Enrichment of low molecular weight fraction of serum for MS analysis of peptides associated with hepatocellular carcinoma

A challenging aspect of biomarker discovery in serum is the interference of abundant proteins with identification of disease-related proteins and peptides. This study describes enrichment of serum by denaturing ultrafiltration, which enables an efficient profiling and identification of peptides up to 5 kDa. We consistently detect several hundred peptide-peaks in MALDI-TOF and SELDI-TOF spectra of enriched serum. The sample preparation is fast and reproducible with an average CV for all 276 peaks in the MALDI-TOF spectrum of 11%. Compared to unenriched serum, the number of peaks in enriched spectra is 4 times higher at an S/N ratio of 5 and 20 times higher at an S/N ratio of 10. To demonstrate utility of the methods, we compared 20 enriched sera of patients with hepatocellular carcinoma (HCC) and 20 age-matched controls using MALDI-TOF. The comparison of 332 peaks at p < 0.001 identified 45 differentially abundant peaks that classified HCC with 90% accuracy in this small pilot study. Direct TOF/TOF sequencing of the most abundant peptide matches with high probability des-Ala-fibrinopeptide A. This study shows that enrichment of the low molecular weight fraction of serum facilitates an efficient discovery of peptides that could serve as biomarkers for detection of HCC as well as other diseases.     

Super-Resolution Imaging of Molecular Emission Spectra and Single Molecule Spectral Fluctuations

Localization microscopy can image nanoscale cellular details. To address biological questions, the ability to distinguish multiple molecular species simultaneously is invaluable. Here, we present a new version of fluorescence photoactivation localization microscopy (FPALM) which detects the emission spectrum of each localized molecule, and can quantify changes in emission spectrum of individual molecules over time. This information can allow for a dramatic increase in the number of different species simultaneously imaged in a sample, and can create super-resolution maps showing how single molecule emission spectra vary with position and time in a sample.     

Optimization of SELDI-TOF protein profiling for analysis of cervical mucous

Cervical mucous, produced in the region where cervical neoplasia occurs, is thought to be a good choice for discovery of biomarkers to improve cervical cancer screening. In this study, SELDI-TOF MS analysis was used to evaluate parameters for protein profiling of mucous. Proteins were extracted from mucous collected with Weck-Cel^® sponges. Several parameters like extraction reagent, loading protein concentration, matrix type, bind/wash conditions and sample fractionation, on different protein chip surfaces were evaluated. SELDI peak number and consistency in the resulting spectra were used to evaluate each condition. Analysis of spectra generated by different protein chips revealed an average of 30 peaks in the 2.5–30 kDa mass range using sinnapinic acid in the unfractionated sample. Sample concentration and buffer conditions evaluated did not lead to large alterations in the profiles. Quality control spectra were reproducible with intra- and inter-assay intensity CV for CM10, H50 and Q10 arrays being less than 20% and 30% respectively. IMAC30-Cu chips had higher intra- and inter-assay CV's at 25% and 35%. Current data showed that optimizing pre-analytical parameters can help in standardization and reproducibility of protein profiles produced by cervical mucous, and thus can be used for protein biomarker discovery with the SELDI platform.     

Optimization of SELDI-TOF protein profiling for analysis of cervical mucous

Cervical mucous, produced in the region where cervical neoplasia occurs, is thought to be a good choice for discovery of biomarkers to improve cervical cancer screening. In this study, SELDI-TOF MS analysis was used to evaluate parameters for protein profiling of mucous. Proteins were extracted from mucous collected with Weck-Cel^® sponges. Several parameters like extraction reagent, loading protein concentration, matrix type, bind/wash conditions and sample fractionation, on different protein chip surfaces were evaluated. SELDI peak number and consistency in the resulting spectra were used to evaluate each condition. Analysis of spectra generated by different protein chips revealed an average of 30 peaks in the 2.5–30 kDa mass range using sinnapinic acid in the unfractionated sample. Sample concentration and buffer conditions evaluated did not lead to large alterations in the profiles. Quality control spectra were reproducible with intra- and inter-assay intensity CV for CM10, H50 and Q10 arrays being less than 20% and 30% respectively. IMAC30-Cu chips had higher intra- and inter-assay CV's at 25% and 35%. Current data showed that optimizing pre-analytical parameters can help in standardization and reproducibility of protein profiles produced by cervical mucous, and thus can be used for protein biomarker discovery with the SELDI platform.     

Improved candidate biomarker detection based on mass spectrometry data using the Hilbert-Huang transform

Mass spectrometry biomarker discovery may assist patient's diagnosis in time and realize the characteristics of new diseases. Our previous work built a preprocess method called HHTmass which is capable of removing noise, but HHTmass only a proof of principle to be peak detectable and did not tested for peak reappearance rate and used on medical data. We developed a modified version of biomarker discovery method called Enhance HHTMass (E-HHTMass) for MALDI-TOF and SELDI-TOF mass spectrometry data which improved old HHTMass method by removing the interpolation and the biomarker discovery process. E-HHTMass integrates the preprocessing and classification functions to identify significant peaks. The results show that most known biomarker can be found and high peak appearance rate achieved comparing to MSCAP and old HHTMass2. E-HHTMass is able to adapt to spectra with a small increasing interval. In addition, new peaks are detected which can be potential biomarker after further validation.     

Species differentiation of a diverse suite of Bacillus spores by mass spectrometry-based protein profiling

In this study, we demonstrate the versatility of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOFMS) protein profiling for the species differentiation of a diverse suite of Bacillus spores. MALDI-TOFMS protein profiles of 11 different strains of Bacillus spores, encompassing nine different species, were evaluated. Bacillus species selected for MALDI-TOFMS analysis represented the spore-forming bacterial diversity of typical class 100K clean room spacecraft assembly facilities. A one-step sample treatment and MALDI-TOFMS preparation were used to minimize the sample preparation time. A library of MALDI-TOFMS spectra was created from these nine Bacillus species, the most diverse protein profiling study of the genus reported to date. Linear correlation analysis was used to successfully differentiate the MALDI-TOFMS protein profiles from all strains evaluated in this study. The MALDI-TOFMS protein profiles were compared with 16S rDNA sequences for their bacterial systematics and molecular phylogenetic affiliations. The MALDI-TOFMS profiles were found to be complementary to the 16S rDNA analysis. Proteomic studies of Bacillus subtilis 168 were pursued to identify proteins represented by the biomarker peaks in the MALDI-TOFMS spectrum. Four small, acid-soluble proteins (A, B, C, and D), one DNA binding protein, hypothetical protein ymf J, and four proteins associated with the spore coat and spore coat formation (coat JB, coat F, coat T, and spoIVA) were identified. The ability to visualize higher-molecular-mass coat proteins (10 to 25 kDa) as well as smaller proteins (<10 kDa) with MALDI-TOFMS profiling is critical for the complete and effective species differentiation of the Bacillus genus.     

Improved quality of 1H NMR spectroscopic data for enhanced metabolic profiling of low molecular weight metabolites in human serum

NMR based metabolic profiling of blood samples in epidemiological studies can be used for molecular phenotyping and biomarker discovery. Often metabolic changes in blood are more subtle and demand a high quality spectrum especially when looking at low molecular weight compounds. In order to improve ¹H NMR spectroscopic data we compared different serum sample preparation methods. Application of phosphate buffer reduces chemical shift variation, enhances resolution of signal multiplicity, facilitates visual inspection of NMR spectra and annotation of signals compared to traditionally used saline. For analysis of low molecular weight compounds we found that standard 1D spectra of ultrafiltrated serum samples show enhanced spectral quality of small metabolites as compared to transverse relaxation edited spectra (also called Carr–Purcell–Meiboom–Gill, CPMG) spectra of unfiltered serum samples due to improved signal-to-noise ratio. Thus, NMR signals attributable to different amino acids and other small metabolites could readily be detected in spectra of ultrafiltrated serum, but remained invisible in the corresponding CPMG spectra. An OPLS model of fasting blood glucose showed an increase of Q² when using spectra from ultrafiltrated serum (Q² = 0.261) compared to using CPMG spectra (Q² = 0.173). Similar results were observed for OPLS models of BMI (Q² = 0.253 and Q² = 0.216, respectively). Furthermore, a reduction in model dimensionality was observed when using ultrafiltrated serum data. In conclusion we recommend sample preparation of serum samples in phosphate buffer instead of saline. Ultrafiltration of serum samples prior to NMR analysis is beneficial especially for low concentrated small metabolites.     

Validation of a multiplexed and targeted lipidomics assay for accurate quantification of lipidomes

A major challenge of lipidomics is to determine and quantify the precise content of complex lipidomes to the exact lipid molecular species. Often, multiple methods are needed to achieve sufficient lipidomic coverage to make these determinations. Multiplexed targeted assays offer a practical alternative to enable quantitative lipidomics amenable to quality control standards within a scalable platform. Herein, we developed a multiplexed normal phase liquid chromatography-hydrophilic interaction chromatography multiple reaction monitoring method that quantifies lipid molecular species across over 20 lipid classes spanning wide polarities in a single 20-min run. Analytical challenges such as in-source fragmentation, isomer separations, and concentration dynamics were addressed to ensure confidence in selectivity, quantification, and reproducibility. Utilizing multiple MS/MS product ions per lipid species not only improved the confidence of lipid identification but also enabled the determination of relative abundances of positional isomers in samples. Lipid class-based calibration curves were applied to interpolate lipid concentrations and guide sample dilution. Analytical validation was performed following FDA Bioanalytical Method Validation Guidance for Industry. We report repeatable and robust quantitation of 900 lipid species measured in NIST-SRM-1950 plasma, with over 700 lipids achieving inter-assay variability below 25%. To demonstrate proof of concept for biomarker discovery, we analyzed plasma from mice treated with a glucosylceramide synthase inhibitor, benzoxazole 1. We observed expected reductions in glucosylceramide levels in treated animals but, more notably, identified novel lipid biomarker candidates from the plasma lipidome. These data highlight the utility of this qualified lipidomic platform for enabling biological discovery.     

Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms

Background

There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached.

Results

In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased.

Conclusions

Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.     

Peak quantification in surface-enhanced laser desorption/ionization by using mixture models

Surface-enhanced laser desorption/ionization (SELDI) time of flight (TOF) is a mass spectrometry technology for measuring the composition of a sampled protein mixture. A mass spectrum contains peaks corresponding to proteins in the sample. The peak areas are proportional to the measured concentrations of the corresponding proteins. Quantifying peak areas is difficult for existing methods because peak shapes are not constant across a spectrum and because peaks often overlap. We present a new method for quantifying peak areas. Our method decomposes a spectrum into peaks and a baseline using so-called statistical finite mixture models. We illustrate our method in detail on 8 samples from culture media of adipose tissue and globally on 64 samples from serum to compare our method to the standard Ciphergen method. Both methods give similar estimates for singleton peaks, but not for overlapping peaks. The Ciphergen method overestimates the heights of such peaks while our method still gives appropriate estimates. Peak quantification is an important step in pre-processing SELDI-TOF data and improvements therein will pay off in the later biomarker discovery phase.     

A computational platform for MALDI-TOF mass spectrometry data: Application to serum and plasma samples

BackgroundMass spectrometry (MS) is becoming the gold standard for biomarker discovery. Several MS-based bioinformatics methods have been proposed for this application, but the divergence of the findings by different research groups on the same MS data suggests that the definition of a reliable method has not been achieved yet. In this work, we propose an integrated software platform, MASCAP, intended for comparative biomarker detection from MALDI-TOF MS data.ResultsMASCAP integrates denoising and feature extraction algorithms, which have already shown to provide consistent peaks across mass spectra; furthermore, it relies on statistical analysis and graphical tools to compare the results between groups. The effectiveness in mass spectrum processing is demonstrated using MALDI-TOF data, as well as SELDI-TOF data. The usefulness in detecting potential protein biomarkers is shown comparing MALDI-TOF mass spectra collected from serum and plasma samples belonging to the same clinical population.ConclusionsThe analysis approach implemented in MASCAP may simplify biomarker detection, by assisting the recognition of proteomic expression signatures of the disease. A MATLAB implementation of the software and the data used for its validation are available at http://www.unich.it/proteomica/bioinf.     

Improving feature detection and analysis of surface-enhanced laser desorption/ionization-time of flight mass spectra

Discovering valid biological information from surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS) depends on clear experimental design, meticulous sample handling, and sophisticated data processing. Most published literature deals with the biological aspects of these experiments, or with computer-learning algorithms to locate sets of classifying biomarkers. The process of locating and measuring proteins across spectra has received less attention. This process should be tunable between sensitivity and false-discovery, and should guarantee that features are biologically meaningful in that they represent chemical species that can be identified and investigated. Existing feature detection in SELDI-TOF MS is not optimal for acquiring biologically relevant data. Most methods have so many user-defined settings that reproducibility and comparability among studies suffer considerably. To address these issues, we have developed an approach, called simultaneous spectrum analysis (SSA), which (i) locates proteins across spectra, (ii) measures their abundance, (iii) subtracts baseline, (iv) excludes irreproducible measurements, and (v) computes normalization factors for comparing spectra. SSA uses only two key parameters for feature detection and one parameter each for quality thresholds on spectra and peaks. The effectiveness of SSA is demonstrated by identifying proteins differentially expressed in SELDI-TOF spectra from plasma of wild-type and knockout mice for plasma glutathione peroxidase. Comparing analyses by SSA and CiphergenExpress Data Manager 2.1 finds similar results for large signal peaks, but SSA improves the number and quality of differences betweens groups among lower signal peaks. SSA is also less likely to introduce systematic bias when normalizing spectra.     

利用基因组学和MALDI-TOF MS技术鉴定放线菌纲细菌的核糖体蛋白质标志物

【目的】基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)法基于微生物的特征蛋白指纹图谱鉴定菌种,本研究利用基因组学和MALDI-TOFMS技术鉴定放线菌纲细菌的核糖体蛋白质标志物。【方法】从MALDI-TOF MS图谱数据库选取放线菌纲代表菌种,在基因组数据库检索目标菌种,获取目标菌株或其参比菌株的核糖体蛋白质序列,计算获得分子质量理论值,用于注释目标菌株MALDI-TOFMS指纹图谱中的核糖体蛋白质信号。【结果】从8目,24科,53属,114种,142株放线菌的MALDI-TOFMS图谱中总共注释出31种核糖体蛋白质。各菌株的指纹图谱中核糖体蛋白质信号数量差异显著。各种核糖体蛋白质信号的注释次数差异显著。总共15种核糖体蛋白质在超过半数图谱中得到注释,注释次数最高的是核糖体大亚基蛋白质L36。【结论】本研究找到了放线菌纲细菌MALDI-TOF MS图谱中常见的15种核糖体蛋白质信号,可为通过识别核糖体蛋白质的质谱特征峰鉴定放线菌的方法建立提供依据。     

A novel peak detection approach with chemical noise removal using short‐time FFT for prOTOF MS data

Peak detection is a pivotal first step in biomarker discovery from MS data and can significantly influence the results of downstream data analysis steps. We developed a novel automatic peak detection method for prOTOF MS data, which does not require a priori knowledge of protein masses. Random noise is removed by an undecimated wavelet transform and chemical noise is attenuated by an adaptive short‐time discrete Fourier transform. Isotopic peaks corresponding to a single protein are combined by extracting an envelope over them. Depending on the S/N, the desired peaks in each individual spectrum are detected and those with the highest intensity among their peak clusters are recorded. The common peaks among all the spectra are identified by choosing an appropriate cut‐off threshold in the complete linkage hierarchical clustering. To remove the 1 Da shifting of the peaks, the peak corresponding to the same protein is determined as the detected peak with the largest number among its neighborhood. We validated this method using a data set of serial peptide and protein calibration standards. Compared with MoverZ program, our new method detects more peaks and significantly enhances S/N of the peak after the chemical noise removal. We then successfully applied this method to a data set from prOTOF MS spectra of albumin and albumin‐bound proteins from serum samples of 59 patients with carotid artery disease compared to vascular disease‐free patients to detect peaks with S/N≥2. Our method is easily implemented and is highly effective to define peaks that will be used for disease classification or to highlight potential biomarkers.     

LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise

Background  

Mass spectrometry protein profiling is a promising tool for biomarker discovery in clinical proteomics. However, the development of a reliable approach for the separation of protein signals from noise is required. In this paper, LIMPIC, a computational method for the detection of protein peaks from linear-mode MALDI-TOF data is proposed. LIMPIC is based on novel techniques for background noise reduction and baseline removal. Peak detection is performed considering the presence of a non-homogeneous noise level in the mass spectrum. A comparison of the peaks collected from multiple spectra is used to classify them on the basis of a detection rate parameter, and hence to separate the protein signals from other disturbances.     

Automated SPE-RP-HPLC fractionation of biofluids combined to off-line NMR spectroscopy for biomarker identification in metabonomics

NMR-based metabonomics is a valuable and straightforward approach to measuring hundreds of metabolites in complex biofluids. However, metabolite identification is sometimes limited by overlapped signals in NMR spectra. We describe a new methodology using an automated hyphenation of solid phase extraction (SPE) with RP-HPLC combined to NMR spectroscopy, which allowed identification of 72 metabolites of various molecular classes in human urine. This methodology was also successfully applied to the fractionation of a cat urine sample to aid identification of aromatic compounds and felinine. The SPE-RP-HPLC method appears to be a reliable tool to support biomarker discovery in metabonomic studies.     

Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.     

Antibody‐based proteomics and biomarker research—Current status and limitations

Antibody‐based proteomics play a very important role in biomarker discovery and validation, facilitating the high‐throughput evaluation of candidate markers. Most proteomics‐driven discovery is nowadays based on the use of MS. MS has many advantages, including its suitability for hypothesis‐free biomarker discovery, since information on protein content of a sample is not required prior to analysis. However, MS presents one main caveat which is the limited sensitivity in complex samples, especially for body fluids, where protein expression covers a huge dynamic range. Antibody‐based technologies remain the main solution to address this challenge since they reach higher sensitivity. In this article, we review the benefits and limitations of antibody‐based proteomics in preclinical and clinical biomarker research for discovery and validation in body fluids and tissue. The combination of antibodies and MS, utilizing the best of both worlds, opens new avenues in biomarker research.     

Systematic Internal Standard Selection for Capillary Liquid Chromatography–Mass Spectrometry Time Normalization to Facilitate Serum Proteomics

Because blood interacts with almost all tissues of the body, it is likely that changes in the overall health of an organism will be reflected in the quantities of specific serum peptides and proteins, making them biomarkers. Due to the complexity of serum, pre-analytical sample simplification and separation are needed prior to mass spectrometric analysis. Use of a reverse-phase capillary column coupled to a mass spectrometer allows for separation and analysis of serum as part of efforts to discover biomarkers. Even after sample simplification by organic solvent precipitation, data files for a single sample typically exceed one gigabyte, making it difficult to analyze complete serum mass spectrometry profiles with currently available software. However, with adequate safeguards, it appears possible to consider portions of mass spectra to find differences in peak intensities between clinical comparison groups visually. To facilitate this, the elution profile was divided into 2-min intervals in which mass spectrometry data were averaged. This required that molecular species had defined reproducible elution times. Given liquid chromatography coupled to mass spectrometry variation, misalignment of elution times of individual peaks occurred often. Hence, internal time controls were identified within each window and used for elution time normalization. This significantly reduced variability in data. This approach allowed for peak alignment across samples, improving biomarker discovery.