1. iTRAQ Based Quantitative Proteomics Approach Validated the Role of Calcyclin Binding Protein (CacyBP) in Promoting Colorectal Cancer Metastasis. Molecular & Cellular Proteomics, 1865–1880, 2013.
Fig. 1. Schematic representation of the experimental design for iTRAQ labeling showing biological replicates. In iTRAQ1, two biological replicates from HCT116-Control cells were labeled with 114 and 115 respectively, and from HCT116-CacyBP OE cells were labeled with 116 and 117 respectively. In iTRAQ2, two biological replicates from SW620-Scr Control cells were labeled with 114 and 115 respectively, and from SW620-CacyBP KD cells were labeled with 116 and 117 respectively.
Fig. 2. Determination of experimental variation using all the identified proteins (Unused score > 1. 3) common in both biological replicates: The horizontal axis represents % variation of iTRAQ ratios of same protein from different biological replicate samples from 1. The primary vertical axis represents the corresponding number of proteins (bars) having different % variation. The secondary vertical axis represents the cumulative % of the counted proteins (lines). Variation against 88% coverage of population was considered for selecting cut-off. A, Corresponds to CacyBP-OE data set and B, Corresponds to CacyBP-KD data set.
Fig. 3. Validation of iTRAQ results. A, Western-blot studies to validate differential expression pattern of 3 candidate proteins from OE data set: Annexin A2 and nucleolin were down-regulated whereas MARCKS was up-regulated in CacyBP- OE cells compared with the control cells which were in conformity with the iTRAQ results. B, Western blot validation of 5 candidate proteins from KD data set: clathrin, kinesin, calpain and VDAC-1 were found to be down-regulated whereas nucleolin was up-regulated. Actin was used as a loading control in both the cases.
2. Label-free Quantitative Proteomics of Mouse Cerebrospinal Fluid Detects β-Site APP Cleaving Enzyme (BACE1) Protease SubstratesIn Vivo. Molecular & Cellular Proteomics, 2015, 14 (10) 2550-2563
Fig. 1. Workflow for the analysis of individual BACE1?/? and wild-type CSF proteomes. The CSF of BACE1-/- mice and wild-type littermates was obtained from the cisterna magna. CSF was digested with Lys-C and trypsin in-solution. Peptides were collected and purified on C18 STAGE Tips. Samples were measured on a 50 cm column using 4 h gradients in two technical replicates on the Q Exactive mass spectrometer, followed by label-free analysis using the MaxQuant algorithm.
Fig. 2. Quality control of the label-free comparative CSF analysis of BACE1?/? mice. A, Upper panel: The log10 LFQ intensities of two selected technical replicates are plotted against each other. Lower panel: The log10 LFQ intensities of two selected biological replicates (two individual BACE1?/? mice) are plotted against each other. Note the high correlation for both biological and technical replicates as indicated by the coefficient of determination being close to 1. B, Number of identified proteins that were detected by at least 1 unique peptide, at least 2 unique peptides and at least 2 unique peptides that were quantifiable in 3 or more biological replicates. LFQ: label-free quantification. ID: protein identification. C, Histogram displaying the relative protein abundances in the BACE1?/? CSF. The log2 fold change of the mean BACE1?/? : wild-type ratio is shown on the x axis. The bin size for the log2 fold change is 0.2. The vast majority of all quantified proteins remained unchanged. Ko: BACE1?/?. Wt: wild type.
Fig. 3. Comparison and distribution of protein identifications with at least 2 unique peptides. A, An area-proportional Venn diagram displays the number of protein identifications of our and tworecent murine CSF analyses (6, 7). * Smith et al. identified a total of 261 proteins with at least two unique peptides. One hundred twenty eight of them were previously found in mouse brain. Only those proteins were used for this Venn diagram, as the other non brain specific proteins were not supplied in the supplementary table of their manuscript. B, UniProt Keyword subcellular location analysis for cellular components of proteins identified by at least two unique peptides. C, Subanalysis of annotated membrane proteins to GPI-anchored, or transmembrane (TM) type 1 or 2 and multipass according to UniProt subcellular location.
Fig. 4. Identification and validation of putative BACE1 substrates in mouse CSF. Each circle represents a protein that was quantifiable in at least three biological replicates. The log2 fold change of the mean BACE1?/? : wild-type ratio is plotted against the -log10 of the p value. The vertical bars mark a fold change of at least 1.5 and greater than the twofold standard deviation (corresponding to a reduction of 1.86-fold). The horizontal bar shows the cutoff for statistical significance (p value 0.05 or below). Proteins above this line have a p value＜0.05. Proteins shown as black and gray filled circles are regarded putative BACE1 substrates, as they are decreased in the absence of BACE1 and are annotated as membrane proteins. Gene names in “bold” mark previously known BACE1 substrates. The circle filled in black indicates that this protein remains significantly changed after a false discovery rate based multiple hypothesis testing. Gene names in regular font show proteins that are significantly decreased or up-regulated in the CSF of BACE1?/? mice by at least 1.5-fold. Gene names in “italic” show integral or GPI-anchored membrane proteins that are significantly decreased in the CSF of BACE1?/? mice, but show a fold change below 1.5. One non-significant outlier is not shown in Fig. 3 in order to save space for visualizing the data (UniProt ID P05063; -3.39, 0.71).
Fig. 5. Top 5 proteins that are decreased in the BACE1?/? CSF. A, The LFQ values of the top 5 significantly regulated proteins are shown for each biological replicate. LFQ values are normalized to the wild-type mean of all biological replicates (set to 1) for a given protein. B, Relative abundance of all observed proteins relative to each other as calculated by the iBAQ algorithm. Each protein identified in the CSF data set is displayed as a black circle and ranked by its relative abundance in the CSF. The iBAQ (intensity Based Absolute Quantification) score is calculated by dividing the total ion intensity of all observed peptides by the theoretical number of all observable peptides of a protein (18). The iBAQ score thus ranks the proteins in a data set by their approximate abundance, the proteins of highest abundance on top. LFQ: label-free quantification.
1、纳升液相串联高分辨质谱仪（SCIEX TripleTOF6600 nanoLCMS）
主要性能参数：
离子源：nano-ESI离子源；
扫描速度：100次MS/MS图谱/秒；
分辨率：MS分辨率40000（FWHM）；
扫描范围：MS/MS:100-2250 amu；
动态范围：5个数量级；
扫描模式：TOF-MS、DDA、DIA(SWATH)、MRMHR等。
2、液相色谱质谱联用仪（SCIEX QTRAP 6500+ LCMS）
主要性能参数：
离子源：NanoSpray? III、ESI离子源；
极高灵敏度；
扫描范围：最高 2000 Da；
动态范围：5个数量级；
扫描模式：母离子扫描、中性丢失扫描、子离子扫描、MRM、MS3;
iTRAQ、SILAC稳定同位素标记相对定量分析
（1）每组样品蛋白总量>150 μg，浓度在0.5-10 mg/mL之间,蛋白溶液的SDS、Triton、NP-40的含量不能超过0.1%；
（2）参考材料量：动物细胞样品：2X106; 动物组织：>50 mg; 血清：>200 μL; 植物组织：>300 mg；
（3）干冰寄送。
iTRAQ、SILAC稳定同位素标记相对定量分析
同位素标记分子与不同来源的蛋白进行标记，然后进行质谱分析具有相同的洗脱时间和离子化效率等化学行为，因此根据质谱的响应强度可以确定蛋白质在不同样品中的相对表达丰度。采用稳定同位素标记相对定量蛋白质组学一次可以得到上千（人细胞样品可以达5000多种蛋白）个蛋白质表达的相对定量信息。稳定同位素化学标记：iTRAQ、ICAT、DM（二甲基化）、TMT；稳定同位素代谢标记：SILAC。
相对定量比较分析两组或者多组样品之间的蛋白质成员的差异,以一组为对照,其余组为处理,处理组中的各个蛋白成员相对于对照组中的各个蛋白成员的百分比情况，通常若小于67%则视为表达下调，大于150%则视为表达上调。
iTRAQ标记技术是目前定量蛋白质组学研究中最常用的方法之一，iTRAQ分子主要包括三部分：报告基团、平衡基团、反应基团，8-plex报告基团加上平衡基团的总分子量为305Da。
反应基团会跟酶切多肽的N-端氨基和赖氨酸侧链氨基反应。iTRAQ化学标记的效率可以达到99%以上，不同样品的酶切多肽可以跟不同标签的iTRAQ试剂反应，最后将全部样品混合，在LCMS的一级质谱分析中，各个不同来源的多肽具有相同的质荷比，在二级质谱分析中，报告基团将从肽链上碎下来，代表各个样品来源的报告基团的信号强度便可以反映每个样品中蛋白质的相对量的情况。
iTRAQ试验流程
非标记蛋白定量分析
蛋白质非标记定量技术（label-free）是通过液质联用技术对蛋白质酶解肽段进行质谱分析，无需使用昂贵的稳定同位素标签做内部标准，只需分析大规模鉴定蛋白质时所产生的质谱数据，比较不同样品中相应肽段的信号强度，从而对肽段对应的蛋白质进行相对定量。目前非标记定量方法的原理是通过专业分析软件如Skyline、 MaxQuant、以MS1为基础，计算样品中每个蛋白质各个特征肽段在LC-MS色谱上各个MS1的信号强度的积分面积。然后进行比较分析，每个样品必须至少重复进样3次。
缺点：需要保证各个重复进样具有良好的重现性，对质谱仪器的分析水平要求较高；数据分析难度较大；
优点：可以对复杂程度不高、样品量很少的样品进行准确相对定量。
靶向蛋白定量分析
通过iTRAQ、SILAC、label free等各种高通量的相对定量分析得到很多差异表达蛋白后，需要对这些差异蛋白进行进一步的验证确定，比如进行western检测，利用质谱对目标蛋白进行靶向定量分析其准确性和灵敏度可以与western相媲美，而通量更高，一次可以验证10-50个蛋白，时间更短，一周时间即可以完成上20例样品，成本也更低。
目前基于质谱的蛋白靶向相对/绝对定量分析有两种，一种是经典的三重四级杆质谱的MRM（多重反应监测），也称SRM（选择反应监测）；另一种是在高分辨质谱上也能实现的PRM/MRM-HR（平行反应监测）分析。PRM和MRM已经成为蛋白质表达丰度检测的另一个重要手段，特别适合因缺少商业化抗体而无法进行western检测的样品。
SRM/MRM质谱工作流程示意图
PRM质谱工作流程示意图