Mass spectrometry (MS) is widely used in science and industry. It allows accurate, specific, sensitive, and reproducible detection and quantification of a huge range of analytes. Across MS applications, quantification by MS has grown most dramatically, with >50 million experiments/year in the USA alone. However, quantification performance varies between instruments, compounds, different samples, and within- and across runs, necessitating normalization with analyte-similar internal standards (IS) and use of IS-corrected multipoint external calibration curves for each analyte, a complicated and resource-intensive approach, which is particularly ill-suited for multi-analyte measurements. We have developed an internal calibration method that utilizes the natural isotope distribution of an IS for a given analyte to provide internal multipoint calibration. Multiple isotope distribution calibrators for different targets in the same sample facilitate multiplex quantification, while the emerging random-access automated MS platforms should also greatly benefit from this approach. Finally, isotope distribution calibration allows mathematical correction for suboptimal experimental conditions. This might also enable quantification of hitherto difficult, or impossible to quantify, targets, if the distribution is adjusted in silico to mimic the analyte. The approach works well for high resolution, accurate mass MS for analytes with at least a modest-sized isotopic envelope. As shown herein, the approach can also be applied to lower molecular weight analytes, but the reduction in calibration points does reduce quantification performance.

中文翻译：

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质谱的同位素分布校准

质谱 (MS) 广泛应用于科学和工业领域。它允许对大量分析物进行准确、特异、灵敏和可重现的检测和定量。在 MS 应用中，MS 的量化增长最为显着，仅在美国就有超过 5000 万次实验/年。然而，仪器、化合物、不同样品以及运行内和运行之间的定量性能各不相同，因此需要使用与分析物类似的内标 (IS) 进行标准化，并对每个分析物使用经过 IS 校正的多点外部校准曲线，这是一种复杂且资源丰富的校准曲线。密集的方法，特别不适合多分析物测量。我们开发了一种内部校准方法，该方法利用给定分析物的 IS 的天然同位素分布来提供内部多点校准。同一样品中不同目标的多个同位素分布校准器促进了多重定量，而新兴的随机存取自动化 MS 平台也应从这种方法中受益匪浅。最后，同位素分布校准允许对次优实验条件进行数学校正。如果调整分布，这也可能使迄今为止难以量化或无法量化的目标量化 而新兴的随机访问自动化 MS 平台也应该从这种方法中受益匪浅。最后，同位素分布校准允许对次优实验条件进行数学校正。如果调整分布，这也可能使迄今为止难以量化或无法量化的目标量化 而新兴的随机访问自动化 MS 平台也应该从这种方法中受益匪浅。最后，同位素分布校准允许对次优实验条件进行数学校正。如果调整分布，这也可能使迄今为止难以量化或无法量化的目标量化在计算机模拟分析物。该方法适用于至少具有中等大小同位素包络的分析物的高分辨率、精确质量 MS。如本文所示，该方法也可应用于较低分子量的分析物，但校准点的减少确实降低了定量性能。