Classification of Large Cellular Populations and Discovery of Rare Cells Using Single Cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry
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- 作者：Ta-Hsuan Ong ; David J. Kissick ; Erik T. Jansson ; Troy J. Comi ; Elena V. Romanova ; Stanislav S. Rubakhin ; Jonathan V. Sweedler
- 刊名：Analytical Chemistry
- 出版年：2015
- 出版时间：July 21, 2015
- 年：2015
- 卷：87
- 期：14
- 页码：7036-7042
- 全文大小：336K
- ISSN：1520-6882
文摘
Cell-to-cell variability and functional heterogeneity are integral features of multicellular organisms. Chemical classification of cells into cell type is important for understanding cellular specialization as well as organismal function and organization. Assays to elucidate these chemical variations are best performed with single cell samples because tissue homogenates average the biochemical composition of many different cells and oftentimes include extracellular components. Several single cell microanalysis techniques have been developed but tend to be low throughput or require preselection of molecular probes that limit the information obtained. Mass spectrometry (MS) is an untargeted, multiplexed, and sensitive analytical method that is well-suited for studying chemically complex individual cells that have low analyte content. In this work, populations of cells from the rat pituitary, the rat pancreatic islets of Langerhans, and from the Aplysia californica nervous system, are classified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI) MS by their peptide content. Cells were dispersed onto a microscope slide to generate a sample where hundreds to thousands of cells were separately located. Optical imaging was used to determine the cell coordinates on the slide, and these locations were used to automate the MS measurements to targeted cells. Principal component analysis was used to classify cellular subpopulations. The method was modified to focus on the signals described by the lower principal components to explore rare cells having a unique peptide content. This approach efficiently uncovers and classifies cellular subtypes as well as discovers rare cells from large cellular populations.