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发表文章

1. “Development of A Combined Strategy forAccurate Lipid Structural Identification and Quantification in Ion-MobilityMass Spectrometry based Untargeted Lipidomics” .X. Chen†, Y. Yin†, Z. Zhou, T.Li, and Z.-J. Zhu* (correspondingauthor)  Analytica Chimica Acta, 2020,1136, 115-124.

Invited contribution for the Special Issue"Analytical Lipidomics"


2. “Ion Mobility Collision Cross-SectionAtlas for Known and Unknown Metabolite Annotation in Untargeted Metabolomics” .Z.Zhou, M. Luo, X. Chen, Y. Yin, X. Xiong, R. Wang, and Z.-J. Zhu* (corresponding author) .Nature Communications, 2020, 11:4334.


3. “ A lipidome atlas in MS-DIAL 4” . H.Tsugawa , K. Ikeda, M. Takahashi , A. Satoh, Y. Mori, H. Uchino, N. Okahashi,Y. Yamada, I. Tada, P. Bonini, Y. Higashi, Y. Okazaki, Z. Zhou, Z.-J. Zhu, J. Koelmel, T. Cajka , O.Fiehn, K. Saito, M. Arita, and M. Arita* .Nature Biotechnology, 2020, 38,1159-1163.


4. “The Application of Ion Mobility-MassSpectrometry in Untargeted Metabolomics: from Separation to Identification” .M.Luo, Z. Zhou, and Z.-J. Zhu*(corresponding author) .Journal of Analysis and Testing, 2020, 4, 163-174.Invited review for the Special Issue "Metabolomics: state of art in methoddevelopment and applications"


5. “NormAE: Deep Adversarial Learning Modelto Remove Batch Effects in Liquid Chromatography Mass Spectrometry-BasedMetabolomics Data” .Z. Rong, Q. Tan, L. Cao, L. Zhang, K. Deng, Y. Huang, Z.-J. Zhu, Z. Li, and K. Li* .AnalyticalChemistry, 2020, 92, 5082-5090.


6. “Different Regions of Synaptic VesicleMembrane Regulate VAMP2 Conformation for the SNARE Assembly” .C. Wang, J. Tu,S. Zhang, B. Cai, Z. Liu, S. Hou, Q. Zhong, X. Hu, W. Liu, G. Li, Z. Liu, L.He, J. Diao, Z.-J. Zhu, Dan Li *,and C. Liu* .Nature Communications, 2020, 11: 1531.


7. “Overview of Tandem Mass Spectral andMetabolite Databases for Metabolite Identification in Metabolomics” .Z. Yi, and Z.-J. Zhu* (corresponding author)  .Methods in Molecular Biology, 2020, 2124,139-148. An invited book chapter for Computational Methods and Data Analysisfor Metabolomics


8. “Daily Oscillation of theExcitation-Inhibition Balance in Visual Cortical Circuits” .M.C.D. Bridi, F. J.Zong, X. Min, N. Luo, T. Tran, J. Qiu, D. Severin, X.T. Zhang, G. Wang, Z.-J. Zhu, K.W. He,* and A. Kirkwood* .Neuron,2020, 105, 621-629


9. “The Use of LipidIMMS Analyzer for LipidIdentification in Ion Mobility-Mass Spectrometry-Based Untargeted Lipidomics” .X.Chen, Z. Zhou, and Z.-J. Zhu*(Corresponding author) .Methods in Molecular Biology, 2020, 2084, 269-282. Aninvited book chapter for Ion Mobility-Mass Spectrometry: Methods and Protocols


10. “DecoMetDIA: Deconvolution ofMultiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS basedUntargeted Metabolomics” .Y. Yin†, R. Wang †, Y. Cai, Z. Wang, and Z.-J. Zhu* (Corresponding Author).AnalyticalChemistry, 2019, 91, 11897-11904.


11. “A Vitamin-C-derived DNA ModificationCatalysed by An Algal TET Homologue” .J. Xue†, G. Chen†, F. Hao†, H. Chen†, Z.Fang, F.-F. Chen, B. Pang, Q. Yang, X. Wei, Q. Fan, C. Xin, J. Zhao, X. Deng,B. Wang, X. Zhang, Y. Chu, H. Tang, H. Yin, W. Ma, L. Chen, J. Ding, E.Weinhold, R. M. Kohli, W. Liu, Z.-J.Zhu, K. Huang*, H. Tang* , and G.-L. Xu*.Nature, 2019, 569, 581–585.


12. “Metabolic Reaction Network-basedRecursive Metabolite Annotation for Untargeted Metabolomics” .X. Shen, R. Wang,X. Xiong, Y. Yin, Y. Cai, Z. Ma, N. Liu, and Z.-J. Zhu* (Corresponding Author) .Nature Communications, 2019,10: 1516.


13. “The Emerging Role of Ion Mobility-MassSpectrometry in Lipidomics to Facilitate Lipid Separation and Identification” .J. Tu†, Z. Zhou†,T. Li†, and Z.-J. Zhu*(Corresponding Author).Trends in Analytical Chemistry, 2019, 116, 332-339. Aninvited contribution to the special issue of “Ion Mobility Spectrometry: FromFundamentals to Applications”.


14. “MetFlow: An Interactive and IntegratedWorkflow for Metabolomics Data Cleaning and Differential Metabolite Discovery” .X.Shen, and Z.-J. Zhu*(Correspondingauthor) .Bioinformatics, 2019, 35, 2870-2872.

 

15. “Advancing Untargeted MetabolomicsUsing Data Independent Acquisition Mass Spectrometry Technology” .R. Wang†, Y.Yin†, and Z.-J. Zhu* (Correspondingauthor) ,  Analytical and BioanalyticalChemistry, 2019, 411, 4235-4250. An invited contribution to the special issueof “Young Investigators in Analytical and Bioanalytical Science”.

 

16. “WaveICA: A novel algorithm to removebatch effects for large-scale untargeted metabolomics data based on waveletanalysis” .K. Deng, F. Zhang, Q. Tan, Y. Huang, W. Song, Z. Rong, Z.-J. Zhu,  K. Li*, and Z. Li*, Analytica Chimica Acta,2019, 1061, 60-69.

 

17. “Development of A Correlative Strategyto Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in PlasmaUsing Untargeted Metabolomics” . Z. Wang†, B. Cui†, F. Zhang, Y. Yang, X. Shen,Z. Li, W. Zhao, Y. Zhang, K. Deng, Z. Rong, K. Yang, X. Yu, K. Li*, P. Han*,and Z.-J. Zhu* .AnalyticalChemistry, 2019, 91, 2401-2408.

 

18. “LipidIMMS Analyzer: IntegratingMulti-dimensional Information to Support Lipid Identification in IonMobility–Mass Spectrometry based Lipidomics” . Z. Zhou, X. Shen, X. Chen, J.Tu, X. Xiong, and Z.-J. Zhu*(Corresponding Author) .Bioinformatics, 2019, 35,698-700.

 

19. “A High-Throughput Targeted MetabolomicsWorkflow for the Detection of 200 Polar Metabolites in Central CarbonMetabolism” .Y. Cai, and Z.-J. Zhu*(Corresponding author)  .Methods inMolecular Biology, 2019, 1859,263-274.

 

20. “Metabolomics Approach for PredictingResponse to Neoadjuvant Chemotherapy for Colorectal Cancer”  .K. Yang, F. Zhang, P. Han, Z. Z. Wang, K.Deng, Y. Y. Zhang, W. W. Zhao, W. Song, Y. Q. Cai, K. Li*, B. B. Cui, * and Z.J. Zhu*(Co-Corresponding author).Metabolomics,2018,14: 110.

 

21.  “Stable-isotopeLabeled Metabolic Analysis in Drosophila melanogaster: From Experimental Setupto Data Analysis.” Y. Cai, N. Liu*, and Z. J. Zhu* (Co-Corresponding Author) .Bio-protocol, 2018, 8(18): e3015.

 

22. “Predicting the Pathological Responseto Neoadjuvant Chemoradiation Using Untargeted Metabolomics in Locally AdvancedRectal Cancer” .H. Jia†, X. Shen†, Y. Guan, M. Xu, J. Tu, M. Mo, L. Xie, J.Yuan, J. Zhu*, and Z.J. Zhu*(†,Co-first authors; *, Co-Corresponding authors) .Radiotherapy and Oncology,2018, 128, 548-556.

 

23. “ Epigenetic Drift of H3K27me3 in AgingLinks Glycolysis to Healthy Longevity in Drosophila”.

Z. Ma†, H. Wang†, Y. Cai†, H. Wang†, K.Niu, X. Wu, H. Ma, Y. Yang, W. Tong, F. Liu, Z. Liu, Y. Zhang, R. Liu, Z.-J. Zhu*, and N. Liu* .eLife, 2018,7: e35368

 

24. “SWATHtoMRM: Development ofHigh-Coverage Targeted Metabolomics Method Using SWATH Technology for BiomarkerDiscovery”.H. Zha†, Y. Cai†, Y. Yin†, Z. Wang, K. Li, and Z.-J. Zhu* (†, Co-first authors; *, Corresponding author) .AnalyticalChemistry, 2018, 90, 4062-4070.

 

25. “Absolute Quantitative LipidomicsReveals Lipidome-Wide Alterations in Aging Brain” .J. Tu, Y. Yin, M. Xu, R.Wang, and Z.-J. Zhu*(Correspondingauthor) .Metabolomics, 2018,14: 5.

 

26. “Advancing the Large-Scale CCS Databasefor Metabolomics and Lipidomics at the Machine-Learning Era” .Z. Zhou†, J. Tu†,and Z.-J. Zhu* (†, Co-first authors;*,Corresponding author) .Current Opinion in Chemical Biology, 2018, 42, 34-41.

 

27. “CLOCK Acetylates ASS1 to DriveCircadian Rhythm of Ureagenesis”. R. Lin*, Y. Mo, H. H. Zha, Z. Qu, P. Xie, Z.-J. Zhu, Y. Xu, Y. Xiong*, K.-L.Guan*.MolecularCell, 2017, 68, 198–209.

 

28. “Proteome-Wide Analysis ofN-Glycosylation Stoichiometry Using SWATH Technology”.X.Yang, Z. Wang, L. Guo, Z.-J. Zhu, and Y. Zhang*,.Technology,Journal of Proteome Research, 2017, 16, 3830–3840.

 

29. “LipidCCS: Prediction of CollisionCross-Section Values for Lipids with High Precision to Support IonMobility-Mass Spectrometry based Lipidomics”.Z. Zhou, J. Tu, X. Xiong, X. Shen,and Z.-J. Zhu*(Corresponding author).AnalyticalChemistry, 2017, 89, 9559–9566.

 

30. “MetCCS Predictor: A Web Server forPredicting Collision Cross-Section Values of Metabolites in Metabolomics”.Z.Zhou, X. Xiong, and Z.-J. Zhu*(Corresponding author).Bioinformatics, 2017, 33, 2235-2237.

 

31. “Comprehensive Metabolomics IdentifiedLipid Peroxidation as A Prominent Feature in Human Plasma of Patients withCoronary Heart Diseases”.J. Lu, B. Chen, T. T. Chen, S. Guo, X. Xue, Q. Chen,M. Zhao, L. Xia, Z. J. Zhu, L.Zheng*,  H. Yin*.RedoxyBiology,2017,12,899-907.

 

32. “Discovery of novel1,2,3,4-tetrahydrobenzo[4, 5]thieno[2, 3-c]pyridine derivatives as potent andselective CYP17 inhibitors”. M. Wang, Y. Fang, S. Gu, F. F. Chen, Z. J. Zhu, X. Sun*, J. Zhu*.European Journal of MedicinalChemistry, 2017, 132, 157-172.

 

33. “Large-Scale Prediction of CollisionCross-Section Values for Metabolites in Ion Mobility – Mass Spectrometry”.Z.Zhou, X. Shen, J. Tu, and Z.-J. Zhu*(Corresponding author). Analytical Chemistry, 2016, 88, 11084-11091.

 

34. “MetDIA: Targeted Metabolite Extractionof Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition”.H. Li,Y. Cai, Y. Guo, F. Chen, and Z.-J. Zhu*(Correspondingauthor).Analytical Chemistry, 2016, 88, 8757-8764.

 

35. “Serum Metabolomics for Early Diagnosisof Esophageal Squamous Cell Carcinoma by UHPLC-QTOF/MS”.J. Wang*,†, T. Zhang†,X. Shen†, J. Liu, D. Zhao, Y. Sun, L. Wang, Y. Liu, X. Gong, Y. Liu, Z.-J. Zhu* (Co-corresponding author).Metabolomics,2016, 12: 116

 

36. “ Normalization and Integration ofLarge-Scale Metabolomics Data Using Support Vector Regression”.X. Shen, X.Gong, Y. Cai, Y. Guo, J. Tu, H. Li, T.Zhang, J. Wang, F. Xue, and Z.-J. Zhu*.Metabolomics, 2016, 12: 89

 

37. “ An Integrated Targeted MetabolomicPlatform for High-Throughput Metabolite Profiling and Automated Data Processing”.Y.Cai, K. Weng, Y. Guo, J. Peng, and Z.-J.Zhu* (Corresponding author).Metabolomics, 2015,11,1575-1586.

 

38. “Degradation of HK2 byChaperone-Mediated Autophagy Promotes Metabolic Catastrophe and Cell Death”. H.-G. Xia, A. Najafov,. J.Geng,. L. Galan-Acosta, X. Han,. Y. Guo, B. Shan, Y.Zhang, E. Norberg, T. Zhang, L.Pan, J.Liu, J. L. Coloff, D. Ofengeim, H. Zhu,K. Wu, Y. Cai, J. R. Yates, Z.- J. Zhu,J. Yuan, and H. Vakifahmetoglu-Norberg.Journal of Cell Biology, 2015,210,705-716.

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