SpheroidPicker for automated 3D cell culture manipulation using deep learning

Recent statistics report that more than 3.7 million new cases of cancer occur in Europe yearly, and the disease accounts for approximately 20% of all deaths. High-throughput screening of cancer cell cultures has dominated the search for novel, effective anticancer therapies in the past decades. Recently, functional assays with patient-derived ex vivo 3D cell culture have gained importance for drug discovery and precision medicine. We recently evaluated the major advancements and needs for the 3D cell culture screening, and concluded that strictly standardized and robust sample preparation is the most desired development. Here we propose an artificial intelligence-guided low-cost 3D cell culture delivery system. It consists of a light microscope, a micromanipulator, a syringe pump, and a controller computer. The system performs morphology-based feature analysis on spheroids and can select uniform sized or shaped spheroids to transfer them between various sample holders. It can select the samples from standard sample holders, including Petri dishes and microwell plates, and then transfer them to a variety of holders up to 384 well plates. The device performs reliable semi- and fully automated spheroid transfer. This results in highly controlled experimental conditions and eliminates non-trivial side effects of sample variability that is a key aspect towards next-generation precision medicine.Peer reviewe

Cell lines and clearing approaches : a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids

Nowadays, three dimensional (3D) cell cultures are widely used in the biological laboratories and several optical clearing approaches have been proposed to visualize individual cells in the deepest layers of cancer multicellular spheroids. However, defining the most appropriate clearing approach for the different cell lines is an open issue due to the lack of a gold standard quantitative metric. In this article, we describe and share a single-cell resolution 3D image dataset of human carcinoma spheroids imaged using a light-sheet fluorescence microscope. The dataset contains 90 multicellular cancer spheroids derived from 3 cell lines (i.e. T-47D, 5-8F, and Huh-7D12) and cleared with 5 different protocols, precisely Clear(T) , Clear(T2) , CUBIC, ScaleA2, and Sucrose. To evaluate image quality and light penetration depth of the cleared 3D samples, all the spheroids have been imaged under the same experimental conditions, labelling the nuclei with the DRAQ(5) stain and using a Leica SP8 Digital LightSheet microscope. The clearing quality of this dataset was annotated by 10 independent experts and thus allows microscopy users to qualitatively compare the effects of different optical clearing protocols on different cell lines. It is also an optimal testbed to quantitatively assess different com putational metrics evaluating the image quality in the deepest layers of the spheroids. (C) 2021 The Author(s). Published by Elsevier Inc.Peer reviewe

3D-Cell-Annotator : an open-source active surface tool for single-cell segmentation in 3D microscopy images

aSummary: Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids and embryos) show that the precision of the segmentation reaches the level of a human expert.Peer reviewe

A quantitative metric for the comparative evaluation of optical clearing protocols for 3D multicellular spheroids

3D multicellular spheroids quickly emerged as in vitro models because they represent the in vivo tumor environment better than standard 2D cell cultures. However, with current microscopy technologies, it is difficult to visualize individual cells in the deeper layers of 3D samples mainly because of limited light penetration and scattering. To overcome this problem several optical clearing methods have been proposed but defining the most appropriate clearing approach is an open issue due to the lack of a gold standard metric. Here, we propose a guideline for 3D light microscopy imaging to achieve single-cell resolution. The guideline includes a validation experiment focusing on five optical clearing protocols. We review and compare seven quality metrics which quantitatively characterize the imaging quality of spheroids. As a test environment, we have created and shared a large 3D dataset including approximately hundred fluorescently stained and optically cleared spheroids. Based on the results we introduce the use of a novel quality metric as a promising method to serve as a gold standard, applicable to compare optical clearing protocols, and decide on the most suitable one for a particular experiment. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.Peer reviewe

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2

We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics

MISpheroID: a knowledgebase and transparency tool for minimum information in spheroid identity

Spheroids are three-dimensional cellular models with widespread basic and translational application across academia and industry. However, methodological transparency and guidelines for spheroid research have not yet been established. The MISpheroID Consortium developed a crowdsourcing knowledgebase that assembles the experimental parameters of 3,058 published spheroid-related experiments. Interrogation of this knowledgebase identified heterogeneity in the methodological setup of spheroids. Empirical evaluation and interlaboratory validation of selected variations in spheroid methodology revealed diverse impacts on spheroid metrics. To facilitate interpretation, stimulate transparency and increase awareness, the Consortium defines the MISpheroID string, a minimum set of experimental parameters required to report spheroid research. Thus, MISpheroID combines a valuable resource and a tool for three-dimensional cellular models to mine experimental parameters and to improve reproducibility. © 2021, The Author(s)

2024_Hollandi-6_Melanoma_100-masks

ARTICLE (when using these files, please, cite the following article):Réka Hollandi, David Bauer, Akos Diosdi, Bálint Schrettner, Timea Toth, Dominik Hirling, Gábor Hollandi, Maria Harmati, József Molnár, Peter Horvath, "When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling". (2024)DESCRIPTION OF THE FILES:2D: Consists of HeLa Kyoto cells imaged by confocal images with multiple fluorescent channels including DAPI-405 (C001), Alexa Fluor-488 (C002), and Alexa Fluor-633 (C003). Each fluorescent image is 2048 × 2048 pixel resolution with 0.103 µm pixel size. All images were annotated by 3 experts, who annotated both the cytoplasm and nuclei of the images. Each of the images was labeled twice: once using only manual labor (manual) and again using Minimal contour (minimal). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are included.Embryo: 3D fluorescent image data of a mouse embryo [1]. All three experts annotated the data separately. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Annotations made with webKnossos, Paintera, and ITK-Snap were included. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlNeurosphere: 3D image data captured using light-sheet fluorescence microscopy, which included a spheroid with fluorescently labeled nuclei [2]. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlCo-culture: Multicellular spheroids were generated by using the HeLa Kyoto EGFP-alpha-tubulin/H2B-mCherry and MRC-5 fibroblast cell lines. 4 channels were acquired: DAPI-405 (ch00), Tubulin-488 (ch01), mCherry-552 (ch02), and Aktin-638 (ch03). The multi-page 16-bit Tif files have a resolution of 2048 × 2048, with pixel size of 0.14370117 µm. The distance between each image in each z-stack is 3.7 µm. To reduce the blurry effect, a deconvolution post-processing step was included only on DAPI channel provided by the LIGHTNING software (available with the LAS-X 4.4 software, Leica). Each folder includes the annotation (Labels.tiff) and the time for each annotation (annotation_times.csv). Both original images (channels) and post-processed images (LIGHTNING_DAPI_only) are included.Mitotic spheroid data: Consists of 90 multicellular cancer spheroids generated from different cell lines (T-47D, 5-8F, and Huh-7D12) and imaged with a light-sheet microscope [3]. The nucleus is fluorescently labeled in the images. For each spheroid, only cells with mitotic phenotype were annotated (Labels.tiff). Images are free to download from: https://doi.org/10.6084/m9.figshare.12620078.v1Melanoma_100: Benign and malignant cases were annotated by an expert in the subset of 100 images that were chosen from the HAM10000 dataset [4]. The annotations outline the lesion and separate the object from the background. Images are free to download from: https://doi.org/10.7910/DVN/DBW86TReferences:1. Saiz N. et al. Quantitative analysis of protein expression to study lineage specification in mouse preimplantation embryos. J. Vis. Exp., 108, e53654 (2016)2. Gole L. et al. OpenSegSPIM: a user-friendly segmentation tool for SPIM data. Bioinformatics, 32, 2075–2077 (2016)3. Diosdi A. et al. Cell lines and clearing approaches: a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids. Data in Brief Volume 36, 107090 (2021)4. Tschandl, P. et al. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018)Napari-biomag-annotator is freely available here: https://github.com/biomag-lab/napari-biomag-annotatorAll additional details about the annotation methods and image acquisition have been reported in the article cited above.MAIN CONTACT:Peter Horvath, Synthetic and Systems Biology Unit, Biological Research Centre (BRC) HUN-REN, 6726 Szeged, Hungary Email: [email protected]:* Copyright (c) 2024, Akos Diosdi, Peter Horvath* Biological Research Centre (BRC) HUN-REN, Szeged, Hungary* All rights reserved.** Redistribution and use of the material, with or without modification, is provided for academic research purposes only.** This material is free; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 (or higher) as published by the Free Software Foundation.* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.* See the GNU General Public License for more details</p

2024_Hollandi-1_2D_images_and_masks

ARTICLE (when using these files, please, cite the following article):Réka Hollandi, David Bauer, Akos Diosdi, Bálint Schrettner, Timea Toth, Dominik Hirling, Gábor Hollandi, Maria Harmati, József Molnár, Peter Horvath, "When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling". (2024)DESCRIPTION OF THE FILES:2D: Consists of HeLa Kyoto cells imaged by confocal images with multiple fluorescent channels including DAPI-405 (C001), Alexa Fluor-488 (C002), and Alexa Fluor-633 (C003). Each fluorescent image is 2048 × 2048 pixel resolution with 0.103 µm pixel size. All images were annotated by 3 experts, who annotated both the cytoplasm and nuclei of the images. Each of the images was labeled twice: once using only manual labor (manual) and again using Minimal contour (minimal). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are included.Embryo: 3D fluorescent image data of a mouse embryo [1]. All three experts annotated the data separately. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Annotations made with webKnossos, Paintera, and ITK-Snap were included. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlNeurosphere: 3D image data captured using light-sheet fluorescence microscopy, which included a spheroid with fluorescently labeled nuclei [2]. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlCo-culture: Multicellular spheroids were generated by using the HeLa Kyoto EGFP-alpha-tubulin/H2B-mCherry and MRC-5 fibroblast cell lines. 4 channels were acquired: DAPI-405 (ch00), Tubulin-488 (ch01), mCherry-552 (ch02), and Aktin-638 (ch03). The multi-page 16-bit Tif files have a resolution of 2048 × 2048, with pixel size of 0.14370117 µm. The distance between each image in each z-stack is 3.7 µm. To reduce the blurry effect, a deconvolution post-processing step was included only on DAPI channel provided by the LIGHTNING software (available with the LAS-X 4.4 software, Leica). Each folder includes the annotation (Labels.tiff) and the time for each annotation (annotation_times.csv). Both original images (channels) and post-processed images (LIGHTNING_DAPI_only) are included.Mitotic spheroid data: Consists of 90 multicellular cancer spheroids generated from different cell lines (T-47D, 5-8F, and Huh-7D12) and imaged with a light-sheet microscope [3]. The nucleus is fluorescently labeled in the images. For each spheroid, only cells with mitotic phenotype were annotated (Labels.tiff). Images are free to download from: https://doi.org/10.6084/m9.figshare.12620078.v1Melanoma_100: Benign and malignant cases were annotated by an expert in the subset of 100 images that were chosen from the HAM10000 dataset [4]. The annotations outline the lesion and separate the object from the background. Images are free to download from: https://doi.org/10.7910/DVN/DBW86TReferences:1. Saiz N. et al. Quantitative analysis of protein expression to study lineage specification in mouse preimplantation embryos. J. Vis. Exp., 108, e53654 (2016)2. Gole L. et al. OpenSegSPIM: a user-friendly segmentation tool for SPIM data. Bioinformatics, 32, 2075–2077 (2016)3. Diosdi A. et al. Cell lines and clearing approaches: a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids. Data in Brief Volume 36, 107090 (2021)4. Tschandl, P. et al. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018)Napari-biomag-annotator is freely available here: https://github.com/biomag-lab/napari-biomag-annotatorAll additional details about the annotation methods and image acquisition have been reported in the article cited above.MAIN CONTACT:Peter Horvath, Synthetic and Systems Biology Unit, Biological Research Centre (BRC) HUN-REN, 6726 Szeged, Hungary Email: [email protected]:* Copyright (c) 2024, Akos Diosdi, Peter Horvath* Biological Research Centre (BRC) HUN-REN, Szeged, Hungary* All rights reserved.** Redistribution and use of the material, with or without modification, is provided for academic research purposes only.** This material is free; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 (or higher) as published by the Free Software Foundation.* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.* See the GNU General Public License for more details</p

2024_Hollandi-2_Embryo_masks

ARTICLE (when using these files, please, cite the following article):Réka Hollandi, David Bauer, Akos Diosdi, Bálint Schrettner, Timea Toth, Dominik Hirling, Gábor Hollandi, Maria Harmati, József Molnár, Peter Horvath, "When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling". (2024)DESCRIPTION OF THE FILES:2D: Consists of HeLa Kyoto cells imaged by confocal images with multiple fluorescent channels including DAPI-405 (C001), Alexa Fluor-488 (C002), and Alexa Fluor-633 (C003). Each fluorescent image is 2048 × 2048 pixel resolution with 0.103 µm pixel size. All images were annotated by 3 experts, who annotated both the cytoplasm and nuclei of the images. Each of the images was labeled twice: once using only manual labor (manual) and again using Minimal contour (minimal). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are included.Embryo: 3D fluorescent image data of a mouse embryo [1]. All three experts annotated the data separately. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Annotations made with webKnossos, Paintera, and ITK-Snap were included. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlNeurosphere: 3D image data captured using light-sheet fluorescence microscopy, which included a spheroid with fluorescently labeled nuclei [2]. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlCo-culture: Multicellular spheroids were generated by using the HeLa Kyoto EGFP-alpha-tubulin/H2B-mCherry and MRC-5 fibroblast cell lines. 4 channels were acquired: DAPI-405 (ch00), Tubulin-488 (ch01), mCherry-552 (ch02), and Aktin-638 (ch03). The multi-page 16-bit Tif files have a resolution of 2048 × 2048, with pixel size of 0.14370117 µm. The distance between each image in each z-stack is 3.7 µm. To reduce the blurry effect, a deconvolution post-processing step was included only on DAPI channel provided by the LIGHTNING software (available with the LAS-X 4.4 software, Leica). Each folder includes the annotation (Labels.tiff) and the time for each annotation (annotation_times.csv). Both original images (channels) and post-processed images (LIGHTNING_DAPI_only) are included.Mitotic spheroid data: Consists of 90 multicellular cancer spheroids generated from different cell lines (T-47D, 5-8F, and Huh-7D12) and imaged with a light-sheet microscope [3]. The nucleus is fluorescently labeled in the images. For each spheroid, only cells with mitotic phenotype were annotated (Labels.tiff). Images are free to download from: https://doi.org/10.6084/m9.figshare.12620078.v1Melanoma_100: Benign and malignant cases were annotated by an expert in the subset of 100 images that were chosen from the HAM10000 dataset [4]. The annotations outline the lesion and separate the object from the background. Images are free to download from: https://doi.org/10.7910/DVN/DBW86TReferences:1. Saiz N. et al. Quantitative analysis of protein expression to study lineage specification in mouse preimplantation embryos. J. Vis. Exp., 108, e53654 (2016)2. Gole L. et al. OpenSegSPIM: a user-friendly segmentation tool for SPIM data. Bioinformatics, 32, 2075–2077 (2016)3. Diosdi A. et al. Cell lines and clearing approaches: a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids. Data in Brief Volume 36, 107090 (2021)4. Tschandl, P. et al. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018)Napari-biomag-annotator is freely available here: https://github.com/biomag-lab/napari-biomag-annotatorAll additional details about the annotation methods and image acquisition have been reported in the article cited above.MAIN CONTACT:Peter Horvath, Synthetic and Systems Biology Unit, Biological Research Centre (BRC) HUN-REN, 6726 Szeged, Hungary Email: [email protected]:* Copyright (c) 2024, Akos Diosdi, Peter Horvath* Biological Research Centre (BRC) HUN-REN, Szeged, Hungary* All rights reserved.** Redistribution and use of the material, with or without modification, is provided for academic research purposes only.** This material is free; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 (or higher) as published by the Free Software Foundation.* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.* See the GNU General Public License for more details</p

2024_Hollandi-4_Co_culture_images_and_masks

ARTICLE (when using these files, please, cite the following article):Réka Hollandi, David Bauer, Akos Diosdi, Bálint Schrettner, Timea Toth, Dominik Hirling, Gábor Hollandi, Maria Harmati, József Molnár, Peter Horvath, "When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling". (2024)DESCRIPTION OF THE FILES:2D: Consists of HeLa Kyoto cells imaged by confocal images with multiple fluorescent channels including DAPI-405 (C001), Alexa Fluor-488 (C002), and Alexa Fluor-633 (C003). Each fluorescent image is 2048 × 2048 pixel resolution with 0.103 µm pixel size. All images were annotated by 3 experts, who annotated both the cytoplasm and nuclei of the images. Each of the images was labeled twice: once using only manual labor (manual) and again using Minimal contour (minimal). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are included.Embryo: 3D fluorescent image data of a mouse embryo [1]. All three experts annotated the data separately. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Annotations made with webKnossos, Paintera, and ITK-Snap were included. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlNeurosphere: 3D image data captured using light-sheet fluorescence microscopy, which included a spheroid with fluorescently labeled nuclei [2]. Each annotator did the manual annotation twice (manual and manual_2), while also annotating with the Minimal contour (minimal), and with the combination of Minimal contour and interpolation (manual_minimal_interpolation). Each folder includes the annotation (Labels.tiff), the time for each annotation (annotation_times.csv), and a video file that visualizes the annotation process. Images are free to download from: https://www.3d-cell-annotator.org/download.htmlCo-culture: Multicellular spheroids were generated by using the HeLa Kyoto EGFP-alpha-tubulin/H2B-mCherry and MRC-5 fibroblast cell lines. 4 channels were acquired: DAPI-405 (ch00), Tubulin-488 (ch01), mCherry-552 (ch02), and Aktin-638 (ch03). The multi-page 16-bit Tif files have a resolution of 2048 × 2048, with pixel size of 0.14370117 µm. The distance between each image in each z-stack is 3.7 µm. To reduce the blurry effect, a deconvolution post-processing step was included only on DAPI channel provided by the LIGHTNING software (available with the LAS-X 4.4 software, Leica). Each folder includes the annotation (Labels.tiff) and the time for each annotation (annotation_times.csv). Both original images (channels) and post-processed images (LIGHTNING_DAPI_only) are included.Mitotic spheroid data: Consists of 90 multicellular cancer spheroids generated from different cell lines (T-47D, 5-8F, and Huh-7D12) and imaged with a light-sheet microscope [3]. The nucleus is fluorescently labeled in the images. For each spheroid, only cells with mitotic phenotype were annotated (Labels.tiff). Images are free to download from: https://doi.org/10.6084/m9.figshare.12620078.v1Melanoma_100: Benign and malignant cases were annotated by an expert in the subset of 100 images that were chosen from the HAM10000 dataset [4]. The annotations outline the lesion and separate the object from the background. Images are free to download from: https://doi.org/10.7910/DVN/DBW86TReferences:1. Saiz N. et al. Quantitative analysis of protein expression to study lineage specification in mouse preimplantation embryos. J. Vis. Exp., 108, e53654 (2016)2. Gole L. et al. OpenSegSPIM: a user-friendly segmentation tool for SPIM data. Bioinformatics, 32, 2075–2077 (2016)3. Diosdi A. et al. Cell lines and clearing approaches: a single-cell level 3D light-sheet fluorescence microscopy dataset of multicellular spheroids. Data in Brief Volume 36, 107090 (2021)4. Tschandl, P. et al. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5, 180161 (2018)Napari-biomag-annotator is freely available here: https://github.com/biomag-lab/napari-biomag-annotatorAll additional details about the annotation methods and image acquisition have been reported in the article cited above.MAIN CONTACT:Peter Horvath, Synthetic and Systems Biology Unit, Biological Research Centre (BRC) HUN-REN, 6726 Szeged, Hungary Email: [email protected]:* Copyright (c) 2024, Akos Diosdi, Peter Horvath* Biological Research Centre (BRC) HUN-REN, Szeged, Hungary* All rights reserved.** Redistribution and use of the material, with or without modification, is provided for academic research purposes only.** This material is free; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 (or higher) as published by the Free Software Foundation.* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.* See the GNU General Public License for more details</p