Improving mass spectrometry peak detection using multiple peak alignment results

Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study

The current study reports an integrated approach of machine learning and tryptophan fluorescence and photoacoustic spectral properties to assess the mitochondrial status under oral pathological conditions. The mitochondria in the study were isolated from oral cancer tissues and adjacent normal counterparts, and the corresponding fluorescence and photoacoustic spectra of tryptophan were recorded at 281 nm pulsed laser excitations. A set of features were selected from the pre-processed spectra and were used to classify the data using support vector machine (SVM) learning in the MATLAB platform. SVM analysis demonstrated clear differentiation between mitochondria isolated from normal and cancer tissues for fluorescence (sensitivity, 86.6%; specificity, 90%) and photoacoustic (sensitivity, 86.6%; specificity, 96.6%) measurements. Further investigation into the influence of change in protein conformation on the nature of tryptophan spectral properties was evaluated by 8-anilino-1-naphthalene sulfonic acid (ANS) fluorescence assay. The impact of protein structural changes on the mitochondrial functions was also estimated by mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and cytochrome c oxidase (COX) assays, suggesting an altered mitochondrial function. The findings indicate that tryptophan fluorescence and photoacoustic spectral properties together with machine learning algorithms may delineate the mitochondrial functional status in vitro, indicating its translational potential.

Targeted and non-targeted unexpected food contaminants analysis by LC/HRMS: Feasibility study on rice

A widely applicable analytical LC/HRMS method based on ion source optimization, data treatment optimization on rice matrix was developed. The effects of key parameters of ion source, and their interactions on ESI response were studied on HPLC-QTOF. Compared with center points, 40% and 20% increase of response factors in the positive and negative mode can be achieved by ion source optimization, respectively. Data processing strategies inspired from metabolomics and multi-targeted analysis were compared and developed using case and control rice samples. Highly automated workflow using XCMS achieved highest mass accuracy, highest detection rate of 96% for 5 μg/kg in a non-targeted way. A clear distinction between the control and contaminated samples by PCA and PLS-DA was also achieved by this workflow using XCMS, even for the concentration of 5 μg/kg.

General strategies to increase the repeatability in non-target screening by liquid chromatography-high resolution mass spectrometry

This article focuses on the data evaluation of non-target high-resolution LC-MS profiles of water samples. Taking into account multiple technical replicates, the difficulties in peak recognition and the related problems of false positive and false negative findings are systematically demonstrated. On the basis of a combinatorial approach, different models involving sophisticated workflows are evaluated, particularly with regard to the repeatability. In addition, the improvement resulting from data processing was systematically taken into consideration where the recovery of spiked standards emphasized that real peaks of interest were barely or not removed by the derived filter criteria. The comprehensive evaluation included different matrix types spiked with up to 263 analytical standards which were analyzed repeatedly leading to a total number of more than 250 injections that were incorporated in the assessment of different models of data processing. It was found that the analysis of multiple replicates is the key factor as, on the one hand, it provides the option of integrating valuable filters in order to minimize the false positive rate and, on the other hand, allows correcting partially false negative findings occurring during the peak recognition. The developed processing strategies including replicates clearly point to an enhanced data quality since both the repeatability as well as the peak recognition could be considerably improved. As proof of concept, four different matrix types, including a wastewater treatment plant (WWTP) effluent, were spiked with 130 isotopically labeled standards at different concentration levels. Despite the stringent filter criteria, at 100 ng L(-1) recovery rates of up to 93% were reached in the positive ionization mode. The proposed model, comprising three technical replicates, filters less than 5% and 2% of the standards recognized at 100 and 500 ng L(-1), respectively and thus indicates the general applicability of the presented strategies.

Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Isotopic signature transfer and mass pattern prediction (IsoStamp): an enabling technique for chemically-directed proteomics

Directed proteomics applies mass spectrometry analysis to a subset of information-rich proteins. Here we describe a method for targeting select proteins by chemical modification with a tag that imparts a distinct isotopic signature detectable in a full-scan mass spectrum. Termed isotopic signature transfer and mass pattern prediction (IsoStamp), the technique exploits the perturbing effects of a dibrominated chemical tag on a peptide’s mass envelope, which can be detected with high sensitivity and fidelity using a computational method. Applying IsoStamp, we were able to detect femtomole quantities of a single tagged protein from total mammalian cell lysates at signal-to-noise ratios as low as 2.5:1. To identify a tagged-peptide’s sequence, we performed an inclusion list-driven shotgun proteomics experiment where peptides bearing a recoded mass envelope were targeted for fragmentation, allowing for direct site mapping. Using this approach, femtomole quantities of several targeted peptides were identified in total mammalian cell lysate, while traditional data-dependent methods were unable to identify as many peptides. Additionally, the isotopic signature imparted by the dibromide tag was detectable on a 12-kDa protein, suggesting applications in identifying large peptide fragments, such as those containing multiple or large posttranslational modifications (e.g., glycosylation). IsoStamp has the potential to enhance any proteomics platform that employs chemical labeling for targeted protein identification, including isotope coded affinity tagging, isobaric tagging for relative and absolute quantitation, and chemical tagging strategies for posttranslational modification.

Peak tree: a new tool for multiscale hierarchical representation and peak detection of mass spectrometry data

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

The evolving field of imaging mass spectrometry and its impact on future biological research

Within the past decade, imaging mass spectrometry (IMS) has been increasingly recognized as an indispensable technique for studying biological systems. Its rapid evolution has resulted in an impressive array of instrument variations and sample applications, yet the tools and data are largely confined to specialists. It is therefore important that at this junction the IMS community begin to establish IMS as a permanent fixture in life science research thereby making the technology and/or the data approachable by non-mass spectrometrists, leading to further integration into biological and clinical research. In this perspective article, we provide insight into the evolution and current state of IMS and propose some of the directions that IMS could develop in order to stay on course to become one of the most promising new tools in life science research.

Signal processing in proteomics

Computational proteomics applications are often imagined as a pipeline, where information is processed in each stage before it flows to the next one. Independent of the type of application, the first stage invariably consists of obtaining the raw mass spectrometric data from the spectrometer and preparing it for use in the later stages by enhancing the signal of interest while suppressing spurious components. Numerous approaches for preprocessing MS data have been described in the literature. In this chapter, we will describe both, standard techniques originating from classical signal and image processing, and novel computational approaches specifically tailored to the analysis of MS data sets. We will focus on low level signal processing tasks such as baseline reduction, denoising, and feature detection.

An insight into high-resolution mass-spectrometry data

Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical preprocessing steps that are taken in order to interpret the data and use it in downstream statistical analyses. Because of the complexity of data acquisition, statistical methods developed for gene expression microarray data are not directly applicable to proteomic data. Areas in need of statistical research for proteomic data include alignment, experimental design, abundance normalization, and statistical analysis.

Application of nucleic-acid-based therapeutics for viral infections in shrimp aquaculture

Viral infections are one of the major reasons for the huge economic losses in shrimp farming. The control of viral diseases in shrimp remains a serious challenge for the shrimp aquacultural industry, with major pathogens, such as the white spot syndrome virus, yellow head virus, Taura syndrome virus, hepatopancreatic parvovirus, and baculoviruses, being geographically widespread. In the absence of a true adaptive immune response system in invertebrates such as shrimp, one of the alternative and more specific approaches to counteract viral infections in shrimp could be the use of molecular-based gene transfer technologies, such as RNA interference (RNAi). The RNAi mechanism is initiated by double-stranded RNAs (dsRNAs), which are fragmented into shorter 21-23 nucleotides of short interfering RNAs (siRNAs) by a type III endonuclease, the Dicer. RNAi, which is mediated by small interfering RNA (siRNA), results in the sequence-specific post-transcriptional silencing of a target gene. This gene-silencing mechanism is universally conserved and is a well-known phenomenon that exists in many eukaryotes, including invertebrates. It has been recently extended to shrimp as an important potential tool in viral disease prevention. RNAi technology shows considerable promise as a therapeutic approach and efficient strategy for shrimp virus control in the aquaculture industry. Further progress in understanding the mechanism of siRNAs at the molecular level, as well as strategies to achieve their tightly regulated, stable, prolonged and tissue-specific expression in an effective manner, will definitely revolutionize therapeutic approaches for counteracting viral diseases of shrimp. In the present review, the recent development and potential use of RNAi in combating shrimp viral infections is discussed.