Label-free analysis of inflammatory tissue remodeling in murine lung tissue based on multiphoton microscopy, Raman spectroscopy and machine learning

Inflammatory fibrotic tissue remodeling can lead to severe morbidity. Histopathology grading requires extraction of biopsies and elaborate tissue processing. Label-free optical technologies can provide diagnostic readout without preparation and artificial stainings and show potential for in vivo applications. Here, we present an integration of Raman spectroscopy (RS) and multiphoton microscopy for joint investigation of the bio-chemical composition and morphological features related to cellular components and connective tissue. Both modalities show that collagen signatures were significantly increased in a murine fibrosis model. Furthermore, autofluorescence signatures assigned to immune cells show high correlation with disease severity. RS indicates increased levels of elastin and lipids. Further, we investigated the effect of joint data sets on prediction performance in machine learning models. Although binary classification did not benefit from adding more features, multi-class classification was improved by integrated data sets.