This upload is associated with the software, Gut Analysis Toolbox (GAT). It contains StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and UNet model for enteric ganglia in 2D in gut wholemount tissue. 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 Python notebooks (Google Colab), training and test data as well as reports on model performance. The model files are located in the respective folders as zip files. The folders have also been zipped: Neuron (Hu; StarDist model): Main folder: 2D_enteric_neuron_model_QA.zip Model File:2D_enteric_neuron_v4_1.zip Neuronal subtype (StarDist model): Main folder: 2D_enteric_neuron_subtype_model_QA.zip Model File: 2D_enteric_neuron_subtype_v4.zip Enteric ganglia (2D UNet model; Use in FIJI with deepImageJ) Main folder: 2D_enteric_ganglia_model_QA.zip Model File: 2D_Ganglia_RGB_v2.bioimage.io.model.zip (Compatible with deepimageJ v3) For the all models, files included are: Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet. Training and Test datasets used for training. Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic notebooks). Quality assurance reports generated from above notebooks. StarDist model exported for use in QuPath. The model files can be used within can be used within the software, StarDist. 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. Data: All the images were collected from 4 different research labs and a public database (SPARC database) to account for variations in image acquisition, sample preparation and immunolabelling. For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue. For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used.. 25 images were used from the following entries in the SPARC database: Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. 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. The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) Human tissue images were acquired using: IX71 Olympus microscope (10X HC PL APO NA 0.3) For more information, visit: https://github.com/pr4deepr/GutAnalysisToolbox/wiki NOTE: 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 Average neuron area for neuronal model: 701.2 ± 195.9 pixel2 (Mean ± SD, 6267 cells) Average neuron area for neuronal subtype model: 880.9 ± 316 pixel2 (Mean ± SD, 924 cells) Software References: Stardist Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer, Cham. deepImageJ 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. Nature Methods, 18(10), pp.1192-1195. ZeroCost DL4Mic 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. Nature communications, 12(1), pp.1-18.
This upload is associated with the software, Gut Analysis Toolbox (GAT). It contains StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and UNet model for enteric ganglia in 2D in gut wholemount tissue. 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 Python notebooks (Google Colab), training and test data as well as reports on model performance. The model files are located in the respective folders as zip files. The folders have also been zipped: Neuron (Hu; StarDist model): Main folder: 2D_enteric_neuron_model_QA.zip Model File:2D_enteric_neuron_v4_1.zip Neuronal subtype (StarDist model): Main folder: 2D_enteric_neuron_subtype_model_QA.zip Model File: 2D_enteric_neuron_subtype_v4.zip Enteric ganglia (2D UNet model; Use in FIJI with deepImageJ) Main folder: 2D_enteric_ganglia_model_QA.zip Model File:2D_enteric_ganglia_v2.bioimage.io.model.zip For the all models, files included are: Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet. Training and Test datasets used for training. Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic notebooks). Quality assurance reports generated from above notebooks. StarDist model exported for use in QuPath. The model files can be used within can be used within the software, StarDist. 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. Data: All the images were collected from 4 different research labs and a public database (SPARC database) to account for variations in image acquisition, sample preparation and immunolabelling. For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue. For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used.. 25 images were used from the following entries in the SPARC database: Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. 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. The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) Human tissue images were acquired using: IX71 Olympus microscope (10X HC PL APO NA 0.3) For more information, visit: https://github.com/pr4deepr/GutAnalysisToolbox/wiki NOTE: 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 Average neuron area for neuronal model: 701.2 ± 195.9 pixel2 (Mean ± SD, 6267 cells) Average neuron area for neuronal subtype model: 880.9 ± 316 pixel2 (Mean ± SD, 924 cells) Software References:Stardist Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer, Cham. deepImageJ 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. Nature Methods, 18(10), pp.1192-1195. ZeroCost DL4Mic 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. Nature communications, 12(1), pp.1-18.
Summary All cells face the challenge of integrating multiple extracellular signals to produce relevant physiological responses. Different combinations of G protein-coupled receptors, when co-expressed, can lead to distinct cellular outputs, yet the molecular basis for this co-operativity is controversial. One such interaction is the reversal, from inhibition to excitation, at the dopamine D2 receptor in the ghrelin receptor’s presence, relevant for defecation control. Here we demonstrate that this reversal of dopamine D2 activity, to excitatory, occurs through a dominant switch in downstream signaling. This dominant switch, mediated by downstream signaling, enables fidelity in cellular responses not possible under alternative models, and provides an explanation for previously unresolved observations. Importantly, the switch in D2 signaling does not require ghrelin receptor agonism, rather its constitutive activity, thus accounting for the importance of central nervous system-ghrelin receptor in the absence of endogenous ligands. This re-coding has important implications for our understanding of how atypical receptor pharmacology can occur as well as how sequential signaling at individual neurons may be encoded to produce new outputs.
This upload is associated with the software, Gut Analysis Toolbox (GAT). It contains StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and UNet model for enteric ganglia in 2D in gut wholemount tissue. 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 Python notebooks (Google Colab), training and test data as well as reports on model performance. The model files are located in the respective folders as zip files. The folders have also been zipped: Neuron (Hu; StarDist model): Main folder: 2D_enteric_neuron_model_QA.zip Model File:2D_enteric_neuron_v4_1.zip Neuronal subtype (StarDist model): Main folder: 2D_enteric_neuron_subtype_model_QA.zip Model File: 2D_enteric_neuron_subtype_v4.zip Enteric ganglia (2D UNet model; Use in FIJI with deepImageJ) Main folder: 2D_enteric_ganglia_model_QA.zip Model File: 2D_Ganglia_RGB_v2.bioimage.io.model.zip (Compatible with deepimageJ v3) For the all models, files included are: Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet. Training and Test datasets used for training. Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic notebooks). Quality assurance reports generated from above notebooks. StarDist model exported for use in QuPath. The model files can be used within can be used within the software, StarDist. 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. Data: All the images were collected from 4 different research labs and a public database (SPARC database) to account for variations in image acquisition, sample preparation and immunolabelling. For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue. For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used.. 25 images were used from the following entries in the SPARC database: Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. 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. The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) Human tissue images were acquired using: IX71 Olympus microscope (10X HC PL APO NA 0.3) For more information, visit: https://github.com/pr4deepr/GutAnalysisToolbox/wiki NOTE: 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 Average neuron area for neuronal model: 701.2 ± 195.9 pixel2 (Mean ± SD, 6267 cells) Average neuron area for neuronal subtype model: 880.9 ± 316 pixel2 (Mean ± SD, 924 cells) Software References: Stardist Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer, Cham. deepImageJ 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. Nature Methods, 18(10), pp.1192-1195. ZeroCost DL4Mic 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. Nature communications, 12(1), pp.1-18.
Abstract The enteric nervous system (ENS) plays an important role in coordinating gut function. The ENS consists of an extensive network of neurons and glial cells within the wall of the gastrointestinal tract. Alterations in neuronal distribution, function, and type are strongly associated with enteric neuropathies and gastrointestinal (GI) dysfunction and can serve as biomarkers for disease. However, current methods for assessing neuronal counts and distribution suffer from undersampling. This is partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. Here, we present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using 2D images of GI wholemount preparations. GAT is developed for the Fiji distribution of ImageJ. It has a user-friendly interface and offers rapid and accurate cell segmentation. Custom deep learning (DL) based cell segmentation models were developed using StarDist. GAT also includes a ganglion segmentation model which was developed using deepImageJ. In addition, GAT allows importing of segmentation generated by other software. DL models have been trained using ZeroCostDL4Mic on diverse datasets sourced from different laboratories. This captures the variability associated with differences in animal species, image acquisition parameters, and sample preparation across research groups. We demonstrate the robustness of the cell segmentation DL models by comparing them against the state-of-the-art cell segmentation software, Cellpose. To quantify neuronal distribution GAT applies proximal neighbor-based spatial analysis. We demonstrate how the proximal neighbor analysis can reveal differences in cellular distribution across gut regions using a published dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput allowing unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples rapidly.
The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using 2D images of GI wholemount preparations. It is developed in Fiji, has a user-friendly interface and offers rapid and accurate segmentation via custom deep learning (DL) based cell segmentation models developed using StarDist, and a ganglion segmentation model in deepImageJ. We use proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput allowing unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples rapidly.
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