Automated Analysis of Quantitative NMR Spectra

NMR spectroscopy is an invaluable tool for structure elucidation in chemistry and molecular biology, which is able to provide unique information not easily obtained by other analytical methods. However, performing quantitative NMR experiments and mixture analysis is considerably less common due to constraints in sensitivity/resolution and the fact that NMR observes individual nuclei, not molecules. The advances in instrument design in the last 25 years have substantially increased the sensitivity of NMR spectrometers, diminishing the main weakness of NMR, while increases in field strength and ever more intricate experiments have improved the resolving power and expanded the attainable information. The minimal need for sample preparation and its non-specific nature make quantitative NMR suitable for many applications ranging from quality control to metabolome characterization. Furthermore, the development of automated sample changers and fully automated acquisition have made high-throughput NMR acquisition a more feasible and attractive, yet expensive, possibility. This work discusses the fundamental principles and limitations of quantitative liquid state NMR spectroscopy, and tries to put together a summary of its various aspects scattered across literature. Many of these more subtle features can be neglected in simple routine spectroscopy, but become important when extracting quantitative data and/or when trying to acquire and process vast amounts of spectra consistently. The original research presented in this thesis provides improved methods for data acquisition of quantitative 13C detected NMR spectra in the form of modified INEPT based experiments (Q-INEPT-CT and Q-INEPT-2D), while software tools for automated processing and analysis of NMR spectra are also presented (ImatraNMR and SimpeleNMR). The application of these tools is demonstrated in the analysis of complex hydrocarbon mixtures (base oils), plant extracts and blood plasma samples. The increased capability of NMR spectroscopy, the rising interest in metabolomics and for example the recent introduction of benchtop NMR spectrometers are likely to expand the future use of quantitative NMR in the analysis of complex mixtures. For this reason, the further development of robust, accurate and feasible analysis methods and tools is essential.NMR-spektroskopia on keskeinen mm. kemiassa ja molekyylibiologiassa käytetty analyysimenetelmä, joka perustuu atomiydinten havaitsemiseen voimakkaassa magneettikentässä radioaaltojen avulla. Menetelmä soveltuu erityisen hyvin molekyylirakenteiden selvittämiseen, ja sillä voidaan saada tietoa myös molekyylien kolmiulotteisesta rakenteesta sekä niiden välisistä interaktioista. NMR-spektroskopia on myös epäselektiivinen menetelmä, jolla on helppo tutkia erityyppisiä näytteitä ilman monimutkaista esikäsittelyä. Perinteisesti NMR-spektroskopian heikkoutena on ollut spektrometrien kalleus ja huono herkkyys, joka on rajannut sen käyttöä laimeiden näytteiden ja etenkin seosten analysoinnissa. Laitteistojen ja analyysitekniikoiden parantuminen viimeisten 20-30 vuoden aikana on kuitenkin kohentanut tilannetta merkittävästi, ja NMR-spektroskopian käyttäminen seosten kvantitatiiviseen analyysiin on selvässä kasvussa. Etenkin metaboliittien analysoimisesta erilaisista biologisista näytteistä on muodostunut tärkeä sovellus. Tätä kehitystä on vauhdittanut myös näytteenkäsittelyn ja spektrien prosessoinnin automaation kehittyminen, joka helpottaa suurien näytemäärien tutkimista. Suurin osa NMR-spektrien käsittelyyn tarkoitetuista ohjelmistoista ei kuitenkaan vielä ole suunniteltu ensisijaisesti suurten näytesarjojen tai seosten analysointiin. Tämä työ keskittyy kvantitatiiviseen NMR-spektroskopiaan ja sen sovelluksiin. Työssä kehitettiin kvantitatiivisia NMR-menetelmiä (pulssisarjat), sekä spektrien analyysiin soveltuvia ohjelmistotyökaluja (ImatraNMR ja SimpeleNMR), joiden tavoitteena on etenkin suurten näytesarjojen automaattisen analysoinnin helpottaminen. Kehitettyjä työkaluja käytettiin hiilivetyseosten (perusöljyt) ja kasviekstraktien analysointiin, mutta niitä voidaan soveltaa myös moniin muihin näytesarjoihin tai esimerkiksi reaktioseosten analysointiin

Beyond the noise : high fidelity MR signal processing

This thesis describes a variety of methods developed to increase the sensitivity and resolution of liquid state nuclear magnetic resonance (NMR) experiments. NMR is known as one of the most versatile non-invasive analytical techniques yet often suffers from low sensitivity. The main contribution to this low sensitivity issue is a presence of noise and level of noise in the spectrum is expressed numerically as “signal-to-noise ratio”. NMR signal processing involves sensitivity and resolution enhancement achieved by noise reduction using mathematical algorithms. A singular value decomposition based reduced rank matrix method, composite property mapping, in particular is studied extensively in this thesis to present its advantages, limitations, and applications. In theory, when the sum of k noiseless sinusoidal decays is formatted into a specific matrix form (i.e., Toeplitz), the matrix is known to possess k linearly independent columns. This information becomes apparent only after a singular value decomposition of the matrix. Singular value decomposition factorises the large matrix into three smaller submatrices: right and left singular vector matrices, and one diagonal matrix containing singular values. Were k noiseless sinusoidal decays involved, there would be only k nonzero singular values appearing in the diagonal matrix in descending order providing the information of the amplitude of each sinusoidal decay. The number of non-zero singular values or the number of linearly independent columns is known as the rank of the matrix. With real NMR data none of the singular values equals zero and the matrix has full rank. The reduction of the rank of the matrix and thus the noise in the reconstructed NMR data can be achieved by replacing all the singular values except the first k values with zeroes. This noise reduction process becomes difficult when biomolecular NMR data is to be processed due to the number of resonances being unknown and the presence of a large solvent peak

Advanced methods in fourier transform ion cyclotron resonance mass spectrometry

Mass spectrometry (MS) is a powerful analytical technique used to characterize various compounds by measuring the mass-to-charge ratio (m/z). Among different types of mass analyzers, Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) is the instrument of choice for those working at the forefront of research, as it offers incomparable mass accuracy, resolving power, and the highest flexibility for hybrid instrumentation and fragmentation techniques. The FT-ICR MS requires professional and careful tuning to achieve its superior performance. Our work aims to review, develop and apply advanced methods to improve the data quality of FT-ICR and push the limits of the instrument. FT-ICR spectrometry has been limited to the magnitude-mode for 40 years due to the complexity of the phase-wrapping problem. However, it is well known that by correcting phase of the data, the spectrum can be plotted in the absorption-mode with a mass resolving power that is as much as two times higher than conventional magnitude-mode. Based on the assumption that the frequency sweep excitation produces a quadratic accumulation in an ion’s phase value, a robust manual method to correct all ions’ phase shifts has been developed, which allows a broadband FT-ICR spectrum to be plotted in the absorption-mode. The developed phasing method has then been applied to a large variety of samples (peptides, proteins, crude oil), different spectral acquisition-mode (broadband, narrowband), and different design of ICR cells (Infinity cell, ParaCell) to compare the performance with the conventional magnitude-mode spectra. The outcome shows that, by plotting the absorption-mode spectrum, not only is the spectral quality improved at no extra cost, but the number of detectable peaks is also increased. Additionally, it has been found that artifactual peaks, such as noise or harmonics in the spectrum can be diagnosed immediately in the absorption-mode. Given the improved characteristics of the absorption-mode spectrum, the following research was then focused on a data processing procedure for phase correction and the features of the phase function. The results demonstrate that in the vast majority of cases, the phase function needs to be calculated just once, whenever the instrument is calibrated. In addition, an internal calibration method for calculating the phase function of spectra with insufficient peak density across the whole mass range has been developed. The above research is the basis of the Autophaser program which allows spectra recorded on any FT-ICR MS to be phase corrected in an automated manner

Advanced techniques for diagnostics and control applied to particle accelerators

201 p.Esta tesis versa en torno a tecnologías y técnicas novedosas orientadas al diagnóstico y control para aceleradores de partículas. Se centra principalmente en el desarrollo de dos aplicaciones para dicho propósito; un monitor de posición de haz (beam position monitor o BPM en inglés) por un lado, y un control de RF denominado sistema de RF de bajo nivel (low-level RF o LLRF en inglés) por el otro. Además, se han desarrollado completos bancos de pruebas, permitiendo de esta manera el testeo de las mencionadas soluciones en el laboratorio. El estudio de técnicas de muestreo y procesamiento digital para su posterior implementación también juega un papel importante en este trabajo.De esta manera, las principales contribuciones de esta tesis son el desarrollo de un BPM y un sistema de control LLRF altamente flexibles y reconfigurables, estando ambos basados en hardware digital. Las soluciones presentadas han sido diseñadas con el objetivo de crear herramientas especialmente adecuadas para labores de investigación en laboratorio. Las aplicaciones obtenidas cumplen este objetivo, mostrando características especialmente valiosas como una rápida etapa de prototipado y alta modularidad.Otra línea de la presente tesis está dirigida al estudio de técnicas avanzadas de muestreo y procesamiento digital de señal, las cuales son posteriormente implementadas en las citadas aplicaciones. Finalmente, la última parte de este trabajo trata sobre la integración de la información producida por estas herramientas de diagnóstico y control en EPICS, un sistema de control ampliamente utilizado en el campo de los aceleradores de partículas

Improvement of sample classification and metabolite profiling in 1H-NMR by a machine learning-based modelling of signal parameters

RMN és una plataforma analítica utilitzada per quantificar els metabòlits presents en les mostres de metabolòmica. Els espectres de 1H-RMN mostren múltiples senyals de metabòlits amb tres paràmetres específics (desplaçament químic, ample mitjà de banda, intensitat) que poden mostrar reactivitat a les condicions de la mostra. Aquesta reactivitat perjudica l'optimització del fitat dels espectres necessari per a realitzar el perfilat automàtic de metabòlits de les mostres. L'objectiu d'aquesta tesi va ser l'exploració de l'ús de tècniques de tendència basades en Machine Learning (ML) amb l'ús de fluxos de treball robustos per modelar i explotar la informació present en els diferents paràmetres de senyal durant el perfilat de metabòlits dels conjunts de dades 1H-NMR. En particular, les aplicacions considerades van ser la millora de la classificació de les mostres en els estudis de metabolòmica i la millora de la qualitat del perfilat automàtic. A més d'assolir aquests objectius, també es van obtenir èxits addicionals (per exemple, la generació d'una nova eina de codi obert capaç de resoldre els reptes en l'elaboració de perfils de matrius complexes).RMN es una plataforma analítica utilizada para cuantificar los metabolitos presentes en las muestras de metabolómica. Los espectros de 1H-RMN muestran múltiples señales de metabolitos con tres parámetros específicos (desplazamiento químico, ancho medio de banda, intensidad) que pueden mostrar reactividad a las condiciones de la muestra. Esta reactividad perjudica a la optimización del fitado de los espectros necesario para realizar el perfilado automático de metabolitos de las muestras. El objetivo de esta tesis fue la exploración del uso de técnicas de tendencia basadas en Machine Learning (ML) con el uso de flujos de trabajo robustos para modelar y explotar la información presente en los diferentes parámetros de señal durante el perfilado de metabolitos de los conjuntos de datos 1H-NMR. En particular, las aplicaciones consideradas fueron la mejora de la clasificación de las muestras en los estudios de metabolómica y la mejora de la calidad del perfilado automático. Además de lograr estos objetivos, también se obtuvieron logros adicionales (por ejemplo, la generación de una nueva herramienta de código abierto capaz de resolver los retos en la elaboración de perfiles de matrices complejas).NMR is an analytical platform used to quantify the metabolites present in metabolomics samples. 1H-NMR spectra show multiple metabolite signals, each one with three parameters (chemical shift, half bandwidth, intensity) which can show reactivity to the sample conditions. This reactivity is a challenge for the optimization of the lineshape fitting of spectra necessary to perform the automatic metabolite profiling of samples. The aim of this PhD thesis was the exploration of the use of trending machine learning (ML)-based techniques and of robust ML-based workflows to model and then exploit the information present in the different parameters collected for each signal during the metabolite profiling of 1H-NMR datasets. In particular, the applications considered were the enhanced classification of samples in metabolomics studies and the enhancement of the quality of automatic profiling in 1H-NMR datasets. in addition to the achievement of these goals, additional achievements (e.g., the generation of a new open-source tool able to solve challenges in the profiling of complex matrices) was also fulfilled