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Guo S, Xue J, Liu J, Ye X, Guo Y, Liu D, Zhao X, Xiong F, Han X, Peng H. Smart imaging to empower brain-wide neuroscience at single-cell levels. Brain Inform 2022; 9:10. [PMID: 35543774 PMCID: PMC9095808 DOI: 10.1186/s40708-022-00158-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/12/2022] [Indexed: 11/10/2022] Open
Abstract
A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.
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Affiliation(s)
- Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China.
| | - Jie Xue
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Jian Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Xiangqiao Ye
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Yichen Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Di Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Xuan Zhao
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Feng Xiong
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Xiaofeng Han
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China.
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, 210096, Jiangsu, China
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2
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Guo S, Zhao X, Jiang S, Ding L, Peng H. Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons. Bioinformatics 2022; 38:503-512. [PMID: 34515755 DOI: 10.1093/bioinformatics/btab638] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 08/05/2021] [Accepted: 09/09/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast (SBC) and the large dynamic range of signal and background both within and across images. RESULTS We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high SBC and better within- and between-image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. In addition, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased five times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience by providing better 3D morphological reconstruction and lower cost of data storage and transfer. AVAILABILITY AND IMPLEMENTATION The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Xuan Zhao
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Shengdian Jiang
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Liya Ding
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
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3
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Hu T, Xu X, Chen S, Liu Q. Accurate Neuronal Soma Segmentation Using 3D Multi-Task Learning U-Shaped Fully Convolutional Neural Networks. Front Neuroanat 2021; 14:592806. [PMID: 33551758 PMCID: PMC7860594 DOI: 10.3389/fnana.2020.592806] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 12/02/2020] [Indexed: 12/12/2022] Open
Abstract
Neuronal soma segmentation is a crucial step for the quantitative analysis of neuronal morphology. Automated neuronal soma segmentation methods have opened up the opportunity to improve the time-consuming manual labeling required during the neuronal soma morphology reconstruction for large-scale images. However, the presence of touching neuronal somata and variable soma shapes in images brings challenges for automated algorithms. This study proposes a neuronal soma segmentation method combining 3D U-shaped fully convolutional neural networks with multi-task learning. Compared to existing methods, this technique applies multi-task learning to predict the soma boundary to split touching somata, and adopts U-shaped architecture convolutional neural network which is effective for a limited dataset. The contour-aware multi-task learning framework is applied to the proposed method to predict the masks of neuronal somata and boundaries simultaneously. In addition, a spatial attention module is embedded into the multi-task model to improve neuronal soma segmentation results. The Nissl-stained dataset captured by the micro-optical sectioning tomography system is used to validate the proposed method. Following comparison to four existing segmentation models, the proposed method outperforms the others notably in both localization and segmentation. The novel method has potential for high-throughput neuronal soma segmentation in large-scale optical imaging data for neuron morphology quantitative analysis.
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Affiliation(s)
- Tianyu Hu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaofeng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,School of Biomedical Engineering, Hainan University, Haikou, China
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4
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Conte D, Borisyuk R, Hull M, Roberts A. A simple method defines 3D morphology and axon projections of filled neurons in a small CNS volume: Steps toward understanding functional network circuitry. J Neurosci Methods 2020; 351:109062. [PMID: 33383055 DOI: 10.1016/j.jneumeth.2020.109062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/11/2020] [Accepted: 12/22/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Fundamental to understanding neuronal network function is defining neuron morphology, location, properties, and synaptic connectivity in the nervous system. A significant challenge is to reconstruct individual neuron morphology and connections at a whole CNS scale and bring together functional and anatomical data to understand the whole network. NEW METHOD We used a PC controlled micropositioner to hold a fixed whole mount of Xenopus tadpole CNS and replace the stage on a standard microscope. This allowed direct recording in 3D coordinates of features and axon projections of one or two neurons dye-filled during whole-cell recording to study synaptic connections. Neuron reconstructions were normalised relative to the ventral longitudinal axis of the nervous system. Coordinate data were stored as simple text files. RESULTS Reconstructions were at 1 μm resolution, capturing axon lengths in mm. The output files were converted to SWC format and visualised in 3D reconstruction software NeuRomantic. Coordinate data are tractable, allowing correction for histological artefacts. Through normalisation across multiple specimens we could infer features of network connectivity of mapped neurons of different types. COMPARISON WITH EXISTING METHODS Unlike other methods using fluorescent markers and utilising large-scale imaging, our method allows direct acquisition of 3D data on neurons whose properties and synaptic connections have been studied using whole-cell recording. CONCLUSIONS This method can be used to reconstruct neuron 3D morphology and follow axon projections in the CNS. After normalisation to a common CNS framework, inferences on network connectivity at a whole nervous system scale contribute to network modelling to understand CNS function.
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Affiliation(s)
- Deborah Conte
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
| | - Roman Borisyuk
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter, EX4 4QF, United Kingdom; Institute of Mathematical Problems of Biology, the Branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, 142290, Russia; School of Computing, Engineering and Mathematics, University of Plymouth, PL4 8AA, United Kingdom.
| | - Mike Hull
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom; Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
| | - Alan Roberts
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
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5
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Radojević M, Meijering E. Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation. Neuroinformatics 2020; 17:423-442. [PMID: 30542954 PMCID: PMC6594993 DOI: 10.1007/s12021-018-9407-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods.
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Affiliation(s)
- Miroslav Radojević
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
| | - Erik Meijering
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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6
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Jin DZ, Zhao T, Hunt DL, Tillage RP, Hsu CL, Spruston N. ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology. Front Neuroinform 2019; 13:68. [PMID: 31736735 PMCID: PMC6834530 DOI: 10.3389/fninf.2019.00068] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 10/14/2019] [Indexed: 11/18/2022] Open
Abstract
Neurons perform computations by integrating inputs from thousands of synapses-mostly in the dendritic tree-to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neurons with a substance that allows subsequent staining, reconstruct the three-dimensional architectures of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic processes, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.
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Affiliation(s)
- Dezhe Z. Jin
- Department of Physics and Center for Neural Engineering, The Pennsylvania State University, University Park, PA, United States
| | - Ting Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - David L. Hunt
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Rachel P. Tillage
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Ching-Lung Hsu
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Nelson Spruston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
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7
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Winnubst J, Bas E, Ferreira TA, Wu Z, Economo MN, Edson P, Arthur BJ, Bruns C, Rokicki K, Schauder D, Olbris DJ, Murphy SD, Ackerman DG, Arshadi C, Baldwin P, Blake R, Elsayed A, Hasan M, Ramirez D, Dos Santos B, Weldon M, Zafar A, Dudman JT, Gerfen CR, Hantman AW, Korff W, Sternson SM, Spruston N, Svoboda K, Chandrashekar J. Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain. Cell 2019; 179:268-281.e13. [PMID: 31495573 DOI: 10.1016/j.cell.2019.07.042] [Citation(s) in RCA: 250] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/14/2019] [Accepted: 07/23/2019] [Indexed: 01/15/2023]
Abstract
Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons constitute more than 85 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.
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Affiliation(s)
- Johan Winnubst
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Erhan Bas
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Tiago A Ferreira
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Zhuhao Wu
- Laboratory of Molecular Genetics, The Rockefeller University, New York, NY 10065, USA
| | - Michael N Economo
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | | | - Ben J Arthur
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Christopher Bruns
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Konrad Rokicki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - David Schauder
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Donald J Olbris
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Sean D Murphy
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - David G Ackerman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Cameron Arshadi
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Perry Baldwin
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Regina Blake
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Ahmad Elsayed
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Mashtura Hasan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Daniel Ramirez
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Bruno Dos Santos
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Monet Weldon
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Amina Zafar
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Joshua T Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Charles R Gerfen
- Intramural Research Program, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Adam W Hantman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Wyatt Korff
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Scott M Sternson
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Nelson Spruston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
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8
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Li S, Quan T, Zhou H, Huang Q, Guan T, Chen Y, Xu C, Kang H, Li A, Fu L, Luo Q, Gong H, Zeng S. Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method. Neuroinformatics 2019; 18:199-218. [PMID: 31396858 DOI: 10.1007/s12021-019-09434-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.
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Affiliation(s)
- Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,School of Mathematics and Economics, Hubei University of Education, Wuhan, 430205, Hubei, China.
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Qing Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Tao Guan
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yijun Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Cheng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Hongtao Kang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
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9
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Li S, Quan T, Zhou H, Yin F, Li A, Fu L, Luo Q, Gong H, Zeng S. Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites. Neuroinformatics 2019; 17:497-514. [PMID: 30635864 PMCID: PMC6841657 DOI: 10.1007/s12021-018-9414-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.
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Affiliation(s)
- Shiwei Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China. .,School of Mathematics and Economics, Hubei University of Education, Wuhan, 430205, Hubei, China.
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - FangFang Yin
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.,MOE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
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10
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Lin R, Wang R, Yuan J, Feng Q, Zhou Y, Zeng S, Ren M, Jiang S, Ni H, Zhou C, Gong H, Luo M. Cell-type-specific and projection-specific brain-wide reconstruction of single neurons. Nat Methods 2018; 15:1033-1036. [PMID: 30455464 DOI: 10.1038/s41592-018-0184-y] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 08/31/2018] [Indexed: 11/09/2022]
Abstract
We developed a dual-adeno-associated-virus expression system that enables strong and sparse labeling of individual neurons with cell-type and projection specificity. We demonstrated its utility for whole-brain reconstruction of midbrain dopamine neurons and striatum-projecting cortical neurons. We further extended the labeling method for rapid reconstruction in cleared thick brain sections and simultaneous dual-color labeling. This labeling system may facilitate the process of generating mesoscale single-neuron projectomes of mammalian brains.
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Affiliation(s)
- Rui Lin
- School of Life Sciences, Peking University, Beijing, China.,National Institute of Biological Sciences, Beijing, China
| | - Ruiyu Wang
- School of Life Sciences, Peking University, Beijing, China.,National Institute of Biological Sciences, Beijing, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Qiru Feng
- National Institute of Biological Sciences, Beijing, China.,School of Life Sciences, Tsinghua University, Beijing, China
| | - Youtong Zhou
- National Institute of Biological Sciences, Beijing, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Miao Ren
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Siqi Jiang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Ni
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Can Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China. .,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China. .,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.
| | - Minmin Luo
- National Institute of Biological Sciences, Beijing, China. .,School of Life Sciences, Tsinghua University, Beijing, China. .,Chinese Institute for Brain Research, Beijing, China.
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11
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Zhang C, Yan C, Ren M, Li A, Quan T, Gong H, Yuan J. A platform for stereological quantitative analysis of the brain-wide distribution of type-specific neurons. Sci Rep 2017; 7:14334. [PMID: 29085023 PMCID: PMC5662727 DOI: 10.1038/s41598-017-14699-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 10/16/2017] [Indexed: 01/15/2023] Open
Abstract
Quantifying the distribution of specific neurons throughout the whole brain is crucial for understanding physiological actions, pathological alterations and pharmacological treatments. However, the precise cell number and density of specific neurons in the entire brain remain unknown because of a lack of suitable research tools. Here, we propose a pipeline to automatically acquire and analyse the brain-wide distribution of type-specific neurons in a mouse brain. We employed a Brain-wide Positioning System to collect high-throughput anatomical information with the co-localized cytoarchitecture of the whole brain at subcellular resolution and utilized the NeuroGPS algorithm to locate and count cells in the whole brain. We evaluated the data continuity of the 3D dataset and the accuracy of stereological cell counting in 3D. To apply this pipeline, we acquired and quantified the brain-wide distributions and somatic morphology of somatostatin-expressing neurons in transgenic mouse brains. The results indicated that this whole-brain cell counting pipeline has the potential to become a routine tool for cell type neuroscience studies.
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Affiliation(s)
- Chen Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Cheng Yan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Miao Ren
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China. .,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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12
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Abstract
Neuronal soma segmentation is essential for morphology quantification analysis. Rapid advances in light microscope imaging techniques have generated such massive amounts of data that time-consuming manual methods cannot meet requirements for high throughput. However, touching soma segmentation is still a challenge for automatic segmentation methods. In this paper, we propose a soma segmentation method that combines the Rayburst sampling algorithm and ellipsoid fitting. The improved Rayburst sampling algorithm is used to detect the soma surface; the ellipsoid fitting method then refines jagged sampled soma surface to generate smooth ellipsoidal shapes for efficient analysis. In experiments, we validated the proposed method by applying it to datasets from the fluorescence micro-optical sectioning tomography (fMOST) system. The results indicate that the proposed method is comparable to the manual segmented gold standard with accurate soma segmentation at a relatively high speed. The proposed method can be extended to large-scale image stacks in the future.
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Affiliation(s)
- Tianyu Hu
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Qiufeng Xu
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Wei Lv
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Qian Liu
- Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, 430074, China.
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13
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Radojevic M, Meijering E. Automated neuron tracing using probability hypothesis density filtering. Bioinformatics 2017; 33:1073-1080. [PMID: 28065895 DOI: 10.1093/bioinformatics/btw751] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 11/22/2016] [Indexed: 01/18/2023] Open
Abstract
Motivation The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed. Results Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi-object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image datasets of real neurons indicate the proposed method performs comparably or even better than the state of the art. Availability and Implementation Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non-commercial use at https://bitbucket.org/miroslavradojevic/phd . Contact meijering@imagescience.org. Supplementary information Supplementary data are available at Bioinformatics online.
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14
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Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons. Neuroinformatics 2016; 14:201-19. [PMID: 26701809 PMCID: PMC4823367 DOI: 10.1007/s12021-015-9287-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.
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15
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Improved detection of soma location and morphology in fluorescence microscopy images of neurons. J Neurosci Methods 2016; 274:61-70. [PMID: 27688018 DOI: 10.1016/j.jneumeth.2016.09.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/20/2016] [Accepted: 09/21/2016] [Indexed: 01/15/2023]
Abstract
BACKGROUND Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. NEW METHOD In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. RESULTS Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. COMPARISON WITH EXISTING METHODS We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. CONCLUSIONS Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.
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16
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Nanda S, Allaham MM, Bergamino M, Polavaram S, Armañanzas R, Ascoli GA, Parekh R. Doubling up on the fly: NeuroMorpho.Org Meets Big Data. Neuroinformatics 2015; 13:127-9. [PMID: 25576225 DOI: 10.1007/s12021-014-9257-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Sumit Nanda
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
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17
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Yuan J, Gong H, Li A, Li X, Chen S, Zeng S, Luo Q. Visible rodent brain-wide networks at single-neuron resolution. Front Neuroanat 2015; 9:70. [PMID: 26074784 PMCID: PMC4446545 DOI: 10.3389/fnana.2015.00070] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2015] [Accepted: 05/13/2015] [Indexed: 01/05/2023] Open
Abstract
There are some unsolvable fundamental questions, such as cell type classification, neural circuit tracing and neurovascular coupling, though great progresses are being made in neuroscience. Because of the structural features of neurons and neural circuits, the solution of these questions needs us to break through the current technology of neuroanatomy for acquiring the exactly fine morphology of neuron and vessels and tracing long-distant circuit at axonal resolution in the whole brain of mammals. Combined with fast-developing labeling techniques, efficient whole-brain optical imaging technology emerging at the right moment presents a huge potential in the structure and function research of specific-function neuron and neural circuit. In this review, we summarize brain-wide optical tomography techniques, review the progress on visible brain neuronal/vascular networks benefit from these novel techniques, and prospect the future technical development.
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Affiliation(s)
- Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan, China ; Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan, China ; Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan, China ; Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan, China ; Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan, China ; Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan, China ; Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Wuhan, China ; Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology Wuhan, China
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18
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Ozcan B, Negi P, Laezza F, Papadakis M, Labate D. Automated detection of soma location and morphology in neuronal network cultures. PLoS One 2015; 10:e0121886. [PMID: 25853656 PMCID: PMC4390318 DOI: 10.1371/journal.pone.0121886] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 02/04/2015] [Indexed: 01/05/2023] Open
Abstract
Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.
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Affiliation(s)
- Burcin Ozcan
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Pooran Negi
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Fernanda Laezza
- Dept. of Pharmacology and Toxicology, University of Texas Medical Branch, Galveston, Texas, United States of America
| | - Manos Papadakis
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
| | - Demetrio Labate
- Dept. of Mathematics, University of Houston, Houston, Texas, United States of America
- * E-mail:
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19
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Narayanan RT, Egger R, Johnson AS, Mansvelder HD, Sakmann B, de Kock CPJ, Oberlaender M. Beyond Columnar Organization: Cell Type- and Target Layer-Specific Principles of Horizontal Axon Projection Patterns in Rat Vibrissal Cortex. Cereb Cortex 2015; 25:4450-68. [PMID: 25838038 PMCID: PMC4816792 DOI: 10.1093/cercor/bhv053] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Vertical thalamocortical afferents give rise to the elementary functional units of sensory cortex, cortical columns. Principles that underlie communication between columns remain however unknown. Here we unravel these by reconstructing in vivo-labeled neurons from all excitatory cell types in the vibrissal part of rat primary somatosensory cortex (vS1). Integrating the morphologies into an exact 3D model of vS1 revealed that the majority of intracortical (IC) axons project far beyond the borders of the principal column. We defined the corresponding innervation volume as the IC-unit. Deconstructing this structural cortical unit into its cell type-specific components, we found asymmetric projections that innervate columns of either the same whisker row or arc, and which subdivide vS1 into 2 orthogonal [supra-]granular and infragranular strata. We show that such organization could be most effective for encoding multi whisker inputs. Communication between columns is thus organized by multiple highly specific horizontal projection patterns, rendering IC-units as the primary structural entities for processing complex sensory stimuli.
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Affiliation(s)
- Rajeevan T Narayanan
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
| | - Robert Egger
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany Graduate School of Neural Information Processing, University of Tuebingen, Tuebingen, Germany
| | - Andrew S Johnson
- Digital Neuroanatomy, Max Planck Florida Institute for Neuroscience, Jupiter , FL 33458, USA
| | - Huibert D Mansvelder
- Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, The Netherlands
| | - Bert Sakmann
- Digital Neuroanatomy, Max Planck Florida Institute for Neuroscience, Jupiter , FL 33458, USA
| | - Christiaan P J de Kock
- Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, The Netherlands
| | - Marcel Oberlaender
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany Digital Neuroanatomy, Max Planck Florida Institute for Neuroscience, Jupiter , FL 33458, USA Bernstein Center for Computational Neuroscience, Tuebingen, Germany
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20
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Crowe SE, Ellis-Davies GCR. Longitudinal in vivo two-photon fluorescence imaging. J Comp Neurol 2014; 522:1708-27. [PMID: 24214350 DOI: 10.1002/cne.23502] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2013] [Revised: 10/15/2013] [Accepted: 10/15/2013] [Indexed: 12/29/2022]
Abstract
Fluorescence microscopy is an essential technique for the basic sciences, especially biomedical research. Since the invention of laser scanning confocal microscopy in the 1980s, which enabled imaging both fixed and living biological tissue with 3D precision, high-resolution fluorescence imaging has revolutionized biological research. Confocal microscopy, by its very nature, has one fundamental limitation. Due to the confocal pinhole, deep tissue fluorescence imaging is not practical. In contrast (no pun intended), two-photon fluorescence microscopy allows, in principle, the collection of all emitted photons from fluorophores in the imaged voxel, dramatically extending our ability to see deep into living tissue. Since the development of transgenic mice with genetically encoded fluorescent protein in neocortical cells in 2000, two-photon imaging has enabled the dynamics of individual synapses to be followed for up to 2 years. Since the initial landmark contributions to this field in 2002, the technique has been used to understand how neuronal structure are changed by experience, learning, and memory and various diseases. Here we provide a basic summary of the crucial elements that are required for such studies, and discuss many applications of longitudinal two-photon fluorescence microscopy that have appeared since 2002.
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Affiliation(s)
- Sarah E Crowe
- Department of Neuroscience, Mount Sinai School of Medicine, New York, NY, 10029
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21
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Blackman AV, Grabuschnig S, Legenstein R, Sjöström PJ. A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling. Front Neuroanat 2014; 8:65. [PMID: 25071470 PMCID: PMC4092368 DOI: 10.3389/fnana.2014.00065] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2014] [Accepted: 06/23/2014] [Indexed: 01/08/2023] Open
Abstract
Accurate 3D reconstruction of neurons is vital for applications linking anatomy and physiology. Reconstructions are typically created using Neurolucida after biocytin histology (BH). An alternative inexpensive and fast method is to use freeware such as Neuromantic to reconstruct from fluorescence imaging (FI) stacks acquired using 2-photon laser-scanning microscopy during physiological recording. We compare these two methods with respect to morphometry, cell classification, and multicompartmental modeling in the NEURON simulation environment. Quantitative morphological analysis of the same cells reconstructed using both methods reveals that whilst biocytin reconstructions facilitate tracing of more distal collaterals, both methods are comparable in representing the overall morphology: automated clustering of reconstructions from both methods successfully separates neocortical basket cells from pyramidal cells but not BH from FI reconstructions. BH reconstructions suffer more from tissue shrinkage and compression artifacts than FI reconstructions do. FI reconstructions, on the other hand, consistently have larger process diameters. Consequently, significant differences in NEURON modeling of excitatory post-synaptic potential (EPSP) forward propagation are seen between the two methods, with FI reconstructions exhibiting smaller depolarizations. Simulated action potential backpropagation (bAP), however, is indistinguishable between reconstructions obtained with the two methods. In our hands, BH reconstructions are necessary for NEURON modeling and detailed morphological tracing, and thus remain state of the art, although they are more labor intensive, more expensive, and suffer from a higher failure rate due to the occasional poor outcome of histological processing. However, for a subset of anatomical applications such as cell type identification, FI reconstructions are superior, because of indistinguishable classification performance with greater ease of use, essentially 100% success rate, and lower cost.
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Affiliation(s)
- Arne V Blackman
- Department of Neuroscience, Physiology and Pharmacology, University College London London, UK
| | - Stefan Grabuschnig
- Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria
| | - Robert Legenstein
- Institute for Theoretical Computer Science, Graz University of Technology Graz, Austria
| | - P Jesper Sjöström
- Department of Neuroscience, Physiology and Pharmacology, University College London London, UK ; Department of Neurology and Neurosurgery, Centre for Research in Neuroscience, The Research Institute of the McGill University Health Centre, Montreal General Hospital Montreal, QC, Canada
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22
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Gala R, Chapeton J, Jitesh J, Bhavsar C, Stepanyants A. Active learning of neuron morphology for accurate automated tracing of neurites. Front Neuroanat 2014; 8:37. [PMID: 24904306 PMCID: PMC4032887 DOI: 10.3389/fnana.2014.00037] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2014] [Accepted: 04/30/2014] [Indexed: 11/24/2022] Open
Abstract
Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.
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Affiliation(s)
- Rohan Gala
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Julio Chapeton
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Jayant Jitesh
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Chintan Bhavsar
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
| | - Armen Stepanyants
- Department of Physics and Center for Interdisciplinary Research on Complex Systems, Northeastern University Boston, MA, USA
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23
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An anterograde rabies virus vector for high-resolution large-scale reconstruction of 3D neuron morphology. Brain Struct Funct 2014; 220:1369-79. [PMID: 24723034 PMCID: PMC4409643 DOI: 10.1007/s00429-014-0730-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Accepted: 02/07/2014] [Indexed: 12/19/2022]
Abstract
Glycoprotein-deleted rabies virus (RABV ∆G) is a powerful tool for the analysis of neural circuits. Here, we demonstrate the utility of an anterograde RABV ∆G variant for novel neuroanatomical approaches involving either bulk or sparse neuronal populations. This technology exploits the unique features of RABV ∆G vectors, namely autonomous, rapid high-level expression of transgenes, and limited cytotoxicity. Our vector permits the unambiguous long-range and fine-scale tracing of the entire axonal arbor of individual neurons throughout the brain. Notably, this level of labeling can be achieved following infection with a single viral particle. The vector is effective over a range of ages (>14 months) aiding the studies of neurodegenerative disorders or aging, and infects numerous cell types in all brain regions tested. Lastly, it can also be readily combined with retrograde RABV ∆G variants. Together with other modern technologies, this tool provides new possibilities for the investigation of the anatomy and physiology of neural circuits.
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24
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Pani G, De Vos WH, Samari N, de Saint-Georges L, Baatout S, Van Oostveldt P, Benotmane MA. MorphoNeuroNet: an automated method for dense neurite network analysis. Cytometry A 2013; 85:188-99. [PMID: 24222510 DOI: 10.1002/cyto.a.22408] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2012] [Revised: 06/06/2013] [Accepted: 10/05/2013] [Indexed: 02/05/2023]
Abstract
High content cell-based screens are rapidly gaining popularity in the context of neuronal regeneration studies. To analyze neuronal morphology, automatic image analysis pipelines have been conceived, which accurately quantify the shape changes of neurons in cell cultures with non-dense neurite networks. However, most existing methods show poor performance for well-connected and differentiated neuronal networks, which may serve as valuable models for inter alia synaptogenesis. Here, we present a fully automated method for quantifying the morphology of neurons and the density of neurite networks, in dense neuronal cultures, which are grown for more than 10 days. MorphoNeuroNet, written as a script for ImageJ, Java based freeware, automatically determines various morphological parameters of the soma and the neurites (size, shape, starting points, and fractional occupation). The image analysis pipeline consists of a multi-tier approach in which the somas are segmented by adaptive region growing using nuclei as seeds, and the neurites are delineated by a combination of various intensity and edge detection algorithms. Quantitative comparison showed a superior performance of MorphoNeuroNet to existing analysis tools, especially for revealing subtle changes in thin neurites, which have weak fluorescence intensity compared to the rest of the network. The proposed method will help determining the effects of compounds on cultures with dense neurite networks, thereby boosting physiological relevance of cell-based assays in the context of neuronal diseases.
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Affiliation(s)
- Giuseppe Pani
- Radiobiology Unit, Molecular and Cellular Biology Expert Group, Belgian Nuclear Research Centre, SCK•CEN, Mol, Belgium; Cell Systems and Imaging Research Group (CSI), Department of Molecular Biotechnology, Ghent University, Ghent, Belgium
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25
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Automated and accurate detection of soma location and surface morphology in large-scale 3D neuron images. PLoS One 2013; 8:e62579. [PMID: 23638117 PMCID: PMC3634810 DOI: 10.1371/journal.pone.0062579] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Accepted: 03/21/2013] [Indexed: 11/19/2022] Open
Abstract
Automated and accurate localization and morphometry of somas in 3D neuron images is essential for quantitative studies of neural networks in the brain. However, previous methods are limited in obtaining the location and surface morphology of somas with variable size and uneven staining in large-scale 3D neuron images. In this work, we proposed a method for automated soma locating in large-scale 3D neuron images that contain relatively sparse soma distributions. This method involves three steps: (i) deblocking the image with overlap between adjacent sub-stacks; (ii) locating the somas in each small sub-stack using multi-scale morphological close and adaptive thresholds; and (iii) fusion of the repeatedly located somas in all sub-stacks. We also describe a new method for the accurate detection of the surface morphology of somas containing hollowness; this was achieved by improving the classical Rayburst Sampling with a new gradient-based criteria. Three 3D neuron image stacks of different sizes were used to quantitatively validate our methods. For the soma localization algorithm, the average recall and precision were greater than 93% and 96%, respectively. For the soma surface detection algorithm, the overlap of the volumes created by automatic detection of soma surfaces and manually segmenting soma volumes was more than 84% for 89% of all correctly detected somas. Our method for locating somas can reveal the soma distributions in large-scale neural networks more efficiently. The method for soma surface detection will serve as a valuable tool for systematic studies of neuron types based on neuron structure.
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26
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Parekh R, Ascoli GA. Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 2013; 77:1017-38. [PMID: 23522039 PMCID: PMC3653619 DOI: 10.1016/j.neuron.2013.03.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2013] [Indexed: 02/07/2023]
Abstract
The importance of neuronal morphology in brain function has been recognized for over a century. The broad applicability of "digital reconstructions" of neuron morphology across neuroscience subdisciplines has stimulated the rapid development of numerous synergistic tools for data acquisition, anatomical analysis, three-dimensional rendering, electrophysiological simulation, growth models, and data sharing. Here we discuss the processes of histological labeling, microscopic imaging, and semiautomated tracing. Moreover, we provide an annotated compilation of currently available resources in this rich research "ecosystem" as a central reference for experimental and computational neuroscience.
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Affiliation(s)
- Ruchi Parekh
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, 22030, USA
| | - Giorgio A. Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, 22030, USA
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27
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Choromanska A, Chang SF, Yuste R. Automatic reconstruction of neural morphologies with multi-scale tracking. Front Neural Circuits 2012; 6:25. [PMID: 22754498 PMCID: PMC3385559 DOI: 10.3389/fncir.2012.00025] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2011] [Accepted: 04/19/2012] [Indexed: 11/21/2022] Open
Abstract
Neurons have complex axonal and dendritic morphologies that are the structural building blocks of neural circuits. The traditional method to capture these morphological structures using manual reconstructions is time-consuming and partly subjective, so it appears important to develop automatic or semi-automatic methods to reconstruct neurons. Here we introduce a fast algorithm for tracking neural morphologies in 3D with simultaneous detection of branching processes. The method is based on existing tracking procedures, adding the machine vision technique of multi-scaling. Starting from a seed point, our algorithm tracks axonal or dendritic arbors within a sphere of a variable radius, then moves the sphere center to the point on its surface with the shortest Dijkstra path, detects branching points on the surface of the sphere, scales it until branches are well separated and then continues tracking each branch. We evaluate the performance of our algorithm on preprocessed data stacks obtained by manual reconstructions of neural cells, corrupted with different levels of artificial noise, and unprocessed data sets, achieving 90% precision and 81% recall in branch detection. We also discuss limitations of our method, such as reconstructing highly overlapping neural processes, and suggest possible improvements. Multi-scaling techniques, well suited to detect branching structures, appear a promising strategy for automatic neuronal reconstructions.
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Affiliation(s)
- Anna Choromanska
- Department of Electrical Engineering, Columbia University New York, NY, USA
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28
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Halavi M, Hamilton KA, Parekh R, Ascoli GA. Digital reconstructions of neuronal morphology: three decades of research trends. Front Neurosci 2012; 6:49. [PMID: 22536169 PMCID: PMC3332236 DOI: 10.3389/fnins.2012.00049] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 03/02/2012] [Indexed: 01/16/2023] Open
Abstract
The importance of neuronal morphology has been recognized from the early days of neuroscience. Elucidating the functional roles of axonal and dendritic arbors in synaptic integration, signal transmission, network connectivity, and circuit dynamics requires quantitative analyses of digital three-dimensional reconstructions. We extensively searched the scientific literature for all original reports describing reconstructions of neuronal morphology since the advent of this technique three decades ago. From almost 50,000 titles, 30,000 abstracts, and more than 10,000 full-text articles, we identified 902 publications describing ∼44,000 digital reconstructions. Reviewing the growth of this field exposed general research trends on specific animal species, brain regions, neuron types, and experimental approaches. The entire bibliography, annotated with relevant metadata and (wherever available) direct links to the underlying digital data, is accessible at NeuroMorpho.Org.
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Affiliation(s)
- Maryam Halavi
- Molecular Neuroscience Department, Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
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29
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Myatt DR, Hadlington T, Ascoli GA, Nasuto SJ. Neuromantic - from semi-manual to semi-automatic reconstruction of neuron morphology. Front Neuroinform 2012; 6:4. [PMID: 22438842 PMCID: PMC3305991 DOI: 10.3389/fninf.2012.00004] [Citation(s) in RCA: 106] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2011] [Accepted: 02/20/2012] [Indexed: 02/05/2023] Open
Abstract
The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.
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Affiliation(s)
- Darren R Myatt
- School of Systems Engineering, University of Reading Reading, UK
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30
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Lang S, Dercksen VJ, Sakmann B, Oberlaender M. Simulation of signal flow in 3D reconstructions of an anatomically realistic neural network in rat vibrissal cortex. Neural Netw 2011; 24:998-1011. [DOI: 10.1016/j.neunet.2011.06.013] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2011] [Revised: 05/19/2011] [Accepted: 06/16/2011] [Indexed: 11/27/2022]
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31
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Swanger SA, Yao X, Gross C, Bassell GJ. Automated 4D analysis of dendritic spine morphology: applications to stimulus-induced spine remodeling and pharmacological rescue in a disease model. Mol Brain 2011; 4:38. [PMID: 21982080 PMCID: PMC3213078 DOI: 10.1186/1756-6606-4-38] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2011] [Accepted: 10/07/2011] [Indexed: 12/22/2022] Open
Abstract
Uncovering the mechanisms that regulate dendritic spine morphology has been limited, in part, by the lack of efficient and unbiased methods for analyzing spines. Here, we describe an automated 3D spine morphometry method and its application to spine remodeling in live neurons and spine abnormalities in a disease model. We anticipate that this approach will advance studies of synapse structure and function in brain development, plasticity, and disease.
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Affiliation(s)
- Sharon A Swanger
- Department of Cell Biology, Emory University, 615 Michael St, NE, Atlanta, GA 30322, USA
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32
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Boudewijns ZS, Kleele T, Mansvelder HD, Sakmann B, de Kock CP, Oberlaender M. Semi-automated three-dimensional reconstructions of individual neurons reveal cell type-specific circuits in cortex. Commun Integr Biol 2011; 4:486-8. [PMID: 21966579 DOI: 10.4161/cib.4.4.15670] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2011] [Accepted: 03/31/2011] [Indexed: 11/19/2022] Open
Abstract
Despite a long history of anatomical mapping of neuronal networks, we are only beginning to understand the detailed three-dimensional (3D) organization of the cortical micro-circuitry. This is in part due to the lack of complete reconstructions of individual cortical neurons. Morphological studies are either performed on incomplete cells in vitro, or when performed in vivo, lack the necessary cellular resolution. We recently reconstructed the in vivo axonal and dendritic morphology of two types of L(ayer) 5 neurons from vibrissal cortex. The 3D profiles of short-range as well as longrange projections indicate that L5 slender-tufted and L5 thick-tufted neurons represent very different building blocks of the cortical circuitry. In this addendum to Oberlaender et al. (PNAS 2011), we motivate our technical approach and the advancements this may give in reconstructing the cortical micro-circuitry.
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Affiliation(s)
- Zimbo Srm Boudewijns
- Department of Integrative Neurophysiology; Center for Neurogenomics and Cognitive Research; Neuroscience Campus Amsterdam; Vrije Universiteit Amsterdam; Amsterdam, The Netherlands
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33
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Boudewijns ZS, Kleele T, Mansvelder HD, Sakmann B, de Kock CP, Oberlaender M. Semi-automated three-dimensional reconstructions of individual neurons reveal cell type-specific circuits in cortex. Commun Integr Biol 2011. [DOI: 10.4161/cib.15670] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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