Despite being a widely used tool for biomedical optical imaging, confocal microscopy has limitations to its performance due to the diffraction limit of light, sample photobleaching, and limited tissue penetration.
In a recent publication in Nature, Y. Wu et al. developed a multi-pronged approach to address the limitations of confocal microscopy and demonstrated the new capabilities on over 20 distinct fixed and live samples. First, the authors developed a multiview confocal microscopy platform with three objectives integrated using compact multi-electromechanical systems (MEMS)-based scanners. Then, to test the limits of this system, they combined this triple-view method with line-scanning confocal microscopy and deconvolution to visualize the nuclei of an entire fixed Caenorhabditis elegans nematode labeled with NucSpot® Live 488.
Additionally, the authors adapted super-resolution imaging techniques from structured illumination microscopy (SIM) to resolve the diffraction limitations of confocal microscopy. The size discrepancies between the point spread function and fluorescently-labeled targets hinder subcellular imaging, so implementing SIM methods, the authors reasoned, would allow them to interrogate densely labeled samples. Finally, the authors applied their triple-view approach to deep-learning based 3D SIM imaging to improve image resolution over commercially available SIM platforms.
Ultimately, the authors found that implementing their multi-pronged approach to confocal microscopy improved typical performance-based limitations. Their method provides an outline for future studies to implement a multiview confocal microscopy platform to improve imaging of biological tissues.