High Spatiotemporal Resolution Mass Spectrometry for Single Cell Analysis

 描述：我们建议开发一种新的单细胞分析的敞开式电离质谱。我们称这种新方法为解吸/电离液滴传递质谱（LDIDD MS）。它使MS成像具有高分辨率和灵敏度，从而可以分析单个细胞甚至亚细胞组分的化学成分。LDIDD MS利用脉冲激光解吸和离子化基质上的分子;液滴直接喷射到聚焦激光照射点上，将解吸的离子输送到质谱仪。我们目前已经达到的空间分辨率为2~3微米，检测限为50毫微微摩尔。将优化LDIDD MS，以获得更好的空间分辨率和灵敏度。在拟议的研究中，将使用LDIDD MS分析和成像打印的单细胞阵列，以进行高通量单细胞分析。将构建细胞凋亡下单细胞代谢组学变化和分泌分子的数据库，以作为模型系统来解决细胞死亡的发生和进展机制。LDIDD MS还能够直接实时分析液相中的样品。用LDIDD MS成功地分析了溶解在水中的肽和蛋白质。还成功地检测了培养的活PC 12细胞分泌的肽。LDIDD MS活细胞分析的性能将进一步优化，以测量单个活神经元的分泌物。我们建议构建单神经元分泌组学，并通过分析单细胞的分子种类来了解单神经元之间的相互作用。LDIDD MS的这些组合特征将使得能够在单细胞水平上收集关于空间分辨代谢组学谱以及时空分辨分泌组学谱的前所未有的信息。将对收集的代谢组学和分泌组学数据进行统计学分析，以提取与细胞反应模型最显著和最相关的种属。我们选择细胞凋亡作为研究的第一个模型。通过分析细胞间代谢组学和分泌组学的变化，在相同量的凋亡刺激下，在不同的凋亡时间尺度上，我们希望了解导致细胞死亡的因素。该评分的分子数据库将用于构建细胞凋亡的随机模型。目前，据我们所知，这类信息并不存在。

 DESCRIPTION: We propose to develop a new ambient ionization mass spectrometry for single cell analysis. We call this new method desorption/ionization droplet delivery mass spectrometry (LDIDD MS). It enables MS imaging with high resolution and sensitivity such that a single cell or even subcellular components can be analyzed for its chemical content. The LDIDD MS utilizes a pulsed laser for desorption and ionization of molecules on a substrate; liquid droplets directly sprayed onto the focused laser irradiation spot delivers the desorbed ions to a mass spectrometer. The spatial resolution we have currently achieved is 2~3 microns, and limit of detection is 50 femtomoles. LDIDD MS will be optimized for better spatial resolution and sensitivity. In the proposed research, an array of printed single cells will be analyzed and imaged with LDIDD MS for high-throughput single cell analysis. A database of single cell metabolomic changes and secreted molecules under apoptosis will be constructed to address the mechanism of onset and progression of cell death as a model system. LDIDD MS is also capable of direct real-time analysis of samples in the liquid phase. Peptides and protein dissolved in water were successfully analyzed using LDIDD MS. Secreted peptides from cultured live PC12 cells were also successfully detected. The performance of the live cell analysis with LDIDD MS will be further optimized to enable the measurement of secretions from single live neurons. We propose to construct single- neuron secretomics and to learn about the interactions between single neurons by analyzing molecular species from single cells. These combined features of LDIDD MS would enable the collection of unprecedented information on spatially resolved metabolomic profiles as well as the spatiotemporally resolved secretomic profiles at the single cell level. The collected metabolomic and secretomic data will be statistically analyzed to extract the most significant and relevant species to a cellular response model. We have chosen cell apoptosis as the first model for study. By analyzing cell-to-cell variation in metabolomic and secretomic changes upon the same amount of apoptotic stimulus at different time scales of apoptosis, we hope to learn about those factors causing cell death. This scored molecular database will be used for constructing a stochastic model of cell apoptosis. At present, to our knowledge this type of information does not exist.