Multidimensional Separation Research Articles

STATISTICAL DATA ANALYSIS TOOLS IN IMAGE CLASSIFICATION METHODS BASED ON THE DESCRIPTION AS A SET OF BINARY DESCRIPTORS OF KEY POINTS

Context. Modern computer vision systems require effective classification solutions based on the research of the the processed data nature. Statistical distributions are currently the perfect tool for representing and analyzing visual data in image recognition systems. If the description of a recognized object is represented by a set of vectors, the statistical apparatus becomes fundamental for making a classification decision. The study of data distributions in the feature blocks systems for key point descriptors has shown its effectiveness in terms of achieving the necessary quality of classification and processing speed. There is a need for in-depth study of the descriptor sets statistical properties in terms of the main aspect – the multidimensional data separation for classification. This task becomes especially important for constructing new effective feature spaces, for example, by aggregating a set of descriptors by their constituent components, including individual bits. To do this, it is natural to use the apparatus of statistical criteria designed to compare the parameters of the distribution of the studied samples. Despite the widespread use and applied effectiveness of the feature descriptors apparatus for image classification, the statistical basis of these methods in their implementation in aggregate visual data systems and the choice of effective means to assess their effectiveness for distinguishing real images in application databases remains insufficiently studied.
 Objective. Development of an effective images classification method by introducing aggregate statistical features for the description components.
 Method. A metric image classifier based on feature aggregation for a set of image descriptors using statistical criteria for assessing the classification decision significance is proposed.
 Results. The synthesis of the classification method on the basis of the introduction of aggregated statistical features for a set of image description descriptors is carried out. The efficiency and effectiveness of the developed classifier are confirmed. On examples of application of a method for system of real images features its efficiency is experimentally estimated.
 Conclusions. The study makes possible to evaluate the applied effectiveness of the key points descriptors apparatus and build on its basis an aggregate features system for the effective visual objects classification implementation. Our research has shown that the available information in the form of a bit descriptors representation is sufficient for a significant statistical distinction between visual objects descriptions. Analysis of pairs and other blocks for descriptor bits provides a promising opportunity to reduce processing time.
 The scientific novelty of the study is the development of a method of image classification based on an integrated statistical features system for structural description, confirmation of the effectiveness of the method and the importance of the created features classification system in the image database.
 The practical significance of the work is to confirm the efficiency of the proposed methods on the real image descriptions examples.

Insights and prospects for ion mobility-mass spectrometry in clinical chemistry

Introduction : Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements. Areas covered : Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds. Expert Opinion : Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.

Preparative capillary electrophoresis (CE) fractionation of protein digests improves protein and peptide identification in bottom-up proteomics.

Reversed-phase liquid chromatography (RPLC) is widely used to reduce sample complexity prior to mass spectrometry (MS) analysis in bottom-up proteomics. Improving peptide separation in complex samples enables lower-abundance proteins to be identified. Multidimensional separations that combine orthogonal separation modes improve protein and peptide identifications over RPLC alone. Here we report a preparative capillary electrophoresis (CE) fractionation method that combines CE and RPLC separations. Using this method, we demonstrate improved protein and peptide identification in a tryptic digest of E. coli cell lysate, with 132 ± 33% more protein identifications and 185 ± 65% more peptide identifications over non-fractionated samples. Fractionation enables detection of lower-abundance proteins in this complex sample. We demonstrate improved coverage of ovarian cancer biomarker MUC16 isolated from conditioned cell media, with 6.73% sequence coverage using CE fractionation compared to 2.74% coverage without preparative fractionation. This new method will allow researchers performing bottom-up proteomics to harness the advantages of CE separations while using widely available LC-MS/MS instrumentation.

Mass Spectrometry-Based Proteomics for Biomarker Discovery.

Proteomics plays a pivotal role in systems medicine, in which pharmacoproteomics and toxicoproteomics have been developed to address questions related to efficacy and toxicity of drugs. Mass spectrometry is the core technology for quantitative proteomics, providing the capabilities of identification and quantitation of thousands of proteins. The technology has been applied to biomarker discovery and understanding the mechanisms of drug action. Both stable isotope labeling of proteins or peptides and label-free approaches have been incorporated with multidimensional LC separation and tandem mass spectrometry (LC-MS/MS) to increase the coverage and depth of proteome analysis. A protocol of such an approach exemplified by dimethyl labeling in combination with 2D-LC-MS/MS is described. With further development of novel proteomic tools and increase in sample throughput, the full spectrum of mass spectrometry-based proteomic research will greatly advance systems medicine.

Characterisation of a new online nanoLC-CZE-MS platform and application for the glycosylation profiling of alpha-1-acid glycoprotein

The ever-increasing complexity of biological samples to be analysed by mass spectrometry has led to the necessity of sophisticated separation techniques, including multidimensional separation. Despite a high degree of orthogonality, the coupling of liquid chromatography (LC) and capillary zone electrophoresis (CZE) has not gained notable attention in research. Here, we present a heart-cut nanoLC-CZE-ESI-MS platform to analyse intact proteins. NanoLC and CZE-MS are coupled using a four-port valve with an internal nanoliter loop. NanoLC and CZE-MS conditions were optimised independently to find ideal conditions for the combined setup. The valve setup enables an ideal transfer efficiency between the dimensions while maintaining good separation conditions in both dimensions. Due to the higher loadability, the nanoLC-CZE-MS setup exhibits a 280-fold increased concentration sensitivity compared to CZE-MS. The platform was used to characterise intact human alpha-1-acid glycoprotein (AGP), an extremely heterogeneous N-glycosylated protein. With the nanoLC-CZE-MS approach, 368 glycoforms can be assigned at a concentration of 50 μg/mL as opposed to the assignment of only 186 glycoforms from 1 mg/mL by CZE-MS. Additionally, we demonstrate that glycosylation profiling is accessible for dried blood spot analysis (25 μg/mL AGP spiked), indicating the general applicability of our setup to biological matrices. The combination of high sensitivity and orthogonal selectivity in both dimensions makes the here-presented nanoLC-CZE-MS approach capable of detailed characterisation of intact proteins and their proteoforms from complex biological samples and in physiologically relevant concentrations.Graphical abstract

Overcoming the lack of reliability associated to monodimensional gas chromatography coupled to isotopic ratio mass spectrometry data by heart-cut two-dimensional gas chromatography

The use of IRMS as a GC detector has a history going back decades, however the critical issue of wrong δ13C measurements resulting from impure peaks has been often underestimated. To this regard, multidimensional separation techniques are effective tools to improve the reliability of the data, with respect to those obtained after monodimensional analysis. The present research aims to draw attention to one critical issue, related to the reliability of the δ13C data obtained by means of monodimensional GC-C-IRMS. Although already known from the literature, such aspect has been greatly overlooked, as is reflected in the few papers reporting the use of MDGC, among the plethora of published research dealing with GC-C-IRMS applications. Hereby, a set of natural samples of complex composition were analysed to investigate the presence of minor or even undetected coelutions, and to which extent it affected the isotope ratio determination. Apart from chromatographic effects, and issues related to analytes conversion to CO2 prior to IRMS measurement, unpredictable co-elutions with compounds, either resulting from oxidation or intentionally added in fraudulent practices, could also contribute to a shift of the δ13C data, up to 10‰ and higher. Last, the influence of column bleed was investigated, as affecting the determination of the δ13C data for compounds that were eluted at high temperatures. It was finally demonstrated by the selected key studies that implementation of MDGC separation is mandatory to prevent the aforementioned issues, aiming to guarantee accurate results. In the light of the above conclusions, and considering the level of automation of heart-cut devices nowadays available, routine practice of MDGC results highly recommendable in any IRMS applications.

Quantitative Top-Down Proteomics by Isobaric Labeling with Thiol-Directed Tandem Mass Tags.

While identification-centric (qualitative) top-down proteomics (TDP) has seen rapid progress in the recent past, the quantification of intact proteoforms within complex proteomes is still challenging. The by far mostly applied approach is label-free quantification, which, however, provides limited multiplexing capacity, and its use in combination with multidimensional separation is encountered with a number of problems. Isobaric labeling, which is a standard quantification approach in bottom-up proteomics, circumvents these limitations. Here, we introduce the application of thiol-directed isobaric labeling for quantitative TDP. For this purpose, we analyzed the labeling efficiency and optimized tandem mass spectrometry parameters for optimal backbone fragmentation for identification and reporter ion formation for quantification. Two different separation schemes, gel-eluted liquid fraction entrapment electrophoresis × liquid chromatography-mass spectrometry (LC-MS) and high/low-pH LC-MS, were employed for the analyses of either Escherichia coli (E. coli) proteomes or combined E. coli/yeast samples (two-proteome interference model) to study potential ratio compression. While the thiol-directed labeling introduces a bias in the quantifiable proteoforms, being restricted to Cys-containing proteoforms, our approach showed excellent accuracy in quantification, which is similar to that achievable in bottom-up proteomics. For example, 876 proteoforms could be quantified with high accuracy in an E. coli lysate. The LC-MS data were deposited to the ProteomeXchange with the dataset identifier PXD026310.

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein–protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein–protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.

Novel Strategies to Address the Challenges in Top-Down Proteomics.

Top-down mass spectrometry (MS)-based proteomics is a powerful technology for comprehensively characterizing proteoforms to decipher post-translational modifications (PTMs) together with genetic variations and alternative splicing isoforms toward a proteome-wide understanding of protein functions. In the past decade, top-down proteomics has experienced rapid growth benefiting from groundbreaking technological advances, which have begun to reveal the potential of top-down proteomics for understanding basic biological functions, unraveling disease mechanisms, and discovering new biomarkers. However, many challenges remain to be comprehensively addressed. In this Account & Perspective, we discuss the major challenges currently facing the top-down proteomics field, particularly in protein solubility, proteome dynamic range, proteome complexity, data analysis, proteoform-function relationship, and analytical throughput for precision medicine. We specifically review the major technology developments addressing these challenges with an emphasis on our research group's efforts, including the development of top-down MS-compatible surfactants for protein solubilization, functionalized nanoparticles for the enrichment of low-abundance proteoforms, strategies for multidimensional chromatography separation of proteins, and a new comprehensive user-friendly software package for top-down proteomics. We have also made efforts to connect proteoforms with biological functions and provide our visions on what the future holds for top-down proteomics.

Multidimensional separation due to selective spherical agglomeration—Evidence of shape separation via X-ray microtomography

Objective of this work was to develop a novel method for characterizing real 3D shapes of particles smaller than 20 μm by X-ray microtomography (X-RMT). Multidimensional separation of heterogenous solids through agglomeration in suspension will improve recycling processes as the particle shape and the agglomerate size are used for shape-selective separation. In the present paper we discuss the fundamentals of X-ray tomography and the experimental setup for selective spherical agglomeration in suspension. A specific preparation method of the particulate sample for X-RMT followed by 3D image processing, are essential for the shape analysis expressed as sphericity. We also discuss the limitation of this method due to the so-called Partial Volume Effect and particle clusters in the order of magnitude of X-RMT resolution. As proof of concept, we used a mixture of graphite platelets and spheronized graphite particles for a shape selective-agglomeration in suspension. The remaining fines were analyzed and showed more platelets than in the mixture. This indicates that spheronized particles are preferably bound in the agglomerates. These findings show that, based on the discussed sample preparation and a 3D image analysis in connection with X-RMT, particle shapes of micronized particles can be discriminated.

Recent (2018-2020) development in capillary electrophoresis.

Development of new capillary electrophoresis (CE) methodology and instrumentation, as well as application of CE to solve new problems, remains an active research area because of the attractive features of CE compared to other separation techniques. In this review, we focus on the representative works about sample preconcentration, separation media or capillary coating development, detector construction, and multidimensional separation in CE, which are judiciously selected from the papers published in 2018–2020.

Comprehensive two-dimensional gas chromatography as a boosting technology in food-omic investigations.

This review focuses on the role that comprehensive two-dimensional gas chromatography can play within the investigation workflows of food-omics and related disciplines and subdisciplines, including food metabolomics, nutrimetabolomics, sensomics, and food safety. After a short introductory survey, discussing the intriguing context of system biology and integrationist approaches of investigation, the concepts of analytical dimensions and the key characteristics of comprehensive two-dimensional gas chromatography are introduced. Through a selection of relevant examples, the boosting role of comprehensive two-dimensional gas chromatography within food-omics is described, providing to the reader evidence of how comprehensive multidimensional separations based platforms have introduced new concepts and tools in the analytical measurement of complex biological samples.

Numerical and experimental examination of the retention of magnetic nanoparticles in magnetic chromatography

This paper investigates a promising new approach of magnetic chromatography to improve particle fractionation in industrial-scale production. To understand the still challenging multidimensional separation mechanism, we develop a novel method to simulate magnetic nanoparticles’ behavior in a magnetized chromatographic column based on a lattice Boltzmann method. In contrast to conventional numerical studies, the Euler–Euler approach is applied by utilizing the advection–diffusion equation to describe the particle component. As a result, the consideration of the magnetic force, the drag force, and a realistic diffusion are possible. Also, enormous computational costs are saved. We show that the column can be modeled from a combination of two unit cells, and a separation effect can be detected even with small magnetic fields. Furthermore, we compare the numerical results to practical experiments. Both results are in good accordance. We additionally determine potential improvements. Besides improving the setup by using stronger magnetic fields, the structure of the column’s matrix plays an important role. Thus, we find that higher porosity leads to higher retention times.

Different ion mobility-mass spectrometry coupling techniques to promote metabolomics.

Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.

Efficient Rejection of Artifacts for Short-Term Few-Channel EEG Based on Fast Adaptive Multidimensional Sub-Bands Blind Source Separation

Artifacts rejection is crucial to electroencephalogram (EEG) application. Short-term few-channel EEG (e.g., in real-time detection of stress level and motor imagery) brings new challenges for removing artifacts due to less data. The existing artifact removal methods cannot guarantee both effectiveness and efficiency for removing artifacts from short-term few-channel EEG recordings. Consequently, we propose a fast adaptive sub-bands’ blind source separation method to remove artifacts from short-term few-channel EEG recordings effectively and efficiently. First, noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD), as a fast adaptive multidimensional sub-bands’ decomposition method, is used to decompose short-term few-channel EEG recordings into multidimensional sub-bands. Then canonical correlation analysis (CCA), as a blind source separation method, is used to estimate artifact-related and EEG-related sources. Finally, EEG-related sources are intelligently selected and reconstructed as clean EEG recordings. The results demonstrate that our method takes at least five times less computing time for 2-s few-channel EEG recordings than state-of-the-art methods with similar effectiveness, using the same computer and software. Therefore, our method enhances the efficiency of removing artifacts from short-term few-channel EEG recordings while ensuring effectiveness, and it is more suitable for real-time processing.

Average Theoretical Peak Time as a Metric to Analytical Speed in Monodimensional and Multidimensional Gas Chromatographic Separations

Average Theoretical Peak Time as a Metric to Analytical Speed in Monodimensional and Multidimensional Gas Chromatographic Separations

The role of sample preparation in multidimensional gas chromatographic separations for non‐targeted analysis with the focus on recent biomedical, food, and plant applications

In this review, we consider and discuss the affinity and complementarity between a generic sample preparation technique and the comprehensive two-dimensional gas chromatography process. From the initial technical development focus (e.g., on the GC×GC and solid-phase microextraction techniques), the trend is inevitably shifting toward more applied challenges, and therefore, the preparation of the sample should be carefully considered in any GC×GC separation for an overreaching research. We highlight recent biomedical, food, and plant applications (2016-July 2020), and specifically those in which the combination of tailored sample preparation methods and GC×GC-MS has proven to be beneficial in the challenging aspects of non-targeted analysis. Specifically on the sample preparation, we report on gas-phase, solid-phase, and liquid-phase extractions, and derivatization procedures that have been used to extract and prepare volatile and semi-volatile metabolites for the successive GC×GC analysis. Moreover, we also present a milestone section reporting the early works that pioneered the combination of sample preparation techniques with GC×GC for non-targeted analysis.

Two-dimensional separation techniques using supercritical fluid chromatography.

High-resolution separation systems are essential for the analysis of complex mixtures in a wide variety of application areas. To increase resolution, multidimensional chromatographic techniques have been one key solution. Supercritical fluid chromatography provides a unique opportunity in these multidimensional separations based on its potential for high solvent compatibility, rapid duty cycles, and orthogonality to other separation modes. This review focuses on two-dimensional chromatography methods from the past decade that use supercritical fluid chromatography because of these advantages. Valving schemes and modulation strategies used to interface supercritical fluid chromatography with other liquid chromatography and gas chromatography techniques are described. Particular applications of multidimensional separations using supercritical fluid chromatography for the analysis of oils and chiral separations of pharmaceutical compounds are highlighted. Limitations of and a potential trajectory for supercritical fluid chromatography in this field are also discussed.

Recent advances in microchip enantioseparation and analysis.

This review summarizes recent developments (over the past decade) in the field of microfluidics-based solutions for enantiomeric separation and detection. The progress in various formats of microchip electrodriven separations, such as MCE, microchip electrochromatography, and multidimensional separation techniques, is discussed. Innovations covering chiral stationary phases, surface coatings, and modification strategies to improve resolution, as well as integration with detection systems, are reported. Finally, combinations with other microfluidic functional units are also presented and highlighted.

Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis.

BACKGROUNDTuberculosis (TB) kills more people than any other infection, and new diagnostic tests to identify active cases are required. We aimed to discover and verify novel markers for TB in nondepleted plasma.METHODSWe applied an optimized quantitative proteomics discovery methodology based on multidimensional and orthogonal liquid chromatographic separation combined with high-resolution mass spectrometry to study nondepleted plasma of 11 patients with active TB compared with 10 healthy controls. Prioritized candidates were verified in independent UK (n = 118) and South African cohorts (n = 203).RESULTSWe generated the most comprehensive TB plasma proteome to date, profiling 5022 proteins spanning 11 orders-of-magnitude concentration range with diverse biochemical and molecular properties. We analyzed the predominantly low–molecular weight subproteome, identifying 46 proteins with significantly increased and 90 with decreased abundance (peptide FDR ≤ 1%, q ≤ 0.05). Verification was performed for novel candidate biomarkers (CFHR5, ILF2) in 2 independent cohorts. Receiver operating characteristics analyses using a 5-protein panel (CFHR5, LRG1, CRP, LBP, and SAA1) exhibited discriminatory power in distinguishing TB from other respiratory diseases (AUC = 0.81).CONCLUSIONWe report the most comprehensive TB plasma proteome to date, identifying novel markers with verification in 2 independent cohorts, leading to a 5-protein biosignature with potential to improve TB diagnosis. With further development, these biomarkers have potential as a diagnostic triage test.FUNDINGColciencias, Medical Research Council, Innovate UK, NIHR, Academy of Medical Sciences, Program for Advanced Research Capacities for AIDS, Wellcome Centre for Infectious Diseases Research.