Ryu Soyoung, Gallis Byron, Goo Young Ah, Shaffer Scott A, Radulovic Dragan, Goodlett David R
Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195-7610, USA.
Cancer Inform. 2008;6:243-55. doi: 10.4137/cin.s385. Epub 2008 Apr 17.
Recently, several research groups have published methods for the determination of proteomic expression profiling by mass spectrometry without the use of exogenously added stable isotopes or stable isotope dilution theory. These so-called label-free, methods have the advantage of allowing data on each sample to be acquired independently from all other samples to which they can later be compared in silico for the purpose of measuring changes in protein expression between various biological states. We developed label free software based on direct measurement of peptide ion current area (PICA) and compared it to two other methods, a simpler label free method known as spectral counting and the isotope coded affinity tag (ICAT) method. Data analysis by these methods of a standard mixture containing proteins of known, but varying, concentrations showed that they performed similarly with a mean squared error of 0.09. Additionally, complex bacterial protein mixtures spiked with known concentrations of standard proteins were analyzed using the PICA label-free method. These results indicated that the PICA method detected all levels of standard spiked proteins at the 90% confidence level in this complex biological sample. This finding confirms that label-free methods, based on direct measurement of the area under a single ion current trace, performed as well as the standard ICAT method. Given the fact that the label-free methods provide ease in experimental design well beyond pair-wise comparison, label-free methods such as our PICA method are well suited for proteomic expression profiling of large numbers of samples as is needed in clinical analysis.
最近,几个研究小组发表了不使用外源添加稳定同位素或稳定同位素稀释理论,通过质谱法测定蛋白质组表达谱的方法。这些所谓的无标记方法具有这样的优势,即允许独立获取每个样品的数据,之后可以在计算机上与所有其他样品进行比较,以测量不同生物学状态之间蛋白质表达的变化。我们基于肽离子电流面积(PICA)的直接测量开发了无标记软件,并将其与另外两种方法进行比较,一种更简单的无标记方法称为光谱计数法,以及同位素编码亲和标签(ICAT)法。通过这些方法对含有已知但浓度不同的蛋白质的标准混合物进行数据分析表明,它们的表现相似,均方误差为0.09。此外,使用PICA无标记方法分析了掺入已知浓度标准蛋白质的复杂细菌蛋白质混合物。这些结果表明,在这个复杂的生物样品中,PICA方法在90%置信水平下检测到了所有水平的标准掺入蛋白质。这一发现证实,基于单个离子电流曲线下面积直接测量的无标记方法与标准ICAT方法表现相当。鉴于无标记方法在实验设计上提供的便利远远超出成对比较,像我们的PICA方法这样的无标记方法非常适合临床分析中所需的大量样品的蛋白质组表达谱分析。