A National Scale Data Asset to Integrate Molecular Imaging with Bio-analytics

Electron Microscopy (EM) has advanced to the point where it is possible to determine the 3D structure of individual proteins in situ (i.e. in the context of parts of intact cryo-preserved cells). However, a fundamental limitation of this technique is that the identification of proteins in the region of interest is exceptionally challenging, and it relies on exhaustive comparative experiments. A potential solution to this problem arises through integration of EM with biological mass spectrometry (proteomics), but this technique is limited to samples far larger than individual cells. Through novel preparation approaches (e.g. de Marco, US Patent App. 16/225,213), we can surmount these challenges and gain substantive insight into the proteome of portions of individual cells. The challenge now lies in accurately correlating the proteomic data with the information derived from imaging experiments. We anticipate that this process will be significantly enhanced through the use of Deep Learning and Artificial Intelligence (AI) tools and via reference to extensive structural biology data and gene ontology/protein interaction network information. Accordingly, we aim to develop a new resource to organise and navigate multidimensional data and drive connectivity between molecular imaging and proteomic datasets.

To achieve this, we propose to enable a new, publicly accessible national scale data asset to underpin the integration of molecular imaging with bio-analytics, thus driving discovery research across the whole of the life sciences. The resource will permit Australian researchers to attain one of the grand ambitions of biologists and understand the precise molecular makeup of the intracellular milieu. We will use AI-driven bioinformatics approaches to seamlessly integrate and interrogate high-resolution imaging data (derived from optical and electron microscopy (EM) and X-ray crystallography) with proteomic/genomic data and gene ontology/protein interaction network data. Currently, this information is distributed across numerous disparate databases, precluding the ready interpretation and analysis of imaging data such as 3D tomograms output by the latest generation optical and electron microscopes. Our online platform will host the final, released and annotated datasets and permit presentation of the data to the community. Our work will have immediate application in fields such as drug discovery, infectious diseases and molecular diagnostics.