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Morris TA, Eldeen S, Tran RDH, Grosberg A. A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture. BIOPHYSICS REVIEWS 2022; 3:041302. [PMID: 36407035 PMCID: PMC9667907 DOI: 10.1063/5.0057434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.
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Affiliation(s)
| | - Sarah Eldeen
- Center for Complex Biological Systems, University of California, Irvine, California 92697-2700, USA
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2
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Kaseva T, Omidali B, Hippeläinen E, Mäkelä T, Wilppu U, Sofiev A, Merivaara A, Yliperttula M, Savolainen S, Salli E. Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei. BMC Bioinformatics 2022; 23:289. [PMID: 35864453 PMCID: PMC9306214 DOI: 10.1186/s12859-022-04827-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation. We studied whether this method could be improved for the segmentation of densely cultivated 3D nuclei via developing multiple system configurations in which we studied the effect of edge emphasizing CNNs, and optimized H-minima transform for mask and marker generation, respectively. RESULTS The dataset used for training and evaluation consisted of twelve in vitro cultivated densely packed 3D human carcinoma cell spheroids imaged using a confocal microscope. With this dataset, the evaluation was performed using a cross-validation scheme. In addition, four independent datasets were used for evaluation. The datasets were resampled near isotropic for our experiments. The baseline deep learning enhanced marker-controlled watershed obtained an average of 0.69 Panoptic Quality (PQ) and 0.66 Aggregated Jaccard Index (AJI) over the twelve spheroids. Using a system configuration, which was otherwise the same but used 3D-based edge emphasizing CNNs and optimized H-minima transform, the scores increased to 0.76 and 0.77, respectively. When using the independent datasets for evaluation, the best performing system configuration was shown to outperform or equal the baseline and a set of well-known cell segmentation approaches. CONCLUSIONS The use of edge emphasizing U-Nets and optimized H-minima transform can improve the marker-controlled watershed transform for segmentation of densely cultivated 3D cell nuclei. A novel dataset of twelve spheroids was introduced to the public.
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Affiliation(s)
- Tuomas Kaseva
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland
| | - Bahareh Omidali
- Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Eero Hippeläinen
- Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland.,HUS Medical Imaging Centre, Clinical Physiology and Nuclear Medicine, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Teemu Mäkelä
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.,Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Ulla Wilppu
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland
| | - Alexey Sofiev
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.,Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Arto Merivaara
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Centre for Drug Research, University of Helsinki, Helsinki, Finland
| | - Marjo Yliperttula
- Division of Pharmaceutical Biosciences, Faculty of Pharmacy, Centre for Drug Research, University of Helsinki, Helsinki, Finland
| | - Sauli Savolainen
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.,Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Eero Salli
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital and University of Helsinki, P.O. Box 340, FI-00290, Helsinki, Finland.
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van der Laan KWF, Reesink KD, van der Bruggen MM, Jaminon AMG, Schurgers LJ, Megens RTA, Huberts W, Delhaas T, Spronck B. Improved Quantification of Cell Density in the Arterial Wall-A Novel Nucleus Splitting Approach Applied to 3D Two-Photon Laser-Scanning Microscopy. Front Physiol 2022; 12:814434. [PMID: 35095571 PMCID: PMC8790070 DOI: 10.3389/fphys.2021.814434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 12/13/2021] [Indexed: 12/05/2022] Open
Abstract
Accurate information on vascular smooth muscle cell (VSMC) content, orientation, and distribution in blood vessels is indispensable to increase understanding of arterial remodeling and to improve modeling of vascular biomechanics. We have previously proposed an analysis method to automatically characterize VSMC orientation and transmural distribution in murine carotid arteries under well-controlled biomechanical conditions. However, coincident nuclei, erroneously detected as one large nucleus, were excluded from the analysis, hampering accurate VSMC content characterization and distorting transmural distributions. In the present study, therefore, we aim to (1) improve the previous method by adding a "nucleus splitting" procedure to split coinciding nuclei, (2) evaluate the accuracy of this novel method, and (3) test this method in a mouse model of VSMC apoptosis. After euthanasia, carotid arteries from SM22α-hDTR Apoe -/- and control Apoe -/- mice were bluntly dissected, excised, mounted in a biaxial biomechanical tester and brought to in vivo axial stretch and a pressure of 100 mmHg. Nuclei and elastin fibers were then stained using Syto-41 and Eosin-Y, respectively, and imaged using 3D two-photon laser scanning microscopy. Nuclei were segmented from images and coincident nuclei were split. The nucleus splitting procedure determines the likelihood that voxel pairs within coincident nuclei belong to the same nucleus and utilizes these likelihoods to identify individual nuclei using spectral clustering. Manual nucleus counts were used as a reference to assess the performance of our splitting procedure. Before and after splitting, automatic nucleus counts differed -26.6 ± 9.90% (p < 0.001) and -1.44 ± 7.05% (p = 0.467) from the manual reference, respectively. Whereas the slope of the relative difference between the manual and automated counts as a function of the manual count was significantly negative before splitting (p = 0.008), this slope became insignificant after splitting (p = 0.653). Smooth muscle apoptosis led to a 33.7% decrease in VSMC density (p = 0.008). Nucleus splitting improves the accuracy of automated cell content quantification in murine carotid arteries and overcomes the progressively worsening problem of coincident nuclei with increasing cell content in vessels. The presented image analysis framework provides a robust tool to quantify cell content, orientation, shape, and distribution in vessels to inform experimental and advanced computational studies on vascular structure and function.
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Affiliation(s)
- Koen W. F. van der Laan
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Koen D. Reesink
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Myrthe M. van der Bruggen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Armand M. G. Jaminon
- Department of Biochemistry, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Leon J. Schurgers
- Department of Biochemistry, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Remco T. A. Megens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
- Institute for Cardiovascular Prevention, Ludwig Maximilian University, Munich, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Wouter Huberts
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Tammo Delhaas
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Bart Spronck
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
- Department of Biomedical Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT, United States
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4
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Yao K, Huang K, Sun J, Jing L, Huang D, Jude C. Scaffold-A549: A Benchmark 3D Fluorescence Image Dataset for Unsupervised Nuclei Segmentation. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09944-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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5
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Olofsson K, Carannante V, Takai M, Önfelt B, Wiklund M. Single cell organization and cell cycle characterization of DNA stained multicellular tumor spheroids. Sci Rep 2021; 11:17076. [PMID: 34426602 PMCID: PMC8382712 DOI: 10.1038/s41598-021-96288-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/03/2021] [Indexed: 12/27/2022] Open
Abstract
Multicellular tumor spheroids (MCTSs) can serve as in vitro models for solid tumors and have become widely used in basic cancer research and drug screening applications. The major challenges when studying MCTSs by optical microscopy are imaging and analysis due to light scattering within the 3-dimensional structure. Herein, we used an ultrasound-based MCTS culture platform, where A498 renal carcinoma MCTSs were cultured, DAPI stained, optically cleared and imaged, to connect nuclear segmentation to biological information at the single cell level. We show that DNA-content analysis can be used to classify the cell cycle state as a function of position within the MCTSs. We also used nuclear volumetric characterization to show that cells were more densely organized and perpendicularly aligned to the MCTS radius in MCTSs cultured for 96 h compared to 24 h. The method presented herein can in principle be used with any stochiometric DNA staining protocol and nuclear segmentation strategy. Since it is based on a single counter stain a large part of the fluorescence spectrum is free for other probes, allowing measurements that correlate cell cycle state and nuclear organization with e.g., protein expression or drug distribution within MCTSs.
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Affiliation(s)
- Karl Olofsson
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Valentina Carannante
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden
| | - Madoka Takai
- Department of Bioengineering, University of Tokyo, Tokyo, Japan
| | - Björn Önfelt
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden
| | - Martin Wiklund
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
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Hof L, Moreth T, Koch M, Liebisch T, Kurtz M, Tarnick J, Lissek SM, Verstegen MMA, van der Laan LJW, Huch M, Matthäus F, Stelzer EHK, Pampaloni F. Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis. BMC Biol 2021; 19:37. [PMID: 33627108 PMCID: PMC7903752 DOI: 10.1186/s12915-021-00958-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/12/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration, and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangiocarcinoma organoids in parallel using light sheet and bright-field microscopy for up to 7 days. RESULTS We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation, and degeneration and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation, and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright-field microscopy analysis of entire cultures. CONCLUSION Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.
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Affiliation(s)
- Lotta Hof
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Till Moreth
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Michael Koch
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Tim Liebisch
- Frankfurt Institute for Advanced Studies and Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Marina Kurtz
- Department of Physics, Goethe Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Julia Tarnick
- Deanery of Biomedical Science, University of Edinburgh, Edinburgh, UK
| | - Susanna M Lissek
- Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany
| | - Monique M A Verstegen
- Department of Surgery, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Luc J W van der Laan
- Department of Surgery, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
| | - Meritxell Huch
- The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, UK
- Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Franziska Matthäus
- Frankfurt Institute for Advanced Studies and Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Ernst H K Stelzer
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany
| | - Francesco Pampaloni
- Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany.
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Siruvallur Murali V, Cobanoglu MC, Welf ES. Evaluating Melanoma Viability and Proliferation in 3D Microenvironments. Methods Mol Biol 2021; 2265:155-171. [PMID: 33704713 DOI: 10.1007/978-1-0716-1205-7_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Researchers often aim to incorporate microenvironmental variables such as the dimensionality and composition of the extracellular matrix into their cell-based assays. A technical challenge created by introduction of these variables is quantification of single-cell measurements and control of environmental reproducibility. Here, we detail a methodology to quantify viability and proliferation of melanoma cells in 3D collagen-based culture platforms by automated microscopy and 3D image analysis to yield robust, high-throughput results of single-cell responses to drug treatment.
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Affiliation(s)
- Vasanth Siruvallur Murali
- Lyda Hill Department of Bioinformatics and Department of Cell Biolog, UT Southwestern Medical Center, Dallas, TX, USA
| | - Murat Can Cobanoglu
- Lyda Hill Department of Bioinformatics and Department of Cell Biolog, UT Southwestern Medical Center, Dallas, TX, USA
| | - Erik S Welf
- Lyda Hill Department of Bioinformatics and Department of Cell Biolog, UT Southwestern Medical Center, Dallas, TX, USA.
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8
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Khan FA, Voß U, Pound MP, French AP. Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning. FRONTIERS IN PLANT SCIENCE 2020; 11:1275. [PMID: 32983190 PMCID: PMC7483761 DOI: 10.3389/fpls.2020.01275] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/05/2020] [Indexed: 06/11/2023]
Abstract
Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development-a process referred to as plant phenotyping-is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyze the data in microscopy images depicting plant growth, either locating or counting regions of cellular features in images. In this paper, we present to the plant community an introduction to and exploration of two machine learning approaches to address the problem of marker localization in confocal microscopy. First, a comparative study is conducted on the classification accuracy of common conventional machine learning algorithms, as a means to highlight challenges with these methods. Second, a 3D (volumetric) deep learning approach is developed and presented, including consideration of appropriate loss functions and training data. A qualitative and quantitative analysis of all the results produced is performed. Evaluation of all approaches is performed on an unseen time-series sequence comprising several individual 3D volumes, capturing plant growth. The comparative analysis shows that the deep learning approach produces more accurate and robust results than traditional machine learning. To accompany the paper, we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (FIJI) software. Network models and example datasets will also be available online.
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Affiliation(s)
- Faraz Ahmad Khan
- Schools of Computer Science and Biosciences, University of Nottingham, Nottingham, United Kingdom
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9
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Chang YH, Yokota H, Abe K, Tasi MD, Chu SL. Automatic three-dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images. J Microsc 2020; 281:57-75. [PMID: 32720710 DOI: 10.1111/jmi.12949] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 06/13/2020] [Accepted: 07/23/2020] [Indexed: 11/29/2022]
Abstract
Time-lapse confocal fluorescence microscopy images from mouse embryonic stem cells (ESCs) carrying reporter genes, histone H2B-mCherry and Mvh-Venus, have been used to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. Accurate cell nucleus segmentation is required to analyse the ESC dynamics and differentiation at a single cell resolution. Several methods used concavities on nucleus contours to segment overlapping cell nuclei. Our proposed method evaluates not only the concavities but also the size and shape of every 2D nucleus region to determine if any of the strait, extrusion, convexity and large diameter criteria is satisfied to segment overlapping nuclei inside the region. We then use a 3D segmentation method to reconstruct simple, convex, and reasonably sized 3D nuclei along the image stacking direction using the radius and centre of every segmented region in respective microscopy images. To avoid false concavities on nucleus boundaries, fluorescence images of the H2B-mCherry reporter are used for localisation of cell nuclei and Venus fluorescence images are used for determining the cell colony ranges. We use a series of image preprocessing procedures to remove noise outside and inside cell colonies, and in respective nuclei, and to smooth nucleus boundaries based on the colony ranges. We propose dynamic data structures to record every segmented nucleus region and solid in sets (volumes) of 3D confocal images. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei on microscopy images and that the segmentation method effectively segment out every nucleus with a reasonable size and shape. All 3D nuclei in a set (volume) of confocal microscopy images can be accessed by the dynamic data structures for 3D reconstruction. The 3D nuclei in time-lapse confocal microscopy images can be tracked to calculate cell movement and proliferation in consecutive volumes for understanding the dynamics of the differentiation characteristics about ESCs. LAY DESCRIPTION: Embryonic stem cells (ESCs) are considered as an ideal source for basic cell biology study and producing medically useful cells in vitro. This study uses time-lapse confocal fluorescence microscopy images from mouse ESCs carrying reporter gene to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. To automate analyses of ESC differentiation behaviours, accurate cell nucleus segmentation to distinguish respective cells are required. A series of image preprocessing procedures are implemented to remove noise in live-cell fluorescence images but yield overlapping cell nuclei. A segmentation method that evaluates boundary concavities and the size and shape of every nucleus is then used to determine if any of the strait, extrusion, convexity, large and local minimum diameter criteria satisfied to segment overlapping nuclei. We propose a dynamic data structure to record every newly segmented nucleus. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei and that the segmentation method effectively detects overlapping nuclei. All segmented nuclei in confocal images can be accessed using the dynamic data structures to be visualised and manipulated for quantitative analyses of the ESC differentiation behaviours. The manipulation can be tracking of segmented 3D cell nuclei in time-lapse images to calculate their dynamics of differentiation characteristics.
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Affiliation(s)
- Y-H Chang
- Department of Information & Computer Engineering, Chung Yuan Christian University, ROC, Chung-Li, Taiwan
| | - H Yokota
- RIKEN Center for Advanced Photonics, Wako, Japan
| | - K Abe
- RIKEN BioResource Research Center, Tsukuba, Japan
| | - M-D Tasi
- Department of Information & Computer Engineering, Chung Yuan Christian University, ROC, Chung-Li, Taiwan
| | - S-L Chu
- Department of Information & Computer Engineering, Chung Yuan Christian University, ROC, Chung-Li, Taiwan
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Piccinini F, Balassa T, Carbonaro A, Diosdi A, Toth T, Moshkov N, Tasnadi EA, Horvath P. Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates. Comput Struct Biotechnol J 2020; 18:1287-1300. [PMID: 32612752 PMCID: PMC7303562 DOI: 10.1016/j.csbj.2020.05.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/25/2022] Open
Abstract
Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.
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Affiliation(s)
- Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy
| | - Tamas Balassa
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
| | - Antonella Carbonaro
- Department of Computer Science and Engineering, University of Bologna, Italy
| | - Akos Diosdi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Timea Toth
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Nikita Moshkov
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary
- National Research University Higher School of Economics, Moscow, Russia
| | - Ervin A. Tasnadi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, Hungary
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Single-Cell Technologies Ltd., Szeged, Hungary
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11
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Friedmann D, Pun A, Adams EL, Lui JH, Kebschull JM, Grutzner SM, Castagnola C, Tessier-Lavigne M, Luo L. Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network. Proc Natl Acad Sci U S A 2020; 117:11068-11075. [PMID: 32358193 PMCID: PMC7245124 DOI: 10.1073/pnas.1918465117] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.
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Affiliation(s)
- Drew Friedmann
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Albert Pun
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Eliza L Adams
- Department of Biology, Stanford University, Stanford, CA 94305
- Neurosciences Graduate Program, Stanford University, Stanford, CA 94305
| | - Jan H Lui
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Justus M Kebschull
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | - Sophie M Grutzner
- Department of Biology, Stanford University, Stanford, CA 94305
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
| | | | | | - Liqun Luo
- Department of Biology, Stanford University, Stanford, CA 94305;
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305
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12
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Hannig J, Schäfer H, Ackermann J, Hebel M, Schäfer T, Döring C, Hartmann S, Hansmann ML, Koch I. Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma. PLoS Comput Biol 2020; 16:e1007516. [PMID: 31961873 PMCID: PMC6999891 DOI: 10.1371/journal.pcbi.1007516] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 02/04/2020] [Accepted: 10/29/2019] [Indexed: 11/25/2022] Open
Abstract
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significantly favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types. In pathology, histological diagnosis is still challenging, in particular, for tumor diseases. Pathologists diagnose the disease and its stage of development on the basis of evaluation and interpretation of images of tissue sections. The quantification of experimental data to support decisions of diagnosis and prognosis, applying bioinformatics methods, is an important issue. Here, we introduce a new, general approach to analyze tissue images of tumor and non-tumor patients and to evaluate the distribution of tumor cells in the tissue. Moreover, we consider neighborhood relations between immunostained cells of different cell morphology. We focus on a special type of lymph node tumor, the Hodgkin lymphoma, exploring the two main types of the classical Hodgkin lymphoma, the nodular sclerosis and the mixed cellularity, and the non-tumor case, the lymphadenitis, representing an inflammation of the lymph node. We considered more than 400, 000 cells immunohistochemically stained with CD30 in 35 whole slide images of tissue sections. We found that cells of specific morphology exhibited significant relations to cells of certain morphology as spatial nearest neighbor. We could show different neighborhood patterns of CD30-positive cells between tumor and non-tumor. The approach is general and can easily be applied to other tumor types.
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Affiliation(s)
- Jennifer Hannig
- KITE - Kompetenzzentrum für Informationstechnologie, Technische Hochschule Mittelhessen, Friedberg, Germany
| | - Hendrik Schäfer
- Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
| | - Jörg Ackermann
- Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
| | - Marie Hebel
- Institute of Biochemistry II, Johann Wolfgang Goethe-University, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, University Hospital Frankfurt am Main, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
| | - Claudia Döring
- Dr. Senckenberg Institute of Pathology, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
| | - Sylvia Hartmann
- Dr. Senckenberg Institute of Pathology, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
| | - Martin-Leo Hansmann
- Consultation and reference center for lymph node pathology at Dr. Senckenberg Institute of Pathology, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
| | - Ina Koch
- Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany
- * E-mail:
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13
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Neurochemical Organization of the Drosophila Brain Visualized by Endogenously Tagged Neurotransmitter Receptors. Cell Rep 2020; 30:284-297.e5. [DOI: 10.1016/j.celrep.2019.12.018] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 10/19/2019] [Accepted: 12/06/2019] [Indexed: 02/08/2023] Open
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14
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Dunn KW, Fu C, Ho DJ, Lee S, Han S, Salama P, Delp EJ. DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. Sci Rep 2019; 9:18295. [PMID: 31797882 PMCID: PMC6892824 DOI: 10.1038/s41598-019-54244-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 11/08/2019] [Indexed: 12/22/2022] Open
Abstract
The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation.
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Affiliation(s)
- Kenneth W Dunn
- Department of Medicine, Division of Nephrology Indiana University School of Medicine, 950 West Walnut St, R2-202, Indianapolis, IN, 46202, USA.
| | - Chichen Fu
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - David Joon Ho
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Soonam Lee
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Shuo Han
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
| | - Edward J Delp
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
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15
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Ruszczycki B, Pels KK, Walczak A, Zamłyńska K, Such M, Szczepankiewicz AA, Hall MH, Magalska A, Magnowska M, Wolny A, Bokota G, Basu S, Pal A, Plewczynski D, Wilczyński GM. Three-Dimensional Segmentation and Reconstruction of Neuronal Nuclei in Confocal Microscopic Images. Front Neuroanat 2019; 13:81. [PMID: 31481881 PMCID: PMC6710455 DOI: 10.3389/fnana.2019.00081] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 07/31/2019] [Indexed: 12/31/2022] Open
Abstract
The detailed architectural examination of the neuronal nuclei in any brain region, using confocal microscopy, requires quantification of fluorescent signals in three-dimensional stacks of confocal images. An essential prerequisite to any quantification is the segmentation of the nuclei which are typically tightly packed in the tissue, the extreme being the hippocampal dentate gyrus (DG), in which nuclei frequently appear to overlap due to limitations in microscope resolution. Segmentation in DG is a challenging task due to the presence of a significant amount of image artifacts and densely packed nuclei. Accordingly, we established an algorithm based on continuous boundary tracing criterion aiming to reconstruct the nucleus surface and to separate the adjacent nuclei. The presented algorithm neither uses a pre-built nucleus model, nor performs image thresholding, which makes it robust against variations in image intensity and poor contrast. Further, the reconstructed surface is used to study morphology and spatial arrangement of the nuclear interior. The presented method is generally dedicated to segmentation of crowded, overlapping objects in 3D space. In particular, it allows us to study quantitatively the architecture of the neuronal nucleus using confocal-microscopic approach.
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Affiliation(s)
- Błażej Ruszczycki
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | | | - Agnieszka Walczak
- Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland
| | | | - Michał Such
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.,Center of New Technologies, University of Warsaw, Warsaw, Poland
| | | | - Małgorzata Hanna Hall
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.,Okinawa Institute of Science and Technology, Okinawa, Japan
| | - Adriana Magalska
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Marta Magnowska
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Artur Wolny
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Grzegorz Bokota
- Center of New Technologies, University of Warsaw, Warsaw, Poland
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Ayan Pal
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Dariusz Plewczynski
- Center of New Technologies, University of Warsaw, Warsaw, Poland.,Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
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16
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Murali VS, Chang BJ, Fiolka R, Danuser G, Cobanoglu MC, Welf ES. An image-based assay to quantify changes in proliferation and viability upon drug treatment in 3D microenvironments. BMC Cancer 2019; 19:502. [PMID: 31138163 PMCID: PMC6537405 DOI: 10.1186/s12885-019-5694-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 05/08/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Every biological experiment requires a choice of throughput balanced against physiological relevance. Most primary drug screens neglect critical parameters such as microenvironmental conditions, cell-cell heterogeneity, and specific readouts of cell fate for the sake of throughput. METHODS Here we describe a methodology to quantify proliferation and viability of single cells in 3D culture conditions by leveraging automated microscopy and image analysis to facilitate reliable and high-throughput measurements. We detail experimental conditions that can be adjusted to increase either throughput or robustness of the assay, and we provide a stand alone image analysis program for users who wish to implement this 3D drug screening assay in high throughput. RESULTS We demonstrate this approach by evaluating a combination of RAF and MEK inhibitors on melanoma cells, showing that cells cultured in 3D collagen-based matrices are more sensitive than cells grown in 2D culture, and that cell proliferation is much more sensitive than cell viability. We also find that cells grown in 3D cultured spheroids exhibit equivalent sensitivity to single cells grown in 3D collagen, suggesting that for the case of melanoma, a 3D single cell model may be equally effective for drug identification as 3D spheroids models. The single cell resolution of this approach enables stratification of heterogeneous populations of cells into differentially responsive subtypes upon drug treatment, which we demonstrate by determining the effect of RAK/MEK inhibition on melanoma cells co-cultured with fibroblasts. Furthermore, we show that spheroids grown from single cells exhibit dramatic heterogeneity to drug response, suggesting that heritable drug resistance can arise stochastically in single cells but be retained by subsequent generations. CONCLUSION In summary, image-based analysis renders cell fate detection robust, sensitive, and high-throughput, enabling cell fate evaluation of single cells in more complex microenvironmental conditions.
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Affiliation(s)
- Vasanth S. Murali
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX USA
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX USA
| | - Bo-Jui Chang
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX USA
| | - Reto Fiolka
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX USA
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX USA
| | - Gaudenz Danuser
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX USA
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX USA
| | - Murat Can Cobanoglu
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX USA
| | - Erik S. Welf
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX USA
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX USA
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17
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Quachtran B, de la Torre Ubieta L, Yusupova M, Geschwind DH, Shattuck DW. VOTING-BASED SEGMENTATION OF OVERLAPPING NUCLEI IN CLARITY IMAGES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:658-662. [PMID: 32038768 DOI: 10.1109/isbi.2018.8363660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
New tissue-clearing techniques and improvements in optical microscopy have rapidly advanced capabilities to acquire volumetric imagery of neural tissue at resolutions of one micron or better. As sizes for data collections increase, accurate automatic segmentation of cell nuclei becomes increasingly important for quantitative analysis of imaged tissue. We present a cell nucleus segmentation method that is formulated as a parameter estimation problem with the goal of determining the count, shapes, and locations of nuclei that most accurately describe an image. We applied our new voting-based approach to fluorescence confocal microscopy images of neural tissue stained with DAPI, which highlights nuclei. Compared to manual counting of cells in three DAPI images, our method outperformed three existing approaches. On a manually labeled high-resolution DAPI image, our method also outperformed those methods and achieved a cell count accuracy of 98.99% and mean Dice coefficient of 0.6498.
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Affiliation(s)
| | - Luis de la Torre Ubieta
- Program in Neurogenetics, Departments of Neurology and Human Genetics, David Geffen School of Medicine, UCLA
| | | | - Daniel H Geschwind
- Department of Neurology, David Geffen School of Medicine, UCLA.,Program in Neurogenetics, Departments of Neurology and Human Genetics, David Geffen School of Medicine, UCLA.,Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, UCLA
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18
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Saadatifard L, Abbott LC, Montier L, Ziburkus J, Mayerich D. Robust Cell Detection for Large-Scale 3D Microscopy Using GPU-Accelerated Iterative Voting. Front Neuroanat 2018; 12:28. [PMID: 29755325 PMCID: PMC5932171 DOI: 10.3389/fnana.2018.00028] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 04/03/2018] [Indexed: 12/21/2022] Open
Abstract
High-throughput imaging techniques, such as Knife-Edge Scanning Microscopy (KESM),are capable of acquiring three-dimensional whole-organ images at sub-micrometer resolution. These images are challenging to segment since they can exceed several terabytes (TB) in size, requiring extremely fast and fully automated algorithms. Staining techniques are limited to contrast agents that can be applied to large samples and imaged in a single pass. This requires maximizing the number of structures labeled in a single channel, resulting in images that are densely packed with spatial features. In this paper, we propose a three-dimensional approach for locating cells based on iterative voting. Due to the computational complexity of this algorithm, a highly efficient GPU implementation is required to make it practical on large data sets. The proposed algorithm has a limited number of input parameters and is highly parallel.
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Affiliation(s)
- Leila Saadatifard
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Louise C Abbott
- College of Veterinary Medicine and Biomedical Sciences, Texas A & M University, College Station, TX, United States
| | - Laura Montier
- Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
| | - Jokubas Ziburkus
- Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
| | - David Mayerich
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
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19
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Cavo M, Caria M, Pulsoni I, Beltrame F, Fato M, Scaglione S. A new cell-laden 3D Alginate-Matrigel hydrogel resembles human breast cancer cell malignant morphology, spread and invasion capability observed "in vivo". Sci Rep 2018; 8:5333. [PMID: 29593247 PMCID: PMC5871779 DOI: 10.1038/s41598-018-23250-4] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 03/01/2018] [Indexed: 01/17/2023] Open
Abstract
Purpose of this study was the development of a 3D material to be used as substrate for breast cancer cell culture. We developed composite gels constituted by different concentrations of Alginate (A) and Matrigel (M) to obtain a structurally stable-in-time and biologically active substrate. Human aggressive breast cancer cells (i.e. MDA-MB-231) were cultured within the gels. Known the link between cell morphology and malignancy, cells were morphologically characterized and their invasiveness correlated through an innovative bioreactor-based invasion assay. A particular type of gel (i.e. 50% Alginate, 50% Matrigel) emerged thanks to a series of significant results: 1. cells exhibited peculiar cytoskeleton shapes and nuclear fragmentation characteristic of their malignancy; 2. cells expressed the formation of the so-called invadopodia, actin-based protrusion of the plasma membrane through which cells anchor to the extracellular matrix; 3. cells were able to migrate through the gels and attach to an engineered membrane mimicking the vascular walls hosted within bioreactor, providing a completely new 3D in vitro model of the very precursor steps of metastasis.
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Affiliation(s)
- Marta Cavo
- National Research Council (CNR) - IEIIT Institute, Genoa, 16149, Italy.,Department of Biophysical and Electronic Engineering (DIBRIS), University of Genoa, Genoa, 16145, Italy.,React4life S.r.l, Genoa, 16100, Italy
| | - Marco Caria
- National Research Council (CNR) - IEIIT Institute, Genoa, 16149, Italy.,Department of Biophysical and Electronic Engineering (DIBRIS), University of Genoa, Genoa, 16145, Italy
| | - Ilaria Pulsoni
- Department of Biophysical and Electronic Engineering (DIBRIS), University of Genoa, Genoa, 16145, Italy
| | - Francesco Beltrame
- National Research Council (CNR) - IEIIT Institute, Genoa, 16149, Italy.,Department of Biophysical and Electronic Engineering (DIBRIS), University of Genoa, Genoa, 16145, Italy
| | - Marco Fato
- National Research Council (CNR) - IEIIT Institute, Genoa, 16149, Italy.,Department of Biophysical and Electronic Engineering (DIBRIS), University of Genoa, Genoa, 16145, Italy
| | - Silvia Scaglione
- National Research Council (CNR) - IEIIT Institute, Genoa, 16149, Italy.
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20
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Lupperger V, Buggenthin F, Chapouton P, Marr C. Image analysis of neural stem cell division patterns in the zebrafish brain. Cytometry A 2018; 93:314-322. [PMID: 29125897 PMCID: PMC5969287 DOI: 10.1002/cyto.a.23260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 08/30/2017] [Accepted: 09/06/2017] [Indexed: 12/18/2022]
Abstract
Proliferating stem cells in the adult body are the source of constant regeneration. In the brain, neural stem cells (NSCs) divide to maintain the stem cell population and generate neural progenitor cells that eventually replenish mature neurons and glial cells. How much spatial coordination of NSC division and differentiation is present in a functional brain is an open question. To quantify the patterns of stem cell divisions, one has to (i) identify the pool of NSCs that have the ability to divide, (ii) determine NSCs that divide within a given time window, and (iii) analyze the degree of spatial coordination. Here, we present a bioimage informatics pipeline that automatically identifies GFP expressing NSCs in three-dimensional image stacks of zebrafish brain from whole-mount preparations. We exploit the fact that NSCs in the zebrafish hemispheres are located on a two-dimensional surface and identify between 1,500 and 2,500 NSCs in six brain hemispheres. We then determine the position of dividing NSCs in the hemisphere by EdU incorporation into cells undergoing S-phase and calculate all pairwise NSC distances with three alternative metrics. Finally, we fit a probabilistic model to the observed spatial patterns that accounts for the non-homogeneous distribution of NSCs. We find a weak positive coordination between dividing NSCs irrespective of the metric and conclude that neither strong inhibitory nor strong attractive signals drive NSC divisions in the adult zebrafish brain. © 2017 International Society for Advancement of Cytometry.
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Affiliation(s)
- Valerio Lupperger
- Institute of Computational Biology, Helmholtz Zentrum München ‐ German Research Center for Environmental Health, Ingolstädter Landstr. 185764 NeuherbergGermany
| | - Felix Buggenthin
- Institute of Computational Biology, Helmholtz Zentrum München ‐ German Research Center for Environmental Health, Ingolstädter Landstr. 185764 NeuherbergGermany
| | - Prisca Chapouton
- Research Unit Sensory Biology and Organogenesis, Helmholtz Zentrum München ‐ German Research Center for Environmental Health, Ingolstädter Landstr. 185764 NeuherbergGermany
| | - Carsten Marr
- Institute of Computational Biology, Helmholtz Zentrum München ‐ German Research Center for Environmental Health, Ingolstädter Landstr. 185764 NeuherbergGermany
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21
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Dufour AC, Jonker AH, Olivo-Marin JC. Deciphering tissue morphodynamics using bioimage informatics. Philos Trans R Soc Lond B Biol Sci 2017; 372:rstb.2015.0512. [PMID: 28348249 DOI: 10.1098/rstb.2015.0512] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/15/2016] [Indexed: 11/12/2022] Open
Abstract
In recent years developmental biology has greatly benefited from the latest advances in fluorescence microscopy techniques. Consequently, quantitative and automated analysis of this data is becoming a vital first step in the quest for novel insights into the various aspects of development. Here we present an introductory overview of the various image analysis methods proposed for developmental biology images, with particular attention to openly available software packages. These tools, as well as others to come, are rapidly paving the way towards standardized and reproducible bioimaging studies at the whole-tissue level. Reflecting on these achievements, we discuss the remaining challenges and the future endeavours lying ahead in the post-image analysis era.This article is part of the themed issue 'Systems morphodynamics: understanding the development of tissue hardware'.
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Affiliation(s)
- Alexandre C Dufour
- Institut Pasteur, Bioimage Analysis Unit, 25-28 rue du Docteur Roux, Paris, France .,CNRS, UMR 3691, 25-28 rue du Docteur Roux, Paris, France
| | | | - Jean-Christophe Olivo-Marin
- Institut Pasteur, Bioimage Analysis Unit, 25-28 rue du Docteur Roux, Paris, France .,CNRS, UMR 3691, 25-28 rue du Docteur Roux, Paris, France
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22
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A novel toolbox to investigate tissue spatial organization applied to the study of the islets of Langerhans. Sci Rep 2017; 7:44261. [PMID: 28303903 PMCID: PMC5355872 DOI: 10.1038/srep44261] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 02/07/2017] [Indexed: 12/20/2022] Open
Abstract
Thanks to the development of new 3D Imaging techniques, volumetric data of thick samples, especially tissues, are commonly available. Several algorithms were proposed to analyze cells or nuclei in tissues, however these tools are limited to two dimensions. Within any given tissue, cells are not likely to be organized randomly and as such have specific patterns of cell-cell interaction forming complex communication networks. In this paper, we propose a new set of tools as an approach to segment and analyze tissues in 3D with single cell resolution. This new tool box can identify and compute the geographical location of single cells and analyze the potential physical interactions between different cell types and in 3D. As a proof-of-principle, we applied our methodology to investigation of the cyto-architecture of the islets of Langerhans in mice and monkeys. The results obtained here are a significant improvement in current methodologies and provides new insight into the organization of alpha cells and their cellular interactions within the islet’s cellular framework.
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23
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Schmitz A, Fischer SC, Mattheyer C, Pampaloni F, Stelzer EHK. Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids. Sci Rep 2017; 7:43693. [PMID: 28255161 PMCID: PMC5334646 DOI: 10.1038/srep43693] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 01/30/2017] [Indexed: 12/31/2022] Open
Abstract
Three-dimensional multicellular aggregates such as spheroids provide reliable in vitro substitutes for tissues. Quantitative characterization of spheroids at the cellular level is fundamental. We present the first pipeline that provides three-dimensional, high-quality images of intact spheroids at cellular resolution and a comprehensive image analysis that completes traditional image segmentation by algorithms from other fields. The pipeline combines light sheet-based fluorescence microscopy of optically cleared spheroids with automated nuclei segmentation (F score: 0.88) and concepts from graph analysis and computational topology. Incorporating cell graphs and alpha shapes provided more than 30 features of individual nuclei, the cellular neighborhood and the spheroid morphology. The application of our pipeline to a set of breast carcinoma spheroids revealed two concentric layers of different cell density for more than 30,000 cells. The thickness of the outer cell layer depends on a spheroid’s size and varies between 50% and 75% of its radius. In differently-sized spheroids, we detected patches of different cell densities ranging from 5 × 105 to 1 × 106 cells/mm3. Since cell density affects cell behavior in tissues, structural heterogeneities need to be incorporated into existing models. Our image analysis pipeline provides a multiscale approach to obtain the relevant data for a system-level understanding of tissue architecture.
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Affiliation(s)
- Alexander Schmitz
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Sabine C Fischer
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Christian Mattheyer
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Francesco Pampaloni
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Ernst H K Stelzer
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
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24
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Smyrek I, Stelzer EHK. Quantitative three-dimensional evaluation of immunofluorescence staining for large whole mount spheroids with light sheet microscopy. BIOMEDICAL OPTICS EXPRESS 2017; 8:484-499. [PMID: 28270962 PMCID: PMC5330556 DOI: 10.1364/boe.8.000484] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 12/05/2016] [Accepted: 12/05/2016] [Indexed: 05/04/2023]
Abstract
Three-dimensional cell biology and histology of tissue sections strongly benefit from advanced light microscopy and optimized staining procedures to gather the full three-dimensional information. In particular, the combination of optical clearing with light sheet-based fluorescence microscopy simplifies fast high-quality imaging of thick biological specimens. However, verified in toto immunostaining protocols for large multicellular spheroids or for tissue sections have not been published. We present a method for the verification of immunostaining in three-dimensional spheroids. The analysis relies on three criteria to evaluate the immunostaining quality: quality of the antibody stain specificity, signal intensity achieved by the staining procedure and the correlation of the signal intensity with that of a homogeneously dispersed fluorescent dye. We optimized and investigated variations of five immunostaining protocols for three-dimensional cell biology. Our method is an important contribution to three-dimensional cell biology and the histology of tissues since it allows to evaluate the efficiency of immunostaining protocols for large three-dimensional specimens, and to study the distribution of protein expression and cell types within spheroids and spheroid-specific morphological structures without the need of physical sectioning.
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Automated Detection and Tracking of Cell Clusters in Time-Lapse Fluorescence Microscopy Images. J Med Biol Eng 2017. [DOI: 10.1007/s40846-016-0216-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Self-organization of human embryonic stem cells on micropatterns. Nat Protoc 2016; 11:2223-2232. [PMID: 27735934 DOI: 10.1038/nprot.2016.131] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 07/13/2016] [Indexed: 12/20/2022]
Abstract
Fate allocation in the gastrulating embryo is spatially organized as cells differentiate into specialized cell types depending on their positions with respect to the body axes. There is a need for in vitro protocols that allow the study of spatial organization associated with this developmental transition. Although embryoid bodies and organoids can exhibit some spatial organization of differentiated cells, methods that generate embryoid bodies or organoids do not yield consistent and fully reproducible results. Here, we describe a micropatterning approach in which human embryonic stem cells are confined to disk-shaped, submillimeter colonies. After 42 h of BMP4 stimulation, cells form self-organized differentiation patterns in concentric radial domains, which express specific markers associated with the embryonic germ layers, reminiscent of gastrulating embryos. Our protocol takes 3 d; it uses commercial microfabricated slides (from CYTOO), human laminin-521 (LN-521) as extracellular matrix coating, and either conditioned or chemically defined medium (mTeSR). Differentiation patterns within individual colonies can be determined by immunofluorescence and analyzed with cellular resolution. Both the size of the micropattern and the type of medium affect the patterning outcome. The protocol is appropriate for personnel with basic stem cell culture training. This protocol describes a robust platform for quantitative analysis of the mechanisms associated with pattern formation at the onset of gastrulation.
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Toyoshima Y, Tokunaga T, Hirose O, Kanamori M, Teramoto T, Jang MS, Kuge S, Ishihara T, Yoshida R, Iino Y. Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. PLoS Comput Biol 2016; 12:e1004970. [PMID: 27271939 PMCID: PMC4894571 DOI: 10.1371/journal.pcbi.1004970] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 05/03/2016] [Indexed: 11/18/2022] Open
Abstract
To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured.
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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Terumasa Tokunaga
- Department of Systems Design and Informatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka-shi, Fukuoka, Japan
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Osamu Hirose
- Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Japan
| | - Manami Kanamori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Moon Sun Jang
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Sayuri Kuge
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan
- * E-mail:
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Li Y, Rose F, di Pietro F, Morin X, Genovesio A. Detection and tracking of overlapping cell nuclei for large scale mitosis analyses. BMC Bioinformatics 2016; 17:183. [PMID: 27112769 PMCID: PMC4845473 DOI: 10.1186/s12859-016-1030-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 04/09/2016] [Indexed: 11/26/2022] Open
Abstract
Background Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods. Results Here, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters. Conclusions The proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.
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Affiliation(s)
- Yingbo Li
- Scientific Center for Computational Biology, Institut de Biologie de l'Ecole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue d'Ulm, Paris, 75005, France.,Division cellulaire et neurogenèse, Institut de Biologie de l'Ecole Normale Superieure, PSL Research University, 46, rue d'Ulm, Paris, 75005, France
| | - France Rose
- Scientific Center for Computational Biology, Institut de Biologie de l'Ecole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue d'Ulm, Paris, 75005, France
| | - Florencia di Pietro
- Division cellulaire et neurogenèse, Institut de Biologie de l'Ecole Normale Superieure, PSL Research University, 46, rue d'Ulm, Paris, 75005, France
| | - Xavier Morin
- Division cellulaire et neurogenèse, Institut de Biologie de l'Ecole Normale Superieure, PSL Research University, 46, rue d'Ulm, Paris, 75005, France
| | - Auguste Genovesio
- Scientific Center for Computational Biology, Institut de Biologie de l'Ecole Normale Superieure, CNRS-INSERM-ENS, PSL Research University, 46, rue d'Ulm, Paris, 75005, France.
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Scherzinger A, Kleene F, Dierkes C, Kiefer F, Hinrichs KH, Jiang X. Automated Segmentation of Immunostained Cell Nuclei in 3D Ultramicroscopy Images. LECTURE NOTES IN COMPUTER SCIENCE 2016. [DOI: 10.1007/978-3-319-45886-1_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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