Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biological research and medical diagnostics. Label-free cell imaging provides information about endogenous autofluorescent metabolites, enzymes and cofactors in cells. However, extracting high content information from autofluorescence imaging has been hitherto impossible. Here, we developed a multispectral fluorescence imaging technique which allows precise quantification of the native fluorophores in cells and tissues. With that approach we are now able to non-invasively image the aspects of biomolecular composition of cells and tissues; where many of these fluorophores (NADH, flavins, cytochrome C) are relevant to metabolism. We will discuss label-free detection of reactive oxygen species (ROS) and the cell cycle. Cell cycle and metabolism have a tight, bidirectional relationship, with the ability of the cell to commit to growth depending on the availability of metabolites, and the molecular mechanisms of the cell-cycle being linked to the regulation of metabolic networks. Cells entering the cell cycle increase glycolysis as they go from G1-phase into S-phase, this results in accumulation of the NADH relative to FAD which is also fluorescent.
Moreover, metabolic dysregulation is common across the spectrum of diseases, this next-generation methodology is able to detect major health conditions including neurodegeneration and cancer. This work also reports on approaches for early diagnosis of motor neurone disease (MND) and localisation of cancer margins for ocular surface squamous neoplasia. Our optimal discrimination approach (extracted features for treatment monitoring in MND and melanoma) enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent.