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By providing a comprehensive view of protein dynamics, quantitative proteomics has emerged as a powerful tool for a better understanding of disease mechanisms. Here, we present a general workflow for identifying and comparing molecular subtypes of disease using proteomics data using R software. We describe steps for data preprocessing, feature selection, determination of subtypes, and functional interpretation of subtypes. These analyses can help us understand the nature of heterogeneous diseases, which is crucial for accurate diagnosis and personalized treatment. For complete details on the use and execution of this protocol, please refer to Chen et al. 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as "the great imitator," NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Precise spatiotemporal regulation of protein complex assembly is essential for cells to achieve a meaningful rely of information flow via intracellular signaling networks in response to extracellular cues, whose disruption would lead to disease. Although various attempts have been made for spatial and/or temporal analysis of protein complexes, it is still a challenge to track cell-wide dynamics of a particular protein complex under physiological conditions. Here we describe a workflow that combines endogenous expression of tagged proteins, organelle marker distribution-directed subcellular fractionation, scaffold protein-mediated receptor complex purification, and targeted proteomics for spatiotemporal quantification of protein complexes in whole cell scale. We applied our method to investigate the assembly kinetics of EGF-dependent ErbB receptor complexes. After fractionation using the density gradient centrifugation and organelle assignment based on organelle markers, endogenous ErbB complex in different subcellular fractionation was efficiently enriched. By using targeted mass spectrometry, ErbB complex components that expressed medium to low level was precisely quantified with in-depth coverage, simultaneously in time and subcellular spaces. Our results revealed a sophisticated scheme of complex behaviors characterized by multiple subcomplexes with distinct molecular composition formed across subcellular fractions enriched with cytosol, plasma membrane, endosome, or mitochondria, implying organelle-specific ErbB functions. Remarkably, our results demonstrated for the first time that activated ErbB receptors might increase their signaling range through promoting a cytosolic, receptor-free subcomplex, consisting of Shc1, Grb2, Arhgef5, Garem1, and Lrrk1. These findings emphasize the potential of our strategy as a powerful tool to study spatiotemporal dynamics of protein complexes.

Summary: As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces., Availability and Implementation: PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press.)

The ubiquitin-like protein FAT10 (HLA-F adjacent transcript 10) is uniquely expressed in mammals. The fat10 gene is encoded in the MHC class I locus in the human genome and is related to some specific processes, such as apoptosis, immune response, and cancer. However, biological knowledge of FAT10 is limited, owing to the lack of identification of its conjugates. FAT10 covalently modifies proteins in eukaryotes, but only a few substrates of FAT10 have been reported until now, and no FATylated sites have been identified. Here, we report the proteome-scale identification of FATylated proteins by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We identified 175 proteins with high confidence as FATylated candidates. A total of 13 modified sites were identified for the first time by a modified search of the raw MS data. The modified sites were highly enriched with hydrophilic amino acids. Furthermore, the FATylation processes of hnRNP C2, PCNA, and PDIA3 were verified by a coimmunoprecipitation assay. We confirmed that most of the substrates were covalently attached to a FAT10 monomer. The functional distribution of the FAT10 targets suggests that FAT10 participates in various biological processes, such as translation, protein folding, RNA processing, and macromolecular complex assembly. These results should be very useful for investigating the biological functions of FAT10.

Localized scleroderma (LoS) is a rare chronic disease with extensive tissue fibrosis, inflammatory infiltration, microvascular alterations, and epidermal appendage lesions. However, a deeper understanding of the pathogenesis and treatment strategies of LoS is currently limited. In the present work, a proteome map of LoS skin is established, and the pathological features of LoS skin are characterized. Most importantly, a human‐induced pluripotent stem cell‐derived epithelial and mesenchymal (EM) organoids model in a 3D culture system for LoS therapy is established. According to the findings, the application of EM organoids on scleroderma skin can significantly reduce the degree of skin fibrosis. In particular, EM organoids enhance the activity of epidermal stem cells in the LoS skin and promotes the regeneration of sweat glands and blood vessels. These results highlight the potential application of organoids for promoting the recovery of scleroderma associated phenotypes and skin‐associated functions. Furthermore, it can provide a new therapeutic alternative for patients suffering from disfigurement and skin function defects caused by LoS. [ABSTRACT FROM AUTHOR]