Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

In this work, we describe a protocol that uses the software SciGlass to segment cryo-electron tomography data. SciGlass is a virtual-reality-based software that provides an immersive and intuitive interface for segmenting cryo-ET telegrams, we demonstrate that VR is a viable tool that can be integrated into cryo-ET segmentation pipelines. Cryo-ET is advancing rapidly with, you know, innovations and focused.

For thinner samples and faster data-collection methods, and using machine-learning algorithms for improving segmented particle-picking, you know, all these advancements culminate to better cryo-ET and the adoption of really great biological insights. Cryo-ET faces challenges like low-throughput cryo. For samples thicker than, like, 500 nanometers and difficulty targeting regions of interest due to low copy numbers.

Additionally, data processing remains a bottleneck, requiring extensive manual annotation for particle-picking and segmentation, along with specialized expertise, you know, you need in cryo-ET, so it all slows down the overall workflow. We've found that virtual reality improves segmentation efficiency compared to traditional methods. Its immersive environment compliments automated approaches by filling gaps and reducing false positives.

Additionally, this VR platform is highly effective for training and education, making it a versatile tool in cryo-ET data analysis. Our protocol addresses the inefficiency of traditional segmentation, which includes manual and slow, difficult processes, so by leveraging, you know, the immersive and intuitive nature of virtual reality, we aim to streamline segmentation by making the process faster and more user-friendly. To begin, convert raw cryo-ET tomograms into a data format compatible with SciGlass, such as TIFF stacks.

Set the signal using ImageJ to ensure that the particles are white on black. Navigate to Process, followed by Enhance Contrast, and check Equalize histogram"and Process all the slices. Launch the virtual-reality software on the computer.

Navigate to the File"menu and select Create Project. Click on Create New Project, and then, on Add Files"in the software. Navigate to the location of the TIFF files and import them into the project.

When prompted, confirm that the files are not part of a time-series after clicking No.Next, assign a name to the project and click Save"to create the project under the project list. Double-click on the project to open the tomogram and load it into the interactive virtual-reality environment. For setting up virtual reality, or VR, connect the VR headset and hand controllers to the computer.

Follow the onscreen instructions to calibrate the VR environment. Adjust the system to ensure the desired area for segmentation is in the VR environment's field of view, then click on the visualization button in the software interface. Adjust visualization options such as contrast, windowing, brightness, and threshold sliders to enhance the signal and minimize noise.

Use the hand controllers to pull the tomogram closer or push it away for better examination. Activate the cut tool using the left hand controller. Visually inspect different slices within the tomogram.

Navigate through the tomogram to the desired slice where segmentation will begin. Activate the region-of-interest, or ROI, option under the annotation menu using the hand controllers. A green box will appear in the tomogram.

Adjust the size and position of the green box to the area to be segmented. Now, lock the ROI using the left hand controller. The tool will switch to paint mode for segmentation.

Zoom in or out of the tomograph for precise segmentation. Adjust the paintbrush size with clockwise or counterclockwise rotations for optimal control. Carefully segment the region of interest, such as mitochondrial membranes, within the three-dimensional area.

Adjust the ball radius appropriately while performing the segmentation. Engage erase mode using the secondary controller trigger to correct segmentation errors, and use the same motion as segmentation to erase. Repeat the segmentation process for all regions until the tomogram is fully segmented.

After completing the segmentation, click on the completed project to highlight it. Click on the Projects"tab and select ROIs"to proceed. Choose to export the entire volume or a specific region of interest, and specify the export location for the segmented data.

Now, load and analyze the exported segmented data from SciGlass into the desired software of choice for additional analysis of the segmented data. After preparing cryo-ET data, right-click on the project and click Add Mask Data, then navigate to where the initial segmentation is saved and import it under the same project. Engage ROI annotation to make edits to the initial segmentation.

Finally, add or erase segmentation to clean up the initial segmentation. Weighted back-projection of tomograms reconstructed at 16 angstroms per pixel revealed mitochondrial and membranous structures following de-noising and missing wedge correction. Visualization in an immersive virtual-reality environment enabled detailed 3D inspection of membranes after histogram equalization enhanced the contrast.

Manual segmentation delineated mitochondrial and organelle structures with high accuracy using VR tools, including the precise mapping of membrane boundaries and ROIs. Final 3D renderings revealed detailed mitochondrial features such as outer and inner membranes. And calcium phosphate deposits with smoothened meshes.