Translating genomic tools to Raman spectroscopy analysis enables high-dimensional tissue characterization on molecular resolution

Manuel Sigle, Anne-Katrin Rohlfing, Martin Kenny, Sophia Scheuermann, Na Sun, Ulla Graeßner, Verena Haug, Jessica Sudmann, Christian M. Seitz, David Heinzmann, Katja Schenke-Layland, Patricia B. Maguire, Axel Walch, Julia Marzi and Meinrad Paul Gawaz ()
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Manuel Sigle: University Hospital Tuebingen, Eberhard Karls University Tuebingen
Anne-Katrin Rohlfing: University Hospital Tuebingen, Eberhard Karls University Tuebingen
Martin Kenny: University College Dublin
Sophia Scheuermann: University Children’s Hospital Tuebingen
Na Sun: Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH)
Ulla Graeßner: University Children’s Hospital Tuebingen
Verena Haug: University Hospital Tuebingen, Eberhard Karls University Tuebingen
Jessica Sudmann: University Hospital Tuebingen, Eberhard Karls University Tuebingen
Christian M. Seitz: University Children’s Hospital Tuebingen
David Heinzmann: University Hospital Tuebingen, Eberhard Karls University Tuebingen
Katja Schenke-Layland: University of Tuebingen
Patricia B. Maguire: University College Dublin
Axel Walch: Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH)
Julia Marzi: University of Tuebingen
Meinrad Paul Gawaz: University Hospital Tuebingen, Eberhard Karls University Tuebingen

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract 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, leaving a gap of potentially highly valuable information. Raman microspectroscopy provides untargeted spatiomolecular information at high resolution, capable of filling this gap. In this study we demonstrate spatially resolved Raman “spectromics” to reveal homogeneity, heterogeneity and dynamics of cell matrix on molecular levels by repurposing state-of-the-art bioinformatic analysis tools commonly used for transcriptomic analyses. By exploring sections of murine myocardial infarction and cardiac hypertrophy, we identify myocardial subclusters when spatially approaching the pathology, and define the surrounding metabolic and cellular (immune-) landscape. Our innovative, label-free, non-invasive “spectromics” approach could therefore open perspectives for a profound characterization of histological samples, while additionally allowing the combination with consecutive downstream analyses of the very same specimen.

Date: 2023
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DOI: 10.1038/s41467-023-41417-0

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