Evaluation of proteome dynamics: Implications for statistical confidence in mass spectrometric determination

IF 3.4 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Proteomics Pub Date : 2024-05-03 DOI:10.1002/pmic.202300351
Inga Popova, Ekaterina Savelyeva, Tatyana Degtyarevskaya, Dmitrii Babaskin, Andrei Vokhmintsev
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Abstract

Single-cell proteomics is currently far less productive than other approaches. Still, the proteomic community is having trouble adapting to the limitation of having to examine fewer cells than they would like. Studies on a small number of cells should be carefully planned to maximize the chances of success in this situation. This study aims to determine how sample size and measurement speed (slope)/variation affect the accuracy of a protein proteome mass spectrometric determination. The determination accuracy was shown to increase, and the false positive rate was shown to decrease as the sample size increased from 7 to 100 cells and the measurement slope/variation (S/V) ratio increased from 1 to 6. Furthermore, it was discovered that the number of cells in the sample increased the accuracy of this estimate. Thus, for 100 cells, the measurement S/V ratio was typically estimated to be very close to the real-world value, with a standard deviation of 0.35. For sample sizes from 7 to 100 cells, this accuracy was seen when calculating the measurement S/V ratio. The findings can help researchers plan experiments for mass spectroscopic protein proteome determination and other research purposes.

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蛋白质组动态评估:对质谱测定统计置信度的影响
目前,单细胞蛋白质组学的产量远远低于其他方法。尽管如此,蛋白质组学界仍难以适应细胞数量少于预期的限制。在这种情况下,对少量细胞的研究应仔细规划,以获得最大的成功机会。本研究旨在确定样本量和测量速度(斜率)/变化如何影响蛋白质组质谱测定的准确性。结果表明,当样本量从 7 个细胞增加到 100 个细胞,测量斜率/变异率(S/V)从 1 增加到 6 时,测定的准确性提高,假阳性率降低。此外,研究还发现,样本中的细胞数会提高这一估计的准确性。因此,对于 100 个细胞,测量 S/V 比值通常非常接近实际值,标准偏差为 0.35。对于 7 到 100 个细胞的样本量,在计算测量 S/V 比时也能看到这种准确性。这些发现有助于研究人员规划质谱蛋白质组测定实验和其他研究目的。
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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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