Adaptive Optics Enables Deep Super-Resolution Imaging of Thick Biological Tissues Using Structured Illumination Microscopy

Core Concepts

Integrating adaptive optics into an upright structured illumination microscope enables high-quality 3D super-resolution imaging up to 130 μm deep in complex biological tissues and live samples.

Abstract

The authors have developed a novel upright 3D structured illumination microscopy (3D-SIM) system, termed Deep3DSIM, that incorporates adaptive optics (AO) for aberration correction and remote focusing. This allows them to overcome the limitations of current 3D-SIM instruments, which are typically restricted to imaging thin specimens on inverted setups due to optical aberrations.
The key highlights and insights from the paper are:

The Deep3DSIM system uses a water-dipping objective lens and an upright configuration, making it suitable for imaging thick tissue samples and live specimens while allowing access for manipulation (e.g., microinjection, electrophysiology).

Adaptive optics, including a deformable mirror, are integrated into the optical path to correct for specimen-induced aberrations, enabling high-quality 3D-SIM imaging up to 130 μm deep in complex tissues like Drosophila larval brains.

The remote focusing capability, coupled with the AO correction, allows rapid acquisition of 3D image stacks without the need to move the specimen or the objective, further improving imaging of dynamic live samples.

The authors demonstrate the system's capabilities by imaging a range of biological specimens, including mammalian cell cultures, fixed Drosophila larval neuromuscular junctions and brains, as well as live Drosophila embryos undergoing rapid mitotic divisions.

The modular design and open-source software make the Deep3DSIM system user-friendly and adaptable for various research applications requiring deep, high-resolution 3D imaging of thick, live samples.

Stats

The authors report achieving a lateral resolution of 176 nm and an axial resolution of 566 nm using 3D-SIM with adaptive optics, which represents an approximately 5-fold volumetric resolution improvement over widefield microscopy.

Quotes

"AO aberration correction made a small but noticeable improvement to the contrast on the proximal side of the brain lobe. On the distal side, control images bypassing the AO suffered from severe aberration-induced reconstruction artifacts, with the cell membranes not being observable as continuous structures. In contrast, imaging at the same depth with AO correction enabled 3D-SIM reconstructions to produce clearer 3D images with reduced artefacts and enhanced contrast."
"Using AO-based aberration correction with interpolation, combined with remote focusing, we were able to obtain an effective time sequence of 3D-SIM stacks with reduced residual aberrations."

Key Insights Distilled From

Deep super-resolution imaging of thick tissue using structured illumination with adaptive optics

by Wang,J., Sto... at www.biorxiv.org 12-22-2023

https://www.biorxiv.org/content/10.1101/2023.12.21.572818v2

Deeper Inquiries

How could the Deep3DSIM system be further optimized to achieve even higher spatial resolution while maintaining the large working distance and accessibility for live sample manipulation?

To achieve even higher spatial resolution with the Deep3DSIM system while maintaining a large working distance and accessibility for live sample manipulation, several optimizations can be considered:

Optical Components: Upgrading the optical components, such as the objective lens, SLM, and deformable mirror, to higher quality versions with better performance characteristics can enhance the resolution capabilities of the system. Using objectives with higher numerical apertures or specialized designs for super-resolution microscopy can improve resolution.

Advanced Adaptive Optics Algorithms: Implementing more advanced adaptive optics algorithms that can correct for a wider range of aberrations with higher precision can further enhance the system's resolution. This may involve developing custom algorithms tailored to the specific requirements of the Deep3DSIM system.

Improved Calibration Methods: Enhancing the calibration methods for the deformable mirror and adaptive optics system can ensure more accurate correction of aberrations, leading to improved resolution. Fine-tuning the calibration process based on the specific characteristics of the system can optimize performance.

Integration of Advanced Imaging Techniques: Combining the Deep3DSIM system with other advanced imaging techniques, such as single-molecule localization microscopy (SMLM) or stimulated emission depletion (STED) microscopy, can provide complementary information and further enhance the resolution capabilities for specific applications.

Software Enhancements: Continuously updating and refining the control software and reconstruction algorithms can improve the overall performance of the system. Implementing real-time feedback mechanisms and automated optimization routines can streamline the imaging process and maximize resolution.

How could the Deep3DSIM system be further optimized to achieve even higher spatial resolution while maintaining the large working distance and accessibility for live sample manipulation?

The Deep3DSIM system has the potential to benefit a wide range of biological samples and research questions beyond the examples provided in the paper. Some additional samples and research areas that could benefit from the capabilities of the Deep3DSIM system include:

Neuronal Tissue: Studying the intricate structures of neuronal networks in the brain, such as dendritic spines, axonal boutons, and synaptic connections, could benefit from the high-resolution imaging capabilities of Deep3DSIM. This could provide insights into synaptic plasticity, neuronal development, and neurodegenerative diseases.

Developmental Biology: Investigating the dynamic processes of embryonic development, organogenesis, and tissue morphogenesis at high spatial and temporal resolution could be facilitated by Deep3DSIM. Visualizing cellular interactions, signaling pathways, and morphological changes in real-time could advance our understanding of developmental biology.

Cancer Research: Examining the spatial organization of tumor microenvironments, interactions between cancer cells and immune cells, and the effects of therapeutic interventions on tumor architecture could be explored using Deep3DSIM. This could offer insights into tumor progression, metastasis, and treatment responses.

Immunology: Visualizing immune cell interactions, immune synapse formation, and immune responses in complex tissue environments could be enhanced by the high-resolution imaging capabilities of Deep3DSIM. Studying immune cell dynamics, antigen presentation, and immune cell signaling could be advanced using this system.

Plant Biology: Investigating plant cell structures, organelle dynamics, and cellular interactions in plant tissues could benefit from the deep imaging capabilities of Deep3DSIM. Studying plant development, responses to environmental stimuli, and symbiotic interactions could be improved with high-resolution imaging.

Given the modular design and open-source software, how could the Deep3DSIM approach be adapted or scaled to enable high-throughput deep tissue imaging in a more automated fashion?

Adapting the Deep3DSIM approach for high-throughput deep tissue imaging in a more automated fashion can be achieved through the following strategies:

Parallelization: Implementing a multi-sample imaging setup with multiple imaging channels and automated sample handling systems can increase throughput. This could involve designing a robotic sample loader, motorized stage systems, and automated imaging protocols to enable continuous imaging of multiple samples in a streamlined manner.

Batch Processing: Developing software algorithms for batch processing and analysis of imaging data can accelerate data analysis and interpretation. This could involve integrating machine learning algorithms for automated image segmentation, feature extraction, and data quantification to handle large datasets efficiently.

Remote Operation: Enabling remote operation and control of the Deep3DSIM system through a user-friendly interface can facilitate remote monitoring and management of imaging experiments. This could involve cloud-based data storage, real-time data streaming, and remote access for researchers to monitor experiments from anywhere.

High-Content Screening: Integrating the Deep3DSIM system with high-content screening platforms can enable automated screening of large sample libraries or drug compounds. This could involve developing custom software for image analysis, data visualization, and result interpretation to streamline the screening process.

Integration with Robotics: Collaborating with robotics experts to integrate robotic arms, liquid handling systems, and automated sample preparation devices with the Deep3DSIM system can create a fully automated imaging pipeline. This could involve designing custom robotic workflows for sample preparation, imaging, and data analysis to achieve high-throughput imaging capabilities.