Simultaneous measurement of glutamate, glutamine, GABA, and glutathione by spectral editing without subtraction

Miniaturized Electrochemical Sensing Platforms for Quantitative Monitoring of Glutamate Dynamics in the Central Nervous System

Glutamate is one of the most important excitatory neurotransmitters within the mammalian central nervous system. The role of glutamate in regulating neural network signaling transmission through both synaptic and extra-synaptic paths highlights the importance of the real-time and continuous monitoring of its concentration and dynamics in living organisms. Progresses in multidisciplinary research have promoted the development of electrochemical glutamate sensors through the co-design of materials, interfaces, electronic devices, and integrated systems. This review summarizes recent works reporting various electrochemical sensor designs and their applicability as miniaturized neural probes to in vivo sensing within biological environments. We start with an overview of the role and physiological significance of glutamate, the metabolic routes, and its presence in various bodily fluids. Next, we discuss the design principles, commonly employed validation models/protocols, and successful demonstrations of multifunctional, compact, and bio-integrated devices in animal models. The final section provides an outlook on the development of the next generation glutamate sensors for neuroscience and neuroengineering, with the aim of offering practical guidance for future research.

Universal dynamic fitting of magnetic resonance spectroscopy

PURPOSE

Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type.

METHODS

A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting.

RESULTS

Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data.

CONCLUSION

A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Detection of 13C labeling of glutamate and glutamine in human brain by proton magnetic resonance spectroscopy

A proton magnetic resonance spectroscopy (MRS) technique was used to measure ¹³C enrichments of glutamate and glutamine in a 3.5 × 1.8 × 2 cm³ voxel placed in the dorsal anterior cingulate cortex of five healthy participants after oral administration of [U-¹³C]glucose. Strong pseudo singlets of glutamate and glutamine were induced to enhance the signal strength of glutamate and glutamine. This study demonstrated that ¹³C labeling of glutamate and glutamine can be measured with the high sensitivity and spatial resolution of ¹H MRS using a proton-only MRS technique with standard commercial hardware. Furthermore, it is feasible to measure ¹³C labeling of glutamate and glutamine in limbic structures, which play major roles in behavioral and emotional responses and whose abnormalities are involved in many neuropsychiatric disorders.

Glutamine Imaging: A New Avenue for Glioma Management

The glutamine pathway is emerging as an important marker of cancer prognosis and a target for new treatments. In gliomas, the most common type of brain tumors, metabolic reprogramming leads to abnormal consumption of glutamine as an energy source, and increased glutamine concentrations are associated with treatment resistance and proliferation. A key challenge in the development of glutamine-based biomarkers and therapies is the limited number of in vivo tools to noninvasively assess local glutamine metabolism and monitor its changes. In this review, we describe the importance of glutamine metabolism in gliomas and review the current landscape of translational and emerging imaging techniques to measure glutamine in the brain. These techniques include MRS, PET, SPECT, and preclinical methods such as fluorescence and mass spectrometry imaging. Finally, we discuss the roadblocks that must be overcome before incorporating glutamine into a personalized approach for glioma management.

Localized singlet-filtered MRS in vivo

MR is a prominent technology to investigate diseases, with millions of clinical procedures performed every year. Metabolic dysfunction is one common aspect associated with many diseases. Thus, understanding and monitoring metabolic changes is essential to develop cures for many illnesses, including for example cancer and neurodegeneration. MR methodologies are especially suited to study endogenous metabolites and processes within an organism in vivo, which has led to many insights about physiological functions. Advancing metabolic MR techniques is therefore key to further understand physiological processes. Here, we introduce an approach based on nuclear spin singlet states to specifically filter metabolic signals and particularly show that singlet-filtered glutamate can be observed distinctly in the hippocampus of a living mouse in vivo. This development opens opportunities to make use of the singlet spin phenomenon in vivo and besides its use as a filter to provide scope for new contrast agents.

Determination of Brain Metabolite T 1 Without Interference From Macromolecule Relaxation

BACKGROUND

J-coupled metabolites are often measured at a predetermined echo time in the presence of macromolecule signals, which complicates the measurement of metabolite T₁ .

PURPOSE

To evaluate the feasibility and benefits of measuring metabolite T₁ relaxation times without changing the overlapping macromolecule baseline signals.

STUDY TYPE

Prospective.

SUBJECTS

Five healthy volunteers (three females and two males; age = 27 ± 7 years).

FIELD STRENGTH/SEQUENCE

7T scanner using a point resolved spectroscopy (PRESS)-based spectral editing MR spectroscopy (MRS) sequence with inversion recovery (IR).

ASSESSMENT

F-tests were performed to evaluate if the new approach, which fitted all the spectra together and used the same baselines for the three different IR settings, significantly reduced the variances of the metabolite T₁ values compared to a conventional fitting approach.

STATISTICAL TESTS

Cramer-Rao lower bound (CRLB), within-subject coefficient of variation, and F-test.

RESULTS

The T₁ relaxation times of N-acetylaspartate (NAA), total creatine (tCr), total choline (tCho), myo-inositol (mI), and glutamate (Glu) were determined with CRLB values below 6%. Glutamine (Gln) T₁ was determined with a 17% CRLB, and the T₁ of γ-aminobutyric acid (GABA) was determined with a 34% CRLB. The new approach significantly reduced the variances (F-test P < 0.05) of NAA, Glu, Gln, tCr, tCho, and mI T₁ s compared to the conventional approach.

DATA CONCLUSION

Keeping macromolecule signals intact by using only long IR times allowed the use of a single macromolecule spectral model for different IR settings and significantly reduced the variances of NAA, Glu, Gln, tCr, tCho, and mI T₁ s.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1.

Signal enhancement of glutamine and glutathione by single-step spectral editing

A single-step spectral editing approach using an always-on editing pulse was proposed to enhance the signals of strongly coupled spins. Specifically, a single-step spectral editing sequence with an always-on editing pulse applied at 2.12 ppm was used to enhance glutamine (Gln) and glutathione (GSH) signals at TE = 56 ms on a 7 T scanner. Density matrix simulations demonstrated that the current method (TE = 56 ms) led to large signal enhancement of at least 61% for Gln and 51% for GSH compared to a previous single-step method (TE = 106 ms). Monte Carlo simulations showed that the current method reduced noise-originated variations by 31% for Gln and 26% for GSH compared to a previous three-step spectral editing method from which the present method was derived.

Simultaneous editing of GABA and GSH with Hadamard-encoded MR spectroscopic imaging

PURPOSE

To evaluate the feasibility of simultaneous MR spectroscopic imaging (MRSI) of gamma-aminobutyric acid (GABA) and glutathione (GSH) in the human brain using Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy (HERMES).

METHODS

Point RESolved Spectroscopy (PRESS)-localized MRSI was performed in GABA and GSH phantoms and in the human brain (n = 3) using HERMES editing and compared to conventional MEGA editing of each metabolite. Multiplet patterns, signal intensities, and metabolite crosstalk were compared between methods. GABA+ and GSH levels were compared between methods for bias and variability. Linear regression of HERMES-MRSI GABA+/H₂ O and GSH/H₂ O versus gray matter (GM) fraction were performed to assess differences between GM and white matter (WM).

RESULTS

Phantom HERMES-MRSI scans gave comparable GABA+ and GSH signals to MEGA-MRSI across the PRESS-localized volume. In vivo, HERMES-reconstructed GABA+ and GSH values had minimal measurement bias and variability relative to MEGA-MRSI. Intersubject coefficients of variation (CV) from two regions within the PRESS-localized volume for HERMES and MEGA were 6-12% for GABA+ and 6-19% for GSH. Interregion CVs were 5-15% for GABA+ and 3-17% for GSH. The GABA+/H₂ O and GSH/H₂ O ratios were ~1.8 times higher and ~1.9 times higher, respectively, in GM than in WM.

CONCLUSION

HERMES-MRSI of GABA+ and GSH was found to be practical in the human brain with minimal measurement bias and comparable variability to separate MEGA-edited acquisitions of each metabolite performed in double the scan time. The HERMES-MRSI is a promising method for simultaneously mapping the distribution of multiple low-concentration metabolites.

Effects of carrier frequency mismatch on frequency-selective spectral editing

OBJECTIVES

This study sought to investigate the effects of carrier frequency mismatch on spectral editing and its correction by frequency matching of basis functions.

MATERIALS AND METHODS

Full density matrix computations and Monte-Carlo simulations based on magnetic resonance spectroscopy (MRS) data collected from five healthy volunteers at 7 T were used to analyze the effects of carrier frequency mismatch on spectral editing. Relative errors in metabolite quantification were calculated with and without frequency matching of basis functions. The algorithm for numerical computation of basis functions was also improved for higher computational efficiency.

RESULTS

We found significant errors without frequency matching of basis functions when carrier frequency mismatch was generally considered negligible. By matching basis functions with the history of frequency deviation, the mean errors in glutamate, glutamine, γ-aminobutyric acid, and glutathione concentrations were reduced from 3.90%, 1.85%, 11.53%, and 3.43% to 0.18%, 0.34%, 0.40%, and 0.51%, respectively.

CONCLUSION

Matching basis functions to frequency deviation history was necessary even when frequency deviations during frequency-selective spectral editing were fairly small. Basis set frequency matching significantly improved accuracy in the quantification of glutamate, glutamine, γ-aminobutyric acid, and glutathione concentrations.