A comprehensive method to analyze single-cell vibrations.

All living cells vibrate depending on metabolism. It has been hypothesized that vibrations are unique for a given phenotype and thereby suitable to diagnose cancer type and stage and to pre-assess the effectiveness of pharmaceutical treatments in real time. However, cells exhibit highly variable vibrational signals, can be subject to environmental noise, and may be challenging to differentiate, having so far limited the phenomenon's applicability. Here, we combined the sensitive method of force spectroscopy using optical tweezers with comprehensive statistical analysis. After data acquisition, the signal was decomposed into its spectral components via fast Fourier transform. Peaks were parameterized and subjected to principal-component analysis to perform an unbiased multivariate statistical evaluation. This method, which we term cell vibrational profiling (CVP), systematically assesses cellular vibrations. To validate the CVP technique, we conducted experiments on five U251 glioblastoma cells, using 8- to 10-μm polystyrene beads as a control for comparison. We collected raw data using optical tweezers, segmenting into 150+ 5-s intervals. Each segment was converted into power spectra representing a frequency resolution of 10,000 Hz for both cells and controls. U251 glioblastoma cells exhibited significant vibrations at 402.6, 1254.6, 1909.0, 2169.4, and 3462.8 Hz (p < 0.0001). This method was further verified with principal-component analysis modeling, which revealed that, in cell-cell comparisons using the selected frequencies, overlap frequently occurred, and clustering was difficult to discern. In contrast, comparison between cell-bead models showed that clustering was easily distinguishable. Our paper establishes CVP as an unbiased, comprehensive technique to analyze cell vibrations. This technique effectively differentiates between cell types and evaluates cellular responses to therapeutic interventions. Notably, CVP is a versatile, cell-agnostic technique requiring minimal sample preparation and no labeling or external interference. By enabling definitive phenotypic assessments, CVP holds promise as a diagnostic tool and could significantly enhance the evaluation of pharmaceutical treatments.