Particle classification and identification in cryoET of crowded cellular environments

In situ cryogenic electron tomography (cryoET) promises to reveal the distribution and structures of macromolecular complexes across the cell with minimal disturbance to their native context. There have been several proof-of-principle studies but the routine application of this technology is limited by the relatively noisy data, the crowded cellular environment, and the size of the datasets that can be collected. The problem is ideally suited to AI which can learn from the large datasets and give bias-free interpretations of tomograms. There are nevertheless issues with generalisability of trained models and useability by research scientists.

In this proposal, we aim to look into AI techniques for 3D particle classification and identification from in situ tomograms. Specifically, we wish to establish a collaboration with the group of Min Xu at Carnegie Mellon University, who has worked in this area for more than 10 years. We will benchmark a selection of his methods on simulated and real datasets, considering factors from accuracy through to ease-of-use. Within the CCP-EM project, we are developing software pipelines for cryoET, and so we are particularly looking for AI tools that can enhance these pipelines. Part of our evaluation will be to quantify the improvement in downstream results, for example higher resolution sub-tomogram averages, providing essential feedback to Xu.

We also aim to strengthen our collaboration with Zachary Freyberg at the University of Pittsburgh, with whom we are processing in situ cryoET data on disease-associated cell lines and tissues. These datasets will be used to help benchmark the AI tools, while potentially leading to important research outcomes in their own right. By integrating novel AI tools in our CCP-EM tomography pipelines, this work will have a much larger impact. This depends partly on practicalities such as the robustness of the software and the ease with which we can make trained models available, and this will form an important part of the project.

Briefly, we will carry out three tasks: (1) Install selected modules from Xu's AITom package and benchmark on simulated and real datasets, (2) Integrate these tools into the CCP-EM tomography pipeline, and investigate how to optimise the tools in the context of a full investigation, and (3) look into the practicalities of making the software available for general usage, compare with similar tools, and host a workshop for dissemination.

There is obviously a significant amount of work needed to develop in situ cryoET into a routine techqniue. This proposal focusses on one specific aspect, namely the application and adaptation of AI approaches to improve the quality of information that can be obtained. As a proposal to the IPAP scheme, we look to expand our existing network of UK and European collaborators to bring in leading US groups. While the CCP-EM consortium is also developing AI tools, the expertise of Xu's group is complementary, covering different specific AI approaches and with a stronger focus on in situ tomography.