Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics.
Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics.
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DOI：
10.1016/j.mcpro.2021.100085
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Payne SH
中科院分区：
文献类型：
--
作者：
Boekweg H;Guise AJ;Plowey ED;Kelly RT;Payne SH
单细胞测量是表征细胞间异质性的独特能力，并已用于探索组织和其他复杂细胞组合中存在的细胞类型和生理功能的巨大多样性。单细胞蛋白质组学的一个有趣的应用是描述生物转变过程中的蛋白质组动力学，如细胞分化或疾病进展。时间过程实验，在状态转换期间定期测量，依赖于在数据序列中检测动态轨迹的能力。然而，在单细胞蛋白质组学实验中，细胞间异质性使蛋白质组动力学的自信鉴定复杂化，因为测量变异性可能高于预期。因此，这些实验的一个关键问题是在时间过程中需要获得多少数据点才能进行稳健的统计分析。我们在此分析了影响蛋白质组动力学检测统计置信度的最重要变量：折叠变化、测量变异性和在时间过程中测量的细胞数量。重要的是，我们表明，在整个时域内少于16个测量值的数据集精度低，而且假阳性率也很高。我们还演示了如何在实验设计中平衡竞争需求以达到预期结果。检测蛋白质的时间变化依赖于折叠变化和可变性。复制时间过程提高了时间动态检测的可靠性。时间实验需要密集的细胞采样来跟踪渐变。时程轨迹实验比双态比较需要更多的样本。细胞发育和疾病进展是表型阶段之间的逐渐过渡。明确测量这种转变的时间过程测量对于发现蛋白质组动力学非常重要。单细胞测量是理解异质性的有力工具，特别是在表型转变期间。单细胞蛋白质组学测量正在成为表征细胞状态的可用工具。我们创造了一种统计方法来预测时间动力学实验设计的成功。
Single-cell measurements are uniquely capable of characterizing cell-to-cell heterogeneity and have been used to explore the large diversity of cell types and physiological functions present in tissues and other complex cell assemblies. An intriguing application of single-cell proteomics is the characterization of proteome dynamics during biological transitions, like cellular differentiation or disease progression. Time-course experiments, which regularly take measurements during state transitions, rely on the ability to detect dynamic trajectories in a data series. However, in a single-cell proteomics experiment, cell-to-cell heterogeneity complicates the confident identification of proteome dynamics as measurement variability may be higher than expected. Therefore, a critical question for these experiments is how many data points need to be acquired during the time course to enable robust statistical analysis. We present here an analysis of the most important variables that affect statistical confidence in the detection of proteome dynamics: fold change, measurement variability, and the number of cells measured during the time course. Importantly, we show that datasets with less than 16 measurements across the time domain suffer from low accuracy and also have a high false-positive rate. We also demonstrate how to balance competing demands in experimental design to achieve a desired result. Detecting temporal change in proteins depends on fold change and variability. Replicate time courses improve reliability of detecting temporal dynamics. Temporal experiments require a dense sampling of cells to track gradual transitions. Time-course trajectory experiments require more samples than two-state comparisons. Cellular development and disease progression are gradual transitions between phenotypic stages. Time-course measurements that explicitly measure this transition are important to discover proteome dynamics. Single-cell measurements are a powerful tool for understanding heterogeneity, especially during phenotypic transitions. Single-cell proteomics measurements are emerging as an available tool to characterize the cellular state. We created a statistical method that predicts the success of an experimental design for temporal dynamics.
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作者：
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影响因子：
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通讯作者：
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