PDF(Pubmed)
Abstract：
Transcriptomes and proteomes can be normalized with a handful of RNAs or proteins (or their peptides), such as GAPDH, β-actin, RPBMS, and/or GAP43. Even with hundreds of standards, normalization cannot be achieved across different molecular mass ranges for small molecules, such as lipids and metabolites, due to the non-linearity of mass by charge ratio for even the smallest part of the spectrum. We define the amount (or range of amounts) of metabolites and/or lipids per a defined amount of a protein, consistently identified in all samples of a multiple-model organism comparison, as the normative level of that metabolite or lipid. The defined protein amount (or range) is a normalized value for one cohort of complete samples for which intrasample relative protein quantification is available. For example, the amount of citrate (a metabolite) per µg of aconitate hydratase (normalized protein amount) identified in the proteome is the normative level of citrate with aconitase. We define normativity as the amount of metabolites (or amount range) detected when compared to normalized protein levels. We use axon regeneration as an example to illustrate the need for advanced approaches to the normalization of proteins. Comparison across different pharmacologically induced axon regeneration mouse models entails the comparison of axon regeneration, studied at different time points in several models designed using different agents. For the normalization of the proteins across different pharmacologically induced models, we perform peptide doping (fixed amounts of known peptides) in each sample to normalize the proteome across the samples. We develop Regen V peptides, divided into Regen III (SEB, LLO, CFP) and II (HH4B, A1315), for pre- and post-extraction comparisons, performed with the addition of defined, digested peptides (bovine serum albumin tryptic digest) for protein abundance normalization beyond commercial labeled relative quantification (for example, 18-plex tandem mass tags). We also illustrate the concept of normativity by using this normalization technique on regenerative metabolome/lipidome profiles. As normalized protein amounts are different in different biological states (control versus axon regeneration), normative metabolite or lipid amounts are expected to be different for specific biological states. These concepts and standardization approaches are important for the integration of different datasets across different models of axon regeneration.
摘要：
转录组和蛋白质组可以用少量RNA或蛋白质（或它们的肽）标准化，如GAPDH，β-肌动蛋白，RPBMS,和/或GAP43。即使有数百个标准，对于小分子，归一化不能在不同的分子质量范围内实现，如脂质和代谢物，由于即使是光谱的最小部分，质量与电荷比的非线性。我们定义了每限定量蛋白质的代谢物和/或脂质的量（或量的范围）。在多模式生物比较的所有样本中一致识别，作为代谢物或脂质的规范水平。定义的蛋白质量(或范围)是一组完整样品的归一化值,对于所述完整样品,可获得样内相对蛋白质定量。例如,在蛋白质组中确定的每µg乌头酸水合酶的柠檬酸盐（一种代谢物）的量（标准化蛋白质量）是柠檬酸盐与乌头酸酶的标准水平。我们将正常性定义为当与标准化蛋白质水平相比时检测到的代谢物的量(或量范围)。我们以轴突再生为例，说明对蛋白质标准化的高级方法的需求。不同药理学诱导的轴突再生小鼠模型之间的比较需要轴突再生的比较，在使用不同代理设计的几个模型的不同时间点进行了研究。为了在不同的药理学诱导模型中对蛋白质进行标准化，我们在每个样品中进行肽掺杂（已知肽的固定量），以标准化样品中的蛋白质组。我们开发了RegenV肽，分为RegenIII（SEB，LLO,CFP)和II(HH4B，A1315),对于提取前和提取后的比较，在添加定义的情况下执行，消化的肽（牛血清白蛋白胰蛋白酶消化）用于蛋白质丰度标准化，超出商业标记的相对定量（例如，18-plex串联质量标签)。我们还通过在再生代谢组/脂质组谱上使用这种标准化技术来说明规范性的概念。由于标准化的蛋白质量在不同的生物状态下是不同的（对照与轴突再生），对于特定的生物状态，规范的代谢物或脂质含量预计会有所不同。这些概念和标准化方法对于跨轴突再生的不同模型的不同数据集的整合是重要的。
