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Yakovlev EV, Simkin IV, Shirokova AA, Kolotieva NA, Novikova SV, Nasyrov AD, Denisenko IR, Gursky KD, Shishkov IN, Narzaeva DE, Salmina AB, Yurchenko SO, Kryuchkov NP. Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy. Sci Rep 2024; 14:9846. [PMID: 38684715 PMCID: PMC11059356 DOI: 10.1038/s41598-024-59773-2] [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: 12/27/2023] [Accepted: 04/15/2024] [Indexed: 05/02/2024] Open
Abstract
Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent microscopy. However, staining and fixation may impact the properties of astrocytes, thereby affecting the accuracy of the experimental data of astrocytes dynamics and morphology. On the other hand, phase contrast microscopy can be used to study astrocytes morphology without affecting them, but the post-processing of the resulting low-contrast images is challenging. The main result of this work is a novel approach for recognition and morphological analysis of unstained astrocytes based on machine-learning recognition of microscopic images. We conducted a series of experiments involving the cultivation of isolated astrocytes from the rat brain cortex followed by microscopy. Using the proposed approach, we tracked the temporal evolution of the average total length of branches, branching, and area per astrocyte in our experiments. We believe that the proposed approach and the obtained experimental data will be of interest and benefit to the scientific communities in cell biology, biophysics, and machine learning.
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Affiliation(s)
- Egor V Yakovlev
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia.
| | - Ivan V Simkin
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
| | - Anastasiya A Shirokova
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
| | - Nataliya A Kolotieva
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
- Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia
| | - Svetlana V Novikova
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
- Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia
| | - Artur D Nasyrov
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
| | - Ilya R Denisenko
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
| | - Konstantin D Gursky
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
| | - Ivan N Shishkov
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
| | - Diana E Narzaeva
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
- Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia
| | - Alla B Salmina
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
- Research Center of Neurology, 80 Volokolamskoye Shosse, Moscow, 125367, Russia
| | - Stanislav O Yurchenko
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia
| | - Nikita P Kryuchkov
- Scientific-Educational Centre "Soft matter and physics of fluids", Bauman Moscow State Technical University, 2nd Baumanskaya Street 5, Moscow, 105005, Russia.
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Keto L, Manninen T. CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations. Neuroinformatics 2023; 21:483-500. [PMID: 37133688 PMCID: PMC10406679 DOI: 10.1007/s12021-023-09627-5] [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] [Accepted: 03/06/2023] [Indexed: 05/04/2023]
Abstract
Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.
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Affiliation(s)
- Laura Keto
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
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Abdellah M, Cantero JJG, Guerrero NR, Foni A, Coggan JS, Calì C, Agus M, Zisis E, Keller D, Hadwiger M, Magistretti PJ, Markram H, Schürmann F. Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. Brief Bioinform 2022; 24:6847753. [PMID: 36434788 PMCID: PMC9851302 DOI: 10.1093/bib/bbac491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/27/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022] Open
Abstract
Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations.
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Affiliation(s)
- Marwan Abdellah
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
| | | | - Nadir Román Guerrero
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Alessandro Foni
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Corrado Calì
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,Neuroscience Institute Cavalieri Ottolenghi (NICO) Orbassano, Italy,Department of Neuroscience, University of Torino Torino, Italy
| | - Marco Agus
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,College of Science and Engineering Hamad Bin Khalifa University Doha, Qatar
| | - Eleftherios Zisis
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Markus Hadwiger
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Henry Markram
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Felix Schürmann
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
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Zhu X, Liu X, Liu S, Shen Y, You L, Wang Y. Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation. Front Neuroinform 2022; 16:953930. [DOI: 10.3389/fninf.2022.953930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Creating high-quality polygonal meshes which represent the membrane surface of neurons for both visualization and numerical simulation purposes is an important yet nontrivial task, due to their irregular and complicated structures. In this paper, we develop a novel approach of constructing a watertight 3D mesh from the abstract point-and-diameter representation of the given neuronal morphology. The membrane shape of the neuron is reconstructed by progressively deforming an initial sphere with the guidance of the neuronal skeleton, which can be regarded as a digital sculpting process. To efficiently deform the surface, a local mapping is adopted to simulate the animation skinning. As a result, only the vertices within the region of influence (ROI) of the current skeletal position need to be updated. The ROI is determined based on the finite-support convolution kernel, which is convolved along the line skeleton of the neuron to generate a potential field that further smooths the overall surface at both unidirectional and bifurcating regions. Meanwhile, the mesh quality during the entire evolution is always guaranteed by a set of quasi-uniform rules, which split excessively long edges, collapse undersized ones, and adjust vertices within the tangent plane to produce regular triangles. Additionally, the local vertices density on the result mesh is decided by the radius and curvature of neurites to achieve adaptiveness.
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Abstract
In 2001, the concept of the neurovascular unit was introduced at the Stroke Progress Review Group meeting. The neurovascular unit is an important element of the health and disease status of blood vessels and nerves in the central nervous system. Since then, the neurovascular unit has attracted increasing interest from research teams, who have contributed greatly to the prevention, treatment, and prognosis of stroke and neurodegenerative diseases. However, additional research is needed to establish an efficient, low-cost, and low-energy in vitro model of the neurovascular unit, as well as enable noninvasive observation of neurovascular units in vivo and in vitro. In this review, we first summarize the composition of neurovascular units, then investigate the efficacy of different types of stem cells and cell culture methods in the construction of neurovascular unit models, and finally assess the progress of imaging methods used to observe neurovascular units in recent years and their positive role in the monitoring and investigation of the mechanisms of a variety of central nervous system diseases.
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Affiliation(s)
- Taiwei Dong
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Min Li
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Feng Gao
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Peifeng Wei
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
| | - Jian Wang
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Provinve, China
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