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Hoffmann C, Cho E, Zalesky A, Di Biase MA. From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation. Commun Biol 2024; 7:571. [PMID: 38750282 PMCID: PMC11096190 DOI: 10.1038/s42003-024-06264-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.
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Affiliation(s)
- Cassandra Hoffmann
- Systems Neuroscience Lab, Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Australia.
| | - Ellie Cho
- Biological Optical Microscopy Platform, University of Melbourne, Parkville, Australia
| | - Andrew Zalesky
- Systems Neuroscience Lab, Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Australia
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
| | - Maria A Di Biase
- Systems Neuroscience Lab, Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Australia
- Stem Cell Disease Modelling Lab, Department of Anatomy and Physiology, The University of Melbourne, Parkville, Australia
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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Ackerman HD, Gerhard GS. Bile Acids Induce Neurite Outgrowth in Nsc-34 Cells via TGR5 and a Distinct Transcriptional Profile. Pharmaceuticals (Basel) 2023; 16:174. [PMID: 37259326 PMCID: PMC9963315 DOI: 10.3390/ph16020174] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 09/24/2024] Open
Abstract
Increasing evidence supports a neuroprotective role for bile acids in major neurodegenerative disorders. We studied major human bile acids as signaling molecules for their two cellular receptors, farnesoid X receptor (FXR or NR1H4) and G protein-coupled bile acid receptor 1 (GPBAR1 or TGR5), as potential neurotrophic agents. Using quantitative image analysis, we found that 20 μM deoxycholic acid (DCA) could induce neurite outgrowth in NSC-34 cells that was comparable to the neurotrophic effects of the culture control 1 μM retinoic acid (RA), with lesser effects observed for chenodexoycholic acid (CDCA) at 20 μM, and similar though less robust neurite outgrowth in SH-SY5Y cells. Using chemical agonists and antagonists of FXR, LXR, and TGR5, we found that TGR5 agonism was comparable to DCA stimulation and stronger than RA, and that neither FXR nor liver X receptor (LXR) inhibition could block bile acid-induced neurite growth. RNA sequencing identified a core set of genes whose expression was regulated by DCA, CDCA, and RA. Our data suggest that bile acid signaling through TGR5 may be a targetable pathway to stimulate neurite outgrowth.
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Affiliation(s)
- Hayley D Ackerman
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
- Department of Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Glenn S Gerhard
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
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Räsänen N, Harju V, Joki T, Narkilahti S. Practical guide for preparation, computational reconstruction and analysis of 3D human neuronal networks in control and ischaemic conditions. Development 2022; 149:276215. [PMID: 35929583 PMCID: PMC9440753 DOI: 10.1242/dev.200012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 06/23/2022] [Indexed: 11/20/2022]
Abstract
To obtain commensurate numerical data of neuronal network morphology in vitro, network analysis needs to follow consistent guidelines. Important factors in successful analysis are sample uniformity, suitability of the analysis method for extracting relevant data and the use of established metrics. However, for the analysis of 3D neuronal cultures, there is little coherence in the analysis methods and metrics used in different studies. Here, we present a framework for the analysis of neuronal networks in 3D. First, we selected a hydrogel that supported the growth of human pluripotent stem cell-derived cortical neurons. Second, we tested and compared two software programs for tracing multi-neuron images in three dimensions and optimized a workflow for neuronal analysis using software that was considered highly suitable for this purpose. Third, as a proof of concept, we exposed 3D neuronal networks to oxygen-glucose deprivation- and ionomycin-induced damage and showed morphological differences between the damaged networks and control samples utilizing the proposed analysis workflow. With the optimized workflow, we present a protocol for preparing, challenging, imaging and analysing 3D human neuronal cultures. Summary: An optimized protocol is presented that allows morphological, quantifiable differences between the damaged and control human neuronal networks to be detected in three-dimensional cultures.
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Affiliation(s)
- Noora Räsänen
- Tampere University, 33100, Tampere Faculty of Medicine and Health Technology , , Finland
| | - Venla Harju
- Tampere University, 33100, Tampere Faculty of Medicine and Health Technology , , Finland
| | - Tiina Joki
- Tampere University, 33100, Tampere Faculty of Medicine and Health Technology , , Finland
| | - Susanna Narkilahti
- Tampere University, 33100, Tampere Faculty of Medicine and Health Technology , , Finland
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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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