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.

中文翻译：

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分析单细胞振动的综合方法


所有活细胞都根据新陈代谢振动。据推测，振动对于给定的表型是唯一的，因此适用于诊断癌症类型和分期，并实时预先评估药物治疗的有效性。然而，细胞表现出高度可变的振动信号，可能会受到环境噪声的影响，并且可能难以区分，到目前为止限制了该现象的适用性。在这里，我们将使用光镊的力谱的敏感方法与全面的统计分析相结合。数据采集后，通过快速傅里叶变换将信号分解为其频谱分量。对峰进行参数化并进行主成分分析，以进行无偏倚的多变量统计评估。这种方法，我们称之为细胞振动分析 （CVP），系统地评估细胞振动。为了验证 CVP 技术，我们对 5 个 U251 胶质母细胞瘤细胞进行了实验，使用 8 至 10 μm 聚苯乙烯珠作为对照进行比较。我们使用光镊收集原始数据，分为 150+ 5 秒的间隔。每个段都转换为功率谱，代表电池和对照的频率分辨率为 10,000 Hz。U251 胶质母细胞瘤细胞在 402.6 、 1254.6 、 1909.0 、 2169.4 和 3462.8 Hz 时表现出显着的振动 （p < 0.0001）。这种方法通过主成分分析建模进一步验证，结果表明，在使用选定频率的细胞间比较中，重叠经常发生，并且难以辨别聚类。相比之下，细胞珠模型之间的比较表明聚类很容易区分。 我们在本文中将 CVP 确立为一种公正、全面的分析细胞振动的技术。该技术可有效区分细胞类型并评估细胞对治疗干预的反应。值得注意的是，CVP 是一种多功能、与细胞无关的技术，只需最少的样品制备，无需标记或外部干扰。通过进行明确的表型评估，CVP 有望作为一种诊断工具，并且可以显着增强对药物治疗的评估。