SynQuant: an automatic tool to quantify synapses from microscopy images
Abstract Motivation Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data,...
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| Vydané v: | Bioinformatics Ročník 36; číslo 5; s. 1599 - 1606 |
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| Hlavní autori: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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England
Oxford University Press
01.03.2020
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| ISSN: | 1367-4803, 1367-4811, 1460-2059, 1367-4811 |
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| Abstract | Abstract
Motivation
Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.
Results
We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.
Availability and implementation
Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.
Supplementary information
Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.
We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.
Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.
Supplementary data are available at Bioinformatics online. Motivation: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. Results: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. Abstract Motivation Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. Results We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. Availability and implementation Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Supplementary information Supplementary data are available at Bioinformatics online. Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.MOTIVATIONSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.RESULTSWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.AVAILABILITY AND IMPLEMENTATIONJava source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. |
| Author | Wang, Yue Wang, Congchao Shi, Guilai Yu, Guoqiang Wang, Yinxue Lyu, Boyu Wang, Yizhi Tian, Lin Wu, Chiung-Ting Broussard, Gerard Joey Ranefall, Petter |
| AuthorAffiliation | 1 Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University , Blacksburg, VA 22203, USA 3 Department of Molecular Biology and Princeton Neuroscience Institute, Princeton University , Princeton, NJ 08544, USA 4 Department of Biochemistry and Molecular Medicine, University of California Davis School of Medicine , Sacramento, CA 95817, USA 2 Centre for Image Analysis and SciLifeLab, Department of Information Technology, Uppsala University , Uppsala, Sweden |
| AuthorAffiliation_xml | – name: 2 Centre for Image Analysis and SciLifeLab, Department of Information Technology, Uppsala University , Uppsala, Sweden – name: 4 Department of Biochemistry and Molecular Medicine, University of California Davis School of Medicine , Sacramento, CA 95817, USA – name: 3 Department of Molecular Biology and Princeton Neuroscience Institute, Princeton University , Princeton, NJ 08544, USA – name: 1 Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University , Blacksburg, VA 22203, USA |
| Author_xml | – sequence: 1 givenname: Yizhi surname: Wang fullname: Wang, Yizhi organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 22203, USA – sequence: 2 givenname: Congchao surname: Wang fullname: Wang, Congchao organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 22203, USA – sequence: 3 givenname: Petter surname: Ranefall fullname: Ranefall, Petter organization: Centre for Image Analysis and SciLifeLab, Department of Information Technology, Uppsala University, Uppsala, Sweden – sequence: 4 givenname: Gerard Joey orcidid: 0000-0003-1636-2615 surname: Broussard fullname: Broussard, Gerard Joey organization: Department of Molecular Biology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA – sequence: 5 givenname: Yinxue surname: Wang fullname: Wang, Yinxue organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 22203, USA – sequence: 6 givenname: Guilai surname: Shi fullname: Shi, Guilai organization: Department of Biochemistry and Molecular Medicine, University of California Davis School of Medicine, Sacramento, CA 95817, USA – sequence: 7 givenname: Boyu surname: Lyu fullname: Lyu, Boyu organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 22203, USA – sequence: 8 givenname: Chiung-Ting surname: Wu fullname: Wu, Chiung-Ting organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 22203, USA – sequence: 9 givenname: Yue surname: Wang fullname: Wang, Yue organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 22203, USA – sequence: 10 givenname: Lin surname: Tian fullname: Tian, Lin organization: Department of Biochemistry and Molecular Medicine, University of California Davis School of Medicine, Sacramento, CA 95817, USA – sequence: 11 givenname: Guoqiang orcidid: 0000-0002-6743-7413 surname: Yu fullname: Yu, Guoqiang email: yug@vt.edu organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 22203, USA |
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Motivation
Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to... Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the... Motivation: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain... |
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| SubjectTerms | Algorithms Microscopy Original Papers Software Synapses |
| Title | SynQuant: an automatic tool to quantify synapses from microscopy images |
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