Open Access

Johannes Reifenrath, Benjamin Gardner, Alexander Gigler, Friederike Liesche-Starnecker, Suzy Eldershaw, Nick Stone, Jürgen Schlegel

• Raman spectroscopy provides comprehensive biochemical information on a sample’s composition, yet it is often used to analyze aggregated spectra rather than specific shifts. We introduce Fluorescence Guided Raman Spectroscopy (FGRS) as a methodology enabling the isolation of proteins’ spectral signatures and the training of classifiers that generalize across cell lines. We demonstrate the utility of this approach using connexin 43, a marker protein of glioblastoma tumour microtubes. By screening eGFP, sodium fluorescein, and mTagBFP2 for their compatibility with a Raman system operating at 532 nm, we selected mTagBFP2 as the most Raman-compatible fluorophore, whereas the other fluorophores emitting near 532 nm caused spectral interference. mTagBFP2 was cloned into a connexin 43 expression vector, allowing fluorescent tracking and Raman interrogation with subsequent peak identification and correlation to an I-TASSER protein prediction model. We then trained two support vector machinesRaman spectroscopy provides comprehensive biochemical information on a sample’s composition, yet it is often used to analyze aggregated spectra rather than specific shifts. We introduce Fluorescence Guided Raman Spectroscopy (FGRS) as a methodology enabling the isolation of proteins’ spectral signatures and the training of classifiers that generalize across cell lines. We demonstrate the utility of this approach using connexin 43, a marker protein of glioblastoma tumour microtubes. By screening eGFP, sodium fluorescein, and mTagBFP2 for their compatibility with a Raman system operating at 532 nm, we selected mTagBFP2 as the most Raman-compatible fluorophore, whereas the other fluorophores emitting near 532 nm caused spectral interference. mTagBFP2 was cloned into a connexin 43 expression vector, allowing fluorescent tracking and Raman interrogation with subsequent peak identification and correlation to an I-TASSER protein prediction model. We then trained two support vector machines (SVMs) for the classification of cells based on their connexin 43 content and highlighted the impact of different spectral ranges (full spectrum vs. most significant Raman shifts) on specificity and sensitivity in glioblastoma target cell lines. Connexin 43 expression led to a loss of the peaks at 600, 1253, and 1401 cm⁻¹, consistent with an increased α-helical content as predicted by I-TASSER. SVMs achieved up to 79% accuracy on unseen glioblastoma lines, with full-spectrum models reaching 98.7% sensitivity. Thus, FGRS enables the spectral isolation of tumour marker proteins and the development of robust classifiers across cell lines. By focusing on key Raman shifts, this method holds the potential to improve diagnostic accuracy and sensitivity, offering a customizable tool for tumour detection.…