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Single Cell Profiling using Ionic Liquid Matrix-Enhanced Secondary Ion Mass Spectrometry for Neuronal Cell Type Differentiation

机译:使用离子液体基质增强的二次离子质谱分析神经元细胞类型的单细胞分析

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摘要

A high-throughput single cell profiling method has been developed for matrix-enhanced secondary ion mass spectrometry (ME-SIMS) to investigate the lipid profiles of neuronal cells. Populations of cells are dispersed onto the substrate, their locations determined using optical microscopy, and the cell locations used to guide the acquisition of SIMS spectra from the cells. Up to 2,000 cells can be assayed in one experiment at a rate of 6 s per cell. Multiple saturated and unsaturated phosphatidylcholines (PCs) and their fragments are detected and verified with tandem mass spectrometry from individual cells when ionic liquids are employed as a matrix. Optically guided single cell profiling with ME-SIMS is suitable for a range of cell sizes, from Aplysia californica neurons larger than 75 μm to 7-μm rat cerebellar neurons. ME-SIMS analysis followed by t-distributed stochastic neighbor embedding of peaks in the lipid molecular mass range (m/z 700–850) distinguishes several cell types from the rat central nervous system, largely based on the relative proportions of the four dominant lipids, PC(32:0), PC(34:1), PC(36:1), and PC(38:5). Furthermore, subpopulations within each cell type are tentatively classified consistent with their endogenous lipid ratios. The results illustrate the efficacy of a new approach to classify single cell populations and subpopulations using SIMS profiling of lipid and metabolite contents. These methods are broadly applicable for high throughput single cell chemical analyses.
机译:已开发出一种用于基质增强的二次离子质谱(ME-SIMS)的高通量单细胞谱分析方法,以研究神经元细胞的脂质分布。将细胞群体分散到基质上,使用光学显微镜确定其位置,并使用细胞位置指导从细胞中获取SIMS光谱。一个实验中最多可检测2,000个细胞,每个细胞6 s的速率。当使用离子液体作为基质时,可从单个细胞中检测并通过串联质谱法检测并验证多个饱和和不饱和的磷脂酰胆碱(PCs)及其片段。使用ME-SIMS进行光学引导的单细胞谱分析适用于各种细胞大小,从大于75μm的加州Ap神经元到7μm的大鼠小脑神经元。 ME-SIMS分析,然后在脂质分子质量范围(m / z 700–850)中进行t分布随机相邻峰嵌入,从大鼠中枢神经系统中区分出几种细胞类型,这主要基于四种主要脂质的相对比例,PC(32:0),PC(34:1),PC(36:1)和PC(38:5)。此外,暂定将每种细胞类型内的亚群与其内源脂质比率相一致。结果表明,使用脂质和代谢物含量的SIMS分析对单细胞群体和亚群进行分类的新方法的功效。这些方法广泛适用于高通量单细胞化学分析。

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