Approaching cellular resolution and reliable identification in mass spectrometry imaging of tryptic peptides.

On-tissue digestion has become the preferred method to identify proteins in mass spectrometry (MS) imaging. In this study, we report advances in data acquisition and protein identification for MS imaging after on-tissue digestion. Tryptic peptides in a coronal mouse brain section were measured at 50 µm pixel size and revealed detailed histological structures, e.g., the ependyma (consisting of one to two cell layers), which was confirmed by H&E staining. This demonstrates that MS imaging of tryptic peptides at or close to cellular resolution is within reach. We also describe a detailed identification workflow which resulted in the identification of 99 proteins (with 435 corresponding peptides), based on comparison with LC-MS/MS data and in silico digest. These results were obtained with stringent parameters, including high mass accuracy in imaging mode (RSME < 3 ppm) and at least two unique peptides per protein showing consistent spatial distribution. We identified almost 50% of proteins with at least four corresponding peptides. As there is no agreed approach for identification of proteins after on-tissue digestion yet, we discuss our workflow in detail and make the corresponding mass spectral data available as "open data" via ProteomeXchange (identifier PXD003172). With this, we would like to contribute to a more effective discussion and the development of new approaches for tryptic peptide identification in MS imaging. From an experimental point of view, we demonstrate the improvement due to the combination of high spatial resolution and high mass resolution/mass accuracy on a measurement at 25 µm pixel size in mouse cerebellum tissue. A whole body section of a mouse pub imaged at 50 µm pixel size (40 GB, 230,000 spectra) demonstrates the stability of our protocol. For this data set, we developed a workflow that is based on conversion to the common data format imzML and sequential application of freely available software tools. In combination, the presented results for spatial resolution, protein identification, and data processing constitute significant improvements for the field of on-tissue digestion. Graphical abstract MS imaging of coronal mouse brain cerebellum with a pixel size of 25 µm: A Optical image, B myelin staining, C H&E staining, and D MS image overlay (RGB) of tryptic peptides m/z = 726.4045 ± 0.005, HGFLPR + H+ (red), m/z = 536.3173 ± 0.005, AKPAK + Na+ (green), and m/z = 994.5436 ± 0.005, WRQLIEK + Na+ (blue).

Strategy for marker-based differentiation of pro- and anti-inflammatory macrophages using matrix-assisted laser desorption/ionization mass spectrometry imaging.

Macrophages are large phagocytes playing a crucial role in the development and progression of atherosclerosis. The phenotypic polarization and activation of macrophages in atherosclerotic plaques depends on their complex micro-environment and at the same time has a major impact on the vulnerability or stability of advanced atherosclerotic lesions. Many in vitro and in vivo studies have been designed to define markers for macrophage subtypes to better understand the mechanism of plaque progression but they have rather added to the confusion. Nonetheless, some of the in vitro defined macrophage subtypes, like the pro-inflammatory M1 or the anti-inflammatory M2a/b/c macrophage, have been shown to be present in atherosclerotic plaques. Herein, we developed a comprehensive workflow to distinguish between human in vitro differentiated pro-inflammatory M1 and anti-inflammatory M2a and M2c macrophages. The cells were analyzed using qPCR and FACS analyses for defining suitable markers on the transcript (mRNA) and protein level as well as MALDI MSI for the assignment of metabolic markers, which can be used for the identification of the corresponding macrophage subtypes in atherosclerotic plaques. Data obtained using both qPCR and FACS analyses were in agreement with the literature. For the analysis of the macrophages with MALDI MSI, a comprehensive workflow was developed and the obtained data were subjected to different statistical analysis methods like principal component analysis (PCA) to define markers for each macrophage type. Our MALDI MSI results revealed that the method produces reliable and reproducible results but that the heterogeneity of the monocytes derived from different donors is too high to define universal markers on the metabolic level. Moreover, the results show that a sample set of three biological replicates is not sufficient to obtain representative data and therefore we recommend performing ring experiments in which the samples are measured by different laboratories.