Imaging Mass Spectrometry of Biological Tissues

质谱学是从样品中识别和定量化学分子的一种强大的分析工具。它通过分离和测量这些化学物质的分子质量来运作。为了对生物组织进行基质辅助激光解吸/电离成像质谱学(MALDI IMS)，使用激光束将化学物质的采样聚焦到组织切片的非常小的区域(&lt；0.1 mm)。以逐点(或逐像素)的方式记录组织上化学物质的丰度和质量。然后将数据集显示为热图，以揭示任何检测到的分子的二维分布。当与战略性设计的样本处理相结合时，研究人员能够将分子分布的变化与生物功能联系起来。理想情况下，统计软件应该能够以无针对性和不偏不倚的方式识别变化。在实践中，化学噪声的存在可能会掩盖分析物信号中的微小变化，使其无法被统计工具检测到。这对低分子量(MW)化合物的IMS尤其有问题，因为促进分析物激光电离所需的化学基质可能会在低分子量区域产生大量的背景噪声。这个区域由许多重要的代谢物和信号分子组成，如生物胺和神经递质。因此，需要进一步的研究来改进IMS对这些小分析物的非靶向分析。

Mass spectrometry is a powerful analytical tool for the identification and quantification of chemical molecules from a sample. It operates by separating and measuring the molecular masses of these chemicals. To perform matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of biological tissues, the sampling of chemicals is focused to a very small area of a tissue section (< 0.1 mm) using a laser beam. In a spot-by-spot (or pixel-by-pixel) fashion, the abundances and masses of the chemicals on the tissue are recorded. The data set is then displayed as heat maps to reveal the 2-dimensional distribution of any detected molecules. When combined with strategically designed sample treatment, researchers are able to correlate the changes in molecular distribution to biological functions. Ideally, statistical software should be able to identify changes in a non-targeted and unbiased manner. In practice, the presence of chemical noise, signals that do not directly originate from the analytes, can drown out small changes in analyte signals, making them undetectable by statistical tools. This is particularly problematic for the IMS of low molecular weight (MW) compounds, because the chemical matrix required to promote the laser ionization of analytes can generate a substantial level of background noise in the low-MW region. This region consists of many important metabolites and signaling molecules, such as biogenic amines and neurotransmitters. Hence, further research is needed to improve IMS for the non-targeted profiling of these small analytes.