Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, Massachusetts 02115, USA.
J Proteome Res. 2009 Oct;8(10):4732-42. doi: 10.1021/pr900427q.
Liquid Chromatography Mass Spectrometry (LC-MS) based proteomics is an important tool in detecting changes in peptide/protein abundances in samples potentially leading to the discovery of disease biomarker candidates. We present CLUE-TIPS (Clustering Using Euclidean distance in Tanimoto Inter-Point Space), an approach that compares complex proteomic samples for similarity/dissimilarity analysis. In CLUE-TIPS, an intersample distance feature map is generated from filtered, aligned and binarized raw LC-MS data by applying the Tanimoto distance metric to obtain normalized similarity scores between all sample pairs for each m/z value. We developed clustering and visualization methods for the intersample distance map to analyze various samples for differences at the sample level as well as the individual m/z level. An approach to query for specific m/z values that are associated with similarity/dissimilarity patterns in a set of samples was also briefly described. CLUE-TIPS can also be used as a tool in assessing the quality of LC-MS runs. The presented approach does not rely on tandem mass-spectrometry (MS/MS), isotopic labels or gels and also does not rely on feature extraction methods. CLUE-TIPS suite was applied to LC-MS data obtained from plasma samples collected at various time points and treatment conditions from immunosuppressed mice implanted with MCF-7 human breast cancer cells. The generated raw LC-MS data was used for pattern analysis and similarity/dissimilarity detection. CLUE-TIPS successfully detected the differences/similarities in samples at various time points taken during the progression of tumor, and also recognized differences/similarities in samples representing various treatment conditions.
基于液相色谱-质谱联用技术（LC-MS）的蛋白质组学是检测样品中肽/蛋白质丰度变化的重要工具，这些变化可能导致疾病生物标志物候选物的发现。我们提出了 CLUE-TIPS（基于欧式距离的 Tanimoto 内点空间聚类），这是一种用于比较复杂蛋白质组样品相似性/相异性分析的方法。在 CLUE-TIPS 中，通过对过滤、对齐和二值化的原始 LC-MS 数据应用 Tanimoto 距离度量标准，从复杂的蛋白质组样品中生成样本间距离特征图，以获得每个 m/z 值的所有样本对之间的归一化相似得分。我们开发了样本间距离图的聚类和可视化方法，用于在样本水平和单个 m/z 水平上分析各种样本的差异。还简要描述了一种查询特定 m/z 值的方法，这些值与一组样本中的相似性/相异性模式相关。CLUE-TIPS 还可以用作评估 LC-MS 运行质量的工具。所提出的方法不依赖于串联质谱（MS/MS）、同位素标记或凝胶，也不依赖于特征提取方法。CLUE-TIPS 套件应用于从免疫抑制的 MCF-7 人乳腺癌细胞荷瘤小鼠在不同时间点和治疗条件下收集的血浆样本获得的 LC-MS 数据。使用生成的原始 LC-MS 数据进行模式分析和相似性/相异性检测。CLUE-TIPS 成功地检测到肿瘤进展过程中不同时间点的样本之间的差异/相似性，并且还识别出代表不同治疗条件的样本之间的差异/相似性。
