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3 results for “Gut Analysis Toolbox”
Gut Analysis Toolbox: Data and code associated with JCS manuscript
<p>The data and python code in jupyter notebooks are associated with the manuscript: <strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <ul> <li><strong>FigS1_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. S1D,E.</li> <li><strong>Fig3_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. 3D-N. <ul> <li>The images and analysis files associated with analysis in GAT are also uploaded: CalR_CalB_GAT_analysis.zip</li> <li>The images used in this analysis are from EXP174 in this dataset: <a href="https://zenodo.org/records/7236748">https://zenodo.org/records/7236748</a></li> </ul> </li> </ul>
Image datasets associated with Gut Analysis Toolbox
<div>The images are sample image datasets associated with the software: <a href="https://gut-analysis-toolbox.gitbook.io/docs/">Gut Analysis Toolbox (GAT)</a>.</div> <div>The dataset contains immunofluorescence images of enteric neurons and glia labeled with different markers. The data is mostly from mouse and human colon or small intestine. </div> <div>Channels corresponding to Hu labelling can be used for segmenting enteric neurons in GAT. </div> <div>Channels corresponding to GFAP (enteric glia) or neurons with markers labelling the cell body and processes (ChAT, Calbindin, Calretinin) can be used as a ganglia marker for segmenting the ganglia</div> <div>The data is two-dimensional (2D) with some images having multiple channels. The data is mostly in tif format, except for one dataset that is czi (Fiji using bioformats or aicspylibczi in Python). Calcium imaging data is 2D+Time.</div> <div> </div> <div>Data curated by: <a href="https://www.linkedin.com/in/rajapradeep/">Pradeep Rajasekhar, Walter and Eliza Hall Institute of Medical Research</a>, Australia</div> <h2><strong>Data from<a href="https://www.monash.edu/mips/themes/drug-discovery-biology/labs/inm"> INM lab, Monash University</a> (mouse images)</strong></h2> <p><strong>Immunofluorescence images</strong></p> <ul> <li> 181107_ms_distal_colon_GFAP_Hu_40X.tif <ul> <li>Channel 1: GFAP</li> <li>Channel 2: Hu</li> </ul> </li> </ul> <div> <ul> <li>181107_ms_distal_colon_nNOS_GFAP_Hu_40X.tif (Same as above, but got an extra channel)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: nNOS</li> <li>Channel 2: GFAP</li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div>In both images above, GFAP can be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li>ms_distal_colon_Hu_20X.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>ms_distal_colon_Hu_40X_1.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <div> <ul> <li>Tilescan_GAT_ms_distal_colon_MP_hu.tif</li> </ul> </div> <div> <ul> <li> <ul> <li>Hu</li> </ul> </li> </ul> </div> <h3><strong>Calcium imaging data (video)</strong></h3> <div>• calcium_imaging_mouse_distal_colon_25X.tif</div> <div>Tissue was incubated with calcium dye Fluo8-AM calcium dye (Myenteric wholemount from distal colon of mouse)</div> <div>142 frames in total acquired at 1.162 frames per second. At frame 52, 100 uM of ATP is added which causes enteric glia, neurons and blood vessels to respond. This causes slow drifting in the field of view.</div> <div> </div> <div>• mouse_GCamp_calcium_movement.tif</div> <div> </div> <div>Wnt1-GCaMP3 mouse where GCamP3 is a genetically encoded calcium sensor expressed by enteric neurons and enteric glia</div> <div>Imaging performed at Monash University. 745 frames in total acquired at 1.162 frames per second with drifting over time</div> <div>Tissue source: <a href="https://biomedicalsciences.unimelb.edu.au/sbs-research-groups/anatomy-and-physiology-research/neuroscience/development-of-the-enteric-nervous-system">Stamp & Hao laboratory, University of Melbourne.</a></div> <div> </div> <h2><strong>Images from McQuade Lab, University of Melbourne</strong></h2> <ul> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse colon)</li> <li>ms_28_wk_colon_DAPI_nNOS_Hu_10X.tif (mouse ileum) <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <div> <ul> <li>ms_distal_colon_nNOS_Hu_10X.czi (This is a .czi file which can be opened in Fiji using bioformats or aicspylibczi in Python)</li> </ul> </div> <ul> <li> <ul> <li>Channel 1: DAPI</li> <li>Channel 2: nNOS </li> <li>Channel 3: Hu</li> </ul> </li> </ul> <h2><strong>Images from public repository (SPARC)</strong>:</h2> <ul> <li>DYM_22_7_Pr_Chat_BYFP_DIN_GFP-g_nNOS-m_VIP-r_Hu-b.tif is a crop from File 100 05-07-2019 DYM 22 7 Pr Chat%3BYFP DIN GFP-g nNOS-m VIP-r Hu-b (Mouse Proximal Colon)</li> <li>DYM_22_7_Pr_Hu_crop.tif is a crop from above. <ul> <li>Channel 1: Choline acetyltransferase</li> <li>Channel 2: nNOS</li> <li>Channel 3: Calretinin</li> <li>Channel 4: Hu (pan-neuronal marker)</li> </ul> </li> </ul> <div> Channel 1 and 3 can be used as ganglia segmentation channels in GAT using DeepImageJ.</div> <div> </div> <div> <ul> <li>146_02_14_20DYM8_6_mouse_Mid_Chat-g CalB-r CalR-b_max.tif is from File 146 02-14-20 DYM 8 6 Mid Chat-g CalB-r CalR-b (Mouse mid colon)</li> </ul> </div> <div> No Hu staining, any channel could be used as ganglia segmentation channel in GAT using DeepImageJ.</div> <div> <ul> <li> <ul> <li>Channel 1: ChAT</li> <li>Channel 2: Calbindin</li> <li>Channel 3: Calretinin</li> </ul> </li> </ul> </div> <h3><strong>Reference</strong>:</h3> <div>Thanks goes to Marthe Howard for depositing the data in the SPARC repository.</div> <div>Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium.<a href="https://doi.org/10.26275/9FFG-482D"> https://doi.org/10.26275/9FFG-482D</a></div> <div>**************</div> <h2><strong>Multiplex data (Flinders University)</strong></h2> <div><strong>Multiplexing_H2202Desc_Layer 1_Ganglia1_Hu.zip </strong>is from:</div> <div><a href="https://pubmed.ncbi.nlm.nih.gov/37355216/">Chen, B. N., Humenick, A., Yew, W. P., Peterson, R. A., Wiklendt, L., Dinning, P. G., Spencer, N. J., Wattchow, D. A., Costa, M., & Brookes, S. J. H. (2023). Types of Neurons in the Human Colonic Myenteric Plexus Identified by Multilayer Immunohistochemical Coding. Cellular and molecular gastroenterology and hepatology, 16(4), 573–605.</a></div> <div>This data is a myenteric wholemount from the descending colon of a Human. It has 14 different markers, 6 different rounds of staining. Every round has pan-neuronal marker Hu as a reference marker.There are 19 images. The filenames follow the convention:</div> <div> </div> <div>H2202Desc_<em>layer num</em>_<em>ganglia num</em>_<em>markername</em>.tif </div> <div><em>H2202 </em>is the sample name, <em>Desc </em>means descending colon</div> <div>Here <em>layer num</em> corresponds to the round of staining, so Layer3, means its the 3rd round of staining.</div> <div><em>ganglia num</em> is specified as multiple ganglia can be imaged from same tissue. </div> <div><em>markername</em> corresponds to the marker used. </div> <div> </div> <div>Markers used are: Hu, 5HT, ChAT, NOS, CGRP, Enk, SP, Somat, VACht, NPY, Calbindin, Calretinin, NF, VIP</div> <div> </div> <div><strong>Abbreviations:</strong></div> <div> </div> <ul> <li>Hu: Pan-neuronal marker</li> <li>5HT: Serotonin (5-Hydroxytryptamine)</li> <li>ChAT: Choline acetyltransferase</li> <li>nNOS: neuronal Nitric Oxide Synthase (NOS in these images are actually nNOS)</li> <li>CGRP: Calcitonin Gene-Related Peptide</li> <li>Enk: Enkephalin</li> <li>SP: Substance P</li> <li>Somat: Somatostatin</li> <li>VACht: Vasoactive Intestinal Peptide (VIP) </li> <li>NPY: Neuropeptide Y</li> <li>NF: neurofilament 200 </li> </ul>
Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia
<p>This upload is associated with the software, <a href="https://github.com/pr4deepr/GutAnalysisToolbox">Gut Analysis Toolbox</a> (GAT).</p> <p>If you use it please cite:</p> <p><strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <p>The upload contains<strong> StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and FPN+ResNet101 model for enteric ganglia in 2D in gut wholemount tissue.</strong> GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of <strong>Python notebooks (Google Colab)</strong>, training and test data as well as reports on model performance.</p> <p>Note: The enteric ganglia model is has been updated to v3 which uses pytorch and is a different architecture (FPN+ResNet101).</p> <p>The model files are located in the respective folders as zip files. The folders have also been zipped:</p> <ul> <li>Neuron (Hu; <a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_model_QA.zip</li> <li>StarDist Model File:2D_enteric_neuron_v4_1.zip </li> <li>DeepImageJ compatible model: 2D_enteric_neuron.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Neuronal subtype (<a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_subtype_model_QA.zip</li> <li>Model File: 2D_enteric_neuron_subtype_v4.zip</li> <li>DeepImageJ compatible model: 2D_enteric_neuron_subtype.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Enteric ganglia (2D FPN_ResNet101; Use in FIJI with <a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a>) <ul> <li>Main folder: 2D_enteric_ganglia_v3_training.zip</li> <li>Model File: 2D_Ganglia_RGB_v3.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> </ul> <p>For the all models, files included are:</p> <ol> <li>Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet.</li> <li>Training and Test datasets used for training.</li> <li>Google Colab notebooks used for training and quality assurance (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic notebooks</a>).</li> <li>Python notebook and code for training ganglia model with QA.</li> <li>Quality assurance reports generated from above notebooks.</li> <li>StarDist model exported for use in QuPath.</li> </ol> <p>The model files can be used within can be used within the software, <a href="https://github.com/stardist/stardist">StarDist</a>. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D.</p> <p><strong>Data:</strong></p> <p>All the images were collected from 4 different research labs and a public database (<a href="https://sparc.science/data?type=dataset">SPARC database</a>) to account for variations in image acquisition, sample preparation and immunolabelling.</p> <p>For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue.</p> <p>For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used..</p> <p>25 images were used from the following entries in the <a href="https://sparc.science/data?type=dataset">SPARC database</a>:</p> <ul> <li><a href="https://doi.org/10.26275/9FFG-482D">Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. </a></li> <li><a href="https://doi.org/10.26275/PZEK-91WX">Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium.</a></li> <li>Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h</li> </ul> <p>Additional images for new ganglia model:</p> <ul> <li>Hamnett, R., Dershowitz, L. B., Sampathkumar, V., Wang, Z., Gomez-Frittelli, J., De Andrade, V., Kasthuri, N., Druckmann, S. and Kaltschmidt, J. A. (2022b). Regional cytoarchitecture of the adult and developing mouse enteric nervous system. Curr. Biol. 32, 4483-4492.e5.</li> </ul> <p>The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: </p> <ul> <li> <p>Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) </p> </li> <li> <p>Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) </p> </li> <li> <p>Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) </p> </li> <li> <p>Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) </p> </li> </ul> <p>Human tissue images were acquired using: </p> <ul> <li> <p>IX71 Olympus microscope (10X HC PL APO NA 0.3) </p> </li> </ul> <p>For more information, visit the <a href="https://gut-analysis-toolbox.gitbook.io/docs" target="_blank" rel="noopener">Documentation</a> website.</p> <p><strong>NOTE:</strong> The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation </p> <p>Average neuron area for neuronal model: 701.2 ± 195.9 pixel<sup>2 </sup>(Mean ± SD, 6267 cells)</p> <p>Average neuron area for neuronal subtype model: 880.9 ± 316 pixel<sup>2 </sup>(Mean ± SD, 924 cells)</p> <p><strong>Software References:</strong></p> <p><strong><a href="https://github.com/stardist/stardist">Stardist</a></strong></p> <p>Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 265-273). Springer, Cham.</p> <p><strong><a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a></strong></p> <p>Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. <em>Nature Methods</em>, <em>18</em>(10), pp.1192-1195.</p> <p><strong><a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic</a></strong></p> <p>von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. <em>Nature communications</em>, <em>12</em>(1), pp.1-18.</p>
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