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Source paper: PMC12408821
The future of HiP-CT and mapping the kidney vasculature is promising.
|
[
{
"end": 83,
"label": "Tissue",
"start": 65,
"text": "kidney vasculature"
}
] |
Single_Cell
|
The upcoming ESRF beamline (BM18) enables longer propagation distances than shown here, dramatically increasing the contrast sensitivity for the lower resolution scans.
|
[] |
Single_Cell
|
Further developments in scanning and data handling have already extended the capabilities of HiP-CT to create whole kidney overview datasets, with voxel sizes down to 9 µm/voxel, and to submicron voxel sizes in VOIs.
|
[] |
Single_Cell
|
Thus, future studies can leverage the greater detail available on low-resolution scans of the whole kidney, providing the potential to further assess phenomena such as the emergence of glomeruli from non-terminal arterioles, or potentially map entire organs down to the capillary level.
|
[
{
"end": 279,
"label": "Tissue",
"start": 270,
"text": "capillary"
},
{
"end": 194,
"label": "Tissue",
"start": 185,
"text": "glomeruli"
},
{
"end": 106,
"label": "Tissue",
"start": 94,
"text": "whole kidney"
},
{
"end": 223,
"label": "Tissue",
"start": 200,
"text": "non-terminal arterioles"
},
{
"end": 257,
"label": "Tissue",
"start": 244,
"text": "entire organs"
}
] |
Single_Cell
|
As these developments unfold, we have created an open-access data portal ( https://human-organ-atlas.esrf.eu/ ), enabling download and use of HiP-CT data by biomedical researchers across the world.
|
[] |
Single_Cell
|
Source paper: PMC12408821
In summary, we have achieved quantitative mapping of the arterial network of an intact human kidney, from renal artery to interlobular arteries, for the first time.
|
[
{
"end": 146,
"label": "Tissue",
"start": 134,
"text": "renal artery"
},
{
"end": 127,
"label": "Tissue",
"start": 85,
"text": "arterial network of an intact human kidney"
},
{
"end": 171,
"label": "Tissue",
"start": 150,
"text": "interlobular arteries"
}
] |
Single_Cell
|
This vital step progresses our understanding of how physical properties of the kidney vasculature relate to cellular and molecular heterogeneity, whilst generating key inputs for future biophysical modelling of human kidney vascular physiology.
|
[
{
"end": 97,
"label": "Tissue",
"start": 79,
"text": "kidney vasculature"
},
{
"end": 223,
"label": "Tissue",
"start": 211,
"text": "human kidney"
}
] |
Single_Cell
|
Ultimately, we envisage that mapping of microstructural detail will become routine at the scale of the whole kidney, providing a means to link cellular events with organ physiology and pathology.
|
[
{
"end": 115,
"label": "Tissue",
"start": 103,
"text": "whole kidney"
}
] |
Single_Cell
|
Source paper: PMC12408821
An intact human kidney was obtained from a 63-year-old male (cause of death: pancreatic cancer), who consented to body donation to the Laboratoire d’Anatomie des Alpes Françaises before death.
|
[
{
"end": 50,
"label": "Tissue",
"start": 31,
"text": "intact human kidney"
}
] |
Single_Cell
|
Transport and imaging protocols were approved by the French Health Ministry.
|
[] |
Single_Cell
|
Post mortem examination was conducted according to Quality Appraisal for Cadaveric Studies scale recommendations .
|
[] |
Single_Cell
|
The body was embalmed by injecting 4500 mL of 1.15% formalin in lanolin, followed by 1.44% formalin, into the right carotid artery, before storage at 3.6 °C.
|
[
{
"end": 130,
"label": "Tissue",
"start": 110,
"text": "right carotid artery"
}
] |
Single_Cell
|
During evisceration of the right kidney, vessels were exposed, and the surrounding fat and connective tissue were removed.
|
[
{
"end": 48,
"label": "Tissue",
"start": 41,
"text": "vessels"
},
{
"end": 108,
"label": "Tissue",
"start": 91,
"text": "connective tissue"
},
{
"end": 86,
"label": "Tissue",
"start": 83,
"text": "fat"
},
{
"end": 39,
"label": "Tissue",
"start": 27,
"text": "right kidney"
}
] |
Single_Cell
|
The kidney was post-fixed in 4% neutral-buffered formaldehyde at room temperature for one week.
|
[
{
"end": 10,
"label": "Tissue",
"start": 4,
"text": "kidney"
}
] |
Single_Cell
|
The kidney was then dehydrated through an ethanol gradient over 9 days to a final equilibrium of 70% .
|
[
{
"end": 10,
"label": "Tissue",
"start": 4,
"text": "kidney"
}
] |
Single_Cell
|
Each solution was four-fold greater than the volume of the organ, and, during dehydration, the solution was degassed using a diaphragm vacuum pump (Vacuubrand, MV2, 1.9m /h) to remove excess dissolved gas.
|
[
{
"end": 64,
"label": "Tissue",
"start": 59,
"text": "organ"
}
] |
Single_Cell
|
The dehydrated kidney was transferred to a polyethylene terephthalate jar where it was physically stabilised using a crushed agar-agar ethanol mixture, and then imaged .
|
[
{
"end": 21,
"label": "Tissue",
"start": 4,
"text": "dehydrated kidney"
}
] |
Single_Cell
|
Source paper: PMC12408821
Imaging was performed on the BM05 beamline at the ESRF following the HiP-CT protocol .
|
[] |
Single_Cell
|
Initially, the whole kidney was imaged at 25 µm per voxel (isotropic edge length) .
|
[
{
"end": 27,
"label": "Tissue",
"start": 15,
"text": "whole kidney"
}
] |
Single_Cell
|
VOIs within the same kidney were also imaged at 6.5 and 2.6 µm per voxel .
|
[
{
"end": 27,
"label": "Tissue",
"start": 21,
"text": "kidney"
},
{
"end": 4,
"label": "Tissue",
"start": 0,
"text": "VOIs"
}
] |
Single_Cell
|
Tomographic reconstruction was performed using the PyHST2 software and following the steps detailed in previous studies .
|
[] |
Single_Cell
|
Briefly, a filtered back-projection algorithm, with single-distance phase retrieval, coupled to an unsharp mask filter, was applied to the collected radiographs.
|
[] |
Single_Cell
|
Reconstruction and scanning parameters are provided in Supplementary Note 1 , Supplementary Tables 1 and 2 .
|
[] |
Single_Cell
|
The reconstructed volumes were binned (averaged) to 50, 13, and 5.2 µm per voxel, respectively, to increase the signal-to-noise ratio, reduce inter-annotator variability and reduce computational load for subsequent image segmentation and quantification (see Supplementary Fig. 1 ).
|
[] |
Single_Cell
|
All reconstructed image volumes and metadata can be accessed at human-organ-atlas.esrf.eu.
|
[] |
Single_Cell
|
A table for direct DOI links for each dataset is provided in Supplementary Table 2 .
|
[] |
Single_Cell
|
Source paper: PMC12408821
Prior to manual segmentation, images were filtered to enhance blood vessel contrast using Amira-Avizo (v2021.1) software.
|
[
{
"end": 102,
"label": "Tissue",
"start": 90,
"text": "blood vessel"
}
] |
Single_Cell
|
A 3D median filter (iterations = 2 and 26 neighbourhood analysis) was used to reduce image noise.
|
[] |
Single_Cell
|
Image normalisation was performed using background detection correction (default parameter settings).
|
[] |
Single_Cell
|
A manual segmentation of the arterial networks was performed in Amira-Avizo using a combination of methodologies.
|
[
{
"end": 46,
"label": "Tissue",
"start": 29,
"text": "arterial networks"
}
] |
Single_Cell
|
First, a 3D region growing tool was used, where the user selects an initial voxel within a vessel lumen along with set intensity and contrast thresholds.
|
[
{
"end": 103,
"label": "Tissue",
"start": 91,
"text": "vessel lumen"
}
] |
Single_Cell
|
Any voxel within the connected neighbourhood of the initially selected voxel with an intensity and contrast within thresholds are added to the region.
|
[] |
Single_Cell
|
Multiple seeds points and thresholds, as well as manual limits on the region, are used by the annotator to ensure the lumens of all vessels are accurately identified.
|
[
{
"end": 139,
"label": "Tissue",
"start": 132,
"text": "vessels"
},
{
"end": 124,
"label": "Tissue",
"start": 118,
"text": "lumens"
}
] |
Single_Cell
|
In areas of collapsed or blood-filled vessels, annotators manually paint lumen voxels utilising three orthogonal views to ensure connection of the vascular network.
|
[
{
"end": 45,
"label": "Tissue",
"start": 25,
"text": "blood-filled vessels"
},
{
"end": 21,
"label": "Tissue",
"start": 12,
"text": "collapsed"
},
{
"end": 163,
"label": "Tissue",
"start": 147,
"text": "vascular network"
}
] |
Single_Cell
|
An annotator continues this process in an iterative fashion by selecting seed points, altering the thresholds and manually correction, resulting in expansion of an interconnected vascular network (Method shown in Supplementary Movie 3 ).
|
[
{
"end": 195,
"label": "Tissue",
"start": 164,
"text": "interconnected vascular network"
}
] |
Single_Cell
|
Once the first annotator has filled the interior of all vessels, data are passed to a second annotator, who repeats the process, but starting in reverse slice order.
|
[
{
"end": 63,
"label": "Tissue",
"start": 56,
"text": "vessels"
}
] |
Single_Cell
|
A third annotator serves as a proofreader by quantitatively reviewing the labels.
|
[] |
Single_Cell
|
The proofreader is presented with 5–9 randomised 2D slices of the data within any one of three orthogonal planes.
|
[] |
Single_Cell
|
They then count the number of vessels cross-sections present in the slice, recording the true positive and false negative number of vessel cross-sections that have been segmented.
|
[
{
"end": 37,
"label": "Tissue",
"start": 30,
"text": "vessels"
}
] |
Single_Cell
|
The proofreader returns the data to the initial two annotators, highlighting areas where vessels are not identified.
|
[
{
"end": 96,
"label": "Tissue",
"start": 89,
"text": "vessels"
}
] |
Single_Cell
|
This three-annotator process repeats iteratively until the proofreader does not find any false negatives.
|
[] |
Single_Cell
|
This method was applied to segment the kidney arterial network from the intact human kidney from the imaging data at 50 µm per voxel, and portions of the same network in the 13 and 5.2 µm per voxel datasets, approximately 250–300 h were needed to segment the kidney in this way.
|
[
{
"end": 265,
"label": "Tissue",
"start": 259,
"text": "kidney"
},
{
"end": 62,
"label": "Tissue",
"start": 39,
"text": "kidney arterial network"
},
{
"end": 91,
"label": "Tissue",
"start": 72,
"text": "intact human kidney"
}
] |
Single_Cell
|
Source paper: PMC12408821
A second approach to independently and quantitatively validate the segmentation of the lowest resolution data was performed using segmented VOIs of the higher resolution, 13 µm per voxel dataset.
|
[
{
"end": 172,
"label": "Tissue",
"start": 158,
"text": "segmented VOIs"
}
] |
Single_Cell
|
Here, the 13 µm per voxel VOIs were rigidly registered to the whole organ volume using the affine registration toolkit (Amira-Avizo) (See Supplementary Note 1.1 , Supplementary Fig. 2 and Supplementary Tables 3 and 4 ).
|
[
{
"end": 73,
"label": "Tissue",
"start": 62,
"text": "whole organ"
},
{
"end": 30,
"label": "Tissue",
"start": 26,
"text": "VOIs"
}
] |
Single_Cell
|
Overlapping portions of the 13 µm voxel segmentations and 50 µm per voxel datasets were extracted, and the 50 µm per voxel datasets were upsampled to the resolution of the 13 µm voxel dataset.
|
[] |
Single_Cell
|
An overlap measure, termed topological precision and recall score, following Paetzold et al. , was applied (see Supplementary Note 2.1 and Supplementary Fig. 3 ).
|
[] |
Single_Cell
|
Source paper: PMC12408821
To quantify branching metrics of the human kidney vasculature, the segmented 3D vascular network at 50 µm per voxel was skeletonised using the centreline tree algorithm in Amira-Avizo v2021.1.
|
[
{
"end": 89,
"label": "Tissue",
"start": 65,
"text": "human kidney vasculature"
}
] |
Single_Cell
|
The choice of skeletonisation algorithm and the parameterising of the algorithm were optimised by utilising the super-metric approach, outlined by Walsh and Berg et al. (tube parameters: slope = 4 and zeroval = 10, see Supplementary Note 2.2 and Supplementary Fig. 4 for parameter optimisation results).
|
[
{
"end": 260,
"label": "Tissue",
"start": 259,
"text": " "
}
] |
Single_Cell
|
The resulting spatial graph describes the vessel network in terms of ‘nodes’, ‘points’, ‘segments’, and ‘subsegments’.
|
[
{
"end": 56,
"label": "Tissue",
"start": 42,
"text": "vessel network"
}
] |
Single_Cell
|
A segment is defined as being between a start and end node, corresponding to either a branching point leading into another segment branch, or a terminal end where no further branches were detectable.
|
[
{
"end": 101,
"label": "Tissue",
"start": 86,
"text": "branching point"
},
{
"end": 137,
"label": "Tissue",
"start": 123,
"text": "segment branch"
},
{
"end": 156,
"label": "Tissue",
"start": 144,
"text": "terminal end"
},
{
"end": 182,
"label": "Tissue",
"start": 174,
"text": "branches"
}
] |
Single_Cell
|
Between the start and terminal node of each segment lie subsegments with ‘points’, marking the start and end of each subsegment.
|
[] |
Single_Cell
|
Each subsegment has an associated radius and length (Supplementary Fig. 6A ).
|
[] |
Single_Cell
|
A multiscale smoothing approach was applied to the larger vessels (those of Truncated Strahler order greater than 5).
|
[
{
"end": 65,
"label": "Tissue",
"start": 51,
"text": "larger vessels"
}
] |
Single_Cell
|
Iterative weighted smoothing was performed, where the smoothed location of any point is given by iteratively calculating weighted average of the current and two neighbour points.
|
[] |
Single_Cell
|
Parameter values were found empirically as 0.8, 0.1 and 15 for the neighbour points, current point and iterations, respectively.
|
[] |
Single_Cell
|
This reduced the tortuosity in the larger vessels (Fig. 2 , Step 4), which occurs artefactually due to noise on the surface of the segmented large vessels, and which if uncorrected, impacts severely on the correction for collapsed radius vessels.
|
[
{
"end": 49,
"label": "Tissue",
"start": 35,
"text": "larger vessels"
},
{
"end": 154,
"label": "Tissue",
"start": 131,
"text": "segmented large vessels"
},
{
"end": 245,
"label": "Tissue",
"start": 221,
"text": "collapsed radius vessels"
}
] |
Single_Cell
|
Correction for collapsed vessels was delineated into two distinct cases.
|
[
{
"end": 32,
"label": "Tissue",
"start": 15,
"text": "collapsed vessels"
}
] |
Single_Cell
|
One case is a scenario in which there is a small collapsed portion in an otherwise patent vessel (Supplementary Fig. 5Bi, Bii ).
|
[
{
"end": 96,
"label": "Tissue",
"start": 83,
"text": "patent vessel"
},
{
"end": 66,
"label": "Tissue",
"start": 43,
"text": "small collapsed portion"
}
] |
Single_Cell
|
The second case applied when the majority of the vessel is collapsed (Supplementary Fig. 5Cii ).
|
[
{
"end": 55,
"label": "Tissue",
"start": 49,
"text": "vessel"
}
] |
Single_Cell
|
The reduction in radius in the skeletonised form of the networks can be seen in Supplementary Fig. 5Bii, Cii .
|
[] |
Single_Cell
|
Correction for short subsegment collapsed vessels was performed by plotting radius along each segment.
|
[
{
"end": 49,
"label": "Tissue",
"start": 15,
"text": "short subsegment collapsed vessels"
},
{
"end": 101,
"label": "Tissue",
"start": 94,
"text": "segment"
}
] |
Single_Cell
|
Subsegments where the radius was below the 5th percentile for that segment were replaced with the nearest neighbour.
|
[
{
"end": 11,
"label": "Tissue",
"start": 0,
"text": "Subsegments"
},
{
"end": 74,
"label": "Tissue",
"start": 67,
"text": "segment"
}
] |
Single_Cell
|
For larger collapsed vessels, the process is fully described in the “Results” section.
|
[
{
"end": 28,
"label": "Tissue",
"start": 4,
"text": "larger collapsed vessels"
}
] |
Single_Cell
|
Source paper: PMC12408821
Topological/morphological metrics of the network were calculated from the spatial graph as follows, with code provided at https://github.com/HiPCTProject/Skeleton_analysis : i. Branching angle is calculated as either: (a) the angle between the two child segments from a common parent segment, or (b) the angle between a child segment and its parent segment.
|
[] |
Single_Cell
|
In both cases, the vector for the segment of parent and child were calculated between the start node and end node, irrespective of vascular tortuosity.
|
[] |
Single_Cell
|
ii.
|
[] |
Single_Cell
|
Tortuosity is defined as the Euclidean distance between start and end node of a segment, divided by the sum of all subsegment lengths.
|
[] |
Single_Cell
|
iii.
|
[] |
Single_Cell
|
Radius is calculated per segment as the mean of all subsegment radii.
|
[] |
Single_Cell
|
In cases where larger vessels had fully collapsed (See “Results” for details), the radius was defined as the equivalent radius for a circular vessel with the same length perimeter as the vessel cross-section in the binary image.
|
[
{
"end": 148,
"label": "Tissue",
"start": 133,
"text": "circular vessel"
},
{
"end": 29,
"label": "Tissue",
"start": 15,
"text": "larger vessels"
}
] |
Single_Cell
|
iv.
|
[] |
Single_Cell
|
Length is defined as the sum of all subsegment lengths.
|
[] |
Single_Cell
|
v. Inter-vessel distance is calculated by two approaches to facilitate different analyses.
|
[] |
Single_Cell
|
First, using the segmentation binary image, the distance of every non-vessel voxel from its nearest vessel voxel was calculated via a 3D distance transform (ImageJ) applied to the binary vessel segmentation.
|
[] |
Single_Cell
|
Second, using the skeleton form, the Euclidean distance between the midpoint of every segment to its nearest-neighbouring segment midpoint was calculated.
|
[] |
Single_Cell
|
Source paper: PMC12408821
Radius is calculated per segment as the mean of all subsegment radii.
|
[] |
Single_Cell
|
In cases where larger vessels had fully collapsed (See “Results” for details), the radius was defined as the equivalent radius for a circular vessel with the same length perimeter as the vessel cross-section in the binary image.
|
[
{
"end": 29,
"label": "Tissue",
"start": 15,
"text": "larger vessels"
},
{
"end": 148,
"label": "Tissue",
"start": 133,
"text": "circular vessel"
}
] |
Single_Cell
|
Source paper: PMC12408821
In addition to the above metrics, we also assessed vessel generation, or order, using two methods.
|
[] |
Single_Cell
|
First, we used a variation of the centripetal system, known as the Strahler ordering system , wherein the most distal, smallest segments are assigned as the first order.
|
[] |
Single_Cell
|
If two segments with the same order intersect, the resulting segment has one Strahler order greater.
|
[] |
Single_Cell
|
Alternatively, if two segments with different orders intersect, the higher order of the two is given to the resulting segment (Supplementary Fig. 6B ).
|
[] |
Single_Cell
|
We used a variant of the Strahler order approach, which we term the truncated Strahler approach.
|
[] |
Single_Cell
|
Our 50 µm per voxel dataset does not provide sufficient resolution to image or segment down to afferent arterioles.
|
[
{
"end": 114,
"label": "Tissue",
"start": 95,
"text": "afferent arterioles"
}
] |
Single_Cell
|
Thus, the network created from this 50 µm per voxel dataset is truncated at the interlobular arteries.
|
[
{
"end": 101,
"label": "Tissue",
"start": 80,
"text": "interlobular arteries"
}
] |
Single_Cell
|
We assigned the terminal ends of our network as the first Strahler order, as opposed to applying statistical estimates to determine the Strahler order of these terminal ends based on diameter relative to the afferent arterioles, as performed previously .
|
[
{
"end": 44,
"label": "Tissue",
"start": 16,
"text": "terminal ends of our network"
},
{
"end": 173,
"label": "Tissue",
"start": 160,
"text": "terminal ends"
},
{
"end": 227,
"label": "Tissue",
"start": 208,
"text": "afferent arterioles"
}
] |
Single_Cell
|
Detailed discussion of this approach and alternative ordering approaches are discussed in the Supplementary Note 3 .
|
[] |
Single_Cell
|
Second, we took a centrifugal, or ‘topological’ approach, starting with the most proximal artery as generation one.
|
[
{
"end": 96,
"label": "Tissue",
"start": 81,
"text": "proximal artery"
}
] |
Single_Cell
|
At each branching node the generation is increased, an approached which has been previously utilised (Supplementary Fig. 6C ).
|
[] |
Single_Cell
|
From the ordering analyses, we assessed the branching ratio ( ) defined as the anti-log of the reciprocal for the linear fit to the plot of truncated Stahler order ( O ), against the logarithm of the number of segments ( N ) in each order: 1 [12pt] $$N=_^}$$ N = N 0 e − O γ
Source paper: PMC12408821
The radius of the arterial network in the human kidney obtained from this study was compared to those of the rat kidney taken , which was scanned with 20 and 4 µm per voxel using a microfilling approach.
|
[
{
"end": 336,
"label": "Tissue",
"start": 320,
"text": "arterial network"
},
{
"end": 356,
"label": "Tissue",
"start": 344,
"text": "human kidney"
},
{
"end": 421,
"label": "Tissue",
"start": 411,
"text": "rat kidney"
}
] |
Single_Cell
|
Source paper: PMC12408821
The radial scaling exponent for vascular networks refers to the relationship between the radii of parent and daughter vessels at a bifurcation.
|
[
{
"end": 77,
"label": "Tissue",
"start": 60,
"text": "vascular networks"
},
{
"end": 132,
"label": "Tissue",
"start": 126,
"text": "parent"
},
{
"end": 153,
"label": "Tissue",
"start": 137,
"text": "daughter vessels"
}
] |
Single_Cell
|
It is formulated as: 2 [12pt] $$_}^= __,i}^$$ R parent 1 / a = ∑ i R child , i 1 / a Where is the scaling exponent.
|
[] |
Single_Cell
|
Source paper: PMC12408821
Murray’s law is derived from assuming minimal work in maintaining blood transport, leading to vessel radii scaling with the cube root of flow rate.
|
[] |
Single_Cell
|
Murray’s law also assumes, constant uniform metabolic demand of the tissue, laminar flow and that blood is a Newtonian fluid.
|
[
{
"end": 103,
"label": "Tissue",
"start": 98,
"text": "blood"
}
] |
Single_Cell
|
In contrast, the West, Brown, and Enquist (WBE) model describes vascular networks as fractal-like structures that optimise metabolic energy distribution across the vascular network in its entirety.
|
[
{
"end": 81,
"label": "Tissue",
"start": 64,
"text": "vascular networks"
},
{
"end": 180,
"label": "Tissue",
"start": 164,
"text": "vascular network"
}
] |
Single_Cell
|
It predicts a 0.5 scaling exponent for large vessels and 0.33 for small vessels, accounting for hierarchical branching, where the emphasis is on efficiently delivering nutrients and waste exchange throughout the system.
|
[
{
"end": 52,
"label": "Tissue",
"start": 39,
"text": "large vessels"
},
{
"end": 79,
"label": "Tissue",
"start": 66,
"text": "small vessels"
}
] |
Single_Cell
|
Source paper: PMC12408821
Both models provide insights into vascular architecture but differ in scope, with real vascular networks often deviating due to biological variability and tissue-specific adaptations .
|
[
{
"end": 132,
"label": "Tissue",
"start": 110,
"text": "real vascular networks"
}
] |
Single_Cell
|
Source paper: PMC12408821
Calculation of the radial exponent is done in this work following the regression-based method outlined in refs. .
|
[] |
Single_Cell
|
For each vessel in the network, the number of downstream endpoints of the network is counted.
|
[
{
"end": 15,
"label": "Tissue",
"start": 9,
"text": "vessel"
},
{
"end": 30,
"label": "Tissue",
"start": 23,
"text": "network"
}
] |
Single_Cell
|
The radius and number of downstream tips are related by Eq 4.
|
[] |
Single_Cell
|
from ref. :
|
[] |
Single_Cell
|
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