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Kasahara K, Muramatsu J, Kurashina Y, Miura S, Miyata S, Onoe H. Spatiotemporal single-cell tracking analysis in 3D tissues to reveal heterogeneous cellular response to mechanical stimuli. SCIENCE ADVANCES 2023; 9:eadf9917. [PMID: 37831766 PMCID: PMC10575577 DOI: 10.1126/sciadv.adf9917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 09/08/2023] [Indexed: 10/15/2023]
Abstract
Mechanical stimuli have been recognized as important for tissue maturation, homeostasis and constructing engineered three-dimensional (3D) tissues. However, we know little about the cellular mechanical response in tissues that could be considerably heterogeneous and spatiotemporally dynamic due to the complex structure of tissues. Here, we report a spatiotemporal single-cell tracking analysis of in vitro 3D tissues under mechanical stretch, to reveal the heterogeneous cellular behavior by using a developed stretch and optical live imaging system. The system could affect the cellular orientation and directly measure the distance of cells in in vitro 3D myoblast tissues (3DMTs) at the single-cell level. Moreover, we observed the spatiotemporal heterogeneous cellular locomotion and shape changes under mechanical stretch in 3DMTs. This single-cell tracking analysis can become a principal method to investigate the heterogeneous cellular response in tissues and provide insights that conventional analyses have not yet offered.
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Affiliation(s)
- Keitaro Kasahara
- School of Integrated Design Engineering, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Jumpei Muramatsu
- School of Integrated Design Engineering, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Yuta Kurashina
- Department of Mechanical Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
- Division of Advanced Mechanical Systems Engineering, Institute of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei-shi, Tokyo 184-8588, Japan
| | - Shigenori Miura
- Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Shogo Miyata
- Department of Mechanical Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
| | - Hiroaki Onoe
- Department of Mechanical Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
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Safdar S, Zwick BF, Yu Y, Bourantas GC, Joldes GR, Warfield SK, Hyde DE, Frisken S, Kapur T, Kikinis R, Golby A, Nabavi A, Wittek A, Miller K. SlicerCBM: automatic framework for biomechanical analysis of the brain. Int J Comput Assist Radiol Surg 2023; 18:1925-1940. [PMID: 37004646 PMCID: PMC10497672 DOI: 10.1007/s11548-023-02881-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/17/2023] [Indexed: 04/04/2023]
Abstract
PURPOSE Brain shift that occurs during neurosurgery disturbs the brain's anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations. METHODS We created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI. RESULTS Our framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI. CONCLUSION Our framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.
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Affiliation(s)
- Saima Safdar
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia.
| | - Benjamin F Zwick
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Yue Yu
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - George C Bourantas
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Department of Agriculture, University of Patras Nea Ktiria, 30200, Campus Mesologhi, Greece
| | - Grand R Joldes
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Damon E Hyde
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sarah Frisken
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Tina Kapur
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Ron Kikinis
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alexandra Golby
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Arya Nabavi
- Department of Neurosurgery, KRH Klinikum Nordstadt, Hannover, Germany
| | - Adam Wittek
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Karol Miller
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Harvard Medical School, Boston, MA, USA
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Abdolkarimzadeh F, Ashory MR, Ghasemi-Ghalebahman A, Karimi A. A position- and time-dependent pressure profile to model viscoelastic mechanical behavior of the brain tissue due to tumor growth. Comput Methods Biomech Biomed Engin 2023; 26:660-672. [PMID: 35638726 PMCID: PMC9708950 DOI: 10.1080/10255842.2022.2082245] [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: 02/12/2022] [Revised: 04/06/2022] [Accepted: 05/23/2022] [Indexed: 11/03/2022]
Abstract
This study proposed a computational framework to calculate the resultant position- and time-dependent pressure profile on the brain tissue due to tumor growth. A finite element (FE) patch of the brain tissue was constructed and an inverse dynamic FE-optimization algorithm was used to calculate its viscoelastic mechanical properties under compressive uniaxial loading. Two patient-specific post-tumor resection FE models were input to the FE-optimization algorithm to calculate the optimized 3rd-order position-dependent and normal distribution time-dependent pressure profile parameters. The optimized viscoelastic material properties, the most suitable simulation time, and the optimized 3rd-order position- and -time-dependent pressure profiles were calculated.
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Affiliation(s)
| | | | | | - Alireza Karimi
- Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham, AL, United States
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