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Qureshi MH, Ozlu N, Bayraktar H. Adaptive tracking algorithm for trajectory analysis of cells and layer-by-layer assessment of motility dynamics. Comput Biol Med 2022; 150:106193. [PMID: 37859286 DOI: 10.1016/j.compbiomed.2022.106193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/26/2022] [Accepted: 10/08/2022] [Indexed: 11/03/2022]
Abstract
Tracking biological objects such as cells or subcellular components imaged with time-lapse microscopy enables us to understand the molecular principles about the dynamics of cell behaviors. However, automatic object detection, segmentation and extracting trajectories remain as a rate-limiting step due to intrinsic challenges of video processing. This paper presents an adaptive tracking algorithm (Adtari) that automatically finds the optimum search radius and cell linkages to determine trajectories in consecutive frames. A critical assumption in most tracking studies is that displacement remains unchanged throughout the movie and cells in a few frames are usually analyzed to determine its magnitude. Tracking errors and inaccurate association of cells may occur if the user does not correctly evaluate the value or prior knowledge is not present on cell movement. The key novelty of our method is that minimum intercellular distance and maximum displacement of cells between frames are dynamically computed and used to determine the threshold distance. Since the space between cells is highly variable in a given frame, our software recursively alters the magnitude to determine all plausible matches in the trajectory analysis. Our method therefore eliminates a major preprocessing step where a constant distance was used to determine the neighbor cells in tracking methods. Cells having multiple overlaps and splitting events were further evaluated by using the shape attributes including perimeter, area, ellipticity and distance. The features were applied to determine the closest matches by minimizing the difference in their magnitudes. Finally, reporting section of our software were used to generate instant maps by overlaying cell features and trajectories. Adtari was validated by using videos with variable signal-to-noise, contrast ratio and cell density. We compared the adaptive tracking with constant distance and other methods to evaluate performance and its efficiency. Our algorithm yields reduced mismatch ratio, increased ratio of whole cell track, higher frame tracking efficiency and allows layer-by-layer assessment of motility to characterize single-cells. Adaptive tracking provides a reliable, accurate, time efficient and user-friendly open source software that is well suited for analysis of 2D fluorescence microscopy video datasets.
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Affiliation(s)
- Mohammad Haroon Qureshi
- Department of Molecular Biology and Genetics, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey; Center for Translational Research, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey
| | - Nurhan Ozlu
- Department of Molecular Biology and Genetics, Koç University, Rumelifeneri Yolu, Sariyer, 34450, Istanbul, Turkey
| | - Halil Bayraktar
- Department of Molecular Biology and Genetics, Istanbul Technical University, Maslak, Sariyer, 34467, Istanbul, Turkey.
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2
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Chaudhary S, Moon S, Lu H. Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning. Nat Commun 2022; 13:5165. [PMID: 36056020 PMCID: PMC9440141 DOI: 10.1038/s41467-022-32886-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 08/18/2022] [Indexed: 11/08/2022] Open
Abstract
Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applied to denoise images, their applications to downstream analyses, such as recovering high-SNR calcium traces, have been limited. Further, these methods require temporally-sequential pre-registered data acquired at ultrafast rates. Here, we demonstrate a supervised deep-denoising method to circumvent these tradeoffs for several applications, including whole-brain imaging, large-field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans. Our framework has 30× smaller memory footprint, and is fast in training and inference (50-70 ms); it is highly accurate and generalizable, and further, trained with only small, non-temporally-sequential, independently-acquired training datasets (∼500 pairs of images). We envision that the framework will enable faster and long-term imaging experiments necessary to study neuronal mechanisms of many behaviors.
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Affiliation(s)
- Shivesh Chaudhary
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Sihoon Moon
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
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3
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Emmons SW, Yemini E, Zimmer M. Methods for analyzing neuronal structure and activity in Caenorhabditis elegans. Genetics 2021; 218:6303616. [PMID: 34151952 DOI: 10.1093/genetics/iyab072] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/20/2021] [Indexed: 11/12/2022] Open
Abstract
The model research animal Caenorhabditis elegans has unique properties making it particularly advantageous for studies of the nervous system. The nervous system is composed of a stereotyped complement of neurons connected in a consistent manner. Here, we describe methods for studying nervous system structure and function. The transparency of the animal makes it possible to visualize and identify neurons in living animals with fluorescent probes. These methods have been recently enhanced for the efficient use of neuron-specific reporter genes. Because of its simple structure, for a number of years, C. elegans has been at the forefront of connectomic studies defining synaptic connectivity by electron microscopy. This field is burgeoning with new, more powerful techniques, and recommended up-to-date methods are here described that encourage the possibility of new work in C. elegans. Fluorescent probes for single synapses and synaptic connections have allowed verification of the EM reconstructions and for experimental approaches to synapse formation. Advances in microscopy and in fluorescent reporters sensitive to Ca2+ levels have opened the way to observing activity within single neurons across the entire nervous system.
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Affiliation(s)
- Scott W Emmons
- Department of Genetics and Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 1041, USA
| | - Eviatar Yemini
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Manuel Zimmer
- Department of Neuroscience and Developmental Biology, University of Vienna, Vienna 1090, Austria and.,Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna 1030, Austria
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Piccinini F, Balassa T, Carbonaro A, Diosdi A, Toth T, Moshkov N, Tasnadi EA, Horvath P. Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates. Comput Struct Biotechnol J 2020; 18:1287-1300. [PMID: 32612752 PMCID: PMC7303562 DOI: 10.1016/j.csbj.2020.05.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/22/2020] [Accepted: 05/23/2020] [Indexed: 12/25/2022] Open
Abstract
Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.
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Affiliation(s)
- Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Cancer Research Hospital, Meldola, FC, Italy
| | - Tamas Balassa
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
| | - Antonella Carbonaro
- Department of Computer Science and Engineering, University of Bologna, Italy
| | - Akos Diosdi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Timea Toth
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Biology, University of Szeged, Hungary
| | - Nikita Moshkov
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Interdisciplinary Medicine, University of Szeged, Hungary
- National Research University Higher School of Economics, Moscow, Russia
| | - Ervin A. Tasnadi
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Doctoral School of Computer Science, University of Szeged, Hungary
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Single-Cell Technologies Ltd., Szeged, Hungary
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5
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Toyoshima Y, Wu S, Kanamori M, Sato H, Jang MS, Oe S, Murakami Y, Teramoto T, Park C, Iwasaki Y, Ishihara T, Yoshida R, Iino Y. Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans. BMC Biol 2020; 18:30. [PMID: 32188430 PMCID: PMC7081613 DOI: 10.1186/s12915-020-0745-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/29/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons. RESULTS Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging. CONCLUSION Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.
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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Stephen Wu
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
- The Graduate University for Advanced Studies, SOKENDAI, Mishima, 411-8540, Japan
| | - Manami Kanamori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hirofumi Sato
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Moon Sun Jang
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Suzu Oe
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Yuko Murakami
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Chanhyun Park
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Yuishi Iwasaki
- Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan.
- The Graduate University for Advanced Studies, SOKENDAI, Mishima, 411-8540, Japan.
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
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Liu H, Lu T, Kremers GJ, Seynhaeve ALB, Ten Hagen TLM. A microcarrier-based spheroid 3D invasion assay to monitor dynamic cell movement in extracellular matrix. Biol Proced Online 2020; 22:3. [PMID: 32021568 PMCID: PMC6995242 DOI: 10.1186/s12575-019-0114-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 12/19/2019] [Indexed: 12/13/2022] Open
Abstract
Background Cell invasion through extracellular matrix (ECM) is a critical step in tumor metastasis. To study cell invasion in vitro, the internal microenvironment can be simulated via the application of 3D models. Results This study presents a method for 3D invasion examination using microcarrier-based spheroids. Cell invasiveness can be evaluated by quantifying cell dispersion in matrices or tracking cell movement through time-lapse imaging. It allows measuring of cell invasion and monitoring of dynamic cell behavior in three dimensions. Here we show different invasive capacities of several cell types using this method. The content and concentration of matrices can influence cell invasion, which should be optimized before large scale experiments. We also introduce further analysis methods of this 3D invasion assay, including manual measurements and homemade semi-automatic quantification. Finally, our results indicate that the position of spheroids in a matrix has a strong impact on cell moving paths, which may be easily overlooked by researchers and may generate false invasion results. Conclusions In all, the microcarrier-based spheroids 3D model allows exploration of adherent cell invasion in a fast and highly reproducible way, and provides informative results on dynamic cell behavior in vitro.
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Affiliation(s)
- Hui Liu
- 1Laboratory of Experimental Oncology, Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Tao Lu
- 1Laboratory of Experimental Oncology, Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Gert-Jan Kremers
- 2Erasmus Optical Imaging Center, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Ann L B Seynhaeve
- 1Laboratory of Experimental Oncology, Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Timo L M Ten Hagen
- 1Laboratory of Experimental Oncology, Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands
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7
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Vodiasova EA, Chelebieva ES, Kuleshova ON. The new technologies of high-throughput single-cell RNA sequencing. Vavilovskii Zhurnal Genet Selektsii 2019. [DOI: 10.18699/vj19.520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
A wealth of genome and transcriptome data obtained using new generation sequencing (NGS) technologies for whole organisms could not answer many questions in oncology, immunology, physiology, neurobiology, zoology and other fields of science and medicine. Since the cell is the basis for the living of all unicellular and multicellular organisms, it is necessary to study the biological processes at its level. This understanding gave impetus to the development of a new direction – the creation of technologies that allow working with individual cells (single-cell technology). The rapid development of not only instruments, but also various advanced protocols for working with single cells is due to the relevance of these studies in many fields of science and medicine. Studying the features of various stages of ontogenesis, identifying patterns of cell differentiation and subsequent tissue development, conducting genomic and transcriptome analyses in various areas of medicine (especially in demand in immunology and oncology), identifying cell types and states, patterns of biochemical and physiological processes using single cell technologies, allows the comprehensive research to be conducted at a new level. The first RNA-sequencing technologies of individual cell transcriptomes (scRNA-seq) captured no more than one hundred cells at a time, which was insufficient due to the detection of high cell heterogeneity, existence of the minor cell types (which were not detected by morphology) and complex regulatory pathways. The unique techniques for isolating, capturing and sequencing transcripts of tens of thousands of cells at a time are evolving now. However, new technologies have certain differences both at the sample preparation stage and during the bioinformatics analysis. In the paper we consider the most effective methods of multiple parallel scRNA-seq using the example of 10XGenomics, as well as the specifics of such an experiment, further bioinformatics analysis of the data, future outlook and applications of new high-performance technologies.
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Affiliation(s)
- E. A. Vodiasova
- A.O. Kovalevsky Institute of Biology of the Southern Seas, RAS
| | | | - O. N. Kuleshova
- A.O. Kovalevsky Institute of Biology of the Southern Seas, RAS
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8
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Hirose O, Kawaguchi S, Tokunaga T, Toyoshima Y, Teramoto T, Kuge S, Ishihara T, Iino Y, Yoshida R. SPF-CellTracker: Tracking Multiple Cells with Strongly-Correlated Moves Using a Spatial Particle Filter. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1822-1831. [PMID: 29990224 DOI: 10.1109/tcbb.2017.2782255] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Tracking many cells in time-lapse 3D image sequences is an important challenging task of bioimage informatics. Motivated by a study of brain-wide 4D imaging of neural activity in C. elegans, we present a new method of multi-cell tracking. Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells on the basis of shape and size only, (iii) the number of imaged cells in the several-hundred range, (iv) movements of nearly-located cells are strongly correlated, and (v) cells do not divide. We developed a tracking software suite that we call SPF-CellTracker. Incorporating dependency on the cells' movements into the prediction model is the key for reducing the tracking errors: the cell switching and the coalescence of the tracked positions. We model the target cells' correlated movements as a Markov random field and we also derive a fast computation algorithm, which we call spatial particle filter. With the live-imaging data of the nuclei of C. elegans neurons in which approximately 120 nuclei of neurons were imaged, the proposed method demonstrated improved accuracy compared to the standard particle filter and the method developed by Tokunaga et al. (2014).
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10
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Cho Y, Zhao CL, Lu H. Trends in high-throughput and functional neuroimaging in Caenorhabditis elegans. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2017; 9. [PMID: 28221003 DOI: 10.1002/wsbm.1376] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Revised: 11/20/2016] [Accepted: 11/23/2016] [Indexed: 02/03/2023]
Abstract
The nervous system of Caenorhabditis elegans is an important model system for understanding the development and function of larger, more complex nervous systems. It is prized for its ease of handling, rapid life cycle, and stereotyped, well-cataloged development, with the development of all 302 neurons mapped all the way from zygote to adult. The combination of easy genetic manipulation and optical transparency of the worm allows for the direct imaging of its interior with fluorescent microscopy, without physically compromising the normal physiology of the animal itself. By expressing fluorescent markers, biologists study many developmental and cell biology questions in vivo; by expressing genetically encoded fluorescent calcium indicators within neurons, it is also possible to monitor their dynamic activity, answering questions about the structure and function of neural microcircuitry in the worm. However, to successfully image the worm it is necessary to overcome a number of experimental challenges. It is necessary to hold worms within the field of view, collect images efficiently and rapidly, and robustly analyze the data obtained. In recent years, a trend has developed toward imaging a large number of worms or neurons simultaneously, directly exploiting the unique properties of C. elegans to acquire data on a scale, which is not possible in other organisms. Doing this has required the development of new experimental tools, techniques, and data analytic approaches, all of which come together to open new perspectives on the field of neurobiology in C. elegans, and neuroscience in general. WIREs Syst Biol Med 2017, 9:e1376. doi: 10.1002/wsbm.1376 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Yongmin Cho
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Charles L Zhao
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Toyoshima Y, Tokunaga T, Hirose O, Kanamori M, Teramoto T, Jang MS, Kuge S, Ishihara T, Yoshida R, Iino Y. Accurate Automatic Detection of Densely Distributed Cell Nuclei in 3D Space. PLoS Comput Biol 2016; 12:e1004970. [PMID: 27271939 PMCID: PMC4894571 DOI: 10.1371/journal.pcbi.1004970] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 05/03/2016] [Indexed: 11/18/2022] Open
Abstract
To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured.
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Affiliation(s)
- Yu Toyoshima
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Terumasa Tokunaga
- Department of Systems Design and Informatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka-shi, Fukuoka, Japan
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Osamu Hirose
- Faculty of Electrical and Computer Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma, Kanazawa, Japan
| | - Manami Kanamori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Takayuki Teramoto
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Moon Sun Jang
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Sayuri Kuge
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Takeshi Ishihara
- Department of Biology, Faculty of Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
- CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan
- * E-mail:
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12
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Taking Aim at Moving Targets in Computational Cell Migration. Trends Cell Biol 2016; 26:88-110. [DOI: 10.1016/j.tcb.2015.09.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 08/31/2015] [Accepted: 09/03/2015] [Indexed: 01/07/2023]
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13
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Robinson S, Guyon L, Nevalainen J, Toriseva M, Åkerfelt M, Nees M. Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields. PLoS One 2015; 10:e0143798. [PMID: 26630674 PMCID: PMC4668034 DOI: 10.1371/journal.pone.0143798] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Accepted: 11/10/2015] [Indexed: 11/22/2022] Open
Abstract
Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.
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Affiliation(s)
- Sean Robinson
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
- Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland
- Université Grenoble-Alpes, F-38000 Grenoble, France
- CEA, iRTSV, Biologie à Grande Echelle, F-38054 Grenoble, France
- INSERM, U1038, F-38054 Grenoble, France
- * E-mail:
| | - Laurent Guyon
- Université Grenoble-Alpes, F-38000 Grenoble, France
- CEA, iRTSV, Biologie à Grande Echelle, F-38054 Grenoble, France
- INSERM, U1038, F-38054 Grenoble, France
| | - Jaakko Nevalainen
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
- School of Health Sciences, University of Tampere, Tampere, Finland
| | - Mervi Toriseva
- Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
| | - Malin Åkerfelt
- Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
| | - Matthias Nees
- Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
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