Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement.

Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently ill-posed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR. Herein, we propose a proof-of-concept approach that blends a physics-informed strategy with data-driven deep learning for two-dimensional Laplace NMR reconstruction. This approach integrates prior knowledge of mathematical and physical laws governing multidimensional decay signals by constructing a forward process model to simulate relationships among different decay factors. Benefiting from a noniterative neural network algorithm that automatically acquires prior information from synthetic data during training, this approach avoids tedious parameter tuning and enhances user friendliness. Experimental results demonstrate the practical effectiveness of this approach. As an advanced and impactful technique, this approach brings a fresh perspective to multidimensional Laplace NMR inversion.

Pure-Shift-Based Proton Magnetic Resonance Spectroscopy for High-Resolution Studies of Biological Samples.

Proton magnetic resonance spectroscopy (1H MRS) presents a powerful tool for revealing molecular-level metabolite information, complementary to the anatomical insight delivered by magnetic resonance imaging (MRI), thus playing a significant role in in vivo/in vitro biological studies. However, its further applications are generally confined by spectral congestion caused by numerous biological metabolites contained within the limited proton frequency range. Herein, we propose a pure-shift-based 1H localized MRS method as a proof of concept for high-resolution studies of biological samples. Benefitting from the spectral simplification from multiplets to singlet peaks, this method addresses the challenge of spectral congestion encountered in conventional MRS experiments and facilitates metabolite analysis from crowded NMR resonances. The performance of the proposed pure-shift 1H MRS method is demonstrated on different kinds of samples, including brain metabolite phantom and in vitro biological samples of intact pig brain tissue and grape tissue, using a 7.0 T animal MRI scanner. This proposed MRS method is readily implemented in common commercial NMR/MRI instruments because of its generally adopted pulse-sequence modules. Therefore, this study takes a meaningful step for MRS studies toward potential applications in metabolite analysis and disease diagnosis.

A Pure Shift-Based Nuclear Magnetic Resonance Method for In-Phase Three-Dimensional Diffusion-Ordered Spectroscopy.

Diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY) plays a vital role in mixture studies. However, its applications to complex mixture samples are generally limited by spectral congestion along the chemical shift domain caused by extensive J coupling networks and abundant compounds. Herein, we develop the in-phase multidimensional DOSY strategy for complex mixture analyses by simultaneously revealing molecular self-diffusion behaviors and multiplet structures with optimal spectral resolution. As a proof of concept, two pure shift-based three-dimensional (3D) DOSY protocols are proposed to record high-resolution 3D spectroscopic view with separated mixture components and their resolved multiplet coupling structures, thus suitable for analyzing complex mixtures that contain abundant compounds and complicated molecular structures, even under adverse magnetic field conditions. Therefore, this study shows a promising tool for component analyses and multiplet structure studies on practical mixture samples.

High-Quality Reconstruction for Laplace NMR Based on Deep Learning.

Laplace nuclear magnetic resonance (NMR) exploits relaxation and diffusion phenomena to reveal information regarding molecular motions and dynamic interactions, offering chemical resolution not accessible by conventional Fourier NMR. Generally, the applicability of Laplace NMR is subject to the performance of signal processing and reconstruction algorithms involving an ill-posed inverse problem. Here, we propose a proof-of-concept of a deep-learning-based method for rapid and high-quality spectra reconstruction from Laplace NMR experimental data. This reconstruction method is performed based on training on synthetic exponentially decaying data, which avoids a vast amount of practically acquired data and makes it readily suitable for one-dimensional relaxation and diffusion measurements by commercial NMR instruments.

Neural Network Method for Diffusion-Ordered NMR Spectroscopy.

Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of the mixed components. For the interpretation of the diffusion information, intrinsically designed algorithms for a DOSY spectrum reconstruction are required. The estimated diffusion coefficients are desired to have consistency for all the spectral signals from the same molecule and good separation of signals from different molecules. For this purpose, we propose a novel method that adopts a coordinated multiexponential fitting to ensure the consistency of diffusion coefficients and apply a sparse constraint to enhance the robustness. A lightweight neural network is applied as an optimizer to solve this highly nonlinear and nonconvex optimization problem. The proposed method provides estimated diffusion coefficients with excellent distinguishment between species and outperforms the state-of-the-art reconstruction algorithms, such as the Laplacian inversion and the multivariate fitting methods.

Adaptable Singlet-Filtered Nuclear Magnetic Resonance Spectroscopy for Chemical and Biological Applications.

Proton nuclear magnetic resonance (1H NMR) spectroscopy presents a powerful detection tool for studying chemical compositions and molecular structures. In practical chemical and biological applications, 1H NMR experiments are generally confronted with the challenge of spectral congestions caused by abundant observable components and intrinsic limitations of a narrow frequency distribution range and extensive J coupling splitting. Herein, a one-dimensional (1D) general NMR method is proposed to individually extract the signals of targeted proton groups based on their endogenous spin singlet states excited from J coupling interactions, and it is suitable for high-resolution detections on complex chemical and biological samples. The applicability of the proposed method is demonstrated by experimental observations on chemical solutions containing different coupled components, intact grape tissues subjected to crowded resonances, and in vitro pig brain with various metabolites. Moreover, the proposed method is further exploited for magnetic resonance spectroscopy applications by directly combining the spatial localization module, showing promise in in vivo biological metabolite studies.

Multiplet analysis by strong-coupling-artifact-suppression 2D J-resolved NMR spectroscopy.

Benefitting from the capability of recording scalar (J) couplings and bonding information, 2D J-resolved NMR spectroscopy constitutes an important tool for molecular structure analysis and mixture component identification. Unfortunately, conventional 2D J-resolved experiments generally encounter challenges of insufficient spectral resolution and strong coupling artifacts. In this study, a general NMR approach is exploited to record absorption-mode artifact-free 2D J-resolved spectra. This proposal adopts the advanced triple-spin-echo pure shift yielded by chirp excitation element to eliminate J coupling splittings and preserve chemical shifts along the F2 dimension, and it additionally utilizes the echo-train J acquisition to reveal the multiplet structure along the F1 dimension in accelerated experimental acquisition. Thus, it permits one to extract multiplet structure information from crowded spectral regions in one-shot experiments, with considerable resolution advantage resulting from completely decoupling F2 dimension and absorption-mode presentation, thus facilitating analysis on complex samples. More importantly, this method grants the superior performance on suppressing strong coupling artifacts, which have been affirmed by experiments on a series of chemical samples. As a consequence, this proposed method serves as a useful tool for J coupling measurements and multiplet structure analyses on complex samples that contain crowded NMR resonances and strong coupling spin systems, and it may exhibit broad application potentials in fields of physics, chemistry, and medical science, among others.

High-Resolution Reconstruction for Diffusion-Ordered NMR Spectroscopy.

Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of mixed components. The applicability of DOSY measurements on complex mixtures is generally limited by the performance of data reconstruction algorithms. Here, based on constraints on low rank and sparsity of DOSY data, we propose a reconstruction method to achieve high-resolution DOSY spectra with excellent peak alignments and accurate diffusion coefficients for measurements of complex mixtures even when component signals are congested and mixed together along the spectral dimension. This proposed method is robust and suitable for DOSY data acquired from common commercial NMR instruments; thus, it may broaden the scope of DOSY applications.

Dispersive liquid-liquid microextraction combined with acetonitrile stacking through capillary electrophoresis for the determination of three selective serotonin reuptake inhibitor drugs in body fluids.

Dispersive liquid-liquid microextraction was combined with acetonitrile stacking in capillary electrophoresis for the identification of three selective serotonin reuptake inhibitors (citalopram, fluoxetine, and fluvoxamine) in human fluids such as urine and plasma. Parameters that affect the extraction and stacking efficiency, such as the type and volume of the extraction and disperser solvent, extraction time, salt addition for dispersive liquid-liquid microextraction, and sample matrices, pH, and concentration of the separation buffer for stacking, were investigated and optimized. Under optimum conditions, the enrichment factors were in the range of 1195-1441. Limits of detection ranged from 1.4 to 1.7 nM for the target analytes. Calibration graphs displayed satisfied linearity with R2 greater than or equal to 0.9978, and relative standard deviations of the peak area analysis were in the range of 2.9-5.0% (n = 3). The recoveries of all tricyclic antidepressant drugs from urine and plasma were in the range of 77-117 and 79-106%, respectively. The findings of this study show that dispersive liquid-liquid microextraction acetonitrile-stacking capillary electrophoresis is a rapid and convenient method for identifying tricyclic antidepressant drugs in urine and plasma.

Application of strong transverse magneto-optical Kerr effect on high sensitive surface plasmon grating sensors.

A high sensitive sensor is demonstrated by exploiting strong transverse magneto-optical Kerr effect on a ferromagnetic surface plasmon grating. The surface plasmon grating, made of a hybridized Au/Fe/Au layer, exhibits a very dispersive Kerr parameter variation near the surface plasmon polariton (SPP) wavelength via coherent scattering of the SPP on the grating structure. Interrogating this Kerr parameter can be utilized for detecting chemical or biological objects in a fluid medium. The experiment results show the minimal detectable mass concentration of sodium chloride in a saline solution is 4.27 × 10(-3) %, corresponding to a refractive index change of 7.60 × 10(-6) RIU. For an avidin-biotin interaction experiment, the sensitivity of avidin detection in PBS solution is 1.97 nM, which is limited by the index fluctuation of flowing media during measurement.

SCREENES: Enhancing non-uniform sampling reconstruction for symmetrical NMR spectroscopy.

BACKGROUND: Symmetrical NMR spectroscopy, such as Total Correlation Spectroscopy (TOCSY) and other homonuclear spectroscopy, displays symmetry in chemical shift but are generally not symmetrical in terms of intensity, which constitutes a pivotal branch of multidimensional NMR spectroscopy and offers a robust tool for elucidating the structures and dynamics of complex samples, particularly in the context of biological macromolecules. Non-Uniform Sampling (NUS) stands as a critical technique for accelerating multidimensional NMR experiments. However, symmetrical NMR spectroscopy inherently presents dynamic peak intensities, where cross peaks tend to be substantially weaker compared to diagonal peaks. Recovering these weaker cross peaks from NUS data poses a significant challenge, often resulting in compromised data quality. RESULTS: We enhance the reconstruction quality of NUS symmetrical NMR spectroscopy based on the assumption that the asymmetry in intensity is mild. Regarding the sampling schedule, we employ the symmetrical sampling structure integrated with Poisson sampling schedule to enhance the efficiency of data acquisition. In term of the reconstruction algorithm, we propose the new method by incorporating hard and soft symmetrical constraints into our recently developed L1-norm-based Compressed Sensing (CS) method known as Sparse Complex-valued REconstruction Enabled by Newton method (SCREEN). Additionally, we propose a two-step reconstruction strategy that separately addresses diagonal and cross peaks. In this two-step strategy, cross peaks are effectively reconstructed by excluding the stronger diagonal peaks. Extensive experimental results validate the effectiveness of our proposed methodology. SIGNIFICANCE: This method enhances the overall quality of the reconstructed NUS symmetrical NMR spectra, especially in terms of cross peaks, thereby enriching the interpretation of spectral information. Furthermore, it boosts the robustness towards regularization parameters, facilitating a user-friendly experience.

GRIN-toolbox: A versatile and light toolbox for NMR inversion.

NMR technique serves as a powerful analytical tool with diverse applications in fields such as chemistry, biology, and material science. However, the effectiveness of NMR heavily relies on data post-processing which is often modeled as regularized inverse problem. Recently, we proposed the Generally Regularized INversion (GRIN) algorithm and demonstrated its effectiveness in NMR data processing. GRIN has been integrated as a friendly graphic user interface-based toolbox which was not detailed in the original paper. In this paper, to make GRIN more practically accessible to NMR practitioners, we focus on introducing the usage of GRIN-Toolbox with processing examples and the corresponding processing graphic interfaces, and the user manual is attached as Supplementary Material. GRIN-Toolbox is versatile and lightweight, where various kinds of data processing tasks can be completed with one click, including but not limited to diffusion-ordered spectroscopy processing, magnetic resonance imaging under-sampling reconstruction, Laplace (diffusion or relaxation) NMR inversion, spectrum denoising, etc. In addition, GRIN-Toolbox could be extended to more applications with user-designed inversion models and freely available at https://github.com/EricLin1993/GRIN.

High-resolution diffusion-order NMR spectroscopy in inhomogeneous magnetic fields via intermolecular zero-quantum coherences.

Diffusion-order NMR spectroscopy (DOSY) presents a powerful tool for studying solution mixtures by recording diffusion coefficients of individual components and separating their signals into respective 1D NMR spectra. Existing DOSY experiments, however, are generally unsuitable for measurements under adverse magnetic field conditions, because calculations for diffusion coefficients strictly rely on resolved resonances in the 1D NMR spectral domain. Herein, we propose a general DOSY method by introducing intermolecular zero-quantum coherence mechanism into molecular diffusion evolution to overcome the challenge of magnetic field inhomogeneity and to record high-resolution DOSY spectra free of magnetic field inhomogeneity. Our experimental results and theoretical interpretation suggest that the proposed method allows to diffusion analysis and component discrimination on solution mixtures under externally inhomogeneous magnetic field conditions and on intact biological tissues with field inhomogeneity internally caused by magnetic susceptibility variations. This study provides a previously unreported NMR protocol for high-resolution DOSY measurements in inhomogeneous magnetic fields, thus broadening the scope of DOSY applications and showing the promising prospect for studies on chemical and biological mixture samples.

Simultaneous determination of multiple coupling networks by high-resolution 2D J-edited NMR spectroscopy.

J coupling constitutes an important NMR parameter for molecular-level composition analysis and conformation elucidation. Dozens of J-based approaches have been exploited for J coupling measurement and coupling network determination, however, they are generally imposed to insufficient spectral resolution to resolve crowded NMR resonances and low measurement efficiency that a single experiment records one J coupling network. Herein, we propose a general NMR method to collect high-resolution 2D J-edited NMR spectra, which are characterized with advantages of pure absorptive lineshapes, decoupled chemical shift dimension, as well as eliminated axial peaks, thus facilitating J coupling partner assignments and J coupling constant measurements. More meaningfully, this protocol allows simultaneous determination of multiple coupling networks for highly efficient multiplet analyses via addressing multiple protons within one single experiment. Additionally, another variant is proposed for high-resolution applications under adverse magnetic field conditions. Therefore, this study provides a useful NMR protocol for configurational and structural studies with extensive applications in chemistry, biology, and material science.

A General Reconstruction Method for Multidimensional Sparse Sampling Nuclear Magnetic Resonance Spectroscopy.

Multidimensional NMR spectroscopy provides a powerful tool for structure elucidation and dynamic analysis of complex samples, particularly for biological macromolecules. Multidimensional sparse sampling effectively accelerates NMR experiments while an efficient reconstruction method is generally required for unraveling spectra. Various reconstruction methods were proposed for pure Fourier NMR (only involving chemical shifts and J couplings detection). However, reconstruction concerned with Laplace-related NMR (i.e., involving relaxation or diffusion detection) is more challenging due to its ill-posed property. The existing Laplace-related NMR sparse sampling reconstruction methods suffer from poor resolution and possible artifacts in the resulting spectra owing to the pitfalls of the optimization algorithms. Herein, we propose a general approach for fast high-resolution reconstruction of multidimensional sparse sampling NMR, including pure Fourier, mixed Fourier-Laplace, and pure Laplace NMR, benefiting from the comprehensive sparse constraint and effective optimization algorithm and thus showing the promising prospects of multidimensional NMR.

High-Resolution Reconstruction for Multidimensional Laplace NMR.

As a perfect complement to conventional NMR that aims for chemical structure elucidation, Laplace NMR constitutes a powerful technique to study spin relaxation and diffusion, revealing information on molecular motions and spin interactions. Different from conventional NMR adopting Fourier transform to deal with the acquired data, Laplace NMR relies on specially designed signal processing and reconstruction algorithms resembling the inverse Laplace transform, and it generally faces severe challenges in cases where high spectral resolution and high spectral dimensionality are required. Herein, based on the tensor technique for high-dimensional problems and the sparsity assumption, we propose a general method for high-resolution reconstruction of multidimensional Laplace NMR data. We show that the proposed method can reconstruct multidimensional Laplace NMR spectra in a high-resolution manner for exponentially decaying relaxation and diffusion data acquired by commercial NMR instruments. Therefore, it would broaden the scope of multidimensional Laplace NMR applications.

A pure shift and spin echo based approach for high-resolution diffusion-ordered NMR spectroscopy.

Diffusion-ordered NMR spectroscopy (DOSY) can be used for separating mixture components according to their individual diffusion behaviors, thus offering a powerful tool for the analysis of compound mixtures. However, conventional DOSY experiments generally encounter the problem of limited resolution in the spectral domain, particularly for applications to complex mixtures that contains crowed resonances in 1D NMR. In addition, chemical exchange effects, bringing about spurious component signals, pose another limitation for interpreting DOSY measurements. Here, a general DOSY method is proposed based on pure shift extraction and spin echo evolution to obtain high-resolution 2D DOSY spectra, along with the suppression on effects of chemical exchange and J coupling. Both theoretical analyses and experimental results suggest that the proposed method is useful for high-resolution DOSY measurements on complex mixtures that contains crowded or even overlapped NMR resonances and exchanging spin systems.

On-line sample preconcentration by sweeping and poly(ethylene oxide)-mediated stacking for simultaneous analysis of nine pairs of amino acid enantiomers in capillary electrophoresis.

This study proposes a sensitive method for the simultaneous separation and concentration of 9 pairs of amino acid enantiomers by combining poly(ethylene oxide) (PEO)-based stacking, ß-cyclodextrin (ß-CD)-mediated micellar electrokinetic chromatography (MEKC), and 9-fluoroenylmethyl chloroformate (FMOC) derivatization. The 9 pairs of FMOC-derivatized amino acid enantiomers were baseline separated using a discontinuous system, and the buffer vials contained a solution of 150 mM Tris-borate (TB), 12.5% (v/v) isopropanol (IPA), 0.5% (w/v) PEO, 35 mM sodium taurodeoxycholate (STDC), and 35 mM ß-CD, and the capillary was filled with a solution of 1.5 M TB, 12.5% (v/v) IPA, 35 mM STDC, and 35 mM ß-CD. Based on the difference in viscosity between the sample zone and PEO solution and because of the STDC sweeping, the discontinuous system effectively stacked 670 nL of the 9 pairs of FMOC-derivatized amino acid enantiomers without losing chiral resolution. Consequently, the limits of detection for the 9 pairs of FMOC-derivatized amino acid enantiomers were reduced to 40-60 nM. This method was successfully used to determine d-Tryptophan (Trp), l-Trp, d-Phenylalanine (Phe), l-Phe, d-Glutamic acid (Glu), and l-Glu in various types of beers.

On-line concentration and separation of cationic and anionic neurochemicals by capillary electrophoresis with UV absorption detection.

This paper presents on-line simultaneous concentration and separation of cationic and anionic neurochemicals by capillary electrophoresis (CE) with UV absorbance spectroscopy. Neurochemical stacking exploits differences in local electric field and viscosity between the sample zone and the background electrolyte (BGE). To achieve these discontinuous conditions for CE, neurochemicals were prepared in a solution containing 1mM formic acid and 20% (v/v) acetonitrile (ACN). The capillary was filled with a solution of 500 mM Tris-borate (TB) and 10% (v/v) glycerol. The buffer vial contained 500 mM TB and 0.5% (v/v) polyethylene oxide (PEO). After injecting a large sample volume, PEO enters the capillary by electro-osmotic flow (EOF). Anionic neurochemicals stacked at the sample zone and PEO-containing BGE boundary. Simultaneously, cationic neurochemicals were concentrated at the boundary between the sample zone and the glycerol-containing BGE. The concentrated cationic neurochemicals were baseline separated in the presence of glycerol, mainly due to hydrogen bonding interactions between glycerol hydroxyl groups and the neurochemical's hydroxyl and amino groups. Under optimal stacking conditions, we observed the following: (a) the maximum sample injection volume was 720 nL; (b) the limit of detection for signal-to-noise ratio of 3 ranged from 14.7 to 313.4 nM; and (c) sensitivity enhancements compared to normal injection (15 nL) ranged from 116 to 281-fold. We evaluated the proposed method by the determination of neurochemicals in urine samples.