Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States.
Department of Chemistry, Stanford University, Stanford, California 94305, United States.
Anal Chem. 2020 Oct 6;92(19):13281-13289. doi: 10.1021/acs.analchem.0c02519. Epub 2020 Sep 17.
Cell-type-specific metabolic profiling in tissue with heterogeneous composition has been of great interest across all mass spectrometry imaging (MSI) technologies. We report here a powerful new chemical imaging capability in desorption electrospray ionization (DESI) MSI, which enables cell-type-specific and metabolic profiling in complex tissue samples. We accomplish this by combining DESI-MSI with immunofluorescence staining using specific cell-type markers. We take advantage of the variable frequency of each distinct cell type in the lateral septal nucleus (LSN) region of mouse forebrain. This allows computational deconvolution of the cell-type-specific metabolic profile in neurons and astrocytes by convex optimization-a machine learning method. Based on our approach, we observed 107 metabolites that show different distributions and intensities between astrocytes and neurons. We subsequently identified 23 metabolites using high-resolution mass spectrometry (MS) and tandem MS, which include small metabolites such as adenosine and -acetylaspartate previously associated with astrocytes and neurons, respectively, as well as accumulation of several phospholipid species in neurons which have not been studied before. Overall, this method overcomes the relatively low spatial resolution of DESI-MSI and provides a new platform for metabolic investigation at the cell-type level in complex tissue samples with heterogeneous cell-type composition.
在所有质谱成像(MSI)技术中,对具有异质成分的组织中的细胞类型特异性代谢物分析一直具有极大的兴趣。我们在此报告一种新型的解吸电喷雾电离(DESI)MSI 化学成像能力,该能力可实现复杂组织样本中的细胞类型特异性和代谢物分析。我们通过将 DESI-MSI 与使用特定细胞类型标志物的免疫荧光染色相结合来实现这一目标。我们利用了在小鼠前脑侧隔核(LSN)区域中每种不同细胞类型的频率变化。这使得通过凸优化(一种机器学习方法)对神经元和星形胶质细胞中的细胞类型特异性代谢物谱进行计算反卷积成为可能。基于我们的方法,我们观察到 107 种代谢物在星形胶质细胞和神经元之间表现出不同的分布和强度。随后,我们使用高分辨率质谱(MS)和串联 MS 鉴定了 23 种代谢物,其中包括先前分别与星形胶质细胞和神经元相关的腺苷和乙酰天冬氨酸等小分子代谢物,以及神经元中几种以前未研究过的磷脂种类的积累。总的来说,该方法克服了 DESI-MSI 相对较低的空间分辨率,并为具有异质细胞类型组成的复杂组织样本中的细胞类型水平代谢研究提供了新的平台。