Maximising data from 3D human tissue imaging

The use of ex-vivo precision cut lung slices provides a more accurate recapitulation of respiratory diseases - focusing on the biomechanics involved as well as the cellular interplay between phenotypes and existing tissue architecture. Fluorophore labelling during volumetric imaging captures this spatial aspect, which can potentially be used as a readout for quantitative and qualitative analysis. A major issue encountered is the lack of opensource image analysis platforms that can retain and handle the 3D dataset generated, without first converting to a singular maximum- intensity projected image, subsequently losing any additional spatial properties. We have created a baseline ImageJ-based macro pipeline to study changes in epithelial cell population over time in precision cut human lung slices (hPCLS), using immunofluorescent membrane markers for phenotypic identification. Large z stack datasets are kept uncompressed as acquired, saved as TIFFs and processed through the pipeline, automating normalisation for user- independent, high-throughput analysis.

Poster presented as part of the Crick BioImage Analysis Symposium 2024.

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