Context:α-Klotho has emerged as a powerful regulator of the aging process. To date, the expression profile of α-Klotho in human tissues is unknown, and its existence in some human tissue types is subject to much controversy.Objective:This is the first study to characterize systemwide tissue expression of transmembrane α-Klotho in humans. We have employed next-generation targeted proteomic analysis using parallel reaction monitoring in parallel with conventional antibody-based methods to determine the expression and spatial distribution of human α-Klotho expression in health.Results:The distribution of α-Klotho in human tissues from various organ systems, including arterial, epithelial, endocrine, reproductive, and neuronal tissues, was first identified by immunohistochemistry. Kidney tissues showed strong α-Klotho expression, whereas liver did not reveal a detectable signal. These results were next confirmed by Western blotting of both whole tissues and primary cells. To validate our antibody-based results, α-Klotho-expressing tissues were subjected to parallel reaction monitoring mass spectrometry (data deposited at ProteomeXchange, PXD002775) identifying peptides specific for the full-length, transmembrane α-Klotho isoform.Conclusions:The data presented confirm α-Klotho expression in the kidney tubule and in the artery and provide evidence of α-Klotho expression across organ systems and cell types that has not previously been described in humans.

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Protein tyrosine phosphatases (PTPs) constitute a large enzyme family with important biological functions. Inhibition of PTP activity through reversible oxidation of the active-site cysteine residue is emerging as a general, yet poorly characterized, regulatory mechanism. In this study, we describe a generic antibody-based method for detection of oxidation-inactivated PTPs. Previous observations of oxidation of receptor-like PTP (RPTP) ␣ after treatment of cells with H2O2 were confirmed. Platelet-derived growth factor (PDGF)-induced oxidation of endogenous SHP-2, sensitive to treatment with the phosphatidylinositol 3-kinase inhibitor LY294002, was demonstrated. Furthermore, oxidation of RPTP␣ was shown after UV-irradiation. Interestingly, the catalytically inactive second PTP domain of RPTP␣ demonstrated higher susceptibility to oxidation. The experiments thus demonstrate previously unrecognized intrinsic differences between PTP domains to susceptibility to oxidation and suggest mechanisms for regulation of RPTPs with tandem PTP domains. The antibody strategy for detection of reversible oxidation is likely to facilitate further studies on regulation of PTPs and might be applicable to analysis of redox regulation of other enzyme families with active-site cysteine residues.

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis. Molecular &