
"end": 171,
"label": "Tissue",
"start": 159,
"text": "smaller VOIs"
},
{
"end": 97,
"label": "Tissue",
"start": 81,
"text": "vascular network"
}
] |
Single_Cell
|
We perform a quantitative comparison between our human and previously published rodent kidney vascular networks, the latter of which has been used as inputs for biophysical modelling of kidney vascular blood flow .
|
[
{
"end": 111,
"label": "Tissue",
"start": 80,
"text": "rodent kidney vascular networks"
}
] |
Single_Cell
|
We further demonstrate how the label-free nature and exceptional soft tissue contrast of HiP-CT allow vascular heterogeneity to be quantified in the context of other anatomical features, such as the different compartments of the kidney.
|
[
{
"end": 235,
"label": "Tissue",
"start": 209,
"text": "compartments of the kidney"
}
] |
Single_Cell
|
Such spatial variations highlight the link between regional structure and function, reinforcing the importance of quantitative analyses for understanding and modelling regional microenvironments within the human kidney.
|
[
{
"end": 218,
"label": "Tissue",
"start": 206,
"text": "human kidney"
}
] |
Single_Cell
|
Source paper: PMC12408821
Using HiP-CT in a hierarchical fashion, we imaged the whole intact kidney obtained from a 63-year-old male organ donor.
|
[
{
"end": 101,
"label": "Tissue",
"start": 82,
"text": "whole intact kidney"
}
] |
Single_Cell
|
We initially performed an overview scan of the entire kidney at 25 μm per voxel, followed by selecting and imaging representative VOIs at 6.5 μm per voxel and 2.6 μm per voxel (Fig. 1A ).
|
[
{
"end": 60,
"label": "Tissue",
"start": 47,
"text": "entire kidney"
},
{
"end": 134,
"label": "Tissue",
"start": 115,
"text": "representative VOIs"
}
] |
Single_Cell
|
As these image volumes are inherently aligned, expert annotation using renal anatomical landmarks (Fig. 1B ) was applied to the image volumes taken at each resolution to produce a multiscale segmented model of the kidney’s arterial network (Supplementary Movie 1 ).
|
[
{
"end": 239,
"label": "Tissue",
"start": 214,
"text": "kidney’s arterial network"
}
] |
Single_Cell
|
From the segmented data, we were able to identify examples of and interconnect all known anatomical subdivisions of the kidney arterial system (Fig. 1C ).
|
[
{
"end": 142,
"label": "Tissue",
"start": 120,
"text": "kidney arterial system"
}
] |
Single_Cell
|
The segmental pattern of anterior, posterior, superior and inferior territories supplying the kidney parenchyma was clearly delineated.
|
[
{
"end": 33,
"label": "Tissue",
"start": 25,
"text": "anterior"
},
{
"end": 44,
"label": "Tissue",
"start": 35,
"text": "posterior"
},
{
"end": 54,
"label": "Tissue",
"start": 46,
"text": "superior"
},
{
"end": 79,
"label": "Tissue",
"start": 59,
"text": "inferior territories"
},
{
"end": 111,
"label": "Tissue",
"start": 94,
"text": "kidney parenchyma"
}
] |
Single_Cell
|
Each vascular territory (Fig. 1D and Supplementary Movie 2 ) had a corresponding kidney arterial branch originating from the hilum, which bifurcated before hierarchical branching towards the cortical parenchyma.
|
[
{
"end": 23,
"label": "Tissue",
"start": 5,
"text": "vascular territory"
},
{
"end": 103,
"label": "Tissue",
"start": 81,
"text": "kidney arterial branch"
},
{
"end": 130,
"label": "Tissue",
"start": 125,
"text": "hilum"
},
{
"end": 210,
"label": "Tissue",
"start": 187,
"text": "the cortical parenchyma"
}
] |
Single_Cell
|
Source paper: PMC12408821
We next sought to quantitate the arterial network in a reliable and reproducible manner.
|
[] |
Single_Cell
|
As we have previously shown that quantitative features of vascular networks can vary by the image processing pipeline used , we developed our own bespoke image processing pipeline (Fig. 2 ), involving reduction of the initial HiP-CT image to a skeleton, or spatial graph representation, of the arterial network.
|
[
{
"end": 75,
"label": "Tissue",
"start": 58,
"text": "vascular networks"
}
] |
Single_Cell
|
The graph representation comprises a set of ‘nodes’; defined as 3D locations where vessels meet or end, and ‘segments’; defined as the connections between these nodes (see Supplementary Fig. 6A and Fig. 4A ).
|
[
{
"end": 90,
"label": "Tissue",
"start": 83,
"text": "vessels"
}
] |
Single_Cell
|
Our pipeline comprises 8 steps, which are fully detailed in our Supplementary Note 2 , and enables the generation of a spatial graph from segmented HiP-CT data, with quantification of error in segmentation (from multiscale comparison) and skeletonization (through application of the skeletonization metric).
|
[] |
Single_Cell
|
Source paper: PMC12408821
Our pipeline first (Fig. 2 , Step 1) assesses validation of the segmentation.
|
[] |
Single_Cell
|
By aligning segmentations from scans taken at 13 µm per voxel, with VOIs captured at 50 µm per voxel, the higher resolution scans served as ‘ground truth’ for the lower resolution scanning.
|
[
{
"end": 72,
"label": "Tissue",
"start": 68,
"text": "VOIs"
}
] |
Single_Cell
|
We used the cl-DICE metric to quantify the overlapping vessel portions finding that 97% of vessels with a vessel lumen radius greater than 50 µm are detectable at 50 µm per voxel.
|
[
{
"end": 98,
"label": "Tissue",
"start": 91,
"text": "vessels"
},
{
"end": 70,
"label": "Tissue",
"start": 55,
"text": "vessel portions"
}
] |
Single_Cell
|
Our next step (Fig. 2 , Step 2) comprises the optimisation of the skeletonization algorithm.
|
[] |
Single_Cell
|
We applied three different skeletonization algorithms, and utilised the recently developed skeleton super-metric to determine the most suitable algorithm and its parameter optimisation.
|
[] |
Single_Cell
|
We found that the Centerline Tree algorithm (Amira-Avizo v2021.1) was the best candidate algorithm, as indicated by its lower super-metric value in comparison to other skeletonization algorithms (Fig. 2 , Step 2).
|
[] |
Single_Cell
|
Thereafter, several steps were implemented to correct the skeleton for HiP-CT specific challenges (Fig. 2 , Steps 3–8), namely the multiscale nature of the vasculature, and the presence of collapsed vessels as a consequence of the ex vivo, label-free HiP-CT protocol.
|
[
{
"end": 167,
"label": "Tissue",
"start": 156,
"text": "vasculature"
},
{
"end": 206,
"label": "Tissue",
"start": 189,
"text": "collapsed vessels"
}
] |
Single_Cell
|
The challenge of multiscale vascular trees was corrected using a truncated Strahler ordering system, which partitions the network into larger or smaller calibre vessels.
|
[
{
"end": 42,
"label": "Tissue",
"start": 28,
"text": "vascular trees"
},
{
"end": 141,
"label": "Tissue",
"start": 135,
"text": "larger"
},
{
"end": 168,
"label": "Tissue",
"start": 145,
"text": "smaller calibre vessels"
}
] |
Single_Cell
|
Smoothing was then applied to all large calibre vessels to reduce tortuosity in the vessel centreline, an artefact which occurs due to the sensitivity of skeletonization algorithms to noise along the vessel surface (Fig. 2 , Steps 3 and 4).
|
[
{
"end": 55,
"label": "Tissue",
"start": 34,
"text": "large calibre vessels"
},
{
"end": 101,
"label": "Tissue",
"start": 84,
"text": "vessel centreline"
},
{
"end": 214,
"label": "Tissue",
"start": 200,
"text": "vessel surface"
}
] |
Single_Cell
|
Following this multiscale smoothing approach, Fig. 2 Steps 5 and 6 involved the identification and manual verification of collapsed vessels.
|
[
{
"end": 139,
"label": "Tissue",
"start": 122,
"text": "collapsed vessels"
}
] |
Single_Cell
|
Initially, all large-calibre vessels were flagged as potentially collapsed.
|
[
{
"end": 36,
"label": "Tissue",
"start": 15,
"text": "large-calibre vessels"
}
] |
Single_Cell
|
Additionally, smaller calibre vessels that were potentially collapsed were identified based on their categorisation below the 10th percentile for radius in their truncated Strahler order (Fig. 2 , Step 5).
|
[
{
"end": 37,
"label": "Tissue",
"start": 14,
"text": "smaller calibre vessels"
}
] |
Single_Cell
|
Once identified, collapsed vessels were subject to a bounding box, automatically extracted and thereafter manually determined whether correction of the radius was required to account for collapse (Fig. 2 , Step 6).
|
[
{
"end": 34,
"label": "Tissue",
"start": 17,
"text": "collapsed vessels"
}
] |
Single_Cell
|
For vessels requiring correction, cross-sectional planes along the vessel centreline were extracted, and the radius was calculated based on the cross-sectional perimeter (Fig. 2 , Step 6).
|
[
{
"end": 11,
"label": "Tissue",
"start": 4,
"text": "vessels"
},
{
"end": 84,
"label": "Tissue",
"start": 67,
"text": "vessel centreline"
}
] |
Single_Cell
|
Finally, the identification of outlier radii in these cross-sectional planes was performed, using a 95th and 5th percentile windowing for radius along the vessel length.
|
[] |
Single_Cell
|
Additionally, an option was applied to manually flag planes that appeared compromised by residual tortuosity in the vessel centreline (Fig. 2 , Step 8).
|
[
{
"end": 133,
"label": "Tissue",
"start": 116,
"text": "vessel centreline"
}
] |
Single_Cell
|
Source paper: PMC12408821
The result of this novel pipeline, when applied to our HiP-CT data of human kidney, was the generation the first open-source spatial graph of the intact human kidney arterial vasculature, ranging from renal artery to interlobular arteries.
|
[
{
"end": 241,
"label": "Tissue",
"start": 229,
"text": "renal artery"
},
{
"end": 110,
"label": "Tissue",
"start": 98,
"text": "human kidney"
},
{
"end": 214,
"label": "Tissue",
"start": 174,
"text": "intact human kidney arterial vasculature"
},
{
"end": 266,
"label": "Tissue",
"start": 245,
"text": "interlobular arteries"
}
] |
Single_Cell
|
We were able to identify 97% of vessels >50 µm radius across the whole intact human kidney.
|
[
{
"end": 39,
"label": "Tissue",
"start": 32,
"text": "vessels"
},
{
"end": 90,
"label": "Tissue",
"start": 65,
"text": "whole intact human kidney"
}
] |
Single_Cell
|
The final network consisted of 10,193 nodes, 376,603 points and 10,190 vessels.
|
[
{
"end": 78,
"label": "Tissue",
"start": 71,
"text": "vessels"
}
] |
Single_Cell
|
The total network volume was 1.68 × 10 µm , with a length of 2.3 × 10 µm.
|
[] |
Single_Cell
|
This spatial graph, which is provided in our Supplementary Information , captures the morphological features and connectivity of the human kidney arterial vasculature, which was then used for downstream analyses as described below.
|
[
{
"end": 166,
"label": "Tissue",
"start": 133,
"text": "human kidney arterial vasculature"
}
] |
Single_Cell
|
Source paper: PMC12408821
Having created a reproducible spatial graph of the human kidney arterial vasculature, we then performed topological generation and truncated Strahler ordering analyses.
|
[
{
"end": 112,
"label": "Tissue",
"start": 79,
"text": "human kidney arterial vasculature"
}
] |
Single_Cell
|
This resulted in nine truncated Strahler orders (Fig. 3A ) and twenty-five topological generations (Fig. 3B ).
|
[] |
Single_Cell
|
As the main artery supplying the kidney was cut during autopsy, we inferred that 10 truncated Strahler orders, representing 26 topological generations, were imaged over the intact human kidney with HiP-CT.
|
[
{
"end": 39,
"label": "Tissue",
"start": 33,
"text": "kidney"
},
{
"end": 18,
"label": "Tissue",
"start": 7,
"text": "main artery"
},
{
"end": 192,
"label": "Tissue",
"start": 173,
"text": "intact human kidney"
}
] |
Single_Cell
|
Source paper: PMC12408821
Strahler ordering, and other approaches to classify vascular networks, have potential caveats (See Supplementary Note 4 ), for example, the Strahler (or truncated Strahler) order of any individual vessel depends upon the downstream network, and thus on the identification of the network endpoint.
|
[
{
"end": 231,
"label": "Tissue",
"start": 225,
"text": "vessel"
},
{
"end": 97,
"label": "Tissue",
"start": 80,
"text": "vascular networks"
},
{
"end": 267,
"label": "Tissue",
"start": 260,
"text": "network"
},
{
"end": 323,
"label": "Tissue",
"start": 307,
"text": "network endpoint"
}
] |
Single_Cell
|
Ideally, this endpoint would correspond to the afferent arteriole entering the renal glomerulus.
|
[
{
"end": 95,
"label": "Tissue",
"start": 79,
"text": "renal glomerulus"
},
{
"end": 65,
"label": "Tissue",
"start": 47,
"text": "afferent arteriole"
},
{
"end": 22,
"label": "Tissue",
"start": 14,
"text": "endpoint"
}
] |
Single_Cell
|
However, at 50 µm per voxel resolution, we were unable to detect afferent arterioles, and thus, the smallest vessels, defined as truncated Strahler order 1 vessels, were the interlobular arteries.
|
[
{
"end": 84,
"label": "Tissue",
"start": 65,
"text": "afferent arterioles"
},
{
"end": 116,
"label": "Tissue",
"start": 100,
"text": "smallest vessels"
},
{
"end": 163,
"label": "Tissue",
"start": 129,
"text": "truncated Strahler order 1 vessels"
},
{
"end": 195,
"label": "Tissue",
"start": 174,
"text": "interlobular arteries"
}
] |
Single_Cell
|
Source paper: PMC12408821
Diameter-based statistical approaches to estimation of the Strahler order of the kidney vascular network’s terminal ends were not appropriate to correct for this due to the ex vivo and non-perfused nature of HiP-CT, as well as the connectivity of glomeruli relative to terminal vessel ends.
|
[
{
"end": 284,
"label": "Tissue",
"start": 275,
"text": "glomeruli"
},
{
"end": 148,
"label": "Tissue",
"start": 109,
"text": "kidney vascular network’s terminal ends"
},
{
"end": 317,
"label": "Tissue",
"start": 297,
"text": "terminal vessel ends"
}
] |
Single_Cell
|
Thus, we applied truncated Strahler ordering to our spatial graph and report how morphological features of vessels in the network vary with truncated Strahler order.
|
[
{
"end": 114,
"label": "Tissue",
"start": 107,
"text": "vessels"
}
] |
Single_Cell
|
We also mapped our truncated Strahler orders to known anatomical subdivisions of the arterial tree to give anatomical context.
|
[
{
"end": 98,
"label": "Tissue",
"start": 85,
"text": "arterial tree"
}
] |
Single_Cell
|
This resulted in the following classification: truncated Strahler orders 7–9 ( n = 25 segments; mean radius = 929 ± 477 µm) mapped to the branches of the kidney artery entering the kidney hilum.
|
[
{
"end": 193,
"label": "Tissue",
"start": 181,
"text": "kidney hilum"
},
{
"end": 167,
"label": "Tissue",
"start": 138,
"text": "branches of the kidney artery"
}
] |
Single_Cell
|
Orders 5–6 comprised interlobar arteries ( n = 219 segments; mean radius = 417 ± 247 µm), and orders 2–4 arcuate arteries ( n = 4841 segments; mean radius = 78 ± 45 µm).
|
[
{
"end": 40,
"label": "Tissue",
"start": 21,
"text": "interlobar arteries"
},
{
"end": 121,
"label": "Tissue",
"start": 105,
"text": "arcuate arteries"
}
] |
Single_Cell
|
Finally, interlobular arteries fell within orders 1–3 ( n = 9430 segments; mean radius = 55 ± 23 µm).
|
[
{
"end": 30,
"label": "Tissue",
"start": 9,
"text": "interlobular arteries"
}
] |
Single_Cell
|
We further plotted the cumulative volume of the kidney vascular network (Fig. 3Cii ), finding that over 20% of the volume of the network lies within Strahler orders 1–4, corresponding to segments from interlobular arteries and arcuate arteries.
|
[
{
"end": 71,
"label": "Tissue",
"start": 48,
"text": "kidney vascular network"
},
{
"end": 222,
"label": "Tissue",
"start": 201,
"text": "interlobular arteries"
},
{
"end": 243,
"label": "Tissue",
"start": 227,
"text": "arcuate arteries"
}
] |
Single_Cell
|
We found 5105 truncated Strahler order 1 segment and identified a logarithmic relationship between truncated Strahler order and segment number (Fig. 3Ci ).
|
[] |
Single_Cell
|
Using this relationship, we determined the branching ratio within this subsection of the vascular tree to be 2.92, a value which is similar to that of the human pulmonary arterial tree (3.0 ) and the rat kidney vasculature (2.85 ).
|
[
{
"end": 102,
"label": "Tissue",
"start": 71,
"text": "subsection of the vascular tree"
},
{
"end": 184,
"label": "Tissue",
"start": 155,
"text": "human pulmonary arterial tree"
},
{
"end": 222,
"label": "Tissue",
"start": 200,
"text": "rat kidney vasculature"
}
] |
Single_Cell
|
Source paper: PMC12408821
To provide further context to the truncated Strahler order and to investigate the small-calibre vessels within the human kidney, we leveraged the hierarchical capability of HiP-CT.
|
[
{
"end": 131,
"label": "Tissue",
"start": 110,
"text": "small-calibre vessels"
},
{
"end": 155,
"label": "Tissue",
"start": 143,
"text": "human kidney"
}
] |
Single_Cell
|
Using high-resolution VOIs, we segmented and counted all glomeruli within each of the 3 high-resolution VOIs of the HiP-CT data (Fig. 3D ).
|
[
{
"end": 66,
"label": "Tissue",
"start": 57,
"text": "glomeruli"
},
{
"end": 26,
"label": "Tissue",
"start": 6,
"text": "high-resolution VOIs"
},
{
"end": 108,
"label": "Tissue",
"start": 88,
"text": "high-resolution VOIs"
}
] |
Single_Cell
|
We extrapolated from these VOIs to the total of ~1.2 million glomeruli in the intact kidney, which aligned well with estimates for adult males within a similar age range .
|
[
{
"end": 70,
"label": "Tissue",
"start": 61,
"text": "glomeruli"
},
{
"end": 91,
"label": "Tissue",
"start": 78,
"text": "intact kidney"
},
{
"end": 31,
"label": "Tissue",
"start": 27,
"text": "VOIs"
}
] |
Single_Cell
|
Given the 5105 truncated Strahler order 1 segments, the branching ratio of 2.921 and the total number of glomeruli, we estimated that there are a further 4–5 truncated Strahler orders between the end of our whole organ network and the afferent arterioles.
|
[
{
"end": 114,
"label": "Tissue",
"start": 105,
"text": "glomeruli"
},
{
"end": 226,
"label": "Tissue",
"start": 207,
"text": "whole organ network"
},
{
"end": 254,
"label": "Tissue",
"start": 235,
"text": "afferent arterioles"
}
] |
Single_Cell
|
To further evaluate this estimate, we assessed on high-resolution VOI, connecting six afferent arterioles of individual glomeruli back to the main vessel tree (Fig. 3E , Supplementary Movie 4 and Supplementary Fig. 8 ).
|
[
{
"end": 129,
"label": "Tissue",
"start": 120,
"text": "glomeruli"
},
{
"end": 105,
"label": "Tissue",
"start": 86,
"text": "afferent arterioles"
},
{
"end": 158,
"label": "Tissue",
"start": 142,
"text": "main vessel tree"
},
{
"end": 69,
"label": "Tissue",
"start": 50,
"text": "high-resolution VOI"
}
] |
Single_Cell
|
Of the 6 glomeruli, 5 originated from non-terminal arteries and one from a terminal artery (Fig. 3E , red arrows and black arrows respectively).
|
[
{
"end": 18,
"label": "Tissue",
"start": 9,
"text": "glomeruli"
},
{
"end": 59,
"label": "Tissue",
"start": 38,
"text": "non-terminal arteries"
},
{
"end": 90,
"label": "Tissue",
"start": 75,
"text": "terminal artery"
}
] |
Single_Cell
|
This supports recent findings , in the rat kidney, which similarly demonstrated the existence of non-terminal branch arterioles, with potential contributions to the synchronicity of blood flow within the kidney .
|
[
{
"end": 210,
"label": "Tissue",
"start": 204,
"text": "kidney"
},
{
"end": 49,
"label": "Tissue",
"start": 39,
"text": "rat kidney"
},
{
"end": 127,
"label": "Tissue",
"start": 97,
"text": "non-terminal branch arterioles"
}
] |
Single_Cell
|
The existence of non-terminal glomeruli also prevents the application of the statistical methods which have previously been used to estimate the true Strahler order of a truncated network.
|
[
{
"end": 39,
"label": "Tissue",
"start": 17,
"text": "non-terminal glomeruli"
},
{
"end": 187,
"label": "Tissue",
"start": 170,
"text": "truncated network"
}
] |
Single_Cell
|
Given the presence of non-terminal glomeruli, statistical estimation of true Strahler order would necessitate connecting a larger number (~1000) of glomeruli back to the main tree to generate an accurate statistical representation of the proportion of terminal to non-terminal glomeruli.
|
[
{
"end": 157,
"label": "Tissue",
"start": 148,
"text": "glomeruli"
},
{
"end": 179,
"label": "Tissue",
"start": 170,
"text": "main tree"
},
{
"end": 260,
"label": "Tissue",
"start": 252,
"text": "terminal"
},
{
"end": 286,
"label": "Tissue",
"start": 264,
"text": "non-terminal glomeruli"
},
{
"end": 44,
"label": "Tissue",
"start": 22,
"text": "non-terminal glomeruli"
}
] |
Single_Cell
|
Such an estimation cannot be made with this dataset as, even with our highest resolution scans, the small vessels connecting the glomeruli to the main vascular tree could not be annotated reliably for a large number of cases.
|
[
{
"end": 138,
"label": "Tissue",
"start": 129,
"text": "glomeruli"
},
{
"end": 113,
"label": "Tissue",
"start": 100,
"text": "small vessels"
},
{
"end": 164,
"label": "Tissue",
"start": 146,
"text": "main vascular tree"
}
] |
Single_Cell
|
However, our estimation of total glomeruli number provides a data-driven estimate for the number of missing orders, and thus gives the context needed to support our use of the truncated Strahler order for our ongoing analysis.
|
[] |
Single_Cell
|
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