Labelling in cryogenic electron tomography - Bridging the gap between correlative light and electron microscopy and...

Area of Science:

• Cellular and Molecular Biology
• Microscopy Techniques
• Structural Biology

Background:

• Cryogenic electron tomography (cryoET) provides high-resolution imaging of native cellular structures without staining or fixation.
• Identifying specific proteins within crowded cellular environments in cryoET data is challenging.
• Current molecular tags for cryoET are effective for protein identification in reconstructed tomograms but not for initial data collection.

Purpose of the Study:

• To enhance the utility and throughput of cryo-electron tomography (cryoET) for cellular structure determination.
• To propose integrated approaches for efficient protein identification and area-of-interest selection in cryoET.
• To leverage advancements in molecular tagging and correlative microscopy for improved cryoET workflows.

Main Methods:

• Combining correlative light and electron microscopy (CLEM) with molecular tagging strategies.
• Developing automated detection of molecular tags within tomograms using machine learning.
• Implementing machine learning for correlation between light and electron microscopy imaging modalities.

Main Results:

• The proposed integrated approach allows molecular tags to be used at both small and large spatial scales.
• Automation of tag detection and cross-modality correlation significantly increases throughput.
• Enhanced methods facilitate the study of rare cellular events and structure determination via sub-tomogram averaging.

Conclusions:

• Integrating CLEM with molecular tagging and machine learning automation is crucial for advancing cryoET.
• This combined approach addresses the limitations of protein identification and data collection in cryoET.
• Future cryoET applications will benefit from increased efficiency, enabling the study of complex cellular processes and structures.