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1,870 results for “Adipose tissue”
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Datasets and R codes for Prokkola et al. 2022 adipose tissue samples
<p>Data and R codes for the analyses reported in Prokkola et al. (pre-print, 2022) <em>Adipose tissue mitochondrial respiration in Atlantic salmon: implications for sex-dependent life-history variation.</em></p> <p>Overview of files can be found in the README file.</p> <p>The file "Cell size data.zip" contains TIFF-images of adipose tissue cryosections, a README file, the result files for each image file and an R code for parsing the results files.</p> <p>To skip the data parsing steps and get the final data, download the AdiposeTissue_data_all.txt file (tab-separated).</p>
DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours
<p>Latest description of this data set: <a href="https://github.com/MRC-Harwell/cytometer/blob/main/DATA.md">Data.md at cytometer project</a></p> <pre># Publications related to the data The data associated to the DeepCytometer project (https://github.com/MRC-Harwell/cytometer) is available from Zenodo (doi: 10.5281/zenodo.5137433 and 10.5281/zenodo.5149005). The histology and mouse measures were generated as part of the Small et al. 2018 study: > Small et al. "Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition". Nature Genetics, 50:572–580, 2018. The hand traced data set, colour maps, and automatic segmentations were generated for the Casero et al. 2021 paper: > Casero et al. "Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks". bioRxiv, 2021. doi: [10.1101/2021.06.03.444997](https://www.biorxiv.org/content/10.1101/2021.06.03.444997v1.full). # Data protocols ## Histology and laboratory measures To develop and evaluate our methods we used Klf14tm1(KOMP)Vlcg C57BL/6NTac (B6NTac) mice tissue samples and additional data generated as part of the Small et al. 2018 study(Small et al. 2018). It should be noted that the single exon Klf14 gene is imprinted and only expressed from the maternally inherited allele(Parker-Katiraee et al. 2007). This was taken into account by (Small et al. 2018) by crossing a Het parent with a WT parent, so that each offspring inherited a WT allele from the WT parent, and the Klf14 gene knockout or a WT allele from the other parent (from the father, PAT, or the mother, MAT). We also take Klf14 imprinting into account by using as controls the PAT mice and comparing them to the MAT WT and MAT Het (or functional KO, FKO) mice. We used a total of 76 Klf14-B6NTac mice (nfemale=nmale=38), of which 20 mice from the Control and FKO groups were used for training and testing the DeepCytometer pipeline, as well as the hand traced population experiment (summary in Table MICE). The histopathology screen involved fixing, processing and embedding in wax, sectioning and staining with Hematoxylin and Eosin (H&E) both inguinal subcutaneous and gonadal adipose depots. For paraffin-embedded sections, all samples were fixed in 10% neutral buffered formalin (Surgipath) for at least 48 hours at RT and processed using an Excelsior™ AS Tissue Processor (Thermo Scientific). Samples were embedded in molten paraffin wax and 8 μm sections were cut through the respective depots using a Finesse™ ME+ microtome (Thermo Scientific). Sampling was conducted at 2sxns per slide, 3 slides per depot block onto simultaneous charged slides, stained with haematoxylin Gill 3 and eosin (Thermo scientific) and scanned using an NDP NanoZoomer Digital pathology scanner (RS C10730 Series; Hamamatsu). Body weight (BW) and depot weight (DW) were measured with Satorius BAL7000 scales. ## White adipose tissue segmentation For cell area quantification, we applied DeepCytometer v8 to 75 inguinal subcutaneous and 72 gonadal whole histology slides with DeepCytometer (with the Corrected method), including the 20 slides sampled for the hand-traced data set, corresponding to 73 females and 74 males, to produce 2,560,067 subcutaneous and 2,467,686 gonadal cells (on average, 34,134 and 34,273 cells per slide, respectively). Full segmentation of all whole slides was performed with script [klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py). In this case, the segmentation contours were grouped by tiles in the output AIDA annotation `.json` file (one contour per cell, one file per slide). Non-white adipocyte contours were filtered out, and white adipocyte contours were aggregated into an AIDA annotation `.json` file with a single tile with script [klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py) (one contour per cell, one file per slide). # List of directories and files ## Casero et al. (2021) "DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours" (doi: 10.5281/zenodo.5137433) ### `deepcytometer_pipeline_v8.zip` (60.6 MB) Weights, colourmaps, etc. necessary to run the pipeline (v8, with mode colour correction). This is the version of the pipeline described in the paper. There are 10 weight files per convolutional neural network (CNN), corresponding to 10-fold cross-validation * `klf14_b6ntac_exp_0086_cnn_dmap_model_fold_[0..9].h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0089_cnn_segmentation_correction_overlapping_scaled_contours_model_fold_[0..9].h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0091_cnn_contour_after_dmap_model_fold_[0..9].h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0095_cnn_tissue_classifier_fcn_model_fold_[0..9].h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) * `klf14_b6ntac_exp_0094_generate_extra_training_images.pickle`: training dataset description * **'file_list'**: list of SVG files with hand-traced contours for network training. Each SVG file has a corresponding TIFF file with the histology used for segmentation * **'idx_test'**: 10 lists with file indices for testing in 10-fold cross-validation * **'idx_train'**: 10 lists with file indices for training in 10-fold cross-validation * **'fold_seed'**: seed number used for the random number generator to assign file indices to folds * `klf14_b6ntac_exp_0098_filename_area2quantile.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0098_full_slide_size_analysis_v7.py` using the whole Klf14 data set with v7 of the pipeline, and used in earlier experiments, including some where v8 of the pipeline was used for segmentation. * `klf14_b6ntac_exp_0106_filename_area2quantile_v8.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py` using the whole Klf14 data set with v8 of the pipeline, and used in later experiments. * `klf14_training_colour_histogram.npz`: statistics from Klf14 histology images to be used in colour correction * **'xbins_edge'**, **'xbins'**: edges and centres of the bins used for histogram calculations * **'hist_r_q1'**, **'hist_r_q2'**, **'hist_r_q3'** * **'hist_g_q1'**, **'hist_g_q2'**, **'hist_g_q3'** * **'hist_b_q1'**, **'hist_b_q2'**, **'hist_b_q3'**: density quartiles (Q1, Q2, Q3) for RGB channels for each bin the histogram * **'mode_r'**, **'mode_g'**, **'mode_b'**: modes for RGB channels (this corresponds to the most typical background colour in the histology images) * **'mean_l'**, **'mean_a'**, **'mean_b'**: mean intensity for L*a*b channels of the image * **'std_l'**, **'std_a'**, **'std_b'**: intensity standard deviations for L*a*b channels of the image * `klf14_exp_0112_training_colour_histogram.npz`: other statistics from Klf14 histology images to be used in colour correction * **'p'**: vector of quantile values used in ECDF calculations * **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'**: all intensity values for the RGB channels of Klf14 training images that contain at least a white adipocyte * **'f_ecdf_to_val_r_klf14'**, **'f_ecdf_to_val_g_klf14'**, **'f_ecdf_to_val_b_klf14'**: linear interpolation function that maps ECDF quantiles to intensity values in the Klf14 training data set. These functions can be used together with intensity->quantile interpolation functions calculated for a new histology image to perform histogram matching colour correction * **'mean_klf14'**, **'std_klf14'**: mean and standard deviation of the **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'** vectors There are also weight files for the pipeline trained with all the data, instead of the 10-fold cross-validation partition. These were not used for the paper, but could be useful for future experiments * `klf14_b6ntac_exp_0101_cnn_dmap_model.h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0104_cnn_segmentation_correction_overlapping_scaled_contours_model.h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0102_cnn_contour_after_dmap_model.h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0103_cnn_tissue_classifier_fcn_model.h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) ### `histology.7z` (29.1 GB) 165 H&E histology whole slides from Hamamatsu scanner (`.ndpi`). ### `klf14.7z` (2.3 GB) Mice metadata, training/testing data sets for the pipeline, intermediate files created during training, and neural network weights for multiple experiments. * `klf14_b6ntac_meta_info.csv`: Klf14 mice metadata * **Animal Identifier**, **id:** unique ID for each mouse * **ko_parent:** heterozygous parent of origin for the KO allele (father, PAT or mother, MAT) * **sex:** female or male * **genotype:** wild type (KLF14-KO:WT) or heterozygous (KLF14-KO:Het) * **BW:** body weight (g) * **SC:** subcutaneous depot weight (g) * **gWAT:** gonadal depot weight (g) * **Liver:** livel weight (g) * **cull_age:** age at time of culling (days) * **BW_alive:** body weight measured before culling * **BW_alive_date:** age at time of BW_alive measure * **mother:** unique ID for mouse's mother * **mother_genotype:** mouse's mother genotype * `klf14_b6ntac_training`: Directory with hand-traced segmentations of training histology windows. 131 windows sampled from 20 whole slides, plus hand-traced contours that were used for training DeepCytometer and compute population distributions. These segmentations were used for CNN training, but note that there's a cleaned-up version of these data below, and it was the cleaned-up version that was used for the paper experiments * `ndpifile_row_YYYYYY_col_XXXXXX[.tif/.xcf/.svg]`: * **ndpifile:** name of the whole slide file (e.g. `KLF14-B6NTAC 36.1c PAT 98-16 C1 - 2016-02-11 10.45.00`) * **row_YYYYYY:** Y-coordinate of the top-left corner of the sampling window, in pixels * **col_XXXXXX:** X-coordinate of the top-left corner of the sampling window, in pixels * **.tif:** TIFF file with the histology sampling window * **.xcf:** Gimp file with the histology and hand-traced contours (the contours were drawn in Gimp) * **.svg:** SVG (Scalable Vector Graphics) that contains the hand-traced contours in the XCF file * `klf14_b6ntac_training_v2`: Same as `klf14_b6ntac_training`, but the hand-traced data set was cleaned up to remove small contours of dubious cells, or cells that are fully overlapped by others * `klf14_b6ntac_training_non_overlap`: Directory with intermediate images to train the networks. These images are generated by script [`klf14_b6ntac_training_non_overlap`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0077_generate_non_overlap_training_images.py) * `klf14_b6ntac_training_augmented`: Directory with intermediate images used to train the networks (using augmentation to reduce overfitting). These images are generated by script [`klf14_b6ntac_exp_0078_generate_augmented_training_images.py`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0078_generate_augmented_training_images.py) * `klf14_b6ntac_seg`: Deprecated. Directory to store whole slide coarse segmentations in old experiments (e.g. `klf14_b6ntac_exp_0076_generate_training_images.py`). Of little interest for most users * `klf14_b6ntac_results`: Deprecated. Directory to store miscellanea output from some experiments. Of little interest for most users ## Casero et al. (2021). "Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations" (doi: 10.5281/zenodo.5149005) ### `aida_data_Klf14_v8_images.7z` (16.9 GB) Histology images converted to DeepZoom so that they can be visualised with [AIDA](https://github.com/alanaberdeen/AIDA). To use this, decompress this file and put the resulting `images` directory in your `AIDA/dist/data/` directory. ### `aida_data_Klf14_v8_annotations.7z` (18 GB) White adipocyte segmentations in AIDA annotation `.json` files (one contour per cell, one file per whole slide). Each slide has the following files: * `SLIDENAME.json`: Soft link to the annotations file that we want to associate to slide `SLIDENAME.ndpi`, e.g. `SLIDENAME` = `KLF14-B6NTAC-PAT-39.2d 454-16 B1 - 2016-03-17 12.16.06` * `SLIDENAME.lock`: Empty file used to tell the pipeline that `SLIDENAME.ndpi` has already been processed or is being currently processed * `SLIDENAME_coarse_mask.npz`: File with the coarse tissue segmentation of `SLIDENAME.ndpi` and the internal state of the pipeline (execution times, steps, etc) * `SLIDENAME_exp_0106_auto.json`: Annotations (all segmentations without filtering from the Auto algorithm, i.e. segmentation without object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_auto_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Auto algorithm. All contours aggregated into a single tile * `SLIDENAME_exp_0106_corrected.json`: Annotations (all segmentations without filtering from the Corrected algorithm, i.e. segmentation with object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_corrected_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Corrected algorithm. All contours aggregated into a single tile To use this, decompress this file and put the resulting `annotations` directory in your `AIDA/dist/data/` directory. </pre>
Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations
<p>Latest description of this data set: <a href="https://github.com/MRC-Harwell/cytometer/blob/main/DATA.md">Data.md at cytometer project</a></p> <pre># Publications related to the data The data associated to the DeepCytometer project (https://github.com/MRC-Harwell/cytometer) is available from Zenodo (doi: 10.5281/zenodo.5137433 and 10.5281/zenodo.5149005). The histology and mouse measures were generated as part of the Small et al. 2018 study: > Small et al. "Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition". Nature Genetics, 50:572–580, 2018. The hand traced data set, colour maps, and automatic segmentations were generated for the Casero et al. 2021 paper: > Casero et al. "Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks". bioRxiv, 2021. doi: [10.1101/2021.06.03.444997](https://www.biorxiv.org/content/10.1101/2021.06.03.444997v1.full). # Data protocols ## Histology and laboratory measures To develop and evaluate our methods we used Klf14tm1(KOMP)Vlcg C57BL/6NTac (B6NTac) mice tissue samples and additional data generated as part of the Small et al. 2018 study(Small et al. 2018). It should be noted that the single exon Klf14 gene is imprinted and only expressed from the maternally inherited allele(Parker-Katiraee et al. 2007). This was taken into account by (Small et al. 2018) by crossing a Het parent with a WT parent, so that each offspring inherited a WT allele from the WT parent, and the Klf14 gene knockout or a WT allele from the other parent (from the father, PAT, or the mother, MAT). We also take Klf14 imprinting into account by using as controls the PAT mice and comparing them to the MAT WT and MAT Het (or functional KO, FKO) mice. We used a total of 76 Klf14-B6NTac mice (nfemale=nmale=38), of which 20 mice from the Control and FKO groups were used for training and testing the DeepCytometer pipeline, as well as the hand traced population experiment (summary in Table MICE). The histopathology screen involved fixing, processing and embedding in wax, sectioning and staining with Hematoxylin and Eosin (H&E) both inguinal subcutaneous and gonadal adipose depots. For paraffin-embedded sections, all samples were fixed in 10% neutral buffered formalin (Surgipath) for at least 48 hours at RT and processed using an Excelsior™ AS Tissue Processor (Thermo Scientific). Samples were embedded in molten paraffin wax and 8 μm sections were cut through the respective depots using a Finesse™ ME+ microtome (Thermo Scientific). Sampling was conducted at 2sxns per slide, 3 slides per depot block onto simultaneous charged slides, stained with haematoxylin Gill 3 and eosin (Thermo scientific) and scanned using an NDP NanoZoomer Digital pathology scanner (RS C10730 Series; Hamamatsu). Body weight (BW) and depot weight (DW) were measured with Satorius BAL7000 scales. ## White adipose tissue segmentation For cell area quantification, we applied DeepCytometer v8 to 75 inguinal subcutaneous and 72 gonadal whole histology slides with DeepCytometer (with the Corrected method), including the 20 slides sampled for the hand-traced data set, corresponding to 73 females and 74 males, to produce 2,560,067 subcutaneous and 2,467,686 gonadal cells (on average, 34,134 and 34,273 cells per slide, respectively). Full segmentation of all whole slides was performed with script [klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py). In this case, the segmentation contours were grouped by tiles in the output AIDA annotation `.json` file (one contour per cell, one file per slide). Non-white adipocyte contours were filtered out, and white adipocyte contours were aggregated into an AIDA annotation `.json` file with a single tile with script [klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py) (one contour per cell, one file per slide). # List of directories and files ## Casero et al. (2021) "DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours" (doi: 10.5281/zenodo.5137433) ### `deepcytometer_pipeline_v8.zip` (60.6 MB) Weights, colourmaps, etc. necessary to run the pipeline (v8, with mode colour correction). This is the version of the pipeline described in the paper. There are 10 weight files per convolutional neural network (CNN), corresponding to 10-fold cross-validation * `klf14_b6ntac_exp_0086_cnn_dmap_model_fold_[0..9].h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0089_cnn_segmentation_correction_overlapping_scaled_contours_model_fold_[0..9].h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0091_cnn_contour_after_dmap_model_fold_[0..9].h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0095_cnn_tissue_classifier_fcn_model_fold_[0..9].h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) * `klf14_b6ntac_exp_0094_generate_extra_training_images.pickle`: training dataset description * **'file_list'**: list of SVG files with hand-traced contours for network training. Each SVG file has a corresponding TIFF file with the histology used for segmentation * **'idx_test'**: 10 lists with file indices for testing in 10-fold cross-validation * **'idx_train'**: 10 lists with file indices for training in 10-fold cross-validation * **'fold_seed'**: seed number used for the random number generator to assign file indices to folds * `klf14_b6ntac_exp_0098_filename_area2quantile.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0098_full_slide_size_analysis_v7.py` using the whole Klf14 data set with v7 of the pipeline, and used in earlier experiments, including some where v8 of the pipeline was used for segmentation. * `klf14_b6ntac_exp_0106_filename_area2quantile_v8.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py` using the whole Klf14 data set with v8 of the pipeline, and used in later experiments. * `klf14_training_colour_histogram.npz`: statistics from Klf14 histology images to be used in colour correction * **'xbins_edge'**, **'xbins'**: edges and centres of the bins used for histogram calculations * **'hist_r_q1'**, **'hist_r_q2'**, **'hist_r_q3'** * **'hist_g_q1'**, **'hist_g_q2'**, **'hist_g_q3'** * **'hist_b_q1'**, **'hist_b_q2'**, **'hist_b_q3'**: density quartiles (Q1, Q2, Q3) for RGB channels for each bin the histogram * **'mode_r'**, **'mode_g'**, **'mode_b'**: modes for RGB channels (this corresponds to the most typical background colour in the histology images) * **'mean_l'**, **'mean_a'**, **'mean_b'**: mean intensity for L*a*b channels of the image * **'std_l'**, **'std_a'**, **'std_b'**: intensity standard deviations for L*a*b channels of the image * `klf14_exp_0112_training_colour_histogram.npz`: other statistics from Klf14 histology images to be used in colour correction * **'p'**: vector of quantile values used in ECDF calculations * **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'**: all intensity values for the RGB channels of Klf14 training images that contain at least a white adipocyte * **'f_ecdf_to_val_r_klf14'**, **'f_ecdf_to_val_g_klf14'**, **'f_ecdf_to_val_b_klf14'**: linear interpolation function that maps ECDF quantiles to intensity values in the Klf14 training data set. These functions can be used together with intensity->quantile interpolation functions calculated for a new histology image to perform histogram matching colour correction * **'mean_klf14'**, **'std_klf14'**: mean and standard deviation of the **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'** vectors There are also weight files for the pipeline trained with all the data, instead of the 10-fold cross-validation partition. These were not used for the paper, but could be useful for future experiments * `klf14_b6ntac_exp_0101_cnn_dmap_model.h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0104_cnn_segmentation_correction_overlapping_scaled_contours_model.h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0102_cnn_contour_after_dmap_model.h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0103_cnn_tissue_classifier_fcn_model.h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) ### `histology.7z` (29.1 GB) 165 H&E histology whole slides from Hamamatsu scanner (`.ndpi`). ### `klf14.7z` (2.3 GB) Mice metadata, training/testing data sets for the pipeline, intermediate files created during training, and neural network weights for multiple experiments. * `klf14_b6ntac_meta_info.csv`: Klf14 mice metadata * **Animal Identifier**, **id:** unique ID for each mouse * **ko_parent:** heterozygous parent of origin for the KO allele (father, PAT or mother, MAT) * **sex:** female or male * **genotype:** wild type (KLF14-KO:WT) or heterozygous (KLF14-KO:Het) * **BW:** body weight (g) * **SC:** subcutaneous depot weight (g) * **gWAT:** gonadal depot weight (g) * **Liver:** livel weight (g) * **cull_age:** age at time of culling (days) * **BW_alive:** body weight measured before culling * **BW_alive_date:** age at time of BW_alive measure * **mother:** unique ID for mouse's mother * **mother_genotype:** mouse's mother genotype * `klf14_b6ntac_training`: Directory with hand-traced segmentations of training histology windows. 131 windows sampled from 20 whole slides, plus hand-traced contours that were used for training DeepCytometer and compute population distributions. These segmentations were used for CNN training, but note that there's a cleaned-up version of these data below, and it was the cleaned-up version that was used for the paper experiments * `ndpifile_row_YYYYYY_col_XXXXXX[.tif/.xcf/.svg]`: * **ndpifile:** name of the whole slide file (e.g. `KLF14-B6NTAC 36.1c PAT 98-16 C1 - 2016-02-11 10.45.00`) * **row_YYYYYY:** Y-coordinate of the top-left corner of the sampling window, in pixels * **col_XXXXXX:** X-coordinate of the top-left corner of the sampling window, in pixels * **.tif:** TIFF file with the histology sampling window * **.xcf:** Gimp file with the histology and hand-traced contours (the contours were drawn in Gimp) * **.svg:** SVG (Scalable Vector Graphics) that contains the hand-traced contours in the XCF file * `klf14_b6ntac_training_v2`: Same as `klf14_b6ntac_training`, but the hand-traced data set was cleaned up to remove small contours of dubious cells, or cells that are fully overlapped by others * `klf14_b6ntac_training_non_overlap`: Directory with intermediate images to train the networks. These images are generated by script [`klf14_b6ntac_training_non_overlap`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0077_generate_non_overlap_training_images.py) * `klf14_b6ntac_training_augmented`: Directory with intermediate images used to train the networks (using augmentation to reduce overfitting). These images are generated by script [`klf14_b6ntac_exp_0078_generate_augmented_training_images.py`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0078_generate_augmented_training_images.py) * `klf14_b6ntac_seg`: Deprecated. Directory to store whole slide coarse segmentations in old experiments (e.g. `klf14_b6ntac_exp_0076_generate_training_images.py`). Of little interest for most users * `klf14_b6ntac_results`: Deprecated. Directory to store miscellanea output from some experiments. Of little interest for most users ## Casero et al. (2021). "Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations" (doi: 10.5281/zenodo.5149005) ### `aida_data_Klf14_v8_images.7z` (16.9 GB) Histology images converted to DeepZoom so that they can be visualised with [AIDA](https://github.com/alanaberdeen/AIDA). To use this, decompress this file and put the resulting `images` directory in your `AIDA/dist/data/` directory. ### `aida_data_Klf14_v8_annotations.7z` (18 GB) White adipocyte segmentations in AIDA annotation `.json` files (one contour per cell, one file per whole slide). Each slide has the following files: * `SLIDENAME.json`: Soft link to the annotations file that we want to associate to slide `SLIDENAME.ndpi`, e.g. `SLIDENAME` = `KLF14-B6NTAC-PAT-39.2d 454-16 B1 - 2016-03-17 12.16.06` * `SLIDENAME.lock`: Empty file used to tell the pipeline that `SLIDENAME.ndpi` has already been processed or is being currently processed * `SLIDENAME_coarse_mask.npz`: File with the coarse tissue segmentation of `SLIDENAME.ndpi` and the internal state of the pipeline (execution times, steps, etc) * `SLIDENAME_exp_0106_auto.json`: Annotations (all segmentations without filtering from the Auto algorithm, i.e. segmentation without object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_auto_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Auto algorithm. All contours aggregated into a single tile * `SLIDENAME_exp_0106_corrected.json`: Annotations (all segmentations without filtering from the Corrected algorithm, i.e. segmentation with object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_corrected_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Corrected algorithm. All contours aggregated into a single tile To use this, decompress this file and put the resulting `annotations` directory in your `AIDA/dist/data/` directory.</pre>
Adipose tissue plasticity in pheochromocytoma patients reveals a key role of the splicing machinery in human adipose browning
<p>RNA-sequencing counts data from omental adipose tissue from control individuals (C1-3) and patients with pheochromocytoma (P1-4) for whole genes (genes-counts.tsv) and individual isoforms (isoform-counts.tsv). Additional details regarding recruited individuals are available in the associated manuscript.</p> <p>Tissue fragments (~150 mg) of adipose biopsies from controls and pheochromocytoma patients were homogenized using a metal bead-based mechanical procedure in a TissueLyser® (QIAGEN, Düsseldorf, Germany). Total RNA was isolated from tissue homogenates using a NucleoSpin® RNA kit (Macherey-Nagel, Dueren, Germany) following the manufacturer’s protocol. mRNA was purified from 2 μg of total RNA using oligo-dT beads; it was then fragmented, retrotranscribed with random primers, and subjected to second-strand synthesis to create double-stranded cDNA fragments. Adaptor ligation, purification of 200-base pair cDNA fragments, amplification of the purified fragments, and library preparation were performed as previously reported by our laboratory. Before sequencing, the RNA integrity number (RIN) of each sample was determined using an Agilent Bioanalyzer 2100; samples with RIN ≥ 7.5 were used for RNA-sequencing. The cDNA library quality and quantity were further analyzed as previously described. Libraries yielding satisfactory results were sequenced on an Illumina HiSeq 2000 sequencer (DNAvision, Charleroi, Belgium). The average reads per sample was 45 million; this level of coverage was previously shown to provide sufficient sequencing depth for gene expression quantification and transcript detection. Quality control of reads was performed using FastQC (version 0.11.8; bioinformatics.babraham.ac.uk/projects/fastqc). Gene expression was quantified using Salmon version 1.1.0 with the additional parameters “– seqBias – gcBias – validateMappings”. GENCODE version 31 (GRCh38.p12) was used as the reference genome and indexed using default parameters; this resulted in 175,775 transcripts corresponding to 35,183 genes.</p>
Dataset related to the article "Epicardial Adipose Tissue-Derived IL-1β Triggers Postoperative Atrial Fibrillation"
<p>This record contains raw data related to the article "Epicardial Adipose Tissue-Derived IL-1β Triggers Postoperative Atrial Fibrillation"</p> <p><strong>Background and aims:</strong> Post-operative atrial fibrillation (POAF), defined as new-onset AF in the immediate period after surgery, is associated with poor adverse cardiovascular events and a higher risk of permanent AF. Mechanisms leading to POAF are not completely understood and epicardial adipose tissue (EAT) inflammation could be a potent trigger. Here, we aim at exploring the link between EAT-secreted interleukin (IL)-1β, atrial remodeling, and POAF in a population of coronary artery disease (CAD) patients. <strong>Methods:</strong> We collected EAT and atrial biopsies from 40 CAD patients undergoing cardiac surgery. Serum samples and EAT-conditioned media were screened for IL-1β and IL-1ra. Atrial fibrosis was evaluated at histology. The potential role of NLRP3 inflammasome activation in promoting fibrosis was explored <em>in vitro</em> by exposing human atrial fibroblasts to IL-1β and IL-18. <strong>Results:</strong> 40% of patients developed POAF. Patients with and without POAF were homogeneous for clinical and echocardiographic parameters, including left atrial volume and EAT thickness. POAF was not associated with atrial fibrosis at histology. No significant difference was observed in serum IL-1β and IL-1ra levels between POAF and no-POAF patients. EAT-mediated IL-1β secretion and expression were significantly higher in the POAF group compared to the no-POAF group. The <em>in vitro</em> study showed that both IL-1β and IL-18 increase fibroblasts' proliferation and collagen production. Moreover, the stimulated cells perpetuated inflammation and fibrosis by producing IL-1β and transforming growth factor (TGF)-β. <strong>Conclusion:</strong> EAT could exert a relevant role both in POAF occurrence and in atrial fibrotic remodeling.</p>
Model weights for the white adipose tissue atlas (WATLAS)
<p>Model weights to use contextualization of new sxRNA-seq from human adipose tissue using transfer learning. For a guide on how to use these weights, please look at the section ''Perform surgery on reference model and train on query dataset without cell type labels" under scANVI in the scArches documentation: https://docs.scarches.org/en/latest/scanvi_surgery_pipeline.html</p>
Comparing the effect of TGF-β receptor inhibition on human mesenchymal stem/stromal cells derived from endometrium, bone marrow and adipose tissues
<p><strong>Figure S1: Differences between bmMSC donors. A)</strong> Graph showing two groups of bmMSCs with and without effect of A83-01 treatment on % SUSD2<sup>+</sup> cells. <strong>B)</strong> Graph showing no difference in the number of cells following A83-01 treatment in the two groups of donor cells from <strong>A</strong>. Plots are median for n=3 biological samples per treatment group.</p>
collected PTR-ToF-MS data, optical-based data, food frequency questionairre results, adipose tissue measurements, and anthroprometrics
<p><span>This file contains the collected PTR-ToF-MS data, optical-based data, food frequency questionairre results, adipose tissue measurements, and anthroprometrics. These data were used in the Periodic Technical Report Part B covering M37-M54. Graph generated from these data are Figure 16 and Figure 17 under Taks 7.5.</span></p> <p><span> </span></p> <p><span>Abbreviation<span> </span>Explanation</span></p> <p><span>BMI<span> </span>Body Mass Index</span></p> <p><span>SAT<span> </span>Subcutaneous adipose tissue</span></p> <p><span>VAT<span> </span>Visceral adipose tissue</span></p> <p><span>DHD1<span> </span>Vegetable intake</span></p> <p><span>DHD2<span> </span>Fruit intake</span></p> <p><span>DHD3<span> </span>Whole wheat products</span></p> <p><span>DHD4<span> </span>Legumes</span></p> <p><span>DHD5<span> </span>Nuts/seeds</span></p> <p><span>DHD6<span> </span>Dairy</span></p> <p><span>DHD7<span> </span>Fish</span></p> <p><span>DHD8<span> </span>Fat/oils</span></p> <p><span>DHD9<span> </span>High-fat and processed meat</span></p> <p><span>DHD10<span> </span>Sugar-containing beverages</span></p> <p><span>DHD11<span> </span>Unhealthy choices</span></p>
Brown adipose tissue PET-MR after fructose and glucose intervention
<p>Includes the data and MATLAB functions used to produce the manuscript: "High-fructose feeding suppresses cold-stimulated brown adipose tissue glucose uptake in young men independently of changes in thermogenesis and the gut microbiome"</p>
Spectral flow cytometric tracking of mitochondria transfer in adipose tissue by age and diet
<p>Unmixed fcs files for the stromal vascular fraction of epididymal (eWAT), inguinal (iWAT), and brown adipose tissue (BAT) in <em>mtD2<sup>F/+</sup>AdipoqCre<sup>+/-</sup></em> and different ages or diets. </p> <p>For a breakdown of samples and code, please see: <a href="https://github.com/ncborcherding/mtD2">https://github.com/ncborcherding/mtD2</a>. The full github repository at time of manuscript acceptance is also in the Zenodo repository under <strong>mtD2_GitHubRepo.zip</strong>.</p> <p><strong>Antibody Panel: </strong></p> <table align="center"> <tbody> <tr> <td> <p><strong>Laser</strong></p> </td> <td> <p><strong>Channel</strong></p> </td> <td> <p><strong>Antigen</strong></p> </td> <td> <p><strong>Fluorophore</strong></p> </td> <td> <p><strong>Final Dilution Factor</strong></p> </td> <td> <p><strong>Vendor</strong></p> </td> <td> <p><strong>Clone</strong></p> </td> <td> <p><strong>Catalog Number</strong></p> </td> </tr> <tr> <td> <p>Violet</p> </td> <td> <p>V1</p> </td> <td> <p>SiglecF</p> </td> <td> <p>BV 421</p> </td> <td> <p>1:400</p> </td> <td> <p>BD Biosciences</p> </td> <td> <p>E50-2440</p> </td> <td> <p>562681</p> </td> </tr> <tr> <td> </td> <td> <p>V2</p> </td> <td> <p>CD44</p> </td> <td> <p>Super Bright 436</p> </td> <td> <p>1:300</p> </td> <td> <p>eBioscience</p> </td> <td> <p>IM7</p> </td> <td> <p>62-044-180</p> </td> </tr> <tr> <td> </td> <td> <p>V3</p> </td> <td> <p>CD11b</p> </td> <td> <p>Pacific Blue</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>M1/70</p> </td> <td> <p>101224</p> </td> </tr> <tr> <td> </td> <td> <p>V5</p> </td> <td> <p>IL-33R⍺-biotin</p> </td> <td> <p>n/a</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>DIH9</p> </td> <td> <p>145308</p> </td> </tr> <tr> <td> </td> <td> <p>V5</p> </td> <td> <p>Streptavidin (2°)</p> </td> <td> <p>BV 480</p> </td> <td> <p>1:300</p> </td> <td> <p>BD Horizon</p> </td> <td> <p>n/a</p> </td> <td> <p>564876</p> </td> </tr> <tr> <td> </td> <td> <p>V7</p> </td> <td> <p>MHC-II (I-A/I-E)</p> </td> <td> <p>BV 510</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>M5/114.15.2</p> </td> <td> <p>107636</p> </td> </tr> <tr> <td> </td> <td> <p>V8</p> </td> <td> <p>Ly6C</p> </td> <td> <p>BV 570</p> </td> <td> <p>1:400</p> </td> <td> <p>BioLegend</p> </td> <td> <p>HK1.4</p> </td> <td> <p>128030</p> </td> </tr> <tr> <td> </td> <td> <p>V10</p> </td> <td> <p>KLRG1</p> </td> <td> <p>BV 605</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>2F1/KLRG1</p> </td> <td> <p>138419</p> </td> </tr> <tr> <td> </td> <td> <p>V11</p> </td> <td> <p>F4/80</p> </td> <td> <p>BV 650</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>BM8</p> </td> <td> <p>123149</p> </td> </tr> <tr> <td> </td> <td> <p>V13</p> </td> <td> <p>Ly6G</p> </td> <td> <p>BV 711</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>1A8</p> </td> <td> <p>127643</p> </td> </tr> <tr> <td> </td> <td> <p>V14</p> </td> <td> <p>CD3</p> </td> <td> <p>BV 750</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>17A2</p> </td> <td> <p>100249</p> </td> </tr> <tr> <td> </td> <td> <p>V15</p> </td> <td> <p>CD206</p> </td> <td> <p>BV 785</p> </td> <td> <p>1:200</p> </td> <td> <p>BioLegend</p> </td> <td> <p>C068C2</p> </td> <td> <p>141729</p> </td> </tr> <tr> <td> <p>Blue</p> </td> <td> <p>B1</p> </td> <td> <p>mtDendra2</p> </td> <td> <p>n/a</p> </td> <td> <p>n/a</p> </td> <td> <p>n/a</p> </td> <td> <p>n/a</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> </td> <td> <p>B2</p> </td> <td> <p>TCRb</p> </td> <td> <p>AF 488</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>H57-597</p> </td> <td> <p>109215</p> </td> </tr> <tr> <td> </td> <td> <p>B3</p> </td> <td> <p>CD8</p> </td> <td> <p>Spark Blue 550</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>53-6.7</p> </td> <td> <p>100780</p> </td> </tr> <tr> <td> </td> <td> <p>B8</p> </td> <td> <p>CD45</p> </td> <td> <p>PerCP</p> </td> <td> <p>1:200</p> </td> <td> <p>BioLegend</p> </td> <td> <p>30-F11</p> </td> <td> <p>103130</p> </td> </tr> <tr> <td> </td> <td> <p>B9</p> </td> <td> <p>CD71</p> </td> <td> <p>PerCP-Cy5.5</p> </td> <td> <p>1:200</p> </td> <td> <p>BioLegend</p> </td> <td> <p>RI7217</p> </td> <td> <p>113816</p> </td> </tr> <tr> <td> </td> <td> <p>B10</p> </td> <td> <p>NKp46</p> </td> <td> <p>PerCP-eFluor710</p> </td> <td> <p>1:200</p> </td> <td> <p>eBioscience</p> </td> <td> <p>29A1.4</p> </td> <td> <p>46-3351-82</p> </td> </tr> <tr> <td> <p>Green</p> </td> <td> <p>YG1</p> </td> <td> <p>TCRgd</p> </td> <td> <p>PE</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>GL3</p> </td> <td> <p>118108</p> </td> </tr> <tr> <td> </td> <td> <p>YG3</p> </td> <td> <p>CD64</p> </td> <td> <p>PE/Dazzle 594</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>X54-5/7.1</p> </td> <td> <p>139320</p> </td> </tr> <tr> <td> </td> <td> <p>YG4</p> </td> <td> <p>CD4</p> </td> <td> <p>PE/Fire 640</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>GK1.5</p> </td> <td> <p>100482</p> </td> </tr> <tr> <td> </td> <td> <p>YG5</p> </td> <td> <p>CD62L</p> </td> <td> <p>PE-Cyanine5</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>MEL-14</p> </td> <td> <p>104410</p> </td> </tr> <tr> <td> </td> <td> <p>YG7</p> </td> <td> <p>CD11c</p> </td> <td> <p>PE-Cyanine5.5</p> </td> <td> <p>1:300</p> </td> <td> <p>eBioscience</p> </td> <td> <p>N418</p> </td> <td> <p>35-0114-82</p> </td> </tr> <tr> <td> </td> <td> <p>YG9</p> </td> <td> <p>CD25</p> </td> <td> <p>PE/Cyanine7</p> </td> <td> <p>1:200</p> </td> <td> <p>BioLegend</p> </td> <td> <p>PC61</p> </td> <td> <p>102016</p> </td> </tr> <tr> <td> <p>Red</p> </td> <td> <p>R1</p> </td> <td> <p>CD9</p> </td> <td> <p>APC</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>MZ3</p> </td> <td> <p>124812</p> </td> </tr> <tr> <td> </td> <td> <p>R2</p> </td> <td> <p>CD127</p> </td> <td> <p>AF 647</p> </td> <td> <p>1:200</p> </td> <td> <p>BioLegend</p> </td> <td> <p>A7R34</p> </td> <td> <p>135020</p> </td> </tr> <tr> <td> </td> <td> <p>R3</p> </td> <td> <p>CD19</p> </td> <td> <p>Spark NIR 550</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>6D5</p> </td> <td> <p>115568</p> </td> </tr> <tr> <td> </td> <td> <p>R4</p> </td> <td> <p>CD90.2</p> </td> <td> <p>AF 700</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>30-H12</p> </td> <td> <p>105320</p> </td> </tr> <tr> <td> </td> <td> <p>R5</p> </td> <td> <p>CD5</p> </td> <td> <p>BUV 737</p> </td> <td> <p>1:300</p> </td> <td> <p>BD Horizon</p> </td> <td> <p>53-7-3</p> </td> <td> <p>612809</p> </td> </tr> <tr> <td> </td> <td> <p>R6</p> </td> <td> <p>Viability dye</p> </td> <td> <p>Zombie NIR</p> </td> <td> <p>1:1,000</p> </td> <td> <p>BioLegend</p> </td> <td> <p>n/a</p> </td> <td> <p>423106</p> </td> </tr> <tr> <td> </td> <td> <p>R7</p> </td> <td> <p>NK1.1</p> </td> <td> <p>APC/Fire 750</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>PK136</p> </td> <td> <p>108752</p> </td> </tr> <tr> <td> </td> <td> <p>R8</p> </td> <td> <p>B220</p> </td> <td> <p>APC/Fire 810</p> </td> <td> <p>1:300</p> </td> <td> <p>BioLegend</p> </td> <td> <p>RA3-6B2</p> </td> <td> <p>103278</p> </td> </tr> </tbody> </table> <p> </p>
Efficacy of Micro-fragmented Adipose Tissue Injection for Knee Osteoarthritis.
ClinicalTrials.gov study NCT03379168. IPD Sharing: NO. Countries: 1. Publications: 24.
Effects of b3-Adrenergic Receptor Agonists on Brown Adipose Tissue
ClinicalTrials.gov study NCT01783470. IPD Sharing: NO. Countries: 1. Publications: 1.
Retrospective Results of Micronized Adipose Tissue in Patients With Interstitial Cystitis
ClinicalTrials.gov study NCT07104981. IPD Sharing: NO. Countries: 1. Publications: 3.
The Project ATA: Adipose Tissue & Adipokines
ClinicalTrials.gov study NCT05758311. IPD Sharing: NO. Countries: 1. Publications: 3.
Microfragmented Adipose Tissue Versus Platelet-rich Plasma for Knee Osteoarthritis: a Randomized Comparative Trial
ClinicalTrials.gov study NCT04351087. IPD Sharing: NO. Countries: 1. Publications: 16.
Effect of Time-restricted Eating on Catecholamine-sensitivity of Adipose Tissue in Obese Adults
ClinicalTrials.gov study NCT04916730. IPD Sharing: NO. Countries: 1. Publications: 0.
Investigating Brown Adipose Tissue Activation in Humans
ClinicalTrials.gov study NCT01935791. IPD Sharing: NO. Countries: 1. Publications: 1.
Adipose Tissue Extract and Platelet-rich Plasma Use for Wound Healing
ClinicalTrials.gov study NCT02799290. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Brown adipose tissue and skeletal muscle coordinately contribute to thermogenesis in mice
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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.