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13C hyperpolarized NMR by Dissolution-DNP enables snapshot detection of degradation products in lithium-ion battery electrolytes
Chloé Gioiosa (CRMN Lyon, France)
LinkedIn: @Chloé Gioiosa
Abstract: Dissolution Dynamic Nuclear Polarization (dDNP) is a powerful hyperpolarization technique enabling tremendous sensitivity gains in solution nuclear magnetic resonance (NMR). Over the last decades, researchers’ efforts have led to an extension of dDNP applications in numerous research fields. Lithium-ion batteries are among the most widespread rechargeable batteries, and a proper understanding of the physicochemical reactions at stake inside them is paramount to make them safer, more efficient, and sustainable. One of the key challenges lies in better understanding and limiting the degradation of the battery electrolyte, which can significantly impact the battery’s performance. While NMR has been used in attempts to understand these mechanisms, notably by investigating the degradation products, the intrinsic lack of sensitivity of this technique, combined with the limited accessible volume of such compounds, makes its application often challenging. This work combines several state-of-the-art dDNP methodologies, including using recently introduced hyperpolarizing polymers (HYPOP) to acquire hyperpolarized 13C NMR spectra of degraded battery electrolytes. We show that we can successfully detect 13C signals on formulated battery electrolyte solutions in different degradation stages, on a 600 MHz spectrometer, with sensitivity gains of up to 3 orders of magnitude. This work paves the way for studying lithium-ion battery electrolyte degradation under usage conditions (cycling, thermal aging, air exposure…) with a 13C detection limit below the micromolar range. This methodology has the potential to provide new insights into degradation mechanisms and the role and effectiveness of additives to mitigate electrolyte degradation.
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Dear Chloe, I have 2 questions:
1. Considering the transient nature of hyperpolarized states, what is the practical time window available for spectral acquisition? To what extent does this limit your ability to resolve and differentiate between various chemical species?
2. In achieving the observed sensitivity enhancements, did you encounter any trade-offs in spectral resolution, such as line broadening, arising either from polarization transfer mechanisms or sample handling procedures?-
Dear Kshama,
1. If we consider the shortest T1 measured on the methyl moieties of the carbonates, which is of approximately 5 seconds, you have around 10 seconds to dissolve and acquire your 90° pulse while ensuring that you see most of the carbon signals, although there is a possibility that some signals might be missing if the T1 is very short. It has already happened that some fast relaxing nuclei were missing from the spectrum. However, the fast injection system still allows us to see signals with T1 in the order of the second, but with diminished enhancements (hence why we report enhancements ranging from 100 to 1000). We also control the magnetic field during the transfer with solenoids to avoid any polarization losses due to a sudden change in the magnetic field strength.
2. We did. We had to trade off some sensitivity enhancements to achieve a satisfying resolution by adding a “resting time” to the sequence, allowing the solution to stabilize inside the tube prior to the start of acquisition. Here, the fwhm was measured to be 3.5 Hz, and our best reported value was 1.3 Hz. We also had to deal with a lack of repeatability in the injected volume due to the significant pressure drop caused by the addition of the filter, resulting in additional losses of resolution. We are currently working on finding the best compromise to acquire a resolved spectrum with maximzed enhancement, in a repeatable manner.
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Thanks!
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Nice Talk! One question I have is, how do polymers like HYPOP “preserve” the polarization within them for use in DNP? I find these materials to be very interesting and am wondering how they work and whether they are compatible with other radicals (e.g. metal-based radicals like Gd-DOTA).
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Hello! Thank you for your question.
In our case, the polymer is synthesized in a manner that allows the radicals (amino-TEMPO) to be grafted and incorporated within the polymer network. The polymer is therefore filled with radicals. Another important aspect is that it is a high-porosity polymer (up to 80%), which enables us to impregnate the polymer with the solution to hyperpolarize. Then, by shining microwaves on the impregnated powder, you can perform DNP in the same fashion as with a DNP juice.
They can be synthesized with any radicals that :
1. Contains a primary or secondary amine group
2. Can survive at 100°C for 24h (curing process)If you want more details on the synthesis, you can go check this paper in which it is described : https://www.nature.com/articles/s41467-021-24279-2
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Awesome, thanks!
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A BOTTOM-UP APPROACH: COMPLIMENTING NMR RELAXOMETRY WITH THEORY AND SIMULATIONS
Angel Mary Chiramel Tony (University of Rostock, Germany)
LinkedIn: @Angel Mary C T; X: @AngelMaryCT1; Bluesky: @angelchirameltony.bsky.social
Abstract: By using Fast Field Cycling (FFC) NMR spectroscopy, dynamical processes can be studied over many orders of magnitude. However, interpreting FFC-NMR data often requires models that are specific to certain systems. Here we propose a novel approach for computing the inter- and intramolecular contribution to the magnetic dipolar relaxation from molecular dynamics (MD) simulations. This method is enabling us to predict NMR relaxation rates, addressing the full FFC frequency range, covering many orders of magnitude, while also avoiding influences due to limitations in system size and the accessible time interval. Our methodology is based on combining the analytical theory of Hwang and Freed (HF) for the long-range intermolecular contribution of the magnetic dipole-dipole correlation function with MD simulations. Here we apply this approach to compute the inter- and intramolecular NMR relaxation of 19F nuclei in the ionic liquid C5Py-NTf2 to study the dynamics of the NTf2 anion. By employing our MD simulation-based approach, we could show that the correlation functions due to the HF theory does asymptotically converge with our MD simulation results at long times. This approach is successful in disentangling the different contributions to the intramolecular 19F-NMR relaxation rate due to the complex intramolecular dynamics of the anion. We successfully described the rotational anisotropy, differentiating between the overall tumbling of the anion and internal rotation of the CF3 group, which is difficult to decipher with the fitting models.
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Hi Angel, nice presentation! I was wondering—on slide 11, why doesn’t the 283 K data fit as well as the 303 K and 323 K data, which show excellent fit?
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Hello Amit,
Many thanks for the question.
The lines are indicated for the Relaxation rate calculated from MD(with correction term) in the bottom approach manner. It’s not a fit for experimental data points obtained from FFC NMR.
The mismatch we have for 283K can probably be attributed to the quadrupolar nuclei(Deuterium on cation) and dipolar nuclei (Fluorine on anion) interaction. As this effect becomes pronounced at lower temperature, we see the effect for 283K compared to the other two higher temperatures.Let me know if it clarifies your question and if you have any other questions/curiosity.
Best regards
Angel-
Thanks Angel, it clarifies my question.
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Hi Angel, nice talk! I was wondering, is it possible to apply this method or a modified version of this method to vitrified samples (e.g. frozen solutions at ultralow temperatures)? I am assuming this would be in a regime of significantly less tumbling, but there would still be processes (e.g. vibrational, rotational) that contribute to and create a relaxation time.
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Hello Raj,
Yes, it should be ideally possible to use the framework for different kinds of soft matter systems possibly with slight modifications in the fitting part with KWW functions for intramolecular part(mostly rotational and vibrational in nature).
However I have personally used it for water, ionic liquids and electrolytes in the temperature range where it remains liquid state. And it works perfectly predicting the broad frequency range (verification done from both from low resolution and high resolution spectrometers).I will be happy to answer if you write to me at angel.tony@uni-rostock.de if you need more clarification or have more questions/ curiosity.
Best regards
Angel
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A Palm-Top time-domain NMR spectrometer for the research laboratory
Dr. Beau Webber (Lab-Tools Ltd., UK)
LinkedIn: @Beau Webber
Abstract: Take this Lab-Tools NMR TD spectrometer down off the shelf, plug it in, insert your sample, and you are up and measuring. Measure, plot and fit your results real-time in any of a number of ways, at the lab bench or from a remote location. This TD NMR spectrometer has been designed as a compact precision tool to measure quantitatively the physical properties of your sample. This TD NMR spectrometer can be used to study liquids, solids, polymers and porous materials. This gives data on sample component masses and molecular movement of the atoms and molecules, which lead to qualities which are variously described as mobility, dynamics, stiffness, viscosity or rigidity. Two NMR probes typically cover a wide range of NMR active nuclei : 1H, 19F, 11B, 7Li, 23Na. If you need variable-temperature, then plug in the Peltier thermo-electrically cooled module. -60C to +80C. This enables a wide range of materials-science measurements, and is also the basis of a thermodynamic NMR Cryoporometry system for measuring pore-sizes from nano-meters to micro-meters. In a hurry ? Or have another experiment or sample to do ? These spectrometers are priced so you can just add more on your research bench.
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Updates on an Even More Compact Precision NMR Spectrometer and a Wider Range V-T Probe, for General Purpose NMR and for NMR Cryoporometric Nano- to Micro-Pore Measurements. J. Beau W. Webber. Micro. 2024; 4(3):509-529. DOI: 10.3390/micro4030032.
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Very cool! Are the spectrometers controlled on home-built software, and how much customizability is there in the programming for e.g. playing with pulse sequences or other experimental parameters? Are there any plans to add frequency-domain capabilities?
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Hi Riley,
The software has been written in my lab in an array processing language called Apl. It is multi-tasking, and also handles the graphics, and talks to the RF Gate-Array over an Ethernet.
New and modified pulse-sequences can be written, and either down-loaded into the firm-ware pulse sequence pipeline, or run in the high-level Apl.
All the front-panel and menu parameters can be set, or saved / loaded to disc. (Tomorrow we are discussing adding an AI assistant to this.)
There are some frequency-domain capabilities already built in. However my magnets are not homogeneous enough for resolving 1H spectra yet. But I have captured some low-resolution 19F spectra easily.
Cheers,
Beau-
Interesting, thanks!
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Hi Dr. Webber, impressive work! Could you elaborate on how T1rho measurements correlate with viscosity ?
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Thanks Amit,
We have a preliminary equation, but we are still analysing the results.
But we believe we may have publishable results, just need to validate them in other well defined systems. This data is only days old.
Can you please contact me on LinkedIn, and I will let you have more info when we are sure we are happy with the results.
Cheers, Beau
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Thank you Dr. Webber.
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Hi Dr. Webber, nice presentation! I am intrigued by the small size of the NMR spectrometer and have a few questions:
1) How transportable is the spectrometer? I am assuming that its smaller size makes it significantly more mobile than other spectrometers, and perhaps capable of being used “on the field” in certain locations where it would otherwise not be possible for a bigger spectrometer to be used (e.g. near a cave, near a river, etc.)
2) Up to what magnetic field are you able to reach while maintaining the small spectrometer size?-
Hi Raj,
Yes it is very transportable : It fits into a laptop bag, with the 0.5T 20 MHz 1H magnet, and a regulated 8 hour battery supply.
Very suitable for mobile use in the field indeed.
The 0.5T magnet is the highest I yet have – but watch this space !
Thanks for the interest,
Beau -
Hi Raj,
Yes it is very transportable : It fits into a laptop bag, with the 0.5T 20 MHz 1H magnet, and a regulated 8 hour battery supply.
Very suitable for mobile use in the field indeed.
The this magnet is the highest I yet have – but watch this space !
Thanks for the interest,-
Very cool, thank you! I look forward also to seeing how the highest field usable changes in the future!
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If you have a suitable magnet, the MK5 NMR goes up to 120MHz, good enough for 2T 1H.
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Advancing GHz-class NMR: High sensitivity through larger volume cryoprobe and optimal control sequences
David Joseph (Max Planck Institute for Multidisciplinary Sciences, Germany)
X: @DaJo_1729
Abstract: Improving the sensitivity of nuclear magnetic resonance (NMR) spectroscopy requires advancements in both instrument technology and experimental methodology. In this study, we introduce the first proton-detected large volume cryoprobe designed for 1.2 GHz instruments, leveraging optimal control pulse sequences to enhance performance (Sci. Adv. 9,eadj1133, 2023). Our results demonstrate up to a 56% increase in sensitivity and more than a twofold reduction in experimental time compared to the small volume cryoprobes in use at the moment. Additionally, we systematically optimized the experimental conditions to fully exploit the capabilities of GHz-class magnets. To further extend the benefits of our approach, we developed a library of optimal control triple resonance experiments, enabling boosted sensitivity for advanced NMR applications.
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When comparing the results from the 5 mm TCI probe at 1.2 GHz with the 5 mm TCI probe at 950 MHz, what is the most surprising/interesting/useful insight that you have personally encountered? In the future, what do you think might be the most useful/interesting insights enabled by performing experiments at 1.2 GHz?
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The most useful insight is that bio-NMR experiments perform much better using optimal control pulses. A 5 mm TCI at 950 MHz approaches the power availability limit for broadband pulses, particularly for the 13C and 15N channels. At 1.2 GHz, a 5 mm TCI can only be used with optimal control pulses. However, using optimal control pulses with fields starting from 800 MHz would provide free signal enhancement and save valuable experimental time.
The most interesting insights would come from performing experiments at 1.2 GHz to study biomolecular dynamics. All B₀-dependent parameters, such as CSA and alignment, reach their maximum values at this frequency, enabling access to data on motions that would otherwise be impossible to observe with lower field magnets. Increased resolution at 1.2 GHz would also be useful for studying larger proteins and intrinsically disordered proteins.
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Thank you for your response, David.
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These are important reference data.
1) Wouldn’t one expect that the sensitivity obtained with a Shigemi tube is either the same or less than that obtained with a conventional 5 mm tube?
2) Which compound and signal did you use to measure the sensitivities in the presence of different salt concentrations – ubiquitin or sucrose?
3) Does CSA relaxation of ubiquitin amide protons broaden their 1H NMR signals noticeably more than at, say, 950 MHz?-
1) The sensitivity of a Shigemi depends on the amount of sample available. It is especially sensitive when a lower volume of sample is available. There is also an optimal height that provides the best signal-to-noise ratio when using a Shigemi tube. Our concern here was B_1 inhomogeneity, which is lower with a Shigemi tube. However, since the pulses also compensate for ±20% inhomogeneity, we only see only a slight improvement in sensitivity when using a Shigemi tube.
2) It was p53 1-73, a disordered protein, in a Tris-Bis buffer, using optimal control HNCA sequence.
3) Thanks for the question! I just looked it up, and for an HNCO experiment, the difference is around 3 Hz, while for an HSQC, it’s around 1 Hz (along the proton dimension). It is broader at 1.2 GHz.
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1) The sensitivity of a Shigemi depends on the amount of sample available. It is especially sensitive when a lower volume of sample is available. There is also an optimal height that provides the best signal-to-noise ratio when using a Shigemi tube. Our concern here was B_1 inhomogeneity, which is lower with a Shigemi tube. However, since the pulses also compensate for ±20% inhomogeneity, we only see only a slight improvement in sensitivity when using a Shigemi tube.
2) It was p53 1-73, a disordered protein, in a Tris-Bis buffer, using optimal control HNCA sequence.
3) Thanks for the question! I just looked it up, and for an HNCO experiment, the difference is around 3 Hz, while for an HSQC, it’s around 1 Hz (along the proton dimension). It is broader at 1.2 GHz.
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Hi David, brilliant presentation. Clear, concise, and insightful.
You mentioned a useful tip about using buffers with lower conductivity and larger ions. Could you please elaborate on why this is beneficial and how exactly it helps in practice?-
Hi, thank you! This has to do with noise contribution from the sample, which is especially problematic for the cryoprobe. The noise from the sample is proportional to its conductivity and dielectric properties. Using a buffer with larger ions will lower the mobility, thus lowering the conductivity of the buffer and reducing the noise from the sample. This increases the signal-to-noise ratio of the spectrum.
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Hi, thank you! This has to do with noise contribution from the sample, which is especially problematic for the cryoprobe. The noise from the sample is proportional to its conductivity and dielectric properties. Using a buffer with larger ions will lower the mobility, thus lowering the conductivity of the buffer and reducing the noise from the sample. This increases the signal-to-noise ratio of the spectrum.
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An NICS study on Modulation of Aromaticity of Nitroaniline Isomers in Binary Solvent Mixtures
Prince Sebastian (St.Berchmans College, Changanacherry, India)
LinkedIn: @Prince Sebastian
Abstract: The stability and chemical reactivity of substituted benzene derivatives are largely determined by their aromatic nature. Using the Nucleus-Independent Chemical Shift at 1 Å above the ring center [NICS(1)] as an aromaticity probe, we examine how binary solvent mixtures affect the modulation of aromaticity in ortho, meta, and para-nitroaniline isomers. To simulate different solvation environments, binary mixtures comprising water, chloroform, dimethyl sulfoxide, acetonitrile, trifluroethanol, N,N-Dimethylformamide, dioxane, and tetrahydrofuran were employed. Less negative NICS(1) values in solvent combinations indicate that solvation reduces aromaticity for all three nitroaniline isomers, according to our computational analysis. Solvent-induced polarisation and hydrogen bonding effects, which affect electron delocalisation within the aromatic ring, are responsible for this decrease in aromaticity. Interestingly, the type of isomer and solvent composition affect the amount of aromaticity loss, with para-nitroaniline exhibiting the highest sensitivity to solvation effects. The reactivity and mechanism of chemical transformations involving nitroaniline derivatives may be influenced by solvent-dependent aromaticity, according to these findings, which also emphasise the critical role of solvent environment in modulating electronic properties.
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Hi Prince, great presentation. I have two questions.
1) From your data it seems like NICS(0) values in binary mixtures are often lower than the NICS(0) values for each solvent making up the mixture. Can you comment on this?
2) How are your calculated NICS values validated? Or is that the next step? -
Hi Blake,
1)Yes, our observations indicate that the NICS(0) values for binary mixtures are generally lower than those of the individual pure solvents. This suggests a decrease in aromaticity when two solvents are mixed. The possible reason for this decrease is the presence of specific intermolecular interactions, such as hydrogen bonding or other non-covalent interactions, that alter the electronic environment of the aromatic ring.
In particular, inter-hydrogen bonding appears to play a crucial role in modulating aromaticity within the mixtures. These interactions can lead to redistribution of electron density, thereby reducing the magnetic shielding experienced in the aromatic system, which manifests as a lower NICS(0) value. To further support this interpretation, we carried out AIM (Atoms in Molecules) analysis, which confirmed the presence and nature of these intermolecular interactions in the mixtures.
2)The calculated NICS values were validated by comparison with literature data, ensuring that the observed trends match previously reported results, and by verifying consistency across computational methods, confirming that variations in the computational approach did not significantly affect the trends.
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An operational perspective on the Magnus-Fer conundrum in time-dependent quantum mechanics
Kuntal Mukherjee (IISER Mohali, India)
LinkedIn: @Kuntal Mukherjee; X: @kuntalsherlock
Abstract: Development of analytical methods for studying periodically driven quantum systems has been key for gaining insights into the physical phenomena in spectroscopy. The success of analytic methods relies on its operational aspects and exactness in replicating (known) experimental results. The analytical methods based on the Magnus expansion (ME) scheme have been preferred in time-evolution studies, though recently, the splitting of the time-propagator into a product of exponential operators in the Fer expansion (FE) scheme has gained wider attention. Hence, the operational advantages between the two has always remained contentious and is discussed herein with a two-spin model system supported by the numerical simulations with a heteronuclear spin system based on CP (Cross-Polarization) and a homonuclear spin system based on DQ-HORROR (Double Quantum Homonuclear Rotary Resonance) experiments. Here, we highlight the serious discrepancies observed in time-evolution studies based on time-propagators derived from both the FE and ME schemes. The exactness of the FE scheme is problem specific and highly dependent on the commutator relations among propagator operator. Only in certain cases, it results in agreement to those obtained from exact numerical methods. By contrast, the ME scheme in an appropriate interaction frame presents a reliable framework for evaluating the observables at stroboscopic time-intervals.
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Thank you for the presentation. Is there a scenario where you would recommend using the Fer expansion?
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Hi Jonas,
Unfortunately, there are very limited opportunities to use Fer expansion to obtain exact results. In general, It would work when following conditions are satisfied.
[Fn(t),ρ(0)] ≠ 0 & [Fn(t), D] ≠ 0
There must be non-commutating operators present in the F-operators with the initial density matrix operator and detection operator simultaneously, since, the terms showing the effect of anisotropic interactions must be reflected in the final signal expression. In case of CP and DQ-HORROR experiments, one of the F-operator commutes with the density operator, the anisotropic terms do not participate in the final signal expression. Hence, we have to choose such experiments where the above relation holds and the choice of experiment is totally contextual and need to verify the applicability of the Fer expansion.
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Hi Jonas,
Unfortunately, there are very limited opportunities to use Fer expansion to obtain exact results. In general, It would work when following conditions are satisfied.
[Fn(t),ρ(0)] ≠ 0 & [Fn(t), D] ≠ 0
There must be non-commutating operators present in the F-operators with the initial density matrix operator and detection operator simultaneously, since, the terms showing the effect of anisotropic interactions must be reflected in the final signal expression. In case of CP and DQ-HORROR experiments, one of the F-operator commutes with the density operator, the anisotropic terms do not participate in the final signal expression. Hence, we have to choose such experiments where the above relation holds and the choice of experiment is totally contextual and need to verify the applicability of the Fer expansion. -
Thank you for the effort in presenting such a topic!
It seems that the Fer expansion cannot work in the presence of CSA, as you discuss. So how and why was this method introduced in the first place?
Based on your conclusions, would there be any reason moving forward to use the Fer expansion over the Magnus one?-
Hi Nicolas, thank you for watching my presentation.
The original Fer expansion was applied to the classical systems. But for quantum mechanical systems, the commutation relations play a big role. In this presentation as well as the publication, to keep things simple, we have shown the applicability of Fer expansion over cycle time detection or stroboscopic detection. The conclusion is straightforward in this case which is presented. But for continuous detection or non-stroboscopic detection, the form of Fn-operators become very complex upon going to higher order. Hence, to work with Magnus expansion, you need to add all the Fn-operators in a single exponent and operate it on density operator to evaluate signal (through BCH expansion). It is highly probable that in such scenario, deducing closed form expression is quite cumbersome though possible and in worse cases, the presence of off-diagonal terms will not give any closed form solution. On the other hand, Fer expansion would allow to operate the Fn-operators individually and obtain a product of simpler expressions, although, it still needs to satisfy the following two conditions, i.e. [Fn(t),ρ(0)] ≠ 0 & [Fn(t), D] ≠ 0 for which the applicability of Fer expansion becomes limited. To sum up, in stroboscopic detection, Magnus expansion’s efficiency is greater or equal to Fer expansion’s efficiency. For non-stroboscopic detection, Fer expansion is convenient to use if the above two conditions satisfy. There is a paper by Shreyan et. al. (https://doi.org/10.1080/00268976.2023.2231107)on decoupling that discuss on the non-stroboscopic detection also.
I hope this explanation helps and answers your query.
Thank you again.
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Hi Kuntal,
Great to see your work here!
Have you ever explored using the Fer expansion approach for systems involving quadrupolar nuclei?
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Hi Sajith,
Thank you very much for watching my presentation.
I have been exploring the Fer expansion scheme involving spin-1/2 nuclei only. Although, for systems involving quadrupolar nuclei, the method would be same, like evaluating the time-propagator by writing down the F1 and F2 operators. Due to quadrupolar nuclei, there will be addition of Quadrupolar interaction. So, starting with a single spin quadrupolar nuclei under single pulse followed by stroboscopic detection of signal and assuming no anisotropic interaction present with offset=0 kHz, we can have simplest problem to start with. As a time-independent Hamiltonian, we would have external RF-Hamiltonian and as a time-dependent Hamiltonian, we would have internal Quadrupolar Hamiltonian. Then, we can write down the F1 and F2-operators. During detection, we would know how much Fer expansion is exact depending upon the condition it needs to satisfy, i.e. [Fn(t),ρ(0)] ≠ 0 & [Fn(t), D] ≠ 0. Further addition of anisotropies like CSA would only bring new set of challenges that Fer expansion has to deal with. As of now, I can comment on a primitive level about it. For better assessment, we have to calculate it thoroughly.
I hope this satisfies your query.
Thank you again for showing the interest.-
Yes. Thank you.
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Characterizing Metabolic Dysregulation in Early-Stage Chronic Kidney Disease for Diagnostic Insight
Upasna Gupta (Centre of Biomedical Research (CBMR) & Lucknow and Academy of Scientific and Innovative Research(AcSIR), India)
LinkedIn: @Upasna Gupta; X: @Upasnagupta30
Abstract: The progressive illness known as chronic kidney disease (CKD) can often be challenging to diagnose in its early stages with conventional diagnostic approaches such as serum creatinine and albumin assessment. Identifying possible biomarkers for early detection and personalized treatment, as well as physiological changes linked to early CKD—an area that hasn’t been fully investigated before—is the goal of this study to address this gap.
We performed a metabolomic analysis using ¹H NMR on 115 human serum samples (24 healthy controls, 91 patients with early-stage CKD). MetaboAnalyst 6.0 was used for data pre-processing and statistical analyses (PCA, PLS-DA, OPLS-DA, ANOVA, and Wilcoxon Mann-Whitney test). Strong differentiation between CKD stages was shown by random forest modelling. The KEGG database was used to perform pathway enrichment, and ROC analysis evaluated the diagnostic value of important metabolites.
Across CKD stages, significant changes in ten different metabolites: myo-inositol, glycerol, pyruvate, carnitine, phenylalanine, tyrosine, histidine, TMAO, 2-hydroxyisobutyrate, and 3-hydroxyisobutyrate (p 1). AUC values > 0.7 from ROC curves demonstrated its potential for diagnosis. Pathway analysis revealed significant dysregulation in metabolism of inositol phosphate, tyrosine, histidine, pyruvate, and biosynthesis of phenylalanine, tryptophan and tyrosine.
This comprehensive metabolomics investigation identified potential early-stage CKD biomarkers in addition to significant metabolic abnormalities. These findings could help provide individualized care for CKD early management.-
Thanks for a nice presentation. I have following questions regarding the same:
1. In the stack plot showing the 1D NMR spectra shown gradual variation of creatinine and format in different groups but these two do not show up in the contributing metabolic factors of group deafferentation. What can be possible explanation?2. Similarly, my-inositol does not seem to vary much in the 1D plots but its there in contributing factors of group differentiations. What can be the reason?
Thanks again.-
Thank you, sir.
1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.
2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.
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Hey, very interesting work, I was wondering how did you handle the large lipo protein signals arising from the blood samples. Did you filter them out? what kind of NMR pulse sequences did you use? Is there any evidence of these proteins to be a biomarker of the disease?
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Thank You, Dr. Marco Schiavina
Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.
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Thank you, sir.
1. Although creatinine was found to be significantly altered when comparing G3a and G3b groups, indicating that its changes become more prominent in later stages of CKD. However, since our primary aim was to identify early-stage biomarkers beyond conventional markers like creatinine, we did not include it in the final list of contributing factors for group deafferentation, though detailed results are provided in the manuscript.Formate, on the other hand, showed significant differences when comparing early-stage CKD patients to controls. However, it may not have contributed strongly to the variance specifically within the deafferentation group, and thus was not highlighted in the final metabolic signature for that group.
2. Great observation, sir, although myo-inositol does not display a marked shift in the 1D NMR stack plots, it was identified as a significant contributor in the multivariate analysis. This suggests that its variation across groups is subtle yet consistent, not readily apparent to the eye but statistically relevant when analysed in the context of the full metabolic profile.
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Thank You, Dr. Marco Schiavina
Yes, we filtered the serum samples using a 3 kDa Amicon filter to remove larger proteins and lipoproteins. However, as reported in earlier studies, small lipid fragments can still appear in the aliphatic region (δ 0.75–2.5 ppm) due to aggregation or interactions with other macromolecules. To suppress these broad signals and focus on low-molecular-weight metabolites, we used the CPMG pulse sequence, which attenuates macromolecular signals. This approach enhanced the spectral resolution and improved our ability to reliably detect metabolites associated with CKD-related metabolic dysregulation.
While we didn’t focus on lipoproteins as biomarkers in this study, there’s growing evidence supporting their relevance, and it’s a great direction for future research.
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DNP Semianalytical Calculations and Quantum Mechanical Simulations in MAS and Static Conditions
Raj Chaklashiya (Northwestern University and University of California, Santa Barbara, United States)
LinkedIn: @Raj Chaklashiya
Abstract: Dynamic Nuclear Polarization (DNP) continues to transform NMR spectroscopy by enhancing its signal by several orders of magnitude via polarization transfer from unpaired electron spins to nuclear spins, enabling studies of objects consisting of very few spins such as cells. However, maximizing its potential towards subcellular components consisting of even fewer spins would significantly benefit from optimization of signal enhancement towards its theoretical maximum, which is nontrivial to achieve due to the many different factors that go into signal enhancement. DNP Semianalytical Calculations via simple numerical model assumptions and first principles Quantum Mechanical Simulations via time-evolved Hamiltonian propagators are two distinct methods that can be used to predict DNP performance and assess potential sources of missing enhancement via comparison with experimental DNP frequency profiles. This talk is a tutorial that applies these methods to the analysis of DNP from a highly efficient DNP biradical, TEMTriPol-1, which consists of one part Trityl and another part TEMPO. Inclusion of the effects of microwave saturation and electron spin relaxation in semianalytical calculations and use of the QUEST Northwestern computing cluster in Spinevolution quantum mechanical simulations both provide key improvements to these methods that enable closer matching between experiment and simulation. The results point towards the critical need to understand the J-coupling distribution of DNP radicals to fully understand the underlying DNP mechanism and optimize DNP performance.
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Dear Raj, very interesting study. Could you comment more on the role of the J coupling on the shape of the DNP profile? How would a reduction of J affect the profile? Have you studied other biradicals with different combinations of relaxation times/J couplings?
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Dear Arianna, thank you, and thanks for the questions!
So regarding J coupling and the profile shape, we found that reducing the J coupling narrows the Trityl part of the DNP profile significantly, removing the “bump” in the DNP profile that shows up on the left side of the profile during experiment. It seems that reducing J has the net effect of making the overall DNP profile narrower, leading to clear discrepancies with the broader experimental DNP profile.
This is the first established biradical that I have done this kind of study on, but I have done simulation studies in the past of multiradicals and coupled monoradicals with varying dipolar, J, and/or relaxation times, and the results can get quite interesting. I’m happy to go more in-depth on this if you’d like! Here are a couple papers where we discuss those cases in detail:
Multi Electron Spin Cluster Enabled Dynamic Nuclear Polarization with Sulfonated BDPA –in our simulation section we see the impact of J coupling and relaxation times on coupled BDPA and find that there is a combination that matches experimental trends. J coupling matching the nuclear larmor frequency combined with a strong differential in t1e’s can result in an absorptive central feature in the DNP profile lineshape: https://pubs.acs.org/doi/full/10.1021/acs.jpclett.3c02428
Dynamic Nuclear Polarization Using Electron Spin Cluster – this paper has detailed simulations on Trityl-based multiradicals and the conditions in which dipolar couplings are strong enough to result in strong DNP enhancements, as well as the impact of relaxation time differentials on the DNP profile: https://pubs.acs.org/doi/full/10.1021/acs.jpclett.4c00182
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Dear Raj, very nice talk! I would like to know that in second method, when you are observing for static case, the dipolar interaction is also present there (which is primarily averaged out in case of MAS), how the role of dipolar interaction and J-coupling can be separately understood?
Thank you.-
Hi Kuntal,
Thank you! Good question–so first to clarify, the dipolar coupling definitely plays a role in both the MAS and Static simulations–even though there is averaging of dipolar orientations that would make seeing their effects in the NMR spectra harder, its effects can still be seen in the DNP profile itself, because in both cases the dipolar coupling strength directly determines how coupled the two electron spins are–if they are too weakly coupled, the Cross Effect DNP cannot occur, while if they have strong enough coupling it can occur. So in this sense, it needs to be understood for both static and MAS.
As to your question–in these simulations I assume a dipolar coupling constant at 12.5 MHz because I don’t expect the distance between the Trityl electron and the TEMPO electron of TEMTriPol-1 to change. However, J-Coupling is a different story, as it is already known based on liquid state EPR experiments that there is a broad distribution of J couplings.
However, if we assume both the dipolar and J couplings can change, what happens, and how do the effects differentiate from one another? I think in this case, the key difference lies in how they impact the EPR spin populations:
– Dipolar coupling acts as a means to broaden electron spin populations. Stronger dipolar coupling results in broader EPR lines, while weaker dipolar coupling results in narrower EPR lines
– J coupling acts as a means of splitting electron spin populations. Stronger +J coupling results in a wider split in the EPR line, while weaker +J coupling results in a narrower split in the EPR line. And crucially, negative J coupling when strong vs weak can flip this dependence (which results in the better fit with -J as opposed to +J)From this, one can see how dipolar coupling alone would result in different a different input EPR line, and therefore a different DNP profile–while it can broaden the already existing electron spin populations, it cannot necessarily create new populations further away. This means it would be harder for a small “bump” to suddenly appear due to dipolar broadening as opposed to J couplings–that bump however could easily be created if a population were shifted due to J couplings.
The beauty of these simulations, however, is that it can be easy to test these hypotheses! We could input a range of dipolar coupling values and compare that with what happens if we input a range of J coupling values, and vice-versa, all while keeping the other coupling fixed.
Let me know if you have any more questions!
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Ok, that clears the query, thank you!
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No problem!
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Dynamic Nuclear Polarization Mechanisms in Diamond Defect Systems: Analytical Models and Transfer Dynamics
Shubham Kumar Debdatta (Indian Institute of Science, Bangalore, India)
Abstract: Nitrogen-vacancy (NV) centers in diamond have become prominent platforms for dynamic nuclear polarization (DNP), offering a robust route to hyperpolarize surrounding ¹³C nuclear spins under ambient conditions. Experimental observations have revealed both microwave-assisted and microwave-independent DNP pathways, frequently rationalized in terms of level anti-crossings between coupled electronic and nuclear spin manifolds.
In this work, we construct an analytical treatment of spin polarization transfer from NV centers to proximal ¹³C nuclei, employing the density matrix formalism in conjunction with average Hamiltonian theory. Under the condition of selective excitation of a single electronic transition, we invoke a reduced Hilbert space description to derive compact expressions for spin polarization resonance conditions, effective spin Hamiltonians, and transfer efficiency as a function of external magnetic field, hyperfine interaction strength, and applied microwave fields.
The model is further generalized to incorporate NV–P1–¹³C configurations, where P1 centers—substitutional nitrogen defects with spin-½—mediate cross-relaxation pathways that enable microwave-free spin polarization transfer. This extension elucidates key dynamical features such as field-dependent polarization oscillations, resonance-enhanced transfer channels, and timescales associated with transient spin exchange processes.
This theoretical framework offers a detailed understanding of DNP mechanisms across both isolated and interacting defect configurations. The results delineate optimal regimes for maximizing nuclear spin polarization in diamond-based systems, particularly under low magnetic fields and ambient conditions, with direct implications for enhancing the sensitivity of nuclear magnetic resonance (NMR) and other hyperpolarization-enabled techniques.-
Dear Shubham, thank you for the presentation, it is a very interesting study. If I understand correctly, the graph showing “Normalized Signal vs Time” shows the the polarization buildup on 13C. Could you comment on the factors that affect this polarization transfer? Thank you.
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Thank you! I assume you’re referring to the NV–P1–13C cluster-based study. In this context, the transfer rate mostly depends on A_zz^Δ and A_zx^Δ, which represent the differences in secular and pseudo-secular hyperfine couplings. These differences are key factors governing the transfer rate.
In the plot, the purple plot indicates no spin polarization transfer. This occurs when the 13C nuclear spin is positioned exactly midway between the NV center and the P1 center, resulting in zero difference in both secular and pseudo-secular hyperfine couplings—hence, no transfer takes place.
Additionally, there is a dependence on θ (theta) and φ (phi), which are related to nuclear and electronic couplings. These angular dependencies reflect how spin polarization transfer is influenced by the relative positioning of the 13C nucleus—whether it’s closer to the NV or the P1 center—and by the physical distance between NV and P1. -
Nice work, Shubham.
Have you explored the simulations under MAS?
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Thank you! I haven’t looked into the matching conditions under MAS yet.
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Okay.
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Hello Shubham, thank you for your answer. Did you try also to study clusters composed by multiple NV and P1 centres?
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Yes, I am currently exploring cluster-based systems. At the moment, our primary focus is on the 14N nucleus (associated with the P1 center) within the NV–P1–13C spin system, examining its dynamics as a four-spin system.
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Dear Shubham,
Nice presentation! I was wondering, while your simulations show that the optimal matching conditions are at X Band, is there any way that you’ve seen to manipulate the spin system (e.g. via couplings) such that the optimal matching condition can occur at higher field? Like basically–what about the system would have to be changed for the optimal condition to be at high field? -
Hi Raj, Thank you! I believe I may not be fully understanding your question. The matching conditions remain the same and are highly valid in the high-field regime. However, as the magnetic field strength increases, the range over which spin polarization transfer occurs becomes narrower. This is because, at higher fields, the matching condition primarily depends on the nuclear Larmor frequency, with much less influence from hyperfine couplings. This trend is also demonstrated in the presentation at 9.4 Tesla.
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Thanks! That makes sense to me–so it would have to require tuning the nuclear larmor frequency for it to work well at high fields.
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Effects of Membrane Composition on Kindlin-2 Binding to Phosphatidyl Inositol Phosphates in Lipid Bilayers
Zainab Mustapha (Rutgers University, United States)
Abstract: Kindlin-2, K2, is a peripheral membrane protein and co-activator of integrin signaling in the cell, which is implicated in cell migration, adhesion, and cancer. K2 contains a pleckstrin homology (PH) domain, which, like many PH domains, binds to the phosphoinositide components of the cell membrane, specifically phosphatidylinositol-3,4,5-trisphosphate (PIP3), to enhance integrin activation. However, the mechanism of PIP3 recognition and binding is not fully understood, as no structural studies to date use full-length PIP3 in lipid bilayers, focusing instead on the soluble inositol headgroup.
In this study, we use a combination of solid-state and solution NMR to investigate the structure and dynamics of PIP3-bound K2-PH in a model membrane containing PIP3, phosphatidylcholine, phosphatidylserine, and cholesterol. We study changes in the bound state with respect to the model membrane and the unbound protein. Using proton detection and very fast MAS, ssNMR results show chemical shift perturbations in the backbone of the bound protein compared to the unbound form. These perturbations confirm interactions in areas predicted to interact with the membrane (in our MD simulations). We also describe how additional membrane components, such as cholesterol, can stabilize the binding of the protein.
Together, these results suggest that PIP3 binding induces structural changes in membrane-interacting regions of K2-PH, and that additional membrane components may help stabilize this interaction. Our findings help explain the mechanism of K2-PH binding to PIP3s in the context of a full-length lipid bilayer, which has broad implications for the PIP-based regulation of numerous important cellular processes.-
Nice presentation!
I have a few questions:
1) Are the bilayers preserving their structural integrity upon spinning in MAS?
2) T398 seem to split into two. Would you have an explanation for this?
3) Can you quantify the fraction of PH bound to the bilayers?cheers
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Thank you! Very good questions and observation
1) Usually, we spin the rotors containing liposomes only at 15 kHz, because they preserve their structural integrity and give good linewidth at this MAS rate. However, the 31P 1D data of the bound sample was spun at 40 kHz and the 31P static spectra taken before and after shows that the bilayer structure is preserved. So yes, the bilayers preserve their structural integrity2) Very nice observation, I am still trying to make sense of all the changes we see.
3) Yes, so we start out with solution NMR titration, which of course renders the membrane-associated protein invisible as the titration progresses. When we pellet the complex, we collect the supernatant and take 15N-HSQC to estimate how much of it is left in solution. We use this as a measure of what is bound to the bilayer.
I hope this answers your question. Please let me know if you have any suggestions or input. I’ll be happy to take them. Thank you again.
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Dear Zainab,
Thank you for your presentation.1. I was wondering if you were able to determine the binding constants from your titration studies. If so, could you share the binding affinity you observed between the PH domain and PIP3?
2. Looking at your static 31P spectra, I noticed slight differences between the bound and unbound forms. Could you please elaborate on what might be causing these differences?Thank you!
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Hi KSHAMA,
Very good questions.
1. We didn’t determine binding affinity from our solution NMR titrations. However, in a pioneering work with the soluble headgroup of PIP3, the Kd was measured to be about 2.12 uM. It’s worth thinking about if the presence of the tails would give a different measurement.
2. The presence of the protein may be inducing some sort of membrane curvature on the bound sample compared to the unbound. It can be that some of phosphates in the bilayer now have a different orientation as a result of protein binding. Overall, even though there are subtle differences between the two spectra, we think the bilayer may not be completely destroyed because at a different protein:lipid ratio (data not shown), the static spectra completely shows a different powder pattern.
Thanks for engaging with my research and I hope this answers your questions. Happy to take any suggestions or further questions.
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Expanding Solid-State NMR Frontiers: The 1.2 GHz MAS System at CERM
Rebecca Calamandrei (CERM, Italy)
Abstract: The ITACA.SB project (https://www.itaca-sb.it/about/) is dedicated to potentiate the Italian Instruct-ERIC center, CERM/CIRMMP (https://www.cerm.unifi.it/) and significantly enhance structural biology (SB) services at selected laboratories of CNR. By enhancing service capacity and overcoming key access barriers, the project supports high-level life sciences research in Italy, boosts international visibility, and fosters stronger integration with European research infrastructures.
Within this embodiment, a significant enhancement of the instrumentation at CERM/CIRMMP has enabled the expansion of both solution and solid-state NMR research as well as the biotechnologies instrumentation ranging a broad spectrum of experiment set-up and characterization techniques.
As part of the infrastructure upgrades supported by ITACA.SB, the 1.2 GHz NMR spectrometer at CERM has been equipped with a 0.7 mm solid-state MAS probe. This high-field system offers exceptional performance for the investigation of solid-phase materials, including protein crystals and, more critically, non-crystalline systems such as amyloid fibrils, membrane proteins, and complex sediments. The implementation of ultra-fast magic angle spinning at 1.2 GHz enables the acquisition of high-resolution, proton-detected spectra, comparable in quality to those obtained in solution-state NMR. This advancement significantly expands the capabilities of solid-state NMR for probing molecular dynamics and intermolecular interactions in challenging biological and material samples.
As the result of the synergic integration of upgraded infrastructure, targeted user support, and strategic collaboration, ITACA.SB not only strengthens Italy’s contribution to the structural biology landscape but also ensures that CERM/CIRMMP operates as a competitive hub for research, facilitating the alignment within the European Research area.-
Hello Rebecca! Nice presentation!
Among all these beautiful instruments and applications presented here, I was intrigued by the performances of the 1.2 GHz equipped with the 0.7 mm MAS probe.
Could you please comment about the resolution that can be obtained? How fast can you spin and what nuclear spins can be detected?-
Hi Marco,
Thanks for your comment! The 0.7 mm MAS probe is capable of spinning up to 111 kHz and features three channels dedicated to the detection of ¹H, ¹³C, and ¹⁵N. The major benefits of using this type of probe at ultra-high magnetic fields are particularly evident in ¹H-detected spectra, which can achieve a level of resolution comparable to that of solution-state NMR. This enables detailed studies of residue-specific dynamics and protein–ligand interactions. These findings highlight the crucial importance of combining ultra-high magnetic fields with ultra-fast magic angle spinning for the structural and dynamic characterization of biomolecular systems in the solid state.
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Nice presentation. I am curious about the NEO console. Does this mean one can set up two different experiments and both run simultaneously instead of queuing experiments?
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Thank you for your kind and relevant question. In the novel NEO console, each radiofrequency (RF) channel is equipped with both transmission and reception capabilities. This design effectively allows each channel to operate as an independent spectrometer, with its own RF generation, transmission, and receiver architecture.
In practice, this enables the implementation of multi-receiver experiments in a user-friendly way. The multiple receiver approach developed at our research infrastructure exploits the recovery delay of one experiment to acquire additional experiments simultaneously (see: [Biophys. J. 2019, 10.1016/j.bpj.2019.05.017]).For example, ¹³C- and ¹H-detected experiments can be combined to obtain complementary information on multidomain proteins ([Biomolecules 2022, 10.3390/biom12070929]) or to monitor complex protein–protein interactions in real time ([J. Am. Chem. Soc. 2024, 10.1021/jacs.4c09176]).
This simultaneous acquisition strategy is a key advantage of the NEO architecture, going beyond traditional queuing of experiments.
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Nice presentation of the facility!
Is there any plans to use the 1.2 GHz with a liquid state probe?
Smaller rotor means less materials. How does the sensitivity of the 0.7mm rotor compares with 1.3/1.9 mm rotors on a GHz for example?-
Dear Nicolas Bolik-Coulon,
Thank you for your question. Indeed, we have a 5 mm CP-TXO probe for 13C direct detection that is also routinely used at the 1.2 GHz instrument. The gain in resolution at ultra-high fields is significant not only for solid-state but also for solution-state NMR experiments. This is particularly beneficial when working with biomolecules whose spectra display extensive peak overlap. The combination of ultra-high field and 13C detection helps to partially overcome the spectral crowding typically observed in IDPs and IDRs, as demonstrated in this study: [doi: 10.1038/s41596-023-00921-9]. Moreover, although the amount of sample decreases when moving from larger to smaller rotors, the linewidth also narrows due to a greater averaging of dipolar couplings, resulting in more intense signals. Additionally, the sensitivity loss caused by the reduced sample volume is partially compensated by improved inductive coupling between the coil and the sample, which becomes more efficient as the coil size decreases, as illustrated in this review [doi:10.1021/acs.chemrev.1c00918].
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Fibrosis Under the Lens: NMR Metabolomics and Machine Learning Illuminate Hidden Pathways and Offer a Non-Invasive Alternative to Liver Biopsy”
Shreya Pandey (Centre of Biomedical Research, India)
LinkedIn: Shreya Pandey; X: @shreyapandey171
Abstract: The landscape of chronic liver disease has changed significantly, with metabolic dysfunction-associated steatotic liver disease(MASLD) now emerging as the most widespread form worldwide. In Asia, particularly in India, the prevalence of MASLD is increasing, largely driven by poor dietary habits and a sedentary way of life. MASLD spans from fat deposition to inflammation and fibrosis. Fibrosis stands out as the most critical indicator of liver-related complications and overall risk of death in MASLD. Early identification of fibrosis is critical, but current tests are often invasive or unreliable. While studies have explored metabolic changes in MASLD, few have focused on distinguishing early-stage fibrosis from steatosis.
In this study, we used NMR-based metabolomics to analyse serum samples from n = 103 MASLD patients, divided into fibrosis (n = 44) and non-fibrosis (n = 59) groups based on standard non-invasive scoring systems. We identified seven metabolites—arginine, glycerol, aspartate, glucose, phenylalanine, histidine, and citrate—that significantly differed between the two groups and showed good diagnostic potential (AUROC> 0.70). Pathway analysis revealed disruptions in arginine and nitrogen metabolism, associated with liver scarring processes, and in energy and lipid metabolism, pointing to mitochondrial dysfunction and lipotoxic stress. Reduced aspartate levels also suggested loss of natural protection against fibrosis.
This is the first study of MASLD cohort to differentiate early-stage fibrosis from steatosis using metabolomics. Our findings highlight the potential of a simple NMR based blood test to aid early diagnosis, guide treatment decisions, and personalize care—offering a non-invasive alternative to improve MASLD management.-
Thanks for the nice presentation. I have following questions related to the presentation:
1. As per the study enhanced level of arginine leads to increased proline synthesis. Is this enhanced proline level reflected in metabolic profile?
2. How is the lipid profile? Are there specific lipids which are changing?
3. How does enhanced level of collagen synthesis lead to fibrosis?
4. What portion of the result was used in machine learning?Thanks again
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Thank you sir.
1- Yes sir, even the proline level was enhanced in the NMR profiling , however it didnot match the criteria to be considered as significant metabolite ( despite having p value 1 , the AUC value was 0.65 so we had to exclude it).
2- The few lipids that we obtained using diffusion edited pulse program also profile had significant difference. -CO-CH2-CH2- (corresponding to cholesterol and FA{TAG and Phospholipids}) was found to be increased in fibrotic cohort, similarly PUFA was found to be decreased in fibrotic cohort. As we did using NMR we have limited data corresponding to lipids. Once we use LC we might get broader insights which we will be starting soon
3- When there is continuous injury or inflammation to liver , Hepatic stellate cells gets activated due to cascade of events. These HSCs are major contributers for collagen. When the level of collagen increases , it starts accumulating in the liver , distorting the normal structure and function and liver and eventually forming scarred tissue. This condition is called fibrosis.
4- Only the data from bins of significant metabolites was used. We excluded the water region and the regions that were not significant
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Thanks for a nice presentation. I have following questions:
1. As shown in the presentation the enhanced level of arginine leads to increased collagen production via increased proline level. Is increased proline reflected in the NMR profiling?
2. How is does lipid profile look? Any specific lipids which are enhanced?
3. How does increased collagen lead to liver fibrosis?
4. What exact data was used in machine learning?Thanks again
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Thank you sir.
1- Yes sir, even the proline level was enhanced in the NMR profiling , however it didnot match the criteria to be considered as significant metabolite ( despite having p value 1 , the AUC value was 0.65 so we had to exclude it).
2- The few lipids that we obtained using diffusion edited pulse program also profile had significant difference. -CO-CH2-CH2- (corresponding to cholesterol and FA{TAG and Phospholipids}) was found to be increased in fibrotic cohort, similarly PUFA was found to be decreased in fibrotic cohort. As we did using NMR we have limited data corresponding to lipids. Once we use LC we might get broader insights which we will be starting soon
3- When there is continuous injury or inflammation to liver , Hepatic stellate cells gets activated due to cascade of events. These HSCs are major contributers for collagen. When the level of collagen increases , it starts accumulating in the liver , distorting the normal structure and function and liver and eventually forming scarred tissue. This condition is called fibrosis.
4- Sir, the data from the binned sheet that we obtained from chenomx was used in machine learning learning.
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Interesting work! Could you tell the pulse program used? What data did you use for building the model ? Is NMETA available online
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Thank you Daniel, we have used CPMG pulse program which is basically used to suppress large molecules. We have used the binned sheet generated using chenomx for creating model. As far as NMETA is concerned, it is not available online we are still working on it.
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The presentation looks very informative but can you answer me the following question: What is NMeta? What all information does it require?
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Thank you, Karthik,
NMETA is a web-based application we are currently working on as part of our effort to develop a non-invasive alternative to liver biopsy.
The process is simple: perform a 1D NMR experiment on a serum sample and upload the resulting spectrum to our webpage. The tool will then provide the probability of the individual having liver fibrosis.
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Hello Shreya!
Interesting work, I was wondering how did you handle the large lipo protein signals arising from the blood samples. Did you filter them out? Is there any evidence of these proteins to be a biomarker of the disease?-
Thank you, Dr. Schiavina,
We have not filtered the serum as we had to perform diffusion edited experiment on the same serum sample. Instead, we have used the CPMG pulse sequence to suppress signals from large molecules, particularly lipoproteins and lipid fragments, thereby minimizing their interference.
While previous studies have compared MASLD (formerly NAFLD) with hepatocellular carcinoma (reference: ref:-https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(21)00455-2/fulltext), none have specifically examined non-fibrotic MASH versus early fibrotic MASH within the MASLD cohort. We are currently investigating this comparison and expect to share promising results soon.
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