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. the radial scaling exponent for vascular networks refers to the relationship between the radii of parent and daughter vessels at a bifurcation. it is formulated as: 2 [ 12pt ] $ $ _ } ^ = _ _, i } ^ $ $ r parent 1 / a = ∑ i r child, i 1 / a where is the scaling exponent. murray ’ s law describes an optimisation principle that minimises energy costs in blood flow by balancing viscous dissipation and metabolic maintenance, predicting a cubic relation, leading to a scaling exponent a = 0. 33. 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. murray ’ s law also assumes, constant uniform metabolic demand of the tissue, laminar flow and that blood is a newtonian fluid. 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. 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. 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. calculation of the radial exponent is done in this work following the regression - based method outlined in refs.. for each vessel in the network, the number of downstream endpoints of the network is counted. the radius and number of downstream tips are related by eq 4. 3 [ 12pt ] $ $ \, _ ^ $ $ r α n d a where = radius of a vessel in the network, = the number of downstream endpoints from that vessel, = the radial scaling exponent. plotting the log - log relation of these two variables allows a to be estimated by regression analysis. segmentation of the compartments within the human kidney, including cortex, medulla, intermedullary pillars and hilum, was performed in dragonfly ( version: 2021. 3 ) using a 2d convolutional neural network ( cnn ). the final hyperparameter s of the cnn are given in supplementary table 4. correction of the cnn output was manually performed in by an expert in amira - avizo v2021. 1 to provide the final compartment delineations. these compartments were used to group and then analyse vascular network parameters. 40 3d patches ( 512 × 512 × 512 ) of the highest resolution data, captured at 2. 6 – 5. 6 µm per voxel, were extracted from multiple human kidneys scanned by hip - ct, and the glomeruli were manually segmented. the widely utilised network nnu - net was trained using 35: 5 cubes for a train: test split and a 70: 30 training validation split. training using 5 - fold cross validation achieved a final dice score of 0. 928, 0. 860, 0. 906 for training, validation and test data, respectively. see supplementary note 3 for training results and nnu - net configuration. the plan files detailing all parameters for the training nnu - net are provided in supplementary data. this trained network was used to perform inference of two vois of high - resolution data from the human kidney in this study, and count the number of glomeruli in each. utilising the kidney anatomical compartment segmentation from above, the volume of cortical tissue within these high - resolution vois was calculated. for each voi, the number of glomeruli and the volume of cortex in each voi were used to estimate the total number of glomeruli in the entire kidney. estimates of total glomerular number extrapolated to the entire kidney, from each voi, were: 1. 28 × 10 and 1. 12 × 10 for voi 3. 1 and voi 2. 1, respectively. statistical comparisons of vascular network morphology between human and rat kidney were performed in graphpad prism ( version: 10. 1. 2 ). for all statistical tests, a p - value of less than 0. 05 was considered statistically significant. in both the rat and human datasets, the segmental / feeding renal arteries were identified to be at strahler orders 8 and truncated strahler order 9, respectively. radius against strahler order were normalised to the 9th truncated strahler order of the human data. log of radius against truncated strahler generation for the human kidney; and radius against strahler order of the rat kidney, were plotted facilitating a linear least squares regression analysis. a sum of squares f test was performed with the null hypothesis that a single set of global parameters for slope and intercept would fit vessel radius or vessel length for both the rat and human cases. for calculating the fit of the radial scaling exponent ( a ), we followed the approach of ref. applying a standard major axis regression to account for measurement error in both variables. this was performed in matlab 2023a using the gmregress. m function with an alpha significance set to 0. 05. a single - cell and spatial genomics atlas of human skin fibroblasts reveals shared disease-related fibroblast subtypes across tissues source paper: pmc12479362 fibroblasts sculpt the architecture and cellular microenvironments of various tissues. here we constructed a spatially resolved atlas of human skin fibroblasts from healthy skin and 23 skin diseases, with comparison to 14 cross - tissue diseases. we define six major skin fibroblast subtypes in health and three that are disease-specific. we characterize two fibroblast subtypes further as they are conserved across tissues and are immune-related. the first, f3: fibroblastic reticular cell - like fibroblast ( ccl19 cd74 hla - dra ), is a fibroblastic reticular cell - like subtype that is predicted to maintain the superficial perivascular immune niche. the second, f6: inflammatory myof ibroblasts ( il11 mmp1 cxcl8 il7r ), characterizes early human skin wounds, inflammatory diseases with scarring risk and cancer. f6: inflammatory myof ibroblasts were predicted to recruit neutrophils, monocytes and b cells across multiple human tissues. our study provides a harmonized nomenclature for skin fibroblasts in health and disease, contextualized with cross - tissue findings and clinical skin disease profiles. fibroblasts are crucial cells for shaping tissue architecture and immune cell niches. studying the heterogeneity of fibroblast subtypes has been challenging due to the scarcity of unique surface markers and their tendency to adopt activated phenotypes during in vitro culture. single - cell rna sequencing ( scrna - seq ) and spatial transcriptomics technologies have overcome these challenges, enabling the dissection of fibroblast heterogeneity in human tissues. while recent studies have described fibroblast states in human skin, they have not spatially resolved their tissue microanatomical location. very few, if any, have interrogate d fibroblasts in diverse disease conditions in the skin and across human tissues. consequently, the fibroblast composition and function in human skin; how it changes across a range of diseases ( inflammatory, cancer and fibrosis / scarring ); and how these populations relate to other human tissues is still unclear. in this study, we integrated published large - scale scrna - seq datasets of healthy human skin and 23 skin diseases and generated spatial transcriptomics data from two different modalities to construct a high - resolution spatially resolved atlas of more than 350, 000 adult human skin fibroblasts.