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High-definition imaging using line-illumination modulation microscopy
- Source :
- Nature Methods. 18:309-315
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- The microscopic visualization of large-scale three-dimensional (3D) samples by optical microscopy requires overcoming challenges in imaging quality and speed and in big data acquisition and management. We report a line-illumination modulation (LiMo) technique for imaging thick tissues with high throughput and low background. Combining LiMo with thin tissue sectioning, we further develop a high-definition fluorescent micro-optical sectioning tomography (HD-fMOST) method that features an average signal-to-noise ratio of 110, leading to substantial improvement in neuronal morphology reconstruction. We achieve a >30-fold lossless data compression at a voxel resolution of 0.32 × 0.32 × 1.00 μm3, enabling online data storage to a USB drive or in the cloud, and high-precision (95% accuracy) brain-wide 3D cell counting in real time. These results highlight the potential of HD-fMOST to facilitate large-scale acquisition and analysis of whole-brain high-resolution datasets. HD-fMOST is a microscopy technique for imaging large samples at high throughput and with high definition, which is achieved with a line-illumination modulation approach. The technology is illustrated by imaging fluorescently labeled neurons in whole mouse brains.
- Subjects :
- Lossless compression
Microscopy
0303 health sciences
Computer science
Resolution (electron density)
Brain
Microtomy
Cell Biology
Signal-To-Noise Ratio
computer.software_genre
Biochemistry
03 medical and health sciences
Imaging, Three-Dimensional
Signal-to-noise ratio
Voxel
Modulation
Tomography
Molecular Biology
computer
Throughput (business)
030304 developmental biology
Biotechnology
Biomedical engineering
Subjects
Details
- ISSN :
- 15487105 and 15487091
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Nature Methods
- Accession number :
- edsair.doi.dedup.....a643cd5250e7b5ec6f1103ec0072d50e
- Full Text :
- https://doi.org/10.1038/s41592-021-01074-x