[Applications of high performance liquid chromatography-mass spectrometry in proteomics].

Proteomics profiling plays an important role in biomedical studies. Proteomics studies are much more complicated than genome research, mainly because of the complexity and diversity of proteomic samples. High performance liquid chromatography-mass spectrometry (HPLC-MS) is a fundamental tool in proteomics research owing to its high speed, resolution, and sensitivity. Proteomics research targets from the peptides and individual proteins to larger protein complexes, the molecular weight of which gradually increases, leading to sustained increases in structural and compositional complexity and alterations in molecular properties. Therefore, the selection of various separation strategies and stationary-phase parameters is crucial when dealing with the different targets in proteomics research for in-depth proteomics analysis. This article provides an overview of commonly used chromatographic-separation strategies in the laboratory, including reversed-phase liquid chromatography (RPLC), hydrophilic interaction liquid chromatography (HILIC), hydrophobic interaction chromatography (HIC), ion-exchange chromatography (IEC), and size-exclusion chromatography (SEC), as well as their applications and selectivity in the context of various biomacromolecules. At present, no single chromatographic or electrophoretic technology features the peak capacity required to resolve such complex mixtures into individual components. Multidimensional liquid chromatography (MDLC), which combines different orthogonal separation modes with MS, plays an important role in proteomics research. In the MDLC strategy, IEC, together with RPLC, remains the most widely used separation mode in proteomics analysis; other chromatographic methods are also frequently used for peptide/protein fractionation. MDLC technologies and their applications in a variety of proteomics analyses have undergone great development. Two strategies in MDLC separation systems are mainly used in proteomics profiling: the "bottom-up" approach and the "top-down" approach. The "shotgun" method is a typical "bottom-up" strategy that is based on the RPLC or MDLC separation of whole-protein-sample digests coupled with MS; it is an excellent technique for identifying a large number of proteins. "Top-down" analysis is based on the separation of intact proteins and provides their detailed molecular information; thus, this technique may be advantageous for analyzing the post-translational modifications (PTMs) of proteins. In this paper, the "bottom-up" "top-down" and protein-protein interaction (PPI) analyses of proteome samples are briefly reviewed. The diverse combinations of different chromatographic modes used to set up MDLC systems are described, and compatibility issues between mobile phases and analytes, between mobile phases and MS, and between mobile phases in different separation modes in multidimensional chromatography are analyzed. Novel developments in MDLC techniques, such as high-abundance protein depletion and chromatography arrays, are further discussed. In this review, the solutions proposed by researchers when encountering compatibility issues are emphasized. Moreover, the applications of HPLC-MS combined with various sample pretreatment methods in the study of exosomal and single-cell proteomics are examined. During exosome isolation, the combined use of ultracentrifugation and SEC can yield exosomes of higher purity. The use of SEC with ultra-large-pore-size packing materials (200 nm) enables the isolation of exosomal subgroups, and proteomics studies have revealed significant differences in protein composition and function between these subgroups. In the field of single-cell proteomics, researchers have addressed challenges related to reducing sample processing volumes, preventing sample loss, and avoiding contamination during sample preparation. Innovative methods and improvements, such as the utilization of capillaries for sample processing and microchips as platforms to minimize the contact area of the droplets, have been proposed. The integration of these techniques with HPLC-MS shows some progress. In summary, this article focuses on the recent advances in HPLC-MS technology for proteomics analysis and provides a comprehensive reference for future research in the field of proteomics.

DEPDC1 as a metabolic target regulates glycolysis in renal cell carcinoma through AKT/mTOR/HIF1α pathway.

Renal cell carcinoma (RCC) is considered a "metabolic disease" characterized by elevated glycolysis in patients with advanced RCC. Tyrosine kinase inhibitor (TKI) therapy is currently an important treatment option for advanced RCC, but drug resistance may develop in some patients. Combining TKI with targeted metabolic therapy may provide a more effective approach for patients with advanced RCC. An analysis of 14 RCC patients (including three needle biopsy samples with TKI resistance) revealed by sing-cell RNA sequencing (scRNA-seq) that glycolysis played a crucial role in poor prognosis and drug resistance in RCC. TCGA-KIRC and glycolysis gene set analysis identified DEPDC1 as a target associated with malignant progression and drug resistance in KIRC. Subsequent experiments demonstrated that DEPDC1 promoted malignant progression and glycolysis of RCC, and knockdown DEPDC1 could reverse TKI resistance in RCC cell lines. Bulk RNA sequencing (RNA-seq) and non-targeted metabolomics sequencing suggested that DEPDC1 may regulate RCC glycolysis via AKT/mTOR/HIF1α pathway, a finding supported by protein-level analysis. Clinical tissue samples from 98 RCC patients demonstrated that DEPDC1 was associated with poor prognosis and predicted RCC metastasis. In conclusion, this multi-omics analysis suggests that DEPDC1 could serve as a novel target for TKI combined with targeted metabolic therapy in advanced RCC patients with TKI resistance.

Current progression in application of extracellular vesicles in central nervous system diseases.

Early diagnosis and pharmacological treatment of central nervous system (CNS) diseases has been a long-standing challenge for clinical research due to the presence of the blood-brain barrier. Specific proteins and RNAs in brain-derived extracellular vesicles (EVs) usually reflect the corresponding state of brain disease, and therefore, EVs can be used as diagnostic biomarkers for CNS diseases. In addition, EVs can be engineered and fused to target cells for delivery of cargo, demonstrating the great potential of EVs as a nanocarrier platform. We review the progress of EVs as markers and drug carriers in the diagnosis and treatment of neurological diseases. The main areas include visual imaging, biomarker diagnosis and drug loading therapy for different types of CNS diseases. It is hoped that increased knowledge of EVs will facilitate their clinical translation in CNS diseases.