With the maturation of proteomics technologies in recent years, proteomics has made significant achievements in early detection of major diseases, disease classification, drug target discovery, and other fields. To explore the important role of proteomics, especially proteomics-based cutting-edge lifeomics technologies, in promoting the development of precision laboratory medicine and to discuss the opportunities and challenges faced during the clinical translation of innovative outcomes, the National Center for Protein Sciences-Beijing invited renowned experts and scholars in laboratory medicine, lifeomics, and precision medicine. The discussions revolved around the collaborative development of laboratory medicine and lifeomics, the future trends of new technologies in clinical laboratory testing, the innovation and development of lifeomics in laboratory medicine, the translational application of proteomics technologies in laboratory medicine, and the opportunities and challenges in the industrialization of proteomics achievements. All participants agreed that proteomics provides new directions and opportunities for precision diagnosis and treatment of diseases. However, close collaboration between academia, hospitals and industry is required. Additionally, challenges such as clinical applicability of equipment, standardization of detection methods and data, cost and quality control, talent cultivation, and the industrialization pathway need to be addressed.

OBJECTIVE To establish a proteomic pre-processing protocol for low input samples (1×104 cells) in order to map the proteomic aging atlas of individual mouse antral follicles and elucidate the aging patterns of mouse antral follicles.METHODS Using cell lines and primary mouse cells,the protocol was developed by optimizing lysis buffers,enzymatic digestion,and ultrasonication for 1 × 104 cells (about 1 μg protein).The sample pre-processing protocol established in this study was evaluated by comparing it with two commercial sample preparation kits:iST kit,EasyPept-Ex (Ex kit),in terms of protein identification overlap,overall grand average of hydropathy (GRAVY),theoretical isoelectric points,and protein molecular weight.The sample suitability of the current sample pretreatment process was assessed using primary spleen and liver cell samples of mice at three different scales:100,1000,and 10000.Using this sample pre-processing approach in conjunction with the highly sensitive timsTOF Pro 2 mass spectrometer,proteome maps of individual antral follicles from young-aged (2 months),middle-aged (12 months),and old-aged (22 months) mice were generated.Differential and time-series analyses identified age-related proteins,and elucidated the aging patterns of mouse antral follicles.RESULTS A proteomic pre-processing protocol for 1 × 104 cell samples was established,requiring only one single-step operation for lysis,reduction,and alkylation,with enzymatic digestion not necessitating ultrasonication.We found that for cell samples on the order of 1 × 104,increasing the concentration of sodium deoxycholate in the lysis buffer from 1％ to 10％ enhanced the number of identified proteins from 4089 to 4389,demonstrating improved lysis efficiency (P＜0.05).The addition of ammonium bicar-bonate buffer (50 mmol·L-1,90 μL) to the lysis solution significantly increased the number of identified proteins from 2579 to 4389 (P＜0.01).Both single trypsin and mixed enzyme (trypsin/Lys-C) treatments yielded similar proteolytic outcomes,identifying approximately 3950 proteins each.Reducing the diges-tion time from overnight to 0.5 hours increased the number of identified proteins from 4299 to 4632 (P＜0.05),thus saving time while achieving higher protein identification yields.Compared to commercial kits,approximately 92.3％of the proteins identified by our protocol could also be identified by the iST kit or Ex kit.Our protocol demonstrated no significant bias in terms of hydrophobicity,theoretical isoelectric points or molecular weight,indicating robust performance.As the number of cells in the sample increased,the variety of identified proteins increased significantly.For instance,in samples containing 100,1000,and 10000 cells,525,1650,and 3210 proteins were identified in primary mouse spleen cells,respectively,compared with 366,1160,and 3590 proteins in primary mouse liver cells (P＜0.05).Finally,using this protocol,proteomic profiling of individual antral follicles from young,middle-aged,and old mice (three follicles per age group) was performed,with each follicle identifying over 7500 proteins on average.Based on this data,principal component analysis was conducted in this study,revealing significant differences in protein expression profiles of antral follicles at different age stages,confirming that age made a big difference to the physiological state of follicles.Additionally,we observed a signifi-cant downregulation of chromosome separation-associated proteins in aging mouse follicles (P＜0.01),suggesting potential disruptions in chromosome segregation and meiotic dysregulation.Further analysis revealed 739 proteins significantly correlated with age,among which 378 exhibited positive correlations and 361 negative correlations.CONCLUSION This study provides a low-cost,easy-to-operate,high-throughput,and highly sensitive proteomic sample pre-processing protocol for low input samples.It has constructed dynamic proteome maps of individual antral follicles at different ages in mice,offering high-quality data resources for basic research on follicular aging and providing new insights for exploring potential therapeutic targets for ovarian aging and the development of drugs to enhance fertility.

OBJECTIVE To establish a proteomic pre-processing protocol for low input samples (1×104 cells) in order to map the proteomic aging atlas of individual mouse antral follicles and elucidate the aging patterns of mouse antral follicles.METHODS Using cell lines and primary mouse cells,the protocol was developed by optimizing lysis buffers,enzymatic digestion,and ultrasonication for 1 × 104 cells (about 1 μg protein).The sample pre-processing protocol established in this study was evaluated by comparing it with two commercial sample preparation kits:iST kit,EasyPept-Ex (Ex kit),in terms of protein identification overlap,overall grand average of hydropathy (GRAVY),theoretical isoelectric points,and protein molecular weight.The sample suitability of the current sample pretreatment process was assessed using primary spleen and liver cell samples of mice at three different scales:100,1000,and 10000.Using this sample pre-processing approach in conjunction with the highly sensitive timsTOF Pro 2 mass spectrometer,proteome maps of individual antral follicles from young-aged (2 months),middle-aged (12 months),and old-aged (22 months) mice were generated.Differential and time-series analyses identified age-related proteins,and elucidated the aging patterns of mouse antral follicles.RESULTS A proteomic pre-processing protocol for 1 × 104 cell samples was established,requiring only one single-step operation for lysis,reduction,and alkylation,with enzymatic digestion not necessitating ultrasonication.We found that for cell samples on the order of 1 × 104,increasing the concentration of sodium deoxycholate in the lysis buffer from 1％ to 10％ enhanced the number of identified proteins from 4089 to 4389,demonstrating improved lysis efficiency (P＜0.05).The addition of ammonium bicar-bonate buffer (50 mmol·L-1,90 μL) to the lysis solution significantly increased the number of identified proteins from 2579 to 4389 (P＜0.01).Both single trypsin and mixed enzyme (trypsin/Lys-C) treatments yielded similar proteolytic outcomes,identifying approximately 3950 proteins each.Reducing the diges-tion time from overnight to 0.5 hours increased the number of identified proteins from 4299 to 4632 (P＜0.05),thus saving time while achieving higher protein identification yields.Compared to commercial kits,approximately 92.3％of the proteins identified by our protocol could also be identified by the iST kit or Ex kit.Our protocol demonstrated no significant bias in terms of hydrophobicity,theoretical isoelectric points or molecular weight,indicating robust performance.As the number of cells in the sample increased,the variety of identified proteins increased significantly.For instance,in samples containing 100,1000,and 10000 cells,525,1650,and 3210 proteins were identified in primary mouse spleen cells,respectively,compared with 366,1160,and 3590 proteins in primary mouse liver cells (P＜0.05).Finally,using this protocol,proteomic profiling of individual antral follicles from young,middle-aged,and old mice (three follicles per age group) was performed,with each follicle identifying over 7500 proteins on average.Based on this data,principal component analysis was conducted in this study,revealing significant differences in protein expression profiles of antral follicles at different age stages,confirming that age made a big difference to the physiological state of follicles.Additionally,we observed a signifi-cant downregulation of chromosome separation-associated proteins in aging mouse follicles (P＜0.01),suggesting potential disruptions in chromosome segregation and meiotic dysregulation.Further analysis revealed 739 proteins significantly correlated with age,among which 378 exhibited positive correlations and 361 negative correlations.CONCLUSION This study provides a low-cost,easy-to-operate,high-throughput,and highly sensitive proteomic sample pre-processing protocol for low input samples.It has constructed dynamic proteome maps of individual antral follicles at different ages in mice,offering high-quality data resources for basic research on follicular aging and providing new insights for exploring potential therapeutic targets for ovarian aging and the development of drugs to enhance fertility.

After decades of development,protein and peptide drugs have now grown into a major drug class in the marketplace.Target identification and validation are crucial for the discovery of protein and peptide drugs,and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection.However,owing to the developmental history in the pharmaceutical industry,previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs,while studies related to protein and peptide drugs are lacking.Here,we systematically explore the target spaces in the human genome specifically for protein and peptide drugs.Compared with other proteins,both suc-cessful protein and peptide drug targets have many special characteristics,and are also significantly different from those of small-molecule drugs in many aspects.Based on these features,we develop separate effective genome-wide target prediction models for protein and peptide drugs.Finally,a user-friendly web server,Predictor Of Protein and Peptide drugs'therapeutic Targets(POPPIT)(http://poppit.ncpsb.org.cn/),is established,which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.

Genome-wide physical protein-protein interaction(PPI)mapping remains a major chal-lenge for current technologies.Here,we reported a high-efficiency BiFC-seq method,yeast-enhanced green fluorescent protein-based bimolecular fluorescence complementation(yEGFP-BiFC)coupled with next-generation DNA sequencing,for interactome mapping.We first applied yEGFP-BiFC method to systematically investigate an intraviral network of the Ebola virus.Two-thirds(9/14)of known interactions of EBOV were recaptured,and five novel interactions were discovered.Next,we used the BiFC-seq method to map the interactome of the tumor protein p53.We identified 97 interactors of p53,more than three-quarters of which were novel.Furthermore,in a more complex background,we screened potential interactors by pooling two BiFC libraries together and revealed a network of 229 interactions among 205 proteins.These results show that BiFC-seq is a highly sensitive,rapid,and economical method for genome-wide interactome map-ping.

Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand challenge for bioinformatics research. As a typical neural network model, the autoencoder is able to efficiently learn the features of input data by unsupervised training method and further help integrate and mine the biological data. In this article, the primary structure and workflow of the autoencoder model are introduced, followed by summarizing the advances of the autoencoder model in cancer informatics using various types of biomedical data. Finally, the challenges and perspectives of the autoencoder model are discussed.
Algorithms ; Humans ; Informatics ; Neoplasms/diagnosis* ; Neural Networks, Computer

The liver is the metabolic center of mammalian body. Systematic study on liver's proteome expression under different physiological and pathological conditions helps us understand the functional mechanisms of the liver. With the rapid development of liquid chromatography tandem mass spectrometry technique, numerous studies on liver physiology and pathology features produced a large number of proteomics data. In this paper, 834 proteomics experiments of mouse liver were systematically collected and the mouse liver proteome database (Mouse Liver Portal, http://mouseliver.com) was established. The Mouse Liver Portal contains the liver's proteomics data under different physiology and pathology conditions, such as different gender, age, circadian rhythm, cell type and different phase of partial hepatectomy, non-alcoholic fatty liver. This portal provides the changes in proteins' expression in different conditions of the liver, differently expressed proteins and the biological processes which they are involved in, potential signal transduction and regulatory networks. As the most comprehensive mouse liver proteome database, it can provide important resources and clues for liver biology research.
Animals ; Chromatography, Liquid ; Databases, Factual ; Liver ; Mice ; Proteome ; Proteomics

Objective To establish neoplasm transplantation models of breast cancer cells in BALB /c nude mice and to isolate tumor infiltrating myeloid cells.Methods pHAGE-EF-ZsG-DEST plasmid,pMD2.G plasmid and psPAX2 were transfected into BT474 using the method of calcium phosphate transfection .The positive cells were selected by flow cytometry and implanted in the fat pad of nude mice .A tumor model of breast cancer cells implanted in nude mice was constructed, and the tumor infiltrating myeloid cells were isolated .Conclusion Tumor infiltrating myeloid cells are successfully isolated, which will contribute to the study of the functions of tumor infiltrating myeloid cells .

Hepatic Stellate Cells ; Humans ; Liver Cirrhosis

Objective To analyze the construction of mouse liver phosphoproteome and phosphorylated kinases to provide useful information for integrating mouse kinase phosphorylation regulatory networks.Methods A new method was established to identify phosphoproteome from the mouse liver.First of all, liver protein was digested with trypsin before the resulting peptides were subjected to a two-step phosphopeptide enrichment and separation procedure consisting of TiO2 chro-maphy enrichment combined with high pHHPLC separation.Samples were injected onto aNanolC-Ultra-2Dplus system cou-pled to an AB-Sciex 5600 Triple TOF mass spectrometer instrument.Then data analysis was performed to provide information of new identified phosphorylation sites of kinase.Results and Conclusion Using our efficient and high-throughput platform, we reported the identification of 5386 phosophorylation sites and 4553 phosphopeptides from 1533 proteins of the mouse liver.126 new phosphorylation sites were identified from 116 kinases, which provides valuable infor-mation for phosphorylation networks in the mouse liver.