Background:

Spatial transcriptomics of histological sections have revolutionized research in life sciences and enabled unprecedented insights into genetic processes involved in tissue reorganization. However, in contrast to genomic analysis, the actual biomolecular composition of the sample has fallen behind.

In this study, we used untargeted spatiomolecular information acquired by Raman spectroscopy to reveal homogeneity, heterogeneity and dynamics of diseased cardiac tissue and cells on molecular levels. This was achieved by repurposing state-of-the-art bioinformatic analysis tools commonly used for transcriptomic analyses.

Methods:

Raman spectroscopy provides insights into the chemical make-up of a sample, by shining a laser on a sample and measuring the induced scattered photons of a different energy to the incident photons. This shift in energy is indicative of discrete vibrational modes of polarizable molecules. A distinctive biological ‘fingerprint’ can be derived from biological samples, which in turn can be used to detect endogenous macromolecules, metabolites, extracellular matrix, cell types etc. in a non-invasive, label-free manner.

We analyzed paraffin sections from two widely used and robust mouse models in cardiovascular research: a) acute injury: myocardial ischemia/reperfusion injury (MI) by transient ligation of the left anterior descending artery (LAD) as described previously and b) chronic injury: continuous infusion of Angiotensin II in atherosclerosis-prone apolipoprotein E (ApoE)-deficient mice, leading to cardiac hypertrophy and fibrosis. Data acquisition was performed in a label-free, tissue non-destructive manner and on subcellular resolution (1 px/µm).

Results:

We demonstrate the feasibility and relevance of translating tools for single-cell genomics, spatial transcriptomics and pseudotime trajectories into the Raman world.  By exploring sections of murine myocardial infarction and cardiac hypertrophy in an unsupervised manner, we identified myocardial subclusters when spatially approaching the pathology. Intra- and intercluster heterogeneity was analyzed by pseudotime trajectories which replicated and visualized molecular dynamics of remodeling myocardium. We furthermore employed spatial trajectories heading towards pathologies to investigate metabolic alterations and used a multi-omics approach to define the surrounding cellular (immune-) landscape in myocardial infarction.

Conclusion:

Our innovative, label-free, non-invasive “spectromics” approach provides a completely novel, innovative, and expandable framework for comprehensive characterization of histological samples. “Spatially-resolved Raman Spectromics” could therefore open new perspectives for a profound characterization of histological samples, while additionally allowing the combination with consecutive downstream analyses of the very same specimen.