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Image data of co-localization of IgG and HEV ORF2 protein in a case of hepatitis E-associated kidney disease
<p><span>Image data for a co-localization study of IgG with HEV ORF2 protein in a </span><span>de novo immune complex-mediated glomerulonephritis (GN) case in</span><span> a kidney transplant recipient </span><span>with chronic hepatitis E (Leblond and Helmchen, et al. 2024).<span> </span>Immunofluorescence images are provided for 25 glomeruli at low magnification (20x, 0.227 micron/pixel) and for 16 glomeruli at high magnification (100x, 0.0454 micron/pixel). For each example glomeruli the green channel represents IgG antibody staining with FITC, and the magenta channel represent anti-HEV ORF2 staining using Alexa Fluor 546.</span></p> <p><span>Methods: </span></p> <p><span>Mouse monoclonal antibody clone 1E6 against the HEV ORF2 protein was incubated for 1h at a dilution of 1:125 followed by a mix of Alexa Fluor 546-conjugated goat anti-mouse antibody (Invitrogen BV, A11018) and FITC-conjugated Rabbit anti-Human IgG (Gamma chain, Diagnostic Biosystem, F008) for 1hat a dilution of 1:50. Following automated staining, the slides were hand -washed in distilled H<sub>2</sub>O. Tissue was covered with Vectashield® Antifade Mounting Medium with DAPI (VectorLaboratories, H-1200), covered with a coverslip and stored at 4°C until evaluation.</span></p> <p><span>Immunofluorescence images were acquired with an upright fluorescence microscope (AxioImager.Z2 controlled by ZEN Blue software; 89 North Photofluor LM-75 light source, and Axiocam 503 mono camera; Zeiss, Jena, Germany), equipped with the following objectives: 20x (NA 0.5, Plan-NEOFLUAR), 40x (NA 1.4 oil, Plan-APOCHROMAT), and 100x (NA 1.45 oil, Plan-APOCHROMAT) objectives. This setup provides an excellent spatial resolution (nominally about 200 nm lateral resolution in our study; pixel size was 45.4 nm for 100x objective). High resolution images were taken with the 100x objective using the ApoTome.2 module with deconvolution (grid 5 lp/mm; section thickness 0.7 µm). We used Vysis Abbott Chroma filter sets (Blue: excitation (ex) 335-383 nm, emission (em) 420-470; green: ex 481-507 nm; em 521-551 nm; red: ex 534-556 nm, em 574- 606 nm). Co-localization of IgG and HEV ORF2 staining was quantified using Fiji software (Schindelin et al., 2012) and the JACoP ImageJ plug-in. </span></p>
T2-weighted Kidney MRI Segmentation
<p>A dataset containing 100 T<sub>2</sub>-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from Chronic Kidney Disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys. </p> <p>For more information about the dataset please refer to <a href="https://doi.org/10.1002/mrm.28768">this article.</a></p> <p>For an executable that allows automated segmentation of the kidneys from this dataset please refer to <a href="https://github.com/alexdaniel654/Renal_Segmentor">this software.</a></p>
TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
Kidney glomeruli-ROIs extracted from histological slides stained with HE or PAS.
<p>Kidney glomeruli-ROIs extracted from histological slides stained with HE or PAS. The multicenter test set of 78 regions of interest together with annotations. ROIs were extracted from 20 WSIs representing various human kidney pathologies. WSI's came from four sources: three independent medical centers and TCGA. Slides from three sources were stained with HE and slides from one center were stained with PAS. Slides were digitalized on Pannoramic 250 Flash II (3DHISTECH, Budapest, Hungary), Hamamatsu NanoZoomer S60 Digital slide scanner, or using Aperio AT Turbo (Leica Biosystems, Vista, CA). ROIs were extracted for x10, that corresponding to the pixel size ~10um.</p>
Immunofluorescence staining of a human kidney (#4, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#4). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#3, tumor area) obtained by MELC
<p>19 marker MELC run in a human tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#3, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#2, tumor area) obtained by MELC
<p>19 marker MELC run in a human tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#2, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immunofluorescence staining of a human kidney (#1, peri-tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan - Supporting Movie Files
<p>This is a collection of movies related to the manuscript "Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan" by McLaughlin et al to describe kidney organization using 3D light sheet fluorescence microscopy. The preprint manuscript associated with these movies is </p> <p>Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan</p> <p>Liam McLaughlin, Bo Zhang, Siddharth Sharma, Amanda L. Knoten, Madhurima Kaushal, Jeffrey M. Purkerson, Heidy Huyck, Gloria S. Pryhuber, Joseph P. Gaut, Sanjay Jain</p> <p>bioRxiv 2024.07.29.605633; doi: <a href="https://doi.org/10.1101/2024.07.29.605633">https://doi.org/10.1101/2024.07.29.605633</a></p> <p>Movie Legends</p> <p>Movie 1: 3D view of the entire slice showing key structures.<br>3D light sheet fluorescence microscopy 5x movie of reference adult sample SK3, demonstrating glomeruli, collecting<br>ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:10s — Segmentations. Annotations are in the movie.</p> <p><br>Movie 2: Relationship of nerves with glomeruli and juxtaglomerular apparatus.<br>The movie depicts innervation of glomeruli in 2D optical sections, containing glomeruli, Tuj1(labels TUBB3)-stained<br>nerves, and CGRP-stained sensory nerves. 0:22s — Innervation of the JGA. 0:33 s— Innervation of the Macula Densa.<br>0:47s — Innervation of the outer boundary of the Bowman’s Capsules.</p> <p><br>Movie 3: Neuro-nephron connectivity.<br>The movie explores innervation between different structures of the same nephron, and between nephrons in both 3D and<br>2D optical sections, containing glomeruli, Tuj1 (TUBB3)-stained nerves, CGRP-stained sensory nerves, proximal<br>(convoluted) tubule, thick ascending limb, distal convoluted tubule, and collecting duct. 0:00-1:53min — 3D<br>relationships. 0:38s — Innervation of glomerulus JGA. 1:02min — Post-JGA innervation of medullary ray structures.<br>1:54min-end — 2D relationships. 2:38min — Interglomerular/internephron innervation.</p> <p><br>Movie 4: Neurovascular – nephron patterns in the medulla.<br>The movie shows innervation pattern within the medulla. 0:00s—5x adult medullary innervation pattern in 3D;<br>0:34s—in 2D also showing Vasa Recta and Collecting Duct; 0:48s—in 3D at 20x resolution. 1:04min—20x adult<br>medullary innervation of proximal tubule, thick ascending limb, and Vasa Recta in 3D; 1:31min—view if the previous in<br>2D. 2:11min—5x adult medullary innervation pattern in 3D; 2:40min—in 2D also showing Vasa Recta and Collecting<br>Duct; 2:54min—in 3D at 20x resolution; 3:15—in 2D at 20x resolution.</p> <p><br>Movie 5: Network motifs.<br>Exploring 3D neuroglomerular networks at 5x and 20x resolution. 0:00s — Raw 5x signal from young adult sample SK2.<br>0:16s— Segmented 5x SK2 with 20x coregistrations. 0:26 — Exploring SK2 20x FOV. 0:38 — 20x network featuring<br>hourglass motif. 1:19 5x “Type I” network in SK2. 1:41 — Segmented 5x adult SK3 sample featuring a “Type 2” network<br>containing a lattice motif. 2:23 — Exploring SK3 20x FOV, featuring a network with a lattice motif.</p> <p><br>Movie 6: LSFM movie of pediatric kidney<br>3D lightsheet 5x image of neonatal sample SK414, containing glomeruli, collecting ducts, nerves, and blood vessels.<br>0:00s — Raw signal. 0:22s — Segmentations. 1:34min — Overlayed segmentations.</p> <p><br>Movie 7: Neuronephron connectivity time course<br>Exploring neuronephro-networks across a life time course in 1mm3 20x images. 0:00 — Raw neonatal. 0:17sec —<br>Segmented neonatal. 0:47 sec— Raw infant. 0:54 — Segmented infant with network. 1:13min — Raw young adult.<br>1:23min — Segmented young adult with network. 1:33min — Raw adult. 1:43min — Segmented adult featuring network<br>with keychain motif. 1:49min — Raw aged. 1:59min — Segmented aged with network featuring pyramid motif.</p> <p> </p> <p>Movie 8: Mother Glomeruli</p> <p>Evaluating distributions and innervation of mother glomeruli in neuroglomerular networks. 0:00 - Large 20x 3D Network sample SK1 FOV8. 0:13 - Sample SK1 5x Network in 2D. 0:36 - Mother glomerulus neural quantifications SK1. 0:41 Large 20x 3D Network sample SK3 FOV12. 0:56. Large 20x 2D Network sample SK3 FOV12. 1:21 - Mother glomerulus neural quantifications SK3.</p> <p> </p> <p>Movie 9: Segmentations</p> <p>Demonstrating accuracy of segmentations that combine supervised ML with manual validation in Sample SK2 FOV5. 0:00 - AQP2 labelled Collecting Duct and NPHS1 labelled Glomerulus. 0:26 - Tuj1 labelled nerve.</p> <p> </p> <p>metadata_analyzed_images:</p> <p>Metadata for images that were analyzed. Includes metadata .txt files for all stitched, downsampled samples, as well as .csv metadata for certain raw .czi files (pre-processing).</p>
Life Cycle Assessment Dataset for Kidney Care Environmental Optimisations within Haemodialysis
<p>This dataset supports a study on environmental optimizations in haemodialysis (HD) kidney care, focusing on reducing carbon emissions, water usage, and social impacts such as forced labour. It includes detailed analyses of interventions to improve sustainability across multiple domains:</p> <ol> <li> <p><strong>Travel Reduction</strong>: Data explores the impact of reducing patient travel distances by 10%, 50%, and 90%, highlighting significant greenhouse gas (GHG) emission savings (up to 2,540 kg CO2e per patient annually) and associated reductions in water usage and forced labour risks. Interventions include promoting home-based dialysis, telemedicine, and optimized patient facility allocation.</p> </li> <li> <p><strong>Water Management</strong>: The dataset documents innovations such as reclaiming reverse osmosis water for reuse, optimizing water treatment plant operations, and reducing water consumption during dialysis processes. Larger centres and daily operation schedules show better water efficiency compared to smaller, less frequent setups.</p> </li> <li> <p><strong>Waste Management</strong>: Data highlights strategies for diverting waste from clinical to domestic streams, recycling dialysis materials, and adopting advanced technologies like pyrolysis. These measures reduce the environmental and economic burden of waste disposal, including incineration costs.</p> </li> <li> <p><strong>Energy Optimizations</strong>: Included interventions cover energy-saving technologies such as heat exchangers in dialysis machines, solar panel installations, and IT system automation. Solar energy adoption demonstrates varied CO2e savings based on regional energy mixes.</p> </li> <li> <p><strong>Incremental Dialysis</strong>: Data supports the transition to incremental dialysis—starting with fewer weekly sessions—to preserve resources, reduce GHG emissions, and maintain residual kidney function, offering both environmental and clinical benefits.</p> </li> </ol> <p>Each intervention was assessed using Life Cycle Assessment (LCA) methodologies, with functional units based on annual HD use for one patient. Metrics include carbon dioxide equivalent emissions (CO2e), water deprivation, and forced labour hours, aligned with EU Product Environmental Footprint standards. The dataset provides comparative results to guide clinical sites in prioritizing high-impact interventions, offering actionable insights into sustainable HD care.</p>
Human kidney cortex CODEX reference dataset 1
<p>CODEX image stack of human kidney cortex stained with markers as indicated in CODEX_antibody_list_010621.csv and imaged in the order given in CODEX_channel_index_010621.csv. Tissue preparation and analysis as described <a href="https://www.biorxiv.org/content/10.1101/2021.12.27.474025v1">here.</a></p>
Dataset for "Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey"
<p>Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology</p> <p><strong>Summary of study</strong></p> <p>An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).</p> <p>Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.</p> <p>The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.</p> <p>Sixty-five individuals participated in the survey.</p> <p>See the published article for full study details.</p> <p><strong>Description of uploaded files</strong></p> <p>1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.</p> <p>2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. </p> <p>3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.</p> <p>4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.</p> <p>5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.</p>
Two metabolomics data sets (mouse kidney, mouse plasma), generated for the publication Bignon et al., 2023: "Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock".
<p><strong>Publication: </strong>Bignon Y, Wigger L, Ansermet C, Weger BD, Lagarrigue S, Centeno G, Durussel F, Götz L, Ibberson M, Pradervand S, Quadroni M, Weger M, Amati F, Gachon F, Firsov D. Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock. J Clin Invest. 2023 Mar 2:e167133. doi: 10.1172/JCI167133. Epub ahead of print. PMID: 36862511.</p> <p> </p> <p><strong>Abstract: </strong> Circadian rhythmicity in renal function suggests rhythmic adaptations in renal metabolism. To decipher the role of the circadian clock in renal metabolism, we studied diurnal changes in renal metabolic pathways using integrated transcriptomic, proteomic, and metabolomic analysis performed on control mice and mice with inducible deletion of the circadian clock regulator Bmal1 in the renal tubule (cKOt). With this unique resource, we demonstrated that ~30% RNAs, ~20% proteins and ~20% metabolites are rhythmic in kidneys of control mice. Several key metabolic pathways including NAD+ biosynthesis, fatty acid transport, carnitine shuttle,and b-oxidation displayed impairments in kidneys of cKOt, resulting in a perturbed mitochondrial activity. Carnitine reabsorption from the primary urine was one of the most impacted processes with a ~50% reduction in plasma carnitine levels and a parallel systemic decrease in tissues carnitine content. This suggests that the circadian clock in the renal tubule controls both kidney and systemic physiology.</p> <p> </p> <p><strong>This record contains two separate mass-spectrometry metabolomics data sets associated with this study:</strong></p> <ol> <li>Metabolic profile of renal tubules, MS/MS data, Metabolon, Morrisville, NC (N=60)</li> <li>Metabolic profile of blood plasma, MS/MS data, Biocrates, Innsbruck, Austria (N=60)</li> </ol> <p>For each data set, original data as received from the platforms and processed data as used in the data analysis are provided. Preprocessing of kidney data included removal of metabolites with more than 80% missing data values, median normalization, imputation and glog2 transformation. Preprocessing of plasma data included filtering of metabolites with any missing data and log2 transformation. Details of data processing are available in the STAR*methods of the publication.</p> <p> </p> <p><strong>Data sets in other repositories associated with the same study:</strong></p> <p>Additional data sets (transcriptomics, proteomics) pertaining to the same study have been deposited in public repositories:</p> <ul> <li>Gene Expression Omnibus (NCBI GEO), GSE216252</li> <li>PRIDE Archive (EMBL-EBI), PXD036803</li> </ul> <p> </p>
Fig. 4 in Kidney anatomy, histology and histometric traits associated to renosomatic index in Gymnotus inaequilabiatus (Gymnotiformes: Gymnotidae)
Fig. 4. Histological cross-sections of the head and exocrine kidney demonstrating different granulomatous structures in Gymnotus inaequilabiatus. a. severe aggregation of MMCs outer to granuloma in the exocrine kidney; PAS, bar scale = 20 µm. b. a clump of melanogenic macrophages (dark pigmented cells) adjacent to granuloma in the head kidney. The hematopoietic tissue is edematous and atypical. Some rodlet cells (black arrow) are observed. Granuloma presents a compact and thick layer of collagen with internal necrotic content surrounded by epithelioid cells (white arrows); H&E, bar scale = 10 µm. c. two well-defined granulomas with substantial internal necrotic content involved by fibrous tissue. A slight layer of MMCs aggregates close to the external wall of granuloma. TM, bar scale = 10 µm. d. MMCs aggregates both internally and externally to granuloma in the exocrine kidney. HE, bar scale = 10 µm.
Fig. 2 in Kidney anatomy, histology and histometric traits associated to renosomatic index in Gymnotus inaequilabiatus (Gymnotiformes: Gymnotidae)
Fig. 2. Cross-section of the head and exocrine kidney in Gymnotus inaequilabiatus. a. a transitional area between head (HE) and exocrine kidney (EX) drained by postcardinal vein (PCV). Lymphohematopoietic tissue is abundant in the head portion. MMCs are diffusely distributed in both portions. A thick fibrous capsule covers the organ (white arrow); HE, bar scale = 500 µm. b. the general aspect of the head kidney. Vascular sinusoids (white arrows) surrounding the lymphohematopoietic tissue enclosing the MMCs (black arrow); HE, bar scale = 200 µm. c. interstitial lymphohematopoietic tissue with MMCs deposits characterizes the exocrine kidney, in addition, the contoured tubules; renal corpuscle (white arrow); HE, bar scale = 200 µm. d. the exocrine kidney with a proximal convoluted tubule showing hyaline degeneration of the tubular epithelium often found in this species; HE, bar scale = 100 µm. e. exocrine kidney showing the corpuscle of Stannius (*). This structure is not lobulated, and is delimited by a fibrous capsule, and situated at the junction between head and exocrine portions; HE, bar scale = 500 µm. f. detail of junction between lymphohematopoietic tissue and corpuscle of Stannius in the head kidney. Fiber bundles subdivide both structures. Rodlet cells (white arrow), macrophages (M) and eosinophilic granulocytic cells (black arrow); granulocytic (G) and secretory cells (SC) internally fill the corpuscle; HE, bar scale = 50 µm.
Fig. 3 in Water pH and hardness alter ATPases and oxidative stress in the gills and kidney of pacu (Piaractus mesopotamicus)
Fig. 3. Thiobarbituric acid reactive substances (TBARS) content (nmol TMP mg wet tissue-1) in a. gills and b. kidney of pacu (Piaractus mesopotamicus) juveniles under different water hardness and pH at different times. LWH = low water hardness (50 mg CaCO L-1); HWH = high water hardness (120 mg CaCO L-1). Data are presented as the means ± SEM (n = 3 3 9 fish treatment–1). Different uppercase letters indicate statistically differences between pH at the same hardness (P <0.05). Different lowercase letters indicate statistically differences between hardness at the same pH (P <0.05).
Fig. 2 in Water pH and hardness alter ATPases and oxidative stress in the gills and kidney of pacu (Piaractus mesopotamicus)
Fig. 2. Total antioxidant capacity against peroxyl radicals (ACAP) (relative area) in a. gills and b. kidney of pacu (Piaractus mesopotamicus) juveniles under different water hardness and pH at different times. LWH = low water hardness (50 mg CaCO L-1); HWH = high water hardness (120 mg CaCO L-1). Data are presented as the means ± SEM (n = 9 fish treatment–1). 3 3 Different uppercase letters indicate statistically differences between pH at the same hardness (P <0.05).
Full summary statistics of mixQTL for GTEx v8 Kidney Cortex
<p>The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.