High Spatiotemporal Resolution Mass Spectrometry for Single Cell Analysis



 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.