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Biomed Opt Express. 2018 Dec 4;10(1):29-49. doi: 10.1364/BOE.10.000029. eCollection 2019 Jan 1.

Fast, volumetric live-cell imaging using high-resolution light-field microscopy.

Author information

1
Department of Biomedical Engineering, Stony Brook University (SUNY), Stony Brook, New York, USA.
2
Ultra-Precision Optoelectronic Instrument Engineering Center, Harbin Institute of Technology, Harbin, Heilongjiang, China.
3
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
4
Department of Biochemistry and Cell Biology and Department of Chemistry, Stony Brook University (SUNY), Stony Brook, New York, USA.
5
Department of Materials Science and Chemical Engineering, Stony Brook University (SUNY), Stony Brook, New York, USA.
6
Department of Pharmacological Sciences, Stony Brook University (SUNY), Stony Brook, New York, USA.
7
Medical Scientist Training Program, School of Medicine, Stony Brook University (SUNY), Stony Brook, New York, USA.

Abstract

Visualizing diverse anatomical and functional traits that span many spatial scales with high spatio-temporal resolution provides insights into the fundamentals of living organisms. Light-field microscopy (LFM) has recently emerged as a scanning-free, scalable method that allows for high-speed, volumetric functional brain imaging. Given those promising applications at the tissue level, at its other extreme, this highly-scalable approach holds great potential for observing structures and dynamics in single-cell specimens. However, the challenge remains for current LFM to achieve a subcellular level, near-diffraction-limited 3D spatial resolution. Here, we report high-resolution LFM (HR-LFM) for live-cell imaging with a resolution of 300-700 nm in all three dimensions, an imaging depth of several micrometers, and a volume acquisition time of milliseconds. We demonstrate the technique by imaging various cellular dynamics and structures and tracking single particles. The method may advance LFM as a particularly useful tool for understanding biological systems at multiple spatio-temporal levels.

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