Many biological functions depend critically upon fine details of tissue’s molecular architecture that have resisted exploration by existing imaging techniques. Array tomography (AT) encompasses light and electron microscopy modalities that offer unparalleled opportunities to explore 3D cellular architectures of large samples in extremely fine structural and molecular detail (see figure). Fluorescence-AT achieves much higher resolution and molecular multiplexing than most other fluorescence microscopy methods, while electron-AT can capture 3D ultrastructure easily and rapidly compared to traditional serial-section electron microscopy methods. The Correlative mode of Array Tomography (CAT) furthermore offers the unique capacity of merging the molecular discrimination strengths of multichannel fluorescence microscopy with the ultrastructural imaging strengths of electron microscopy.
However, performing CAT has many challenges, including sample preparation, image acquisition and data handling. This approach results in imaging workflows which are notoriously time-consuming and require high levels of expertise. Furthermore, in CAT the microscopes have very different fields of view, and therefore accurately overlaying the different 3D volumes is highly non-trivial and prone to bias. In this framework, the CCI is working on smart microscopy workflows to increase the image acquisition speed of AT in both light and electron microscopy by implementing denoising methods, managing big image data, and automating some of the target identification steps that currently need human intervention.