Henrik Zauber

Publications (3)16.94 Total impact

• Article: Proteomics Wants cRacker: Automated Standardized Data Analysis of LC-MS Derived Proteomic Data.

  Henrik Zauber, Waltraud X Schulze
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  ABSTRACT: The large-scale analysis of thousands of proteins under various experimental conditions or in mutant lines has gained more and more importance in hypothesis-driven scientific research and systems biology in the past years. Quantitative analysis by large scale proteomics using modern mass spectrometry usually results in long lists of peptide ion intensities. The main interest for most researchers, however, is to draw conclusions on the protein level. Postprocessing and combining peptide intensities of a proteomic data set requires expert knowledge, and the often repetitive and standardized manual calculations can be time-consuming. The analysis of complex samples can result in very large data sets (lists with several 1000s to 100 000 entries of different peptides) that cannot easily be analyzed using standard spreadsheet programs. To improve speed and consistency of the data analysis of LC-MS derived proteomic data, we developed cRacker. cRacker is an R-based program for automated downstream proteomic data analysis including data normalization strategies for metabolic labeling and label free quantitation. In addition, cRacker includes basic statistical analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in editable graphic formats and in list files.
  Journal of Proteome Research 09/2012; · 5.11 Impact Factor
• Article: Dynamics of salivary proteins and metabolites during extreme endurance sports - a case study.

  Henrik Zauber, Stephan Mosler, Andreas von Heßberg, Waltraud X Schulze
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  ABSTRACT: As noninvasively accessible body fluid, saliva is of growing interest in diagnostics. To exemplify the diagnostic potential of saliva, we used a mass spectrometry-based approach to gain insights into adaptive physiological processes underlying long-lasting endurance work load in a case study. Saliva was collected from male and female athlete at four diurnal time points throughout a 1060 km nonstop cycling event. Total sampling time covered 180 h comprising 62 h of endurance cycling as well as reference samples taken over 3 days before the event, and over 2 days after. Altogether, 1405 proteins and 62 metabolites were identified in these saliva samples, of which 203 could be quantified across the majority of the sampling time points. Many proteins show clear diurnal abundance patterns in saliva. In many cases, these patterns were disturbed and altered by the long-term endurance stress. During the stress phase, metabolites of energy mobilization, such as creatinine and glucose were of high abundance, as well as metabolites with antioxidant functions. Lysozyme, amylase, and proteins with redox-regulatory function showed significant increase in average abundance during work phase compared to rest or recovery phase. The recovery phase was characterized by an increased abundance of immunoglobulins. Our work exemplifies the application of high-throughput technologies to understand adaptive processes in human physiology.
  Proteomics 07/2012; 12(13):2221-35. · 4.43 Impact Factor
• Article: Precision, proteome coverage, and dynamic range of Arabidopsis proteome profiling using (15)N metabolic labeling and label-free approaches.

  Borjana Arsova, Henrik Zauber, Waltraud X Schulze
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  ABSTRACT: This study reports the comprehensive comparison of (15)N metabolic labeling and label free proteomic strategies for quantitation, with particular focus on plant proteomics. Our investigation of proteome coverage, dynamic range and quantitative precision for a wide range of mixing ratios and protein loadings aim to aid the investigators in the decision making process during experimental design. One of the main characteristics of the label free strategy is the applicability to all starting material, which is a limitation to the metabolic labeling. However, particularly at mixing ratios up to 10-fold the (15)N metabolic labeling proved to be more precise. Contrary to usual practice based on the results from this study, we suggest that nonequal mixing ratios in metabolic labeling could further increase the proteome coverage for quantitation. On the other hand, the label free strategy, in combination with low protein loading allows the extension of the dynamic range for quantitation and it is more precise at very high ratios, which could be important for certain types of experiments.
  Molecular &amp Cellular Proteomics 05/2012; 11(9):619-28. · 7.40 Impact Factor