Papers by Ruth Hüttenhain
Nature Methods, 2011
Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly u... more Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and
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Science Translational Medicine, 2012
The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translatio... more The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.
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PROTEOMICS, 2013
SWATH-MS is a data-independent acquisition method that generates, in a single measurement, a comp... more SWATH-MS is a data-independent acquisition method that generates, in a single measurement, a complete recording of the fragment ion spectra of all the analytes in a biological sample for which the precursor ions are within a predetermined m/z versus retention time window. To assess the performance and suitability of SWATH-MS-based protein quantification for clinical use, we compared SWATH-MS and SRM-MS-based quantification of N-linked glycoproteins in human plasma, a commonly used sample for biomarker discovery. Using dilution series of isotopically labeled heavy peptides representing biomarker candidates, the LOQ of SWATH-MS was determined to reach 0.0456 fmol at peptide level by targeted data analysis, which corresponds to a concentration of 5-10 ng protein/mL in plasma, while SRM reached a peptide LOQ of 0.0152 fmol. Moreover, the quantification of endogenous glycoproteins using SWATH-MS showed a high degree of reproducibility, with the mean CV of 14.90%, correlating well with SRM results (R(2) = 0.9784). Overall, SWATH-MS measurements showed a slightly lower sensitivity and a comparable reproducibility to state-of-the-art SRM measurements for targeted quantification of the N-glycosites in human blood. However, a significantly larger number of peptides can be quantified per analysis. We suggest that SWATH-MS analysis combined with N-glycoproteome enrichment in plasma samples is a promising integrative proteomic approach for biomarker discovery and verification.
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PROTEOMICS, 2012
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Nature Protocols, 2013
Targeted proteomics based on selected reaction monitoring (SRM) mass spectrometry is commonly use... more Targeted proteomics based on selected reaction monitoring (SRM) mass spectrometry is commonly used for accurate and reproducible quantification of protein analytes in complex biological mixtures. Strictly hypothesis-driven, SRM assays quantify each targeted protein by collecting measurements on its peptide fragment ions, called transitions. To achieve sensitive and accurate quantitative results, experimental design and data analysis must consistently account for the variability of the quantified transitions. This consistency is especially important in large experiments, which increasingly require profiling up to hundreds of proteins over hundreds of samples. Here we describe a robust and automated workflow for the analysis of large quantitative SRM data sets that integrates data processing, statistical protein identification and quantification, and dissemination of the results. The integrated workflow combines three software tools: mProphet for peptide identification via probabilistic scoring; SRMstats for protein significance analysis with linear mixed-effect models; and PASSEL, a public repository for storage, retrieval and query of SRM data. The input requirements for the protocol are files with SRM traces in mzXML format, and a file with a list of transitions in a text tab-separated format. The protocol is especially suited for data with heavy isotope-labeled peptide internal standards. We demonstrate the protocol on a clinical data set in which the abundances of 35 biomarker candidates were profiled in 83 blood plasma samples of subjects with ovarian cancer or benign ovarian tumors. The time frame to realize the protocol is 1-2 weeks, depending on the number of replicates used in the experiment.
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Nature Methods, 2011
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Molecular & Cellular Proteomics, 2012
Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensit... more Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a stand-alone tool or in integration with the existing computational pipelines.
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Journal of Proteome Research, 2011
The development of plasma biomarkers has proven to be more challenging than initially anticipated... more The development of plasma biomarkers has proven to be more challenging than initially anticipated. Many studies have reported lists of candidate proteins rather than validated candidate markers with an assigned performance to a specific clinical objective. Biomarker research necessitates a clear rational framework with requirements on a multitude of levels. On the technological front, the platform needs to be effective to detect low abundant plasma proteins and be able to measure them in a high throughput manner over a large amount of samples reproducibly. At a conceptual level, the choice of the technological platform and available samples should be part of an overall clinical study design that depends on a joint effort between basic and clinical research. Solutions to these needs are likely to facilitate more feasible studies. Targeted proteomic workflows based on SRM mass spectrometry show the potential of fast verification of biomarker candidates in plasma and thereby closing the gap between discovery and validation in the biomarker development pipeline. Biological samples need to be carefully chosen based on well-established guidelines either for candidate discovery in the form of disease models with optimal fidelity to human disease or for candidate evaluation as well-designed and annotated clinical cohort groups. Most importantly, they should be representative of the target population and directly address the investigated clinical question. A conceptual structure of a biomarker study can be provided in the form of several sequential phases, each having clear objectives and predefined goals. Furthermore, guidelines for reporting the outcome of biomarker studies are critical to adequately assess the quality of the research, interpretation and generalization of the results. By being attentive to and applying these considerations, biomarker research should become more efficient and lead to directly translatable biomarker candidates into clinical evaluation.
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Expert Review of Molecular Diagnostics, 2013
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Current Opinion in Chemical Biology, 2009
The identification of specific biomarkers will improve the early diagnosis of disease, facilitate... more The identification of specific biomarkers will improve the early diagnosis of disease, facilitate the development of targeted therapies, and provide an accurate method to monitor treatment response. A major challenge in the process of verifying biomarker candidates in blood plasma is the complexity and high dynamic range of proteins. This article reviews the current, targeted proteomic strategies that are capable of quantifying biomarker candidates at concentration ranges where biomarkers are expected in plasma (i.e. at the ng/ml level). In addition, a workflow is presented that allows the fast and definitive generation of targeted mass spectrometry-based assays for most biomarker candidate proteins. These assays are stored in publicly accessible databases and have the potential to greatly impact the throughput of biomarker verification studies.
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Sickle cell disease is caused by one of the 1200 known hemoglobin variations. A single-point muta... more Sickle cell disease is caused by one of the 1200 known hemoglobin variations. A single-point mutation β6(A3)Glu→Val leads to sickling of red blood cells, which in turn causes a lack of oxygen supply to tissue and organs. Although sickle cell disease is well understood, treatment options are currently underdeveloped. The only Food and Drug Administration-approved drug is hydroxyurea, an inducer of fetal γ-hemoglobin, which is known to have a higher oxygen affinity than adult hemoglobins and thus alleviates symptoms. In the search for better cures, Rhesus monkeys (Macaca mulatta) serve as models for monitoring success of induction of fetal γ-hemoglobins and with recent advances in proteomics, MS has become the leading technique to determine globin expression. Similar to humans, Rhesus monkeys possess hemoglobin variants that have not been sufficiently characterized to initiate such a study. Therefore, we developed a combined bottom-up and top-down approach to identify and characterize novel hemoglobin variants of the umbilical cord blood of Rhesus monkeys. A total of four different variants were studied: α, β, γ1 and γ2. A new α- and β-hemoglobin variant was identified, and the two previously hypothesized γ-hemoglobins were identified. In addition, glutathionylation of both γ-hemoglobin variants at their cysteines has been characterized. The combined approach outperformed either bottom-up or top-down alone and can be used for characterization of unknown hemoglobin variants and their PTMs.
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Molecular & Cellular Proteomics, 2013
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Papers by Ruth Hüttenhain