INTRODUCTION: Radiation biodosimetry plays a vital role in assessing radiation exposure levels and determining potential health risks for individuals affected by radiological incidents or accidental exposures. Accurate and rapid classification of radiation-exposed samples is essential for effective triage, medical treatment, and public health management. This study investigates the potential of Raman Tweezer Spectroscopy (RTS) as a label-free, single-cell spectroscopic approach for detecting and discriminatating radiation-induced biochemical alterations in human red blood cells (RBCs). METHODS: Human RBCs were exposed to X-ray doses ranging from 0.5 to 8 Gy and analyzed using Raman tweezers with 785 nm excitation. The resulting vibrational spectra captured molecular signatures arising primarily from hemoglobin, membrane lipids, and protein backbones. Multivariate statistical analysis was applied to the spectral data to identify dose-dependent biochemical variance and to evaluate the feasibility of discriminating between irradiated and non-irradiated samples at the single-cell level. RESULTS: Principal component analysis (PCA) revealed progressive spectral divergence with increasing radiation dose, with distinct response regimes observed at low (0.5 Gy), moderate (2-4 Gy), and high (6-8 Gy) doses. Loading analysis indicated that radiation-induced changes were dominated by lipid- and protein-associated Raman bands, consistent with oxidative modification of membrane and protein structures. Increased intercellular heterogeneity was observed at intermediate-to-high doses. DISCUSSION: These results demonstrate that RTS is sensitive to radiation-induced biochemical alterations in individual RBCs and can differentiate between exposure levels without labels or extensive sample preparation. The findings support the feasibility of this approach as a complementary tool for rapid radiation biodosimetry, with potential applications in emergency response, medical triage, and radiobiological event management. As an unsupervised, proof-of-concept analysis, the present work establishes the detection and discrimination of radiation-induced biochemical change rather than a validated quantitative dose-reconstruction methodology.
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Shil et al. (2026) studied this question.
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