Stable isotope labeling methods hold great potential for many applications in the life and biomedical sciences. It was 20 years ago when John Yates’ laboratory at The Scripps Research Institute developed ‘Stable Isotope Labeling of Mammals’ (SILAM)1. The method was initially designed and primarily applied for relative protein quantification by mass spectrometry using 15N-labeled rodent specimens as a reference.
Subsequently the SILAM method was extended to other applications including the analysis of protein turnover, the identification of long-lived proteins, tissue imaging by ‘Nanoscale Secondary Ion Mass Spectrometry’ (NanoSIMS) and metabolomics analyses2-17.
In the webinar Dr Christoph Turck presents examples for the many uses of SILAM biospecimens that were generated by labeling rodents with Silantes diets.
Watch the Webinar from June 18th, 2024

Speaker: Dr. Christoph W. Turck
Director, Proteomics Platform
Kunming Institute of Zoology, Chinese Academy of Sciences
More information about Dr. Turck
Research Interests
Research in my laboratory continues to center on the identification of biosignatures for psychiatric and neurogenerative disorders using -omics technologies to delineate affected molecular pathways. Our goal is to complement imprecise clinical parameters with molecular biosignatures to improve patient diagnosis, stratification and treatment. Towards this objective, we collaborate with behavioral scientists as well as clinical scientists at the Kunming Institute of Zoology and elsewhere.
Equally important, and near and dear to my heart, is the development and implementation of new technologies and biomarker sources to study molecular mechanisms involved in brain physiology and disease. In this regard, a recent focus has been on exosomes from peripheral body specimens. There are several advantages when it comes to using exosomes for diagnostic applications. They can be isolated non-invasively, their membranous structure stabilizes interior contents, and they represent a proxy of the cells they are derived from, which is particularly relevant for brain disorder biomarker identification efforts. Learn more.
Curriculum Vitae Prof. Dr. Christoph Turck
Education
- 1983 Ph.D. (Dr.rer.nat.) Peptide Chemistry, University of Aachen, Germany
- 1981 Dipl. Chem., Macromolecular Chemistry, University of Aachen, Germany
- 1978 IAESTE Industrial Traineeship, AB Volvo, R&D, Gothenburg, Sweden
- 1975 IAESTE Industrial Traineeship, Hellenic Aspropyrgos Refinery, Quality Control. Athens, Greece
Professional experience
Kunming Institute of Zoology, Chinese Academy of Sciences
- 2023-Present Professor, Chinese Academy of Sciences
- 2023-Present Director, Proteomics Platform, Kunming Institute of Zoology, Chinese Academy of Sciences
Max Planck Institute of Psychiatry, Munich, Germany
- 2002-2022 Head, Proteomics and Biomarkers
- 2003-Present Professor, Department of Biochemistry, Ludwig Maximilians University Munich
- 2004-2023 Faculty, International Max Planck Research School for Molecular Life Sciences
- 2009-Present Faculty, Graduate Program of Systemic Neurosciences, University of Munich
- 2015-2022 Faculty, International Max Planck Research School for Translational Psychiatry
- 2019-Present Adjunct Faculty, CAS-MPG Partner Institute of Computational Biology, Shanghai
Learn more about Dr. Turck’s CV and publications.
References:
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1. Wu CC, MacCoss MJ, Howell KE, Matthews DE, Yates III JR. (2004) Metabolic Labeling of Mammalian Organisms with Stable Isotopes for Quantitative Proteomic Analysis. Anal Chem 76:4951-4959
2. Liu X, Novak B, Namendorf C, Steigenberger B, Zhang Y, Turck CW. (2024) Long-lived proteins and DNA as candidate predictive biomarkers for tissue associated diseases. iScience 27:109642.
3. Turck CW, Webhofer C, Reckow S, Moy J, Wang M, Guillermier C, Poczatek JC, Filiou MD. (2022) Antidepressant treatment effects on hippocampal protein turnover: Molecular and spatial insights from mass spectrometry. Proteomics e2100244.
4. Bonnin EA, Fornasiero EF, Lange F, Turck CW, Rizzoli SO. (2021) NanoSIMS observations of mouse retinal cells reveal strict metabolic controls on nitrogen turnover. BMC Mol Cell Biol 22:5.
5. Ko HG, Park DI, Lee JH, Turck CW, Kaang BK. (2020) Proteomic analysis of synaptic protein turnover in the anterior cingulate cortex after nerve injury. Mol Brain 13:19.
6. Dethloff F, Bueschl C, Heumann H, Schuhmacher R, Turck CW. (2018) Partially 13C-labeled mouse tissue as reference for LC-MS based untargeted metabolomics. Anal Biochem 556:63-69.
7. Ko HG, Choi JH, Park DI, Kang SJ, Lim CS, Sim SE, Shim J, Kim JI, Kim S, Choi TH, Ye S, Lee J, Park P, Kim S, Do J, Park J, Islam MA, Kim HJ, Turck CW, Collingridge GL, Zhuo M, Kaang BK. (2018) Rapid turnover of cortical NCAM1 regulates synaptic reorganization after peripheral nerve injury. Cell Rep 22:748-759.
8. Turck CW, Webhofer C, Nussbaumer M, Teplytska L, Chen A, Maccarrone G, Filiou MD. (2016) Stable isotope metabolic labeling suggests differential turnover of the DPYSL protein family. Proteomics Clin Appl 10:1269-1272.
9. Filiou MD, Moy J, Wang M, Guillermier C, Poczatek JC, Turck C, Lechene C. (2014) Effect of an anti-depressant on mouse hippocampus protein turnover using MIMS. Surf Interface Anal 46 (Suppl 1):144-146.
10. Webhofer C, Zhang Y, Brusis J, Reckow S, Landgraf R, Maccarrone G, Turck CW, Filiou MD. (2012) (15)N metabolic labeling: Evidence for a stable isotope effect on plasma protein levels and peptide chromatographic retention times. J Proteomics 88:27-33.
11. Filiou MD, Varadarajulu J, Teplytska L, Reckow S, Maccarrone G, Turck CW. (2012) The (15) N isotope effect in Escherichia coli: A neutron can make the difference. Proteomics 12:3121-3128.
12. Filiou MD, Webhofer C, Gormanns P, Zhang Y, Reckow S, Bisle B, Teplytska L, Frank E, Kessler MS, Maccarrone G, Landgraf R, Turck CW. (2012) The (15) N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model. Proteomics 12:2421-2427.
13. Gormanns P, Reckow S, Poczatek JC, Turck CW, Lechene C. (2012) Segmentation of multi-isotope imaging mass spectrometry data for semi-automatic detection of regions of interest. PLoS One 7:e30576. 14. Zhang YY, Reckow S, Webhofer C, Boehme M, Gormanns P, Egge-Jacobsen WM, Turck CW. (2011) Proteome scale turnover analysis in live animals using stable isotope metabolic labeling. Anal Chem 83:1665-1672.
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