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Litsios A, Grys BT, Kraus OZ, Friesen H, Ross C, Masinas MPD, Forster DT, Couvillion MT, Timmermann S, Billmann M, Myers C, Johnsson N, Churchman LS, Boone C, Andrews BJ. Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle. Cell 2024; 187:1490-1507.e21. [PMID: 38452761 PMCID: PMC10947830 DOI: 10.1016/j.cell.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/01/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024]
Abstract
Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.
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Affiliation(s)
- Athanasios Litsios
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Benjamin T Grys
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Oren Z Kraus
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Helena Friesen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Catherine Ross
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Myra Paz David Masinas
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Duncan T Forster
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Mary T Couvillion
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Stefanie Timmermann
- Institute of Molecular Genetics and Cell Biology, Department of Biology, Ulm University, Ulm 89081, Germany
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Chad Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nils Johnsson
- Institute of Molecular Genetics and Cell Biology, Department of Biology, Ulm University, Ulm 89081, Germany
| | | | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; RIKEN Center for Sustainable Resource Science, Wako 351-0198 Saitama, Japan.
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
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2
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Jose A, Roy R, Moreno-Andrés D, Stegmaier J. Automatic detection of cell-cycle stages using recurrent neural networks. PLoS One 2024; 19:e0297356. [PMID: 38466708 PMCID: PMC10927108 DOI: 10.1371/journal.pone.0297356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/02/2024] [Indexed: 03/13/2024] Open
Abstract
Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning. We used neural networks to predict different mitosis stages. We extracted video sequences of cells undergoing division and trained a Recurrent Neural Network (RNN) to extract image features. The use of RNN enabled better extraction of features. The RNN-based approach gave better performance compared to classifier based feature extraction methods which do not use time information. Evaluation of precision, recall, and F-score indicates the superiority of the proposed model compared to the baseline. To study the loss in performance due to confusion between adjacent classes, we plotted the confusion matrix as well. In addition, we visualized the feature space to understand why RNNs are better at classifying the mitosis stages than other classifier models, which indicated the formation of strong clusters for the different classes, clearly confirming the advantage of the proposed RNN-based approach.
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Affiliation(s)
- Abin Jose
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Rijo Roy
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Daniel Moreno-Andrés
- Institute of Biochemistry and Molecular Cell Biology, Medical School, RWTH Aachen University, Aachen, Germany
| | - Johannes Stegmaier
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
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3
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Jang S, Kim S, Lee J, Choi WJ, Yoon CH, Yang S, Kim KH. Deep learning framework for automated goblet cell density analysis in in-vivo rabbit conjunctiva. Sci Rep 2023; 13:22839. [PMID: 38129447 PMCID: PMC10739799 DOI: 10.1038/s41598-023-49275-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Goblet cells (GCs) in the conjunctiva are specialized epithelial cells secreting mucins for the mucus layer of protective tear film and playing immune tolerance functions for ocular surface health. Because GC loss is observed in various ocular surface diseases, GC examination is important for precision diagnosis. Moxifloxacin-based fluorescence microscopy (MBFM) was recently developed for non-invasive high-contrast GC visualization. MBFM showed promise for GC examination by high-speed large-area imaging and a robust analysis method is needed to provide GC information. In this study, we developed a deep learning framework for GC image analysis, named dual-channel attention U-Net (DCAU-Net). Dual-channel convolution was used both to extract the overall image texture and to acquire the GC morphological characteristics. A global channel attention module was adopted by combining attention algorithms and channel-wise pooling. DCAU-Net showed 93.1% GC segmentation accuracy and 94.3% GC density estimation accuracy. Further application to both normal and ocular surface damage rabbit models revealed the spatial variations of both GC density and size in normal rabbits and the decreases of both GC density and size in damage rabbit models during recovery after acute damage. The GC analysis results were consistent with histology. Together with the non-invasive high-contrast imaging method, DCAU-Net would provide GC information for the diagnosis of ocular surface diseases.
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Affiliation(s)
- Seunghyun Jang
- Department of Biomedical Engineering, Yonsei University, 1 Yonseidae-gil, Wonju-si, Gangwon-do, 26493, Republic of Korea
| | - Seonghan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyeoungbuk, 37673, Republic of Korea
| | - Jungbin Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyeoungbuk, 37673, Republic of Korea
| | - Wan Jae Choi
- Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Chang Ho Yoon
- Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Laboratory of Ocular Regenerative Medicine and Immunology, Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Sejung Yang
- Department of Precision Medicine, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju-si, Gangwon-do, 26426, Republic of Korea.
- Department of Medical Informatics and Biostatistics, Graduate School, Yonsei University, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| | - Ki Hean Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, Gyeoungbuk, 37673, Republic of Korea.
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Kelch MA, Vera-Guapi A, Beder T, Oswald M, Hiemisch A, Beil N, Wajda P, Ciesek S, Erfle H, Toptan T, Koenig R. Machine learning on large scale perturbation screens for SARS-CoV-2 host factors identifies β-catenin/CBP inhibitor PRI-724 as a potent antiviral. Front Microbiol 2023; 14:1193320. [PMID: 37342561 PMCID: PMC10277617 DOI: 10.3389/fmicb.2023.1193320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/16/2023] [Indexed: 06/23/2023] Open
Abstract
Expanding antiviral treatment options against SARS-CoV-2 remains crucial as the virus evolves under selection pressure which already led to the emergence of several drug resistant strains. Broad spectrum host-directed antivirals (HDA) are promising therapeutic options, however the robust identification of relevant host factors by CRISPR/Cas9 or RNA interference screens remains challenging due to low consistency in the resulting hits. To address this issue, we employed machine learning, based on experimental data from several knockout screens and a drug screen. We trained classifiers using genes essential for virus life cycle obtained from the knockout screens. The machines based their predictions on features describing cellular localization, protein domains, annotated gene sets from Gene Ontology, gene and protein sequences, and experimental data from proteomics, phospho-proteomics, protein interaction and transcriptomic profiles of SARS-CoV-2 infected cells. The models reached a remarkable performance suggesting patterns of intrinsic data consistency. The predicted HDF were enriched in sets of genes particularly encoding development, morphogenesis, and neural processes. Focusing on development and morphogenesis-associated gene sets, we found β-catenin to be central and selected PRI-724, a canonical β-catenin/CBP disruptor, as a potential HDA. PRI-724 limited infection with SARS-CoV-2 variants, SARS-CoV-1, MERS-CoV and IAV in different cell line models. We detected a concentration-dependent reduction in cytopathic effects, viral RNA replication, and infectious virus production in SARS-CoV-2 and SARS-CoV-1-infected cells. Independent of virus infection, PRI-724 treatment caused cell cycle deregulation which substantiates its potential as a broad spectrum antiviral. Our proposed machine learning concept supports focusing and accelerating the discovery of host dependency factors and identification of potential host-directed antivirals.
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Affiliation(s)
- Maximilian A. Kelch
- Institute for Medical Virology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
| | | | - Thomas Beder
- Medical Department II, Hematology and Oncology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Marcus Oswald
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany
| | - Alicia Hiemisch
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany
| | - Nina Beil
- Advanced Biological Screening Facility (ABSF), High-Content Analysis of the Cell (HiCell), BioQuant, Heidelberg University, Heidelberg, Germany
| | - Piotr Wajda
- Advanced Biological Screening Facility (ABSF), High-Content Analysis of the Cell (HiCell), BioQuant, Heidelberg University, Heidelberg, Germany
| | - Sandra Ciesek
- Institute for Medical Virology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
- German Centre for Infection Research (DZIF), External Partner Site Frankfurt, Frankfurt, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt, Germany
| | - Holger Erfle
- Advanced Biological Screening Facility (ABSF), High-Content Analysis of the Cell (HiCell), BioQuant, Heidelberg University, Heidelberg, Germany
| | - Tuna Toptan
- Institute for Medical Virology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
| | - Rainer Koenig
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany
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Zhao B, Zhang K, Liu P, Chen Y. Large-scale time-lapse scanning electron microscopy image mosaic using a smooth stitching strategy. Microsc Res Tech 2023. [PMID: 37119500 DOI: 10.1002/jemt.24334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/23/2023] [Accepted: 04/15/2023] [Indexed: 05/01/2023]
Abstract
Due to the trade-off between the field of view and resolution of various microscopes, obtaining a wide-view panoramic image through high-resolution image tiles is frequently encountered and demanded in numerous applications. Here, we propose an automatic image mosaic strategy for sequential 2D time-lapse scanning electron microscopy (SEM) images. This method can accurately compute pairwise translations among serial image tiles with indeterminate overlapping areas. The detection and matching of feature points are limited by geographical coordinates, thus avoiding accidental mismatching. Moreover, the nonlinear deformation of the mosaic part is also taken into account. A smooth stitching field is utilized to gradually transform the perspective transformation in overlapping regions into the linear transformation in non-overlapping regions. Experimental results demonstrate that better image stitching accuracy can be achieved compared with some other image mosaic algorithms. Such a method has potential applications in high-resolution large-area analysis using serial microscopy images. RESEARCH HIGHLIGHTS: An automatic image mosaic strategy for processing sequential scanning electron microscopy images is proposed. A smooth stitching field is applied in the image mosaic. Improved stitching accuracy is achieved compared with other conventional mosaic methods.
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Affiliation(s)
- Binglu Zhao
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
| | - Kaidi Zhang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
| | - Peng Liu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
| | - Yuhang Chen
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, China
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6
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Dimitriou NM, Flores-Torres S, Kinsella JM, Mitsis GD. Detection and Spatiotemporal Analysis of In-vitro 3D Migratory Triple-Negative Breast Cancer Cells. Ann Biomed Eng 2023; 51:318-328. [PMID: 35896866 DOI: 10.1007/s10439-022-03022-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 07/13/2022] [Indexed: 01/25/2023]
Abstract
The invasion of cancer cells into the surrounding tissues is one of the hallmarks of cancer. However, a precise quantitative understanding of the spatiotemporal patterns of cancer cell migration and invasion still remains elusive. A promising approach to investigate these patterns are 3D cell cultures, which provide more realistic models of cancer growth compared to conventional 2D monolayers. Quantifying the spatial distribution of cells in these 3D cultures yields great promise for understanding the spatiotemporal progression of cancer. In the present study, we present an image processing and segmentation pipeline for the detection of 3D GFP-fluorescent triple-negative breast cancer cell nuclei, and we perform quantitative analysis of the formed spatial patterns and their temporal evolution. The performance of the proposed pipeline was evaluated using experimental 3D cell culture data, and was found to be comparable to manual segmentation, outperforming four alternative automated methods. The spatiotemporal statistical analysis of the detected distributions of nuclei revealed transient, non-random spatial distributions that consisted of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. Overall, the implementation of the proposed framework revealed the spatial organization of cellular nuclei with improved accuracy, providing insights into the 3 dimensional inter-cellular organization and its progression through time.
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Affiliation(s)
| | | | | | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, H3A 0E9, Canada
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7
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Summers HD, Wills JW, Rees P. Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis. CELL REPORTS METHODS 2022; 2:100348. [PMID: 36452868 PMCID: PMC9701617 DOI: 10.1016/j.crmeth.2022.100348] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.
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Affiliation(s)
- Huw D. Summers
- Department of Biomedical Engineering, Swansea University, Swansea SA1 8QQ, UK
| | - John W. Wills
- Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK
| | - Paul Rees
- Department of Biomedical Engineering, Swansea University, Swansea SA1 8QQ, UK
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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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Cuny AP, Schlottmann FP, Ewald JC, Pelet S, Schmoller KM. Live cell microscopy: From image to insight. BIOPHYSICS REVIEWS 2022; 3:021302. [PMID: 38505412 PMCID: PMC10903399 DOI: 10.1063/5.0082799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/18/2022] [Indexed: 03/21/2024]
Abstract
Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.
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Affiliation(s)
| | - Fabian P. Schlottmann
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Jennifer C. Ewald
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Serge Pelet
- Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland
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10
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Marzec M, Piórkowski A, Gertych A. Efficient automatic 3D segmentation of cell nuclei for high-content screening. BMC Bioinformatics 2022; 23:203. [PMID: 35641922 DOI: 10.1186/s12859-022-04737-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 05/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in cells for screening. However, this segmentation is challenging due to variabilities in cell confluency, drug-induced alterations in cell morphology, and gradual degradation of fluorescence with the depth of scanning. Despite advances in algorithms for segmenting nuclei for HCS, robust 3D methods that are insensitive to these conditions are still lacking. RESULTS We have developed an algorithm which first generates a 3D nuclear mask in the original images. Next, an iterative 3D marker-controlled watershed segmentation is applied to downsized images to segment adjacent nuclei under the mask. In the last step, borders of segmented nuclei are adjusted in the original images based on local nucleus and background intensities. The method was developed using a set of 10 3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei demonstrated that our method, in comparison with 6 reference methods, achieved the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation, was similar to that yielded by all reference approaches. Our method was on average more than twice as fast as the reference method that produced the best results. Additional tests carried out on three stacked 3D images comprising heterogenous nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80. CONCLUSIONS The high-performance metrics yielded by the proposed approach suggest that it can be used to reliably delineate nuclei in 3D images of monolayered and stacked cells exposed to cytotoxic drugs.
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Affiliation(s)
- Mariusz Marzec
- Faculty of Science and Technology, Institute of Biomedical Engineering, University of Silesia, Bedzinska St. 39, 41-200, Sosnowiec, Poland.
| | - Adam Piórkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30, 30-059, Cracow, Poland
| | - Arkadiusz Gertych
- Department of Surgery, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.,Faculty of Biomedical Engineering, Silesian University of Technology, Roosvelta 40, 41-800, Zabrze, Poland
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11
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Evans EL, Pocock GM, Einsdorf G, Behrens RT, Dobson ETA, Wiedenmann M, Birkhold C, Ahlquist P, Eliceiri KW, Sherer NM. HIV RGB: Automated Single-Cell Analysis of HIV-1 Rev-Dependent RNA Nuclear Export and Translation Using Image Processing in KNIME. Viruses 2022; 14:903. [PMID: 35632645 PMCID: PMC9145009 DOI: 10.3390/v14050903] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/21/2022] [Accepted: 04/23/2022] [Indexed: 01/27/2023] Open
Abstract
Single-cell imaging has emerged as a powerful means to study viral replication dynamics and identify sites of virus−host interactions. Multivariate aspects of viral replication cycles yield challenges inherent to handling large, complex imaging datasets. Herein, we describe the design and implementation of an automated, imaging-based strategy, “Human Immunodeficiency Virus Red-Green-Blue” (HIV RGB), for deriving comprehensive single-cell measurements of HIV-1 unspliced (US) RNA nuclear export, translation, and bulk changes to viral RNA and protein (HIV-1 Rev and Gag) subcellular distribution over time. Differentially tagged fluorescent viral RNA and protein species are recorded using multicolor long-term (>24 h) time-lapse video microscopy, followed by image processing using a new open-source computational imaging workflow dubbed “Nuclear Ring Segmentation Analysis and Tracking” (NR-SAT) based on ImageJ plugins that have been integrated into the Konstanz Information Miner (KNIME) analytics platform. We describe a typical HIV RGB experimental setup, detail the image acquisition and NR-SAT workflow accompanied by a step-by-step tutorial, and demonstrate a use case wherein we test the effects of perturbing subcellular localization of the Rev protein, which is essential for viral US RNA nuclear export, on the kinetics of HIV-1 late-stage gene regulation. Collectively, HIV RGB represents a powerful platform for single-cell studies of HIV-1 post-transcriptional RNA regulation. Moreover, we discuss how similar NR-SAT-based design principles and open-source tools might be readily adapted to study a broad range of dynamic viral or cellular processes.
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Affiliation(s)
- Edward L. Evans
- McArdle Laboratory for Cancer Research (Department of Oncology), Institute for Molecular Virology, and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI 53706, USA; (E.L.E.III); (G.M.P.); (R.T.B.)
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (G.E.); (E.T.A.D.); (M.W.)
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Ginger M. Pocock
- McArdle Laboratory for Cancer Research (Department of Oncology), Institute for Molecular Virology, and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI 53706, USA; (E.L.E.III); (G.M.P.); (R.T.B.)
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (G.E.); (E.T.A.D.); (M.W.)
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Gabriel Einsdorf
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (G.E.); (E.T.A.D.); (M.W.)
- KNIME GmbH, 78467 Konstanz, Germany;
| | - Ryan T. Behrens
- McArdle Laboratory for Cancer Research (Department of Oncology), Institute for Molecular Virology, and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI 53706, USA; (E.L.E.III); (G.M.P.); (R.T.B.)
| | - Ellen T. A. Dobson
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (G.E.); (E.T.A.D.); (M.W.)
| | - Marcel Wiedenmann
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (G.E.); (E.T.A.D.); (M.W.)
- KNIME GmbH, 78467 Konstanz, Germany;
| | | | - Paul Ahlquist
- McArdle Laboratory for Cancer Research (Department of Oncology), Institute for Molecular Virology, and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI 53706, USA; (E.L.E.III); (G.M.P.); (R.T.B.)
- Morgridge Institute for Research, Madison, WI 53715, USA
- John and Jeanne Rowe Center for Research in Virology, Madison, WI 53715, USA
| | - Kevin W. Eliceiri
- Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI 53706, USA; (G.E.); (E.T.A.D.); (M.W.)
- Morgridge Institute for Research, Madison, WI 53715, USA
| | - Nathan M. Sherer
- McArdle Laboratory for Cancer Research (Department of Oncology), Institute for Molecular Virology, and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI 53706, USA; (E.L.E.III); (G.M.P.); (R.T.B.)
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12
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Arbelle A, Cohen S, Raviv TR. Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; PP:1948-1960. [PMID: 35180079 DOI: 10.1109/tmi.2022.3152927] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Convolutional Neural Networks (CNNs) are considered state of the art segmentation methods for biomedical images in general and microscopy sequences of living cells, in particular. The success of the CNNs is attributed to their ability to capture the structural properties of the data, which enables accommodating complex spatial structures of the cells, low contrast, and unclear boundaries. However, in their standard form CNNs do not exploit the temporal information available in time-lapse sequences, which can be crucial to separating touching and partially overlapping cell instances. In this work, we exploit cell dynamics using a novel CNN architecture which allows multi-scale spatio-temporal feature extraction. Specifically, a novel recurrent neural network (RNN) architecture is proposed based on the integration of a Convolutional Long Short Term Memory (ConvLSTM) network with the U-Net. The proposed ConvLSTM-UNet network is constructed as a dual-task network to enable training with weakly annotated data, in the form of approximate cell centers, termed markers, when the complete cells' outlines are not available. We further use the fast marching method to facilitate the partitioning of clustered cells into individual connected components. Finally, we suggest an adaptation of the method for 3D microscopy sequences without drastically increasing the computational load. The method was evaluated on the Cell Segmentation Benchmark and was ranked among the top three methods on six submitted datasets. Exploiting the proposed built-in marker estimator we also present state-of-the-art cell detection results for an additional, publicly available, weekly annotated dataset. The source code is available at https://gitlab.com/shaked0/lstmUnet.
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13
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Gomariz A, Portenier T, Nombela-Arrieta C, Goksel O. Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression. SCIENCE ADVANCES 2022; 8:eabi8295. [PMID: 35119934 PMCID: PMC8816343 DOI: 10.1126/sciadv.abi8295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable.
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Affiliation(s)
- Alvaro Gomariz
- Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland
- Department of Medical Oncology and Hematology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tiziano Portenier
- Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland
| | - César Nombela-Arrieta
- Department of Medical Oncology and Hematology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Orcun Goksel
- Computer-assisted Applications in Medicine, ETH Zurich, Zurich, Switzerland
- Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden
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14
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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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15
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Zhang C, Freistaedter A, Schmelas C, Gunkel M, Dao Thi VL, Grimm D. An RNA Interference/Adeno-Associated Virus Vector-Based Combinatorial Gene Therapy Approach Against Hepatitis E Virus. Hepatol Commun 2021; 6:878-888. [PMID: 34719133 PMCID: PMC8948557 DOI: 10.1002/hep4.1842] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/10/2021] [Accepted: 10/10/2021] [Indexed: 12/22/2022] Open
Abstract
Hepatitis E virus (HEV) is a major public health problem with limited therapeutic options. Here, we engineered adeno-associated viral vectors of serotype 6 (AAV6) to express short hairpin RNAs (shRNAs) against HEV transcripts with the prospect of down-regulating HEV replication in vivo. We designed 20 different shRNAs, targeting the genome of the HEV genotype 3 (GT3) Kernow-C1 p6 strain, for delivery upon AAV6 transduction. Using an original selectable HEV GT3 reporter replicon, we identified three shRNAs that efficiently down-regulated HEV replication. We further confirmed their inhibitory potency with full-length HEV infection. Seventy-two hours following transduction, HEV replication in both systems decreased by up to 95%. The three most potent inhibitory shRNAs identified were directed against the methyltransferase domain, the junction region between the open reading frames (ORFs), and the 3´ end of ORF2. Targeting all three regions by multiplexing the shRNAs further enhanced their inhibitory potency over a prolonged period of up to 21 days following transduction. Conclusion: Combining RNA interference and AAV vector-based gene therapy has great potential for suppressing HEV replication. Our strategy to target the viral RNA with multiplexed shRNAs should help to counteract viral escape through mutations. Considering the widely documented safety of AAV vector-based gene therapies, our approach is, in principle, amenable to clinical translation.
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Affiliation(s)
- Cindy Zhang
- Department of Infectious Diseases/Virology, Medical Faculty, Heidelberg University, Cluster of Excellence CellNetworks, BioQuant, Center for Integrative Infectious Diseases Research, Heidelberg, Germany.,Schaller Research group at Department of Infectious Diseases/Virology, Medical Faculty, Heidelberg University, Center for Integrative Infectious Diseases Research, Heidelberg, Germany.,German Center for Infection Research, Heidelberg, Germany
| | - Andrew Freistaedter
- Schaller Research group at Department of Infectious Diseases/Virology, Medical Faculty, Heidelberg University, Center for Integrative Infectious Diseases Research, Heidelberg, Germany
| | - Carolin Schmelas
- Department of Infectious Diseases/Virology, Medical Faculty, Heidelberg University, Cluster of Excellence CellNetworks, BioQuant, Center for Integrative Infectious Diseases Research, Heidelberg, Germany
| | - Manuel Gunkel
- High-Content Analysis of the Cell and Advanced Biological Screening Facility, BioQuant, Heidelberg University, Heidelberg, Germany
| | - Viet Loan Dao Thi
- Schaller Research group at Department of Infectious Diseases/Virology, Medical Faculty, Heidelberg University, Center for Integrative Infectious Diseases Research, Heidelberg, Germany.,German Center for Infection Research, Heidelberg, Germany
| | - Dirk Grimm
- Department of Infectious Diseases/Virology, Medical Faculty, Heidelberg University, Cluster of Excellence CellNetworks, BioQuant, Center for Integrative Infectious Diseases Research, Heidelberg, Germany.,German Center for Infection Research, Heidelberg, Germany.,German Center for Cardiovascular Research, Heidelberg, Germany
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16
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Belini VL, Junior OM, Ceccato-Antonini SR, Suhr H, Wiedemann P. Morphometric quantification of a pseudohyphae forming Saccharomyces cerevisiae strain using in situ microscopy and image analysis. J Microbiol Methods 2021; 190:106338. [PMID: 34597736 DOI: 10.1016/j.mimet.2021.106338] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/21/2021] [Accepted: 09/21/2021] [Indexed: 11/30/2022]
Abstract
Yeast morphology and counting are highly important in fermentation as they are often associated with productivity and can be influenced by process conditions. At present, time-consuming and offline methods are utilized for routine analysis of yeast morphology and cell counting using a haemocytometer. In this study, we demonstrate the application of an in situ microscope to obtain a fast stream of pseudohyphae images from agitated sample suspensions of a Saccharomyces cerevisiae strain, whose morphology in cell clusters is frequently found in the bioethanol fermentation industry. The large statistics of microscopic images allow for online determination of the principal morphological characteristics of the pseudohyphae, including the number of constituent cells, cell-size, number of branches, and length of branches. The distributions of these feature values are calculated online, constituting morphometric monitoring of the pseudohyphae population. By providing representative data, the proposed system can improve the effectiveness of morphological characterization, which in turn can help to improve the understanding and control of bioprocesses in which pseudohyphal-like morphologies are found.
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Affiliation(s)
- Valdinei L Belini
- Department of Electrical Engineering, Universidade Federal de São Carlos, Rodovia Washington Luís, km 235, São Carlos, SP CEP 13565-905, Brazil.
| | - Orides M Junior
- Computing Department, Universidade Federal de São Carlos, Rodovia Washington Luís, km 235, São Carlos, SP CEP 13565-905, Brazil
| | - Sandra R Ceccato-Antonini
- Department of Agroindustrial Technology and Rural Socio-Economics, Universidade Federal de São Carlos, Via Anhanguera, km 174, Araras, SP CEP 13600-970, Brazil
| | - Hajo Suhr
- Department of Information Technology, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
| | - Philipp Wiedemann
- Department of Biotechnology, Mannheim University of Applied Sciences, Paul-Wittsack-Straße 10, 68163 Mannheim, Germany
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17
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Bao R, Al-Shakarji NM, Bunyak F, Palaniappan K. DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking. ... IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS. IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION 2021; 2021:3354-3363. [PMID: 35386855 DOI: 10.1109/iccvw54120.2021.00375] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Accurate segmentation and tracking of cells in microscopy image sequences is extremely beneficial in clinical diagnostic applications and biomedical research. A continuing challenge is the segmentation of dense touching cells and deforming cells with indistinct boundaries, in low signal-to-noise-ratio images. In this paper, we present a dual-stream marker-guided network (DMNet) for segmentation of touching cells in microscopy videos of many cell types. DMNet uses an explicit cell marker-detection stream, with a separate mask-prediction stream using a distance map penalty function, which enables supervised training to focus attention on touching and nearby cells. For multi-object cell tracking we use M2Track tracking-by-detection approach with multi-step data association. Our M2Track with mask overlap includes short term track-to-cell association followed by track-to-track association to re-link tracklets with missing segmentation masks over a short sequence of frames. Our combined detection, segmentation and tracking algorithm has proven its potential on the IEEE ISBI 2021 6th Cell Tracking Challenge (CTC-6) where we achieved multiple top three rankings for diverse cell types. Our team name is MU-Ba-US, and the implementation of DMNet is available at, http://celltrackingchallenge.net/participants/MU-Ba-US/.
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Affiliation(s)
- Rina Bao
- University of Missouri-Columbia, MO 65211, USA
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18
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Ogungbemi AO, Teixido E, Massei R, Scholz S, Küster E. Automated measurement of the spontaneous tail coiling of zebrafish embryos as a sensitive behavior endpoint using a workflow in KNIME. MethodsX 2021; 8:101330. [PMID: 34434841 PMCID: PMC8374338 DOI: 10.1016/j.mex.2021.101330] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 03/26/2021] [Indexed: 01/31/2023] Open
Abstract
Neuroactive substances are the largest group of chemicals detected in European surface waters. Mixtures of neuroactive substances occurring at low concentrations can induce adverse neurological effects in humans and organisms in the environment. Therefore, there is a need to develop new screening tools to detect these chemicals. Measurement of behavior or motor effects in rodents and fish are usually performed to assess potential neurotoxicity for risk assessment. However, due to pain and stress inflicted on these animals, the scientific community is advocating for new alternative methods based on the 3R principle (reduce, replace and refine). As a result, the behavior measurement of early stages of zebrafish embryos such as locomotor response, photomotor response and spontaneous tail coiling are considered as a valid alternative to adult animal testing. In this study, we developed a workflow to investigate the spontaneous tail coiling (STC) of zebrafish embryos and to accurately measure the STC effect in the KNIME software. We validated the STC protocol with 3 substances (abamectin, chlorpyrifos-oxon and pyracostrobin) which have different mechanisms of action. The KNIME workflow combined with easy and cost-effective method of video acquisition makes this STC protocol a valuable method for neurotoxicity testing.Video acquisition duration of 60 s at 25 ± 1 hpf was used 20 embryos exposed per dish and acclimatized for 30 min before video acquisition Capability to inspect and correct errors for high accuracy
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Affiliation(s)
- Afolarin O Ogungbemi
- Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, Leipzig 04318, Germany.,Institute for Environmental Sciences, University of Koblenz-Landau, 76829, Fortstraße 7, Landau, Germany
| | - Elisabet Teixido
- Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, Leipzig 04318, Germany
| | - Riccardo Massei
- Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, Leipzig 04318, Germany
| | - Stefan Scholz
- Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, Leipzig 04318, Germany
| | - Eberhard Küster
- Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, Leipzig 04318, Germany
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19
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Lefevre JG, Koh YWH, Wall AA, Condon ND, Stow JL, Hamilton NA. LLAMA: a robust and scalable machine learning pipeline for analysis of large scale 4D microscopy data: analysis of cell ruffles and filopodia. BMC Bioinformatics 2021; 22:410. [PMID: 34412593 PMCID: PMC8375126 DOI: 10.1186/s12859-021-04324-z] [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: 02/18/2021] [Accepted: 08/10/2021] [Indexed: 11/24/2022] Open
Abstract
Background With recent advances in microscopy, recordings of cell behaviour can result in terabyte-size datasets. The lattice light sheet microscope (LLSM) images cells at high speed and high 3D resolution, accumulating data at 100 frames/second over hours, presenting a major challenge for interrogating these datasets. The surfaces of vertebrate cells can rapidly deform to create projections that interact with the microenvironment. Such surface projections include spike-like filopodia and wave-like ruffles on the surface of macrophages as they engage in immune surveillance. LLSM imaging has provided new insights into the complex surface behaviours of immune cells, including revealing new types of ruffles. However, full use of these data requires systematic and quantitative analysis of thousands of projections over hundreds of time steps, and an effective system for analysis of individual structures at this scale requires efficient and robust methods with minimal user intervention. Results We present LLAMA, a platform to enable systematic analysis of terabyte-scale 4D microscopy datasets. We use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, generating detailed object level statistics. Our system is designed to run on high-performance computing to achieve high throughput, with outputs suitable for visualisation and statistical analysis. Advanced visualisation is a key element of LLAMA: we provide a specialised tool which supports interactive quality control, optimisation, and output visualisation processes to complement the processing pipeline. LLAMA is demonstrated in an analysis of macrophage surface projections, in which it is used to i) discriminate ruffles induced by lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1) and ii) determine the autonomy of ruffle morphologies. Conclusions LLAMA provides an effective open source tool for running a cell microscopy analysis pipeline based on semantic segmentation, object analysis and tracking. Detailed numerical and visual outputs enable effective statistical analysis, identifying distinct patterns of increased activity under the two interventions considered in our example analysis. Our system provides the capacity to screen large datasets for specific structural configurations. LLAMA identified distinct features of LPS and CSF-1 induced ruffles and it identified a continuity of behaviour between tent pole ruffling, wave-like ruffling and filopodia deployment. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04324-z.
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Affiliation(s)
- James G Lefevre
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Yvette W H Koh
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Adam A Wall
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas D Condon
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Jennifer L Stow
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Nicholas A Hamilton
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. .,Research Computing Centre, The University of Queensland, Brisbane, QLD, Australia.
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20
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How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction. Histochem Cell Biol 2021; 156:461-478. [PMID: 34383240 DOI: 10.1007/s00418-021-02022-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/02/2021] [Indexed: 10/20/2022]
Abstract
Differences between computer-assisted image analysis (CAI) algorithms may cause discrepancies in the identification of immunohistochemically stained immune biomarkers in biopsies of breast cancer patients. These discrepancies have implications for their association with disease outcome. This study aims to compare three CAI procedures (A, B and C) to measure positive marker areas in post-neoadjuvant chemotherapy biopsies of patients with triple-negative breast cancer (TNBC) and to explore the differences in their performance in determining the potential association with relapse in these patients. A total of 3304 digital images of biopsy tissue obtained from 118 TNBC patients were stained for seven immune markers using immunohistochemistry (CD4, CD8, FOXP3, CD21, CD1a, CD83, HLA-DR) and were analyzed with procedures A, B and C. The three methods measure the positive pixel markers in the total tissue areas. The extent of agreement between paired CAI procedures, a principal component analysis (PCA) and Cox multivariate analysis was assessed. Comparisons of paired procedures showed close agreement for most of the immune markers at low concentration. The probability of differences between the paired procedures B/C and B/A was generally higher than those observed in C/A. The principal component analysis, largely based on data from CD8, CD1a and HLA-DR, identified two groups of patients with a significantly lower probability of relapse than the others. The multivariate regression models showed similarities in the factors associated with relapse for procedures A and C, as opposed to those obtained with procedure B. General agreement among the results of CAI procedures would not guarantee that the same predictive breast cancer markers were consistently identified. These results highlight the importance of developing additional strategies to improve the sensitivity of CAI procedures.
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21
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Nishimura K, Wang C, Watanabe K, Fei Elmer Ker D, Bise R. Weakly supervised cell instance segmentation under various conditions. Med Image Anal 2021; 73:102182. [PMID: 34340103 DOI: 10.1016/j.media.2021.102182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 07/14/2021] [Accepted: 07/14/2021] [Indexed: 10/20/2022]
Abstract
Cell instance segmentation is important in biomedical research. For living cell analysis, microscopy images are captured under various conditions (e.g., the type of microscopy and type of cell). Deep-learning-based methods can be used to perform instance segmentation if sufficient annotations of individual cell boundaries are prepared as training data. Generally, annotations are required for each condition, which is very time-consuming and labor-intensive. To reduce the annotation cost, we propose a weakly supervised cell instance segmentation method that can segment individual cell regions under various conditions by only using rough cell centroid positions as training data. This method dramatically reduces the annotation cost compared with the standard annotation method of supervised segmentation. We demonstrated the efficacy of our method on various cell images; it outperformed several of the conventional weakly-supervised methods on average. In addition, we demonstrated that our method can perform instance cell segmentation without any manual annotation by using pairs of phase contrast and fluorescence images in which cell nuclei are stained as training data.
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Affiliation(s)
- Kazuya Nishimura
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.
| | - Chenyang Wang
- Institute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR
| | | | - Dai Fei Elmer Ker
- Institute for Tissue Engineering and Regenerative Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR; School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR; Key Laboratory for Regenerative Medicine, Ministry of Education, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR; Department of Orthopaedics and Traumatology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.
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22
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Khened M, Kori A, Rajkumar H, Krishnamurthi G, Srinivasan B. A generalized deep learning framework for whole-slide image segmentation and analysis. Sci Rep 2021; 11:11579. [PMID: 34078928 PMCID: PMC8172839 DOI: 10.1038/s41598-021-90444-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 05/04/2021] [Indexed: 12/22/2022] Open
Abstract
Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Whole-slide imaging (WSI), i.e., the scanning and digitization of entire histology slides, are now being adopted across the world in pathology labs. Trained histopathologists can provide an accurate diagnosis of biopsy specimens based on WSI data. Given the dimensionality of WSIs and the increase in the number of potential cancer cases, analyzing these images is a time-consuming process. Automated segmentation of tumorous tissue helps in elevating the precision, speed, and reproducibility of research. In the recent past, deep learning-based techniques have provided state-of-the-art results in a wide variety of image analysis tasks, including the analysis of digitized slides. However, deep learning-based solutions pose many technical challenges, including the large size of WSI data, heterogeneity in images, and complexity of features. In this study, we propose a generalized deep learning-based framework for histopathology tissue analysis to address these challenges. Our framework is, in essence, a sequence of individual techniques in the preprocessing-training-inference pipeline which, in conjunction, improve the efficiency and the generalizability of the analysis. The combination of techniques we have introduced includes an ensemble segmentation model, division of the WSI into smaller overlapping patches while addressing class imbalances, efficient techniques for inference, and an efficient, patch-based uncertainty estimation framework. Our ensemble consists of DenseNet-121, Inception-ResNet-V2, and DeeplabV3Plus, where all the networks were trained end to end for every task. We demonstrate the efficacy and improved generalizability of our framework by evaluating it on a variety of histopathology tasks including breast cancer metastases (CAMELYON), colon cancer (DigestPath), and liver cancer (PAIP). Our proposed framework has state-of-the-art performance across all these tasks and is ranked within the top 5 currently for the challenges based on these datasets. The entire framework along with the trained models and the related documentation are made freely available at GitHub and PyPi. Our framework is expected to aid histopathologists in accurate and efficient initial diagnosis. Moreover, the estimated uncertainty maps will help clinicians to make informed decisions and further treatment planning or analysis.
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Affiliation(s)
- Mahendra Khened
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Avinash Kori
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Haran Rajkumar
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Ganapathy Krishnamurthi
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, 600036, India.
| | - Balaji Srinivasan
- Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, 600036, India
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Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends. MATHEMATICS 2020. [DOI: 10.3390/math8111863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.
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Braiki M, Benzinou A, Nasreddine K, Hymery N. Automatic Human Dendritic Cells Segmentation Using K-Means Clustering and Chan-Vese Active Contour Model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 195:105520. [PMID: 32497772 DOI: 10.1016/j.cmpb.2020.105520] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 03/09/2020] [Accepted: 04/23/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Nowadays, the number of pathologies related to food are multiplied. Mycotoxins are one of the most severe food contaminants that cause serious effects on the human health. Therefore, it is necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, a new investigational method using human dendritic cells was endorsed by biologists. Nevertheless, analysis of the morphological features and the behavior of these cells remains merely visual. In addition, this manual analysis is difficult and time-consuming. Here, we focus mainly on automating the evaluation process by using advanced image processing technology. METHODS An automatic segmentation approach of microscopic dendritic cell images is developed to provide a fast and objective evaluation. First, a combination of K-means clustering and mathematical morphology is used to detect dendritic cells. Second, a region-based Chan-Vese active contour model is used to segment the detected cells more precisely. Finally, dendritic cells are extracted by a filtering based on eccentricity measure. RESULTS The proposed scheme is tested on an actual dataset containing 421 microscopic dendritic cell images. The experimental results show high conformity between the results of the proposed scheme and ground-truth elaborated by biological expert. Moreover, a comparative study with other state-of-art segmentation schemes demonstrates the efficiency of the proposed method. It gives the highest average accuracy rate (99.42 %) compared to recent studied approaches. CONCLUSIONS The proposed image segmentation method for morphological analysis of dendrite inhibition can consistently be used as an assessment tool for biologists to facilitate the evaluation of serious health impacts of mycotoxins.
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Affiliation(s)
- Marwa Braiki
- ENIB, UMR CNRS 6285 LabSTICC, 29238, Brest, France; UTM, ISTMT, LR13ES07 (LRBTM), 1006, Tunis, Tunisie
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25
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ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy. APPLIED SCIENCES-BASEL 2020; 10. [PMID: 34306736 PMCID: PMC8297459 DOI: 10.3390/app10186187] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.
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26
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Weiss T, Semmler L, Millesi F, Mann A, Haertinger M, Salzmann M, Radtke C. Automated image analysis of stained cytospins to quantify Schwann cell purity and proliferation. PLoS One 2020; 15:e0233647. [PMID: 32442229 PMCID: PMC7244157 DOI: 10.1371/journal.pone.0233647] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 05/10/2020] [Indexed: 11/18/2022] Open
Abstract
In response to injury, adult Schwann cells (SCs) re-enter the cell cycle, change their expression profile, and exert repair functions important for wound healing and the re-growth of axons. While this phenotypical instability of SCs is essential for nerve regeneration, it has also been implicated in cancer progression and de-myelinating neuropathies. Thus, SCs became an important research tool to study the molecular mechanisms involved in repair and disease and to identify targets for therapeutic intervention. A high purity of isolated SC cultures used for experimentation must be demonstrated to exclude that novel findings are derived from a contaminating fibroblasts population. In addition, information about the SC proliferation status is an important parameter to be determined in response to different treatments. The evaluation of SC purity and proliferation, however, usually depends on the time consuming, manual assessment of immunofluorescence stainings or comes with the sacrifice of a large amount of SCs for flow cytometry analysis. We here show that rat SC culture derived cytospins stained for SC marker SOX10, proliferation marker EdU, intermediate filament vimentin and DAPI allowed the determination of SC identity and proliferation by requiring only a small number of cells. Furthermore, the CellProfiler software was used to develop an automated image analysis pipeline that quantified SCs and proliferating SCs from the obtained immunofluorescence images. By comparing the results of total cell count, SC purity and SC proliferation rate between manual counting and the CellProfiler output, we demonstrated applicability and reliability of the established pipeline. In conclusion, we here combined the cytospin technique, a multi-colour immunofluorescence staining panel, and an automated image analysis pipeline to enable the quantification of SC purity and SC proliferation from small cell aliquots. This procedure represents a solid read-out to simplify and standardize the quantification of primary SC culture purity and proliferation.
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Affiliation(s)
- Tamara Weiss
- Research Laboratory of the Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
- * E-mail:
| | - Lorenz Semmler
- Research Laboratory of the Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Flavia Millesi
- Research Laboratory of the Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Anda Mann
- Research Laboratory of the Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Maximilian Haertinger
- Research Laboratory of the Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
| | - Manuel Salzmann
- Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Christine Radtke
- Research Laboratory of the Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria
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Boukari F, Makrogiannis S. Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:959-971. [PMID: 30334766 PMCID: PMC6832744 DOI: 10.1109/tcbb.2018.2875684] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score ( TRA) of 99 percent, and the fully automated segmentation and tracking system produces an average TRA of 89 percent.
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28
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Yudistira N, Kavitha M, Itabashi T, Iwane AH, Kurita T. Prediction of Sequential Organelles Localization under Imbalance using A Balanced Deep U-Net. Sci Rep 2020; 10:2626. [PMID: 32060319 PMCID: PMC7021757 DOI: 10.1038/s41598-020-59285-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 01/27/2020] [Indexed: 01/17/2023] Open
Abstract
Assessing the structure and function of organelles in living organisms of the primitive unicellular red algae Cyanidioschyzon merolae on three-dimensional sequential images demands a reliable automated technique in the class imbalance among various cellular structures during mitosis. Existing classification networks with commonly used loss functions were focused on larger numbers of cellular structures that lead to the unreliability of the system. Hence, we proposed a balanced deep regularized weighted compound dice loss (RWCDL) network for better localization of cell organelles. Specifically, we introduced two new loss functions, namely compound dice (CD) and RWCD by implementing multi-class variant dice and weighting mechanism, respectively for maximizing weights of peroxisome and nucleus among five classes as the main contribution of this study. We extended the Unet-like convolution neural network (CNN) architecture for evaluating the ability of our proposed loss functions for improved segmentation. The feasibility of the proposed approach is confirmed with three different large scale mitotic cycle data set with different number of occurrences of cell organelles. In addition, we compared the training behavior of our designed architectures with the ground truth segmentation using various performance measures. The proposed balanced RWCDL network generated the highest area under the curve (AUC) value in elevating the small and obscure peroxisome and nucleus, which is 30% higher than the network with commonly used mean square error (MSE) and dice loss (DL) functions. The experimental results indicated that the proposed approach can efficiently identify the cellular structures, even when the contour between the cells is obscure and thus convinced that the balanced deep RWCDL approach is reliable and can be helpful for biologist to accurately identify the relationship between the cell behavior and structures of cell organelles during mitosis.
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Affiliation(s)
- Novanto Yudistira
- Hiroshima University, Department of Information Engineering, Higashi Hiroshima, 739-8521, Japan.
- Universitas Brawijaya, Fakultas Ilmu Komputer, Malang, 65145, Indonesia.
| | - Muthusubash Kavitha
- Hiroshima University, Department of Information Engineering, Higashi Hiroshima, 739-8521, Japan
| | - Takeshi Itabashi
- Riken, Center for Biosystems Dynamics Research, Laboratory for Cell Field Structure, Higashi Hiroshima, 739-0046, Japan
- Hiroshima University, Graduate School of Integrated Sciences for Life, Higashi Hiroshima, 739-0046, Japan
- Osaka University, Graduate School of Frontier Biosciences, Osaka, 565-0871, Japan
| | - Atsuko H Iwane
- Riken, Center for Biosystems Dynamics Research, Laboratory for Cell Field Structure, Higashi Hiroshima, 739-0046, Japan
- Hiroshima University, Graduate School of Integrated Sciences for Life, Higashi Hiroshima, 739-0046, Japan
- Osaka University, Graduate School of Frontier Biosciences, Osaka, 565-0871, Japan
| | - Takio Kurita
- Hiroshima University, Department of Information Engineering, Higashi Hiroshima, 739-8521, Japan
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30
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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] [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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Caicedo JC, Goodman A, Karhohs KW, Cimini BA, Ackerman J, Haghighi M, Heng C, Becker T, Doan M, McQuin C, Rohban M, Singh S, Carpenter AE. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat Methods 2019; 16:1247-1253. [PMID: 31636459 PMCID: PMC6919559 DOI: 10.1038/s41592-019-0612-7] [Citation(s) in RCA: 238] [Impact Index Per Article: 47.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 09/13/2019] [Indexed: 01/15/2023]
Abstract
Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.
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Affiliation(s)
| | - Allen Goodman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Tim Becker
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Minh Doan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Claire McQuin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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32
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Pontalba JT, Gwynne-Timothy T, David E, Jakate K, Androutsos D, Khademi A. Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks. Front Bioeng Biotechnol 2019; 7:300. [PMID: 31737619 PMCID: PMC6838039 DOI: 10.3389/fbioe.2019.00300] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 10/15/2019] [Indexed: 02/03/2023] Open
Abstract
Image analysis tools for cancer, such as automatic nuclei segmentation, are impacted by the inherent variation contained in pathology image data. Convolutional neural networks (CNN), demonstrate success in generalizing to variable data, illustrating great potential as a solution to the problem of data variability. In some CNN-based segmentation works for digital pathology, authors apply color normalization (CN) to reduce color variability of data as a preprocessing step prior to prediction, while others do not. Both approaches achieve reasonable performance and yet, the reasoning for utilizing this step has not been justified. It is therefore important to evaluate the necessity and impact of CN for deep learning frameworks, and its effect on downstream processes. In this paper, we evaluate the effect of popular CN methods on CNN-based nuclei segmentation frameworks.
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Affiliation(s)
| | | | - Ephraim David
- Image Analysis in Medicine Lab (IAMLAB), Ryerson University, Toronto, ON, Canada
| | | | - Dimitrios Androutsos
- Image Analysis in Medicine Lab (IAMLAB), Ryerson University, Toronto, ON, Canada
| | - April Khademi
- Image Analysis in Medicine Lab (IAMLAB), Ryerson University, Toronto, ON, Canada
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33
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Investigation of the fabric evolution and the stress-transmission behaviour of sands based on X-ray μCT images. ADV POWDER TECHNOL 2019. [DOI: 10.1016/j.apt.2019.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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34
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Caicedo JC, Roth J, Goodman A, Becker T, Karhohs KW, Broisin M, Molnar C, McQuin C, Singh S, Theis FJ, Carpenter AE. Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images. Cytometry A 2019; 95:952-965. [PMID: 31313519 PMCID: PMC6771982 DOI: 10.1002/cyto.a.23863] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 05/31/2019] [Accepted: 06/23/2019] [Indexed: 12/12/2022]
Abstract
Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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Affiliation(s)
- Juan C. Caicedo
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
| | - Jonathan Roth
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
- Institute of Computational BiologyGerman Research Center for Environmental HealthMunichGermany
| | - Allen Goodman
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
| | - Tim Becker
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
| | - Kyle W. Karhohs
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
| | - Matthieu Broisin
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
- Biomedical Imaging GroupEcole polytechnique fédérale de LausanneLausanneSwitzerland
| | - Csaba Molnar
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
- Biological Research Centre of the Hungarian Academy of SciencesSzegedHungary
| | - Claire McQuin
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
| | - Shantanu Singh
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
| | - Fabian J. Theis
- Institute of Computational BiologyGerman Research Center for Environmental HealthMunichGermany
| | - Anne E. Carpenter
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusetts
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35
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Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images. J Clin Med 2019; 8:jcm8081159. [PMID: 31382487 PMCID: PMC6723258 DOI: 10.3390/jcm8081159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 07/29/2019] [Accepted: 07/30/2019] [Indexed: 12/22/2022] Open
Abstract
Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.
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Abstract
Embryonic development is highly complex and dynamic, requiring the coordination of numerous molecular and cellular events at precise times and places. Advances in imaging technology have made it possible to follow developmental processes at cellular, tissue, and organ levels over time as they take place in the intact embryo. Parallel innovations of in vivo probes permit imaging to report on molecular, physiological, and anatomical events of embryogenesis, but the resulting multidimensional data sets pose significant challenges for extracting knowledge. In this review, we discuss recent and emerging advances in imaging technologies, in vivo labeling, and data processing that offer the greatest potential for jointly deciphering the intricate cellular dynamics and the underlying molecular mechanisms. Our discussion of the emerging area of “image-omics” highlights both the challenges of data analysis and the promise of more fully embracing computation and data science for rapidly advancing our understanding of biology.
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Affiliation(s)
- Francesco Cutrale
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, California 90089, USA
| | - Scott E. Fraser
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California 90089, USA
- Translational Imaging Center, University of Southern California, Los Angeles, California 90089, USA
- Division of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA
| | - Le A. Trinh
- Translational Imaging Center, University of Southern California, Los Angeles, California 90089, USA
- Division of Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA
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37
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Payer C, Štern D, Feiner M, Bischof H, Urschler M. Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks. Med Image Anal 2019; 57:106-119. [PMID: 31299493 DOI: 10.1016/j.media.2019.06.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 06/05/2019] [Accepted: 06/26/2019] [Indexed: 11/28/2022]
Abstract
Differently to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same object class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time, which is highly relevant, e.g., in biomedical applications involving cell growth and migration. Our network architecture incorporates convolutional gated recurrent units (ConvGRU) into a stacked hourglass network to utilize temporal information, e.g., from microscopy videos. Moreover, we train our network with a novel embedding loss based on cosine similarities, such that the network predicts unique embeddings for every instance throughout videos, even in the presence of dynamic structural changes due to mitosis of cells. To create the final tracked instance segmentations, the pixel-wise embeddings are clustered among subsequent video frames by using the mean shift algorithm. After showing the performance of the instance segmentation on a static in-house dataset of muscle fibers from H&E-stained microscopy images, we also evaluate our proposed recurrent stacked hourglass network regarding instance segmentation and tracking performance on six datasets from the ISBI celltracking challenge, where it delivers state-of-the-art results.
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Affiliation(s)
- Christian Payer
- Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria
| | - Darko Štern
- Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria
| | - Marlies Feiner
- Division of Phoniatrics, Medical University Graz, Graz, Austria
| | - Horst Bischof
- Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria
| | - Martin Urschler
- Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria; Department of Computer Science, The University of Auckland, New Zealand.
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Lotfollahi M, Berisha S, Saadatifard L, Montier L, Žiburkus J, Mayerich D. Three-dimensional GPU-accelerated active contours for automated localization of cells in large images. PLoS One 2019; 14:e0215843. [PMID: 31173591 PMCID: PMC6555506 DOI: 10.1371/journal.pone.0215843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 04/09/2019] [Indexed: 01/17/2023] Open
Abstract
Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. Successful cell segmentation algorithms rely identifying seed points, and are highly sensitive to variablility in cell size. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional contour evolution that extends previous work on fast two-dimensional snakes. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell localization tasks when compared to existing methods on large 3D brain images.
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Affiliation(s)
- Mahsa Lotfollahi
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
| | - Sebastian Berisha
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
| | - Leila Saadatifard
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
| | - Laura Montier
- Department of Biology and Biochemistry, University of Houston, TX, United States of America
| | - Jokūbas Žiburkus
- Department of Biology and Biochemistry, University of Houston, TX, United States of America
| | - David Mayerich
- Department of Electrical and Computer engineering, University of Houston, Houston, TX, United States of America
- * E-mail:
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A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images. Sci Rep 2019; 9:5654. [PMID: 30948741 PMCID: PMC6449358 DOI: 10.1038/s41598-019-41683-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 03/14/2019] [Indexed: 11/24/2022] Open
Abstract
Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithm, based on sparse coding, that can both enhance very faint and noisy nuclei signal but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96:96% recall for the C. elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99:3%).
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40
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Cheng Z, Wang J. Quantification of the strain field of sands based on X-ray micro-tomography: A comparison between a grid-based method and a mesh-based method. POWDER TECHNOL 2019. [DOI: 10.1016/j.powtec.2018.12.048] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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41
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Iqbal MS, Khan T, Hussain S, Mahmood R, El-Ashram S, Abbasi R, Luo B. Cell Recognition of Microscopy Images of TPEF (Two Photon Excited Florescence) Probes. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.procs.2019.01.188] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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42
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Lanng MB, Møller CB, Andersen ASH, Pálsdóttir ÁA, Røge R, Østergaard LR, Jørgensen AS. Quality assessment of Ki67 staining using cell line proliferation index and stain intensity features. Cytometry A 2018; 95:381-388. [PMID: 30556331 DOI: 10.1002/cyto.a.23683] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 11/07/2022]
Abstract
Breast cancer is the most frequent cancer among women worldwide. Ki67 can be used as an immunohistochemical pseudo marker for cell proliferation to determine how aggressive the cancer is and thereby the treatment of the patient. No standard Ki67 staining protocol exists, resulting in inter-laboratory stain variability. Therefore, it is important to determine the quality control of a staining protocol to ensure correct diagnosis and treatment of patients. Currently, quality control is performed by the organization NordiQC that use an expert panel-based qualitative assessment system. However, no objective method exists to determine the quality of a staining protocol. In this study, we propose an algorithm, to objectively assess staining quality from segmented cell nuclei structures extracted from cell lines. The cell nuclei were classified into either Ki67 positive or negative to determine the Ki67 proliferation index within the cell lines. A Ki67 stain quality model based on ordinal logistic regression was developed to determine the quality of a staining protocol from features extracted from the segmented cell nuclei in the cell lines. The algorithm was able to segment and classify Ki67 positive cell nuclei with a sensitivity and positive predictive value (PPV) of 0.90 and 0.94 and Ki67 negative cell nuclei with a sensitivity and PPV of 0.78 and 0.78. The mean difference between a manual and automatic Ki67 proliferation index was -0.003 with a standard deviation of 0.056. The ordinal logistic regression model found that the stain intensity for both the Ki67 positive and Ki67 negative cell nuclei were statistically significant as parameters determining the stain quality from the cell line cores. The framework shows great promise for using cell nuclei information from cell lines to predict the staining quality of staining protocols. © 2018 International Society for Advancement of Cytometry.
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Affiliation(s)
- Mathias Buus Lanng
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Cecilie Brochdorff Møller
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Anne-Sofie Hendrup Andersen
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Ásgerður Arna Pálsdóttir
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Rasmus Røge
- Institute of Pathology, Aalborg University Hospital, Denmark.,The Department of Clinical Medicine, Aalborg University, Denmark
| | - Lasse Riis Østergaard
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
| | - Alex Skovsbo Jørgensen
- Department of Health Science and Technology, Aalborg University, Fredrik Bajersvej 7D2, 9220, Aalborg, Denmark
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Lock JG, Jones MC, Askari JA, Gong X, Oddone A, Olofsson H, Göransson S, Lakadamyali M, Humphries MJ, Strömblad S. Reticular adhesions are a distinct class of cell-matrix adhesions that mediate attachment during mitosis. Nat Cell Biol 2018; 20:1290-1302. [PMID: 30361699 DOI: 10.1038/s41556-018-0220-2] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 09/21/2018] [Indexed: 12/13/2022]
Abstract
Adhesion to the extracellular matrix persists during mitosis in most cell types. However, while classical adhesion complexes, such as focal adhesions, do and must disassemble to enable mitotic rounding, the mechanisms of residual mitotic cell-extracellular matrix adhesion remain undefined. Here, we identify 'reticular adhesions', a class of adhesion complex that is mediated by integrin αvβ5, formed during interphase, and preserved at cell-extracellular matrix attachment sites throughout cell division. Consistent with this role, integrin β5 depletion perturbs mitosis and disrupts spatial memory transmission between cell generations. Reticular adhesions are morphologically and dynamically distinct from classical focal adhesions. Mass spectrometry defines their unique composition, enriched in phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2)-binding proteins but lacking virtually all consensus adhesome components. Indeed, reticular adhesions are promoted by PtdIns(4,5)P2, and form independently of talin and F-actin. The distinct characteristics of reticular adhesions provide a solution to the problem of maintaining cell-extracellular matrix attachment during mitotic rounding and division.
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Affiliation(s)
- John G Lock
- Department of Pathology, School of Medical Sciences, University of New South Wales, Sydney, Australia.
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden.
| | - Matthew C Jones
- Wellcome Trust Centre for Cell-Matrix Research, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Janet A Askari
- Wellcome Trust Centre for Cell-Matrix Research, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Xiaowei Gong
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Anna Oddone
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- ICFO, Institut de Ciencies Fotoniques, Mediterranean Technology Park, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Helene Olofsson
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Sara Göransson
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Melike Lakadamyali
- ICFO, Institut de Ciencies Fotoniques, Mediterranean Technology Park, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Perelman School of Medicine, Department of Physiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Martin J Humphries
- Wellcome Trust Centre for Cell-Matrix Research, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Staffan Strömblad
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden.
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Casiraghi E, Huber V, Frasca M, Cossa M, Tozzi M, Rivoltini L, Leone BE, Villa A, Vergani B. A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections. BMC Bioinformatics 2018; 19:357. [PMID: 30367588 PMCID: PMC6191943 DOI: 10.1186/s12859-018-2302-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension. Software The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers’ relative location). Additionally, it computes novel measures of markers’ co-existence in tissue volumes depending on their density. Conclusions Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.
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Affiliation(s)
- Elena Casiraghi
- Department of Computer Science "Giovanni Degli Antoni", Università degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.
| | - Veronica Huber
- Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Marco Frasca
- Department of Computer Science "Giovanni Degli Antoni", Università degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy
| | - Mara Cossa
- Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Matteo Tozzi
- Department of medicine and surgery, Vascular Surgery, University of Insubria Hospital, Varese, Italy
| | - Licia Rivoltini
- Unit of Immunotherapy of Human Tumors, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Antonello Villa
- School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.,Consorzio MIA - Microscopy and Image Analysis, University of Milano Bicocca, Monza, Italy
| | - Barbara Vergani
- School of Medicine and Surgery, University of Milano Bicocca, Monza, Italy.,Consorzio MIA - Microscopy and Image Analysis, University of Milano Bicocca, Monza, Italy
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45
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Todorov H, Saeys Y. Computational approaches for high‐throughput single‐cell data analysis. FEBS J 2018; 286:1451-1467. [DOI: 10.1111/febs.14613] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 06/04/2018] [Accepted: 07/25/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Helena Todorov
- Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium
- Department of Applied Mathematics, Computer Science and Statistics Ghent University Belgium
- Centre International de Recherche en Infectiologie Inserm U1111, Université Claude Bernard Lyon 1 CNRS, UMR5308 École Normale Supérieure de Lyon Univ Lyon France
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium
- Department of Applied Mathematics, Computer Science and Statistics Ghent University Belgium
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46
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Khoshdeli M, Winkelmaier G, Parvin B. Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes. BMC Bioinformatics 2018; 19:294. [PMID: 30086715 PMCID: PMC6081825 DOI: 10.1186/s12859-018-2285-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 07/16/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Nuclear segmentation is an important step for profiling aberrant regions of histology sections. If nuclear segmentation can be resolved, then new biomarkers of nuclear phenotypes and their organization can be predicted for the application of precision medicine. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), nuclear phenotypes (e.g., vesicular, aneuploidy), and overlapping nuclei. The problem is further complicated as a result of variations in sample preparation (e.g., fixation, staining). Our hypothesis is that (i) deep learning techniques can learn complex phenotypic signatures that rise in tumor sections, and (ii) fusion of different representations (e.g., regions, boundaries) contributes to improved nuclear segmentation. RESULTS We have demonstrated that training of deep encoder-decoder convolutional networks overcomes complexities associated with multiple nuclear phenotypes, where we evaluate alternative architecture of deep learning for an improved performance against the simplicity of the design. In addition, improved nuclear segmentation is achieved by color decomposition and combining region- and boundary-based features through a fusion network. The trained models have been evaluated against approximately 19,000 manually annotated nuclei, and object-level Precision, Recall, F1-score and Standard Error are reported with the best F1-score being 0.91. Raw training images, annotated images, processed images, and source codes are released as a part of the Additional file 1. CONCLUSIONS There are two intrinsic barriers in nuclear segmentation to H&E stained images, which correspond to the diversity of nuclear phenotypes and perceptual boundaries between adjacent cells. We demonstrate that (i) the encoder-decoder architecture can learn complex phenotypes that include the vesicular type; (ii) delineation of overlapping nuclei is enhanced by fusion of region- and edge-based networks; (iii) fusion of ENets produces an improved result over the fusion of UNets; and (iv) fusion of networks is better than multitask learning. We suggest that our protocol enables processing a large cohort of whole slide images for applications in precision medicine.
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Affiliation(s)
- Mina Khoshdeli
- Electrical and Biomedical Department, University of Nevada, Reno, 1664 N. Virginia, Reno, USA
| | - Garrett Winkelmaier
- Electrical and Biomedical Department, University of Nevada, Reno, 1664 N. Virginia, Reno, USA
| | - Bahram Parvin
- Electrical and Biomedical Department, University of Nevada, Reno, 1664 N. Virginia, Reno, USA
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47
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Arbelle A, Reyes J, Chen JY, Lahav G, Riklin Raviv T. A probabilistic approach to joint cell tracking and segmentation in high-throughput microscopy videos. Med Image Anal 2018; 47:140-152. [PMID: 29747154 PMCID: PMC6217993 DOI: 10.1016/j.media.2018.04.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Revised: 04/12/2018] [Accepted: 04/19/2018] [Indexed: 12/21/2022]
Abstract
We present a novel computational framework for the analysis of high-throughput microscopy videos of living cells. The proposed framework is generally useful and can be applied to different datasets acquired in a variety of laboratory settings. This is accomplished by tying together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. In contrast to most existing approaches, which aim to be general, no assumption of cell shape is made. Spatial, temporal, and cross-sectional variation of the analysed data are accommodated by two key contributions. First, time series analysis is exploited to estimate the temporal cell shape uncertainty in addition to cell trajectory. Second, a fast marching (FM) algorithm is used to integrate the inferred cell properties with the observed image measurements in order to obtain image likelihood for cell segmentation, and association. The proposed approach has been tested on eight different time-lapse microscopy data sets, some of which are high-throughput, demonstrating promising results for the detection, segmentation and association of planar cells. Our results surpass the state of the art for the Fluo-C2DL-MSC data set of the Cell Tracking Challenge (Maška et al., 2014).
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Affiliation(s)
- Assaf Arbelle
- Department of Electrical and Computer Engineering, Ben Gurion University of the Negev, Israel; The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel
| | - Jose Reyes
- Department of Systems Biology, Harvard Medical School, USA
| | - Jia-Yun Chen
- Department of Systems Biology, Harvard Medical School, USA
| | - Galit Lahav
- Department of Systems Biology, Harvard Medical School, USA
| | - Tammy Riklin Raviv
- Department of Electrical and Computer Engineering, Ben Gurion University of the Negev, Israel; The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel.
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48
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Thorne CA, Chen IW, Sanman LE, Cobb MH, Wu LF, Altschuler SJ. Enteroid Monolayers Reveal an Autonomous WNT and BMP Circuit Controlling Intestinal Epithelial Growth and Organization. Dev Cell 2018; 44:624-633.e4. [PMID: 29503158 DOI: 10.1016/j.devcel.2018.01.024] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 12/15/2017] [Accepted: 01/25/2018] [Indexed: 12/15/2022]
Abstract
The intestinal epithelium maintains a remarkable balance between proliferation and differentiation despite rapid cellular turnover. A central challenge is to elucidate mechanisms required for robust control of tissue renewal. Opposing WNT and BMP signaling is essential in establishing epithelial homeostasis. However, it has been difficult to disentangle contributions from multiple sources of morphogen signals in the tissue. Here, to dissect epithelial-autonomous morphogenic signaling circuits, we developed an enteroid monolayer culture system that recapitulates four key properties of the intestinal epithelium, namely the ability to maintain proliferative and differentiated zones, self-renew, polarize, and generate major intestinal cell types. We systematically perturb intrinsic and extrinsic sources of WNT and BMP signals to reveal a core morphogenic circuit that controls proliferation, tissue organization, and cell fate. Our work demonstrates the ability of intestinal epithelium, even in the absence of 3D tissue architecture, to control its own growth and organization through morphogen-mediated feedback.
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Affiliation(s)
- Curtis A Thorne
- Green Center for Systems Biology, Simmons Cancer Center, Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Cellular and Molecular Medicine, University of Arizona Cancer Center, University of Arizona, Tucson, AZ 85724, USA
| | - Ina W Chen
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Laura E Sanman
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Melanie H Cobb
- Green Center for Systems Biology, Simmons Cancer Center, Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Lani F Wu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Steven J Altschuler
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
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49
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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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50
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Mostajer Kheirkhah F, Sadegh Mohammadi HR, Shahverdi A. Modified histogram-based segmentation and adaptive distance tracking of sperm cells image sequences. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 154:173-182. [PMID: 29249341 DOI: 10.1016/j.cmpb.2017.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 10/09/2017] [Accepted: 11/06/2017] [Indexed: 06/07/2023]
Abstract
Proper recognition and tracking of microscopic sperm cells in video images are vital steps of male infertility diagnosis and treatment. The segmentation and detection of sperms in microscopic image analysis is a complicate process as a result of their small sizes, fast movements, and considerable collisions. Histogram-based thresholding schemes are very popular for this purpose, since they are quite fast and provide almost acceptable results. This paper proposes a combined method for sperm cells detection, which consists of a non-linear pre-processing stage, a histogram-based thresholding algorithm, and a tracking method based on an adaptive distance scheme. The results of conducted experiments verify the superiority of the proposed scheme with incorporated Kittler algorithm compared to the other competitive methods in the majority of cases.
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