Integrating light-sheet imaging with virtual reality to recapitulate developmental cardiac mechanics
Abstract
Currently, there is a limited ability to interactively study developmental cardiac mechanics and physiology. We therefore combined light-sheet fluorescence microscopy (LSFM) with virtual reality (VR) to provide a hybrid platform for 3D architecture and time-dependent cardiac contractile function characterization. By taking advantage of the rapid acquisition, high axial resolution, low phototoxicity, and high fidelity in 3D and 4D (3D spatial + 1D time or spectra), this VR-LSFM hybrid methodology enables interactive visualization and quantification otherwise not available by conventional methods, such as routine optical microscopes. We hereby demonstrate multiscale applicability of VR-LSFM to (a) interrogate skin fibroblasts interacting with a hyaluronic acid–based hydrogel, (b) navigate through the endocardial trabecular network during zebrafish development, and (c) localize gene therapy-mediated potassium channel expression in adult murine hearts. We further combined our batch intensity normalized segmentation algorithm with deformable image registration to interface a VR environment with imaging computation for the analysis of cardiac contraction. Thus, the VR-LSFM hybrid platform demonstrates an efficient and robust framework for creating a user-directed microenvironment in which we uncovered developmental cardiac mechanics and physiology with high spatiotemporal resolution.
Additional Information
© 2017 American Society for Clinical Investigation. Submitted: August 30, 2017; Accepted: October 12, 2017; Published: November 16, 2017. Authorship note: Y. Ding and A. Abiri contributed equally to this work. The authors thank Carlos Pedroza for the initial discussion on the VR technique, Rajan P. Kulkarni and Kevin Sung for the tissue clearing, Scott John for invaluable assistance with the preparation of the cardiac reporter gene, and C.-C. Jay Kuo and Hao Xu in the USC Media Communications lab for the computational algorithm. This work was supported by the NIH (5R01HL083015-11, 5R01HL118650-04, 5R01HL129727-03, 2R01HL111437-05A1, U54 EB022002, P41-EB02182), the UCLA Harvey Karp Discovery Award, and the American Heart Association (Scientist Development Grants 13SDG14640095 and 16SDG30910007 and Predoctoral Fellowship 15PRE21400019). Conflict of interest: The authors have declared that no conflict of interest exists.Attached Files
Published - JCI_Insight97180.pdf
Supplemental Material - jci.insight.97180.sd.pdf
Supplemental Material - jci.insight.97180.sdv1.mp4
Supplemental Material - jci.insight.97180.sdv2.mp4
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Supplemental Material - jci.insight.97180.sdv7.mp4
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Additional details
Identifiers
- PMCID
- PMC5752380
- Eprint ID
- 83818
- Resolver ID
- CaltechAUTHORS:20171212-085409495
Funding
- NIH
- 5R01HL083015-11
- NIH
- 5R01HL118650-04
- NIH
- 5R01HL129727-03
- NIH
- 2R01HL111437-05A1
- NIH
- U54 EB022002
- NIH
- P41-EB02182
- UCLA
- American Heart Association
- 13SDG14640095
- American Heart Association
- 16SDG30910007
- American Heart Association
- 15PRE21400019
Dates
- Created
-
2017-12-12Created from EPrint's datestamp field
- Updated
-
2021-11-15Created from EPrint's last_modified field