Project C5: Developing tools for extraction and registration of complex morphological features in cryo electron tomography

Beata Turoňová

Cryo electron tomography (cryoET) allows unique insights into the landscape of cells in their close to native environment. In recent years, most of the software development and data analysis was focused on high-resolution structure determination while very little progress was made to allow for robust analysis of the cellular ultrastructure. In my laboratory we focus on contextual analysis of features found within tomograms. We recently showed the power of template matching (TM) for in situ particle localization when using highly optimized parameters and developed the tool for their optimization [1], [2], followed by GPU-accelerated implementation of the template matching itself called GAPStopTM [3]. The next challenges are extraction of more complex features (such as vesicles, membranes or virion capsids during their assembly) and contextual analysis of all features identified within a tomogram as well as across the whole dataset (statistical analysis).

 

References
[1] S. Cruz-León et al., ‘High-confidence 3D template matching for cryo-electron tomography’. Nature Communications 2024, 15, 3992. https://dx.doi.org/10.1038/s41467-024-47839-8
[2] B. Turoňová, ‘turonova/cryoCAT’. Feb. 23, 2024. [Online]. Available: https://github.com/turonova/cryoCAT
[3] B. Turoňová, ‘GAPStop(TM)’, GitLab. [Online]. Available: https://gitlab.mpcdf.mpg.de/bturo/gapstop_tm