Forrester Jeffrey S, Milne Stephen B, Ivanova Pavlina T, Brown H Alex
Department of Pharmacology, Vanderbilt Instituteof Chemichal Biology, Vanderbilt University School of Medicine, Nashville, TN 37232-6600, USA.
Mol Pharmacol. 2004 Apr;65(4):813-21. doi: 10.1124/mol.65.4.813.
Recent successes in defining the roles of lipids in cell signaling have stimulated greater interest in these versatile biomolecules. Until recently, analysis of these molecules at the species level has required labor-intensive techniques. The development of electrospray ionization mass spectrometry (ESI-MS) has made possible the detection and identification of thermally labile biological molecules, such as phospholipids. The "soft" ionization does not cause extensive fragmentation, is highly sensitive, accurate, and reproducible. Thus, this method is well suited for analyzing a broad range of phospholipids without elaborate chromatographic separation. Evaluating the vast amounts of data resulting from these measurements is a rate-limiting step in the assessment of phospholipid composition, requiring the development and application of computational algorithms for mass spectrometry data. Here we describe computational lipidomics, a novel analytical technique, coupling mass spectrometry with statistical algorithms to facilitate the comprehensive analysis of hundreds of lipid species from cellular extracts. As a result, lipid arrays are generated to indicate qualitative changes that occur in lipid composition between experimental or disease states, similar to proteomic and genomic analyses. This review presents a methodological strategy for using ESI-MS combined with a high-power computational analysis to profile time-dependent changes in cellular phospholipids after the addition of an agonist or to evaluate changes promoted by pathophysiological processes. As an illustration, we describe the methods and approaches used to generate lipid arrays for The Alliance for Cellular Signaling (AfCS). These arrays are contributing to a more complete understanding of the participants of cellular signaling pathways after activation of cell surface receptors.
近期在确定脂质在细胞信号传导中的作用方面所取得的成功，激发了人们对这些多功能生物分子的更大兴趣。直到最近，在物种水平上对这些分子进行分析还需要耗费大量人力的技术。电喷雾电离质谱（ESI-MS）的发展使得检测和鉴定热不稳定生物分子（如磷脂）成为可能。“软”电离不会导致广泛的碎片化，具有高灵敏度、准确性和可重复性。因此，这种方法非常适合在无需精细色谱分离的情况下分析多种磷脂。评估这些测量产生的大量数据是评估磷脂组成的限速步骤，这需要开发和应用用于质谱数据的计算算法。在此，我们描述了计算脂质组学，这是一种新颖的分析技术，它将质谱与统计算法相结合，以促进对细胞提取物中数百种脂质种类的全面分析。结果，生成了脂质阵列以指示实验状态或疾病状态之间脂质组成发生的定性变化，类似于蛋白质组学和基因组学分析。本综述提出了一种方法策略，即使用ESI-MS结合高功率计算分析来描绘添加激动剂后细胞磷脂随时间的变化，或评估病理生理过程促进的变化。作为例证，我们描述了为细胞信号联盟（AfCS）生成脂质阵列所使用的方法和途径。这些阵列有助于更全面地了解细胞表面受体激活后细胞信号通路的参与者。
