NMR-based analysis of metabolite mixtures provides crucial
information on biological systems but mostly relies on 1D 1H
experiments for maximizing sensitivity. However, strong peak overlap of
1H spectra often is a limitation for the analysis of inherently complex biological mixtures. Dissolution dynamic nuclear polarization (d-DNP) improves NMR sensitivity by several orders of magnitude, which enables 13C NMR-based analysis of metabolites at natural abundance. We have recently demonstrated the successful introduction of d-DNP into a full
untargeted metabolomics workflow applied to the study of plant metabolism.
Here we describe the systematic optimization of d-DNP experimental settings
for experiments at natural 13C abundance and show how the resolution,
sensitivity, and ultimately the number of detectable signals improve as a
result. We have systematically optimized the parameters involved (in a
semi-automated prototype d-DNP system, from sample preparation to signal
detection, aiming at providing an optimization guide for potential users of
such a system, who may not be experts in instrumental development). The
optimization procedure makes it possible to detect previously inaccessible
protonated 13C signals of metabolites at natural abundance with at
least 4 times improved line shape and a high repeatability compared to a
previously reported d-DNP-enhanced untargeted metabolomic study. This
extends the application scope of hyperpolarized 13C NMR at natural
abundance and paves the way to a more general use of DNP-hyperpolarized NMR
in metabolomics studies.
Introduction
NMR spectroscopy offers unparalleled robustness and reproducibility for the
analysis of complex metabolite mixtures. Such advantages make NMR an ideal
tool for a number of analytical applications such as targeted or untargeted
metabolomics, stable-isotope-resolved studies of metabolism, pharmacokinetic
studies, and bioprocess optimization (Zhang
et al., 2010; Wang et al., 2013; Zhang et al., 2008; Calvani et al., 2010;
Liu et al., 2010; Strickland et al., 2017; Emwas et al., 2019; Kim et al.,
2013; Wishart, 2008). However, NMR suffers from poor sensitivity, which
limits the detection of metabolites to the micromolar concentration range
(in contrast, the limit of detection of mass spectroscopy can reach
sub-nanomolar concentrations; Grotti
et al., 2009; Liem-Nguyen et al., 2015; Li et al., 2020). Owing to such
a challenge, the analysis of metabolic mixtures by NMR mostly relies on
1H spectroscopy, which is, however, often marred by the strong signal
overlap in 1H spectra of complex biological samples due to limited
spectral dispersion. 13C NMR could be a promising solution as it offers
wide spectral dispersion, which results in a better separation of metabolite
signals. At present, the application of 13C NMR to metabolite mixtures
at natural abundance is limited due to about 2900-fold reduced sensitivity
(owing to its low natural abundance and gyromagnetic ratio) versus 1H.
Therefore, to expand the applicability of 13C NMR metabolomics, it is of
much interest to develop methods which improve the sensitivity of 13C
signal detection while retaining its resolution advantage. Indeed, detecting
major metabolites in biological samples at natural abundance would require
reaching signal-to-noise ratio (SNR) values above 10 for millimolar (mM) concentrations or less, which is not
possible with conventional hardware. The development of homemade 13C
optimized NMR probes for metabolomics has been shown to yield improved
accuracy in the metabolite identification and group separation for mass
limited samples (Clendinen et al., 2014). However, the
13C signal sensitivity generally remains too low for routine
metabolomics applications, and further development is required to improve
sensitivity.
Hyperpolarization techniques are in the forefront among such developing
methods. Hyperpolarization stems from creating a far from equilibrium spin
population distribution, which results in a significant increase of the
nuclear spin polarization compared to thermal equilibrium values, leading to
considerable improvement in sensitivity. Several hyperpolarization
techniques such as dissolution dynamic nuclear polarization
(d-DNP; Jannin et al., 2019; Giraudeau et al., 2009; Singh et al., 2021; Dey et al., 2020; Guduff et al., 2017; Leon Swisher et al., 2015; Dumez et al., 2015), parahydrogen-induced polarization (PHIP; Kiryutin et al., 2019; Ivanov et al., 2009), and its reversible version, signal amplification by reversible exchange (SABRE; Lloyd et
al., 2012; Daniele et al., 2015; Eshuis et al., 2014; Guduff et al.,
2019), have been implemented successfully for the analysis of complex
mixtures. Among all the hyperpolarization techniques, d-DNP
is of particular interest for metabolic mixtures as it has been
known to improve the signal sensitivity by more than 10 000 times in a
nonselective fashion (Ardenkjær-Larsen et al., 2003).
In d-DNP, nuclear spins are polarized in the solid state at cryogenic
temperatures (typically 1–2 K), in a high magnetic field (3–7 T), by
microwave irradiation in the presence of a radical species. This is followed
by rapid dissolution and transfer of the sample to a nearby NMR spectrometer,
where hyperpolarized signals are acquired at room temperature in the
liquid state. Despite offering impressive sensitivity improvements, the
instrumental complexity of d-DNP could appear contradictory with the
high throughput, precision, and robustness needed for analytical
applications. However, several recent studies highlighted the potential of
d-DNP for analyzing complex metabolic mixtures by 13C NMR. Recent studies demonstrated the relevance of d-DNP for fluxomic studies by quantifying the 13C isotopic patterns to understand the metabolic activity of cancer cell extract incubated with 13C-enriched glucose (Frahm et al., 2021; Lerche
et al., 2018; Frahm et al., 2020). Parallel investigations also focused on the
development of d-DNP methods to analyze metabolic samples at natural
13C abundance, taking advantage of 1H →13C cross-polarization (CP) in the solid state to reach high 13C polarization
levels in a short time (Batel et al., 2014; Bornet et al., 2013). In 2015, we showed that d-DNP could be
used to detect metabolites of plant and cell extracts at 13C natural
abundance (Dumez et al., 2015), and we then reported that
the repeatability of this approach was compatible with metabolomics
applications (Bornet et al., 2016a).
In a recent proof-of-concept study, we demonstrated that d-DNP could be
incorporated into a full untargeted metabolomics workflow capable of
separating tomato extract samples at two different ripening stages and of
highlighting corresponding biomarkers (Dey et al., 2020).
In this study, we described preliminary experimental optimizations that
played a key role in achieving the precision needed for the application of
d-DNP to a series of metabolic samples. These included the use of a
Hellmanex™-coated NMR tube which helps to reduce the formation of microbubbles due to the rapid motion of the dissolved sample inside the NMR
tube. Moreover, we showed that with the use of an appropriate internal
standard, the effect of instrumental variability on relative signal
quantification could be reduced from about 10 % to 3 %. Overall, the
study showcases the potential of d-DNP for metabolomics. However, this study
also highlighted that a full utilization of the prototype DNP setting for
such application would require a thorough optimization of several
experimental parameters. Such optimization was beyond the scope of that
report owing to the large number, complexity and interdependence of
parameters involved in the d-DNP setting. A recent review by Elliot et al.
(Elliott et al., 2021) provides a detailed description of
the practical aspects of the d-DNP workflow, highlighting the good
experimental practices that ensure optimized sensitivity and line shape on
the resulting liquid-state spectra. In the perspective of applying d-DNP to
complex diluted mixtures of metabolites at 13C natural abundance, a
particular focus should be made on the experimental parameters that impact
sensitivity and repeatability, two key ingredients for analytical
applications. Such optimization should also be oriented towards potential
users of this equipment, who may not be experts in instrumental development.
In this context, the present study demonstrates a systematic optimization of
a prototype d-DNP setting, focusing on the parameters which could be tuned by
the user without the need of instrumental development. More specifically, we
present the results of a fine optimization of d-DNP settings for the
analysis of metabolic mixtures at 13C natural abundance, showcasing a
significant improvement (about 5 times in the quaternary 13C region and
50 times in protonated 13C region) in sensitivity compared to our
previous study while preserving a high repeatability. We also intend this
optimization procedure to serve as a guideline for the various applications
of d-DNP in the field of analytical chemistry.
Design of experiment
A brief schematic description of the d-DNP experimental setting is presented
in Fig. 1, highlighting the most important components. The figure
represents the operational workflow indicating important steps in sequence.
The complete d-DNP operation is divided into four main experimental steps,
i.e., sample preparation, polarization in the solid state, dissolution and
transfer, and signal acquisition in the liquid state. In the operational
workflow, we have schematically indicated the change of several relevant
parameters as experienced by the sample during the d-DNP experiment.
Schematic description of the semi-automated prototype dissolution
DNP system along with operational timeline, indicating important events
during d-DNP operation in sequence.
List of parameters for the plan of optimization of DNP setting. PA:
polarizing agent. CP: cross-polarization. μw: microwaves. VTI:
variable temperature insert (enclosure where the sample is vitrified,
hyperpolarized, and dissolved at cryogenic temperature).
(a) Sample preparation(b) DNP polarization(c) Dissolution(d) Signal acquisitionParameters optimized(1) PA concentration (2) DNP juice composition and order of mixing (3) Ripening time (RT) (4) Sample sonication parameters(1) μw parameters (2) 1HDNP buildup (3) CP parameters (4) Vitrification parameters(1) Dissolution solvent (CD3OD, D2O) (2) Dissolution duration (3) Sample transfer line(1) Pre-shimming & Pre-tuningParameters not optimized– Choice of PA – Thermal history of the sample– VTI temperature & pressure stability – Field strength of the DNP polarizer – μw source– Dissolution solvent volume – Dissolution temperature & pressure – Other dissolution solvent – Other sample transfer methods– Standard 13C NMR parameters (pulse length, power, decoupling, etc.)
Table 1 provides a list of all parameters involved in the workflow,
indicating those to be optimized from a practical user viewpoint without the
expertise of hardware development. In the next section, we qualitatively
describe all the relevant parameters involved in DNP experimental setting
and identify the parameters which could potentially impact the analytical
performance of the d-DNP workflow for the analysis of metabolic mixtures at
natural 13C abundance.
Most of these parameters are interdependent. Therefore, instead of a
sequential, one-by-one optimization of these parameters, the ideal strategy
would consist in testing all (or many) possible parameter combinations.
However, it would require a number of experimental attempts that would be
unrealistic with respect to the liquid helium consumption, considering that
three experiments for each condition would be required to evaluate the
repeatability. Therefore, we have divided all the parameters into four main
operational subunits that we optimized in two layers, i.e. (i) a systematic
investigation with no repetition to find out the optimum combinations of
impactful parameters on a sensitivity basis and (ii) an evaluation of the
repeatability on the basis of three experiments for the most sensitive
conditions.
Experiments and parameters
In this section, we sequentially describe the different steps of the
experiment, highlighting the key parameters in the perspective of
application to the sensitive and repeatable analysis of metabolite mixtures
at natural 13C abundance. Numbers in paragraph titles refer to the numbering of parameters in Table 1.
Sample preparation (a)
The d-DNP sample preparation procedure is essential to enable uniform nuclear spin polarization across the sample at a cryogenic temperature, which in turn affects the achievable sensitivity and
repeatability (El Daraï and Jannin, 2021). A standard
sample preparation protocol is required with a careful choice of parameters.
Based on previous d-DNP studies and on our own experience, we identified
three potentially important parameters that could impact the d-DNP workflow
and should be optimized: “DNP juice” composition, ripening time, and
polarizing agent (PA) concentration (Plainchont et al.,
2018; Köckenberger, 2014; Elliott et al., 2021). For the experimental
optimization and evaluation of the d-DNP workflow, a mixture of three common
metabolites at natural 13C abundance (L-alanine, sodium acetate, and sodium
pyruvate) was prepared, each at a 5 mM concentration, which is
representative of the concentration of major metabolites in extracts. Sodium
3-trimethylsilylpropionate-d4 (Na-TSP-d4; 98 % D; 20 mM) was added as an
internal standard, as previously reported (Dey et al.,
2020). For DNP experiments, these chemicals were dissolved in a glassy
matrix along with the polarizing agent (PA),
4-hydroxy-2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPOL). To ensure optimal
solubility, samples were stirred for 60 s with a mechanical stirrer. Sample
sonication was also evaluated by sonicating the sample for 60 s before
inserting the sample into the polarizer, but it did not impact the
polarization efficiency.
For the whole study, samples were prepared from the same stock solution to
avoid unwanted variation from the differences in sample measurement. The
stock solution was prepared by solubilizing the metabolites and TEMPOL in
the DNP juice, then DNP samples were equally divided and stored inside a
-80 ∘C freezer. Before the start of the polarization experiment,
samples were taken out from the freezer and stirred at room temperature as
mentioned above and then transferred into the sample cup. Care was taken
to avoid small bubbles and residue of sample droplets residing in the top
part of the sample cup, which is above the active μw irradiation
region. The sample cup was then vitrified at ca. 4 K inside the polarizer.
It is also important to trace the amount of sample (in weight) taken inside
the sample cup before hyperpolarization. In our case, 200 µL of DNP
sample weighed 258 mg, with a standard variation of 1 % of all the samples
used for experiments. Also, we have monitored the 1H signal integral
without microwave at the solid state to investigate the variation of signal
integral without microwave. It is important to note that such a signal
integral differs from the actual thermal signal of the sample as the
acquired signal integral includes the background signal from the sample cup.
In the following section, we discuss the key parameters involved in sample
preparation for d-DNP experiments.
PA concentration (a.1)
The PA plays a central part in DNP polarization. A broad variety of PAs is
available depending on the targeted sample and application. It has been well
discussed in several studies that nitroxide-based radicals such as TEMPOL
are preferred for 1H →13C CP-based d-DNP, as the broad EPR
linewidth of TEMPOL ensures high and rapid 1H polarization, which in
turn is the main source of 13C polarization. Therefore, TEMPOL was
chosen as a PA for this study. A range from 25–100 mM concentration of
TEMPOL has been previously investigated (Elliott et al.,
2021). For the application to metabolite mixtures at natural 13C
abundance, the TEMPOL concentration should be optimized to achieve the
highest DNP polarization through a rapid DNP buildup with minimal
contribution to the polarization losses during sample transfer before
liquid-state signal acquisition.
DNP juice composition and order of mixing (a.2)
The DNP juice composition consists of a mixture of glycerol,
D2O, and H2O, which ensures a uniform distribution of analytes and
polarizing agent (PA) forming a glassy sample at cryogenic temperatures
(about 1–2 K). Previous studies reported that the efficiency of DNP juice
particularly depends on the nature and concentration of
PA (Leavesley et al., 2018). Several studies
reported that a glycerol content > 55 % was sufficient to form
a glass at 1–2 K (Puzenko et al., 2005;
Leavesley et al., 2018; Hayashi et al., 2005). However, a very high percentage
of glycerol in the DNP juice restricts the solubility of the biological
sample in the DNP juice. Previous DNP studies reported up to 60 % of
glycerol content in the DNP juice composition (Jähnig
et al., 2019; Tran et al., 2020; Overall and Barnes, 2021; Kaushik et al.,
2022). Care should be taken to decide the order of mixing the sample in
H2O–D2O and glycerol of DNP juice depending on the solubility in
H2O–D2O compared to glycerol for ensuring complete solubility of
the sample in DNP juice.
Ripening time (a.3)
A recent study reported that following the completion of DNP sample
preparation, a delay before vitrification (ripening time) could lead to the
formation of nanoscopic water vesicles in a glycerol rich matrix, resulting
in an inhomogeneous distribution of PA in the two water and glycerol phases
of the DNP juice (Weber et al., 2018). Such nanoscopic
phase separation was reported at a PA concentration of 10–80 mM, which
could hamper the 1H DNP efficiency by 20 %. The optimum ripening time (RT) was reported to depend on the sample, polarizing agent, and DNP
juice composition. Therefore, it is essential to investigate the impact of
ripening time for diluted metabolite samples at natural 13C abundance.
DNP polarization (b)
In this section, we describe the relevant instrumental details of the
polarizer including the cryostat along with the microwave source, followed
by a discussion on the parameters involved in the optimization that impact
repeatability and sensitivity.
DNP polarizer. The prototype Bruker d-DNP polarizer works at field (7.05 T) and temperature (1.15 K), which offers optimal CP-based capabilities to reach high 13C polarization levels in about 15 min (Bornet et al., 2013; Dey et al.,
2020). It is built on a standard 7.05 T wide-bore magnet and cryostat
design, modified to accommodate a variable temperature insert (VTI). The VTI
enables DNP at 1.15 K, using liquid helium (l-He) introduced from a transport dewar (e.g., 100 L) and custom transfer line into a phase separator (PS) near the top of the VTI. From there, a membrane pump (Vacuubrand MD 4 NT) transfers cold gaseous helium (g-He), whose enthalpy cools the neck, baffles, and radiation shields of the VTI, while l-He flows down from the PS and
enters the sample space via automated needle valves near the VTI tail. A
main pump (Edwards iGx600L) acts on the admitted l-He for final cooling of
the sample space, whose temperature setpoint is chosen via a feedback-controlled butterfly valve to the pump. For DNP, the microwave
source consists of a synthesizer (8–20 GHz) and an amplifier and frequency
multiplier chain (AMC; Virginia Diodes, Inc) to deliver a final frequency of
∼ 198 GHz at ∼ 120 mW. A waveguide carries the
μw into the VTI to irradiate the sample. Frequency modulation
(Bornet et al., 2014) is programmed via the low-frequency
source, while microwave gating (Bornet et
al., 2016b) is achieved via TTL (transistor–transistor logic) pulses from the Bruker AV NEO NMR console to
the AMC. For NMR, the two-channel console runs Topspin 4 and is coupled to a
custom Bruker 1H,13C probe, with an external tuning and matching
(room temperature) for an overall circuit able to achieve simultaneous
nutation frequencies of 50 kHz without arcing.
Optimization. The efficiency of the DNP polarization depends on the
instrumental design (μw source, cryostat, polarizer, radio frequency (RF)
coil, etc.). Therefore, the ability of users to improve sensitivity
and repeatability is limited. Our system was designed to offer a highly
repeatable polarization in the solid state, and further instrumental
modifications are beyond the scope of this study. However, the sensitivity
and repeatability are also impacted by user-dependent parameters, such as
the polarization temperature and the μw and CP parameters, which are
further described below.
Microwave parameters (b.1)
It is essential to find out the optimal μw irradiation frequency and
power as well as associated modulation bandwidth to achieve optimal
polarization. The optimal value of such parameters depends on the temperature
and sample formulation. Here, μw optimization was performed at 1.2 K
and for the optimal sample preparation parameters.
The measurement of 1H polarization buildup rate helps to verify the
PA's integrity and also dictates the optimum μw irradiation time
(which is chosen to be once or twice the 1H DNP buildup time) between
“contact” for polarization transfer from 1H to 13C via CP. For
each sample, before polarizing the 13C spins, the 1H polarization
buildup time was measured using the pulse sequence shown in Fig. B1a at
1.2 K.
Vitrification parameters (b.4)
Formation of a glass during vitrification inside the polarizer is important
to obtain repeatable polarization. Care should be taken while inserting the
sample to maintain a similar rate of vitrification in the cryostat. It is
important to note that in some cases we experienced a sudden drop of 1H
polarization buildup time in spite of an identical sample composition,
which resulted in a reduction of 1H and 13C DNP signal integrals.
This could be due to the impact of the sample insertion rate on the
formation of glassy matrix at cryo-temperature inside the cryostat. However,
such reduction did not impact the liquid-state signal integral, and we
concluded that the vitrification rate did not impact our results. Still, to
limit potential associated effects, we took care of keeping the same sample
insertion time (40 s) inside the cryostat for all experiments. Also,
dissolving the metabolites and PA in H2O and D2O followed by
dissolving the resulting solution in glycerol helped to improve the
solid-state signal repeatability compared to the reverse sequence of
dissolving the metabolites and PA in DNP juice (first in glycerol then in
H2O and D2O).
CP parameters (b.3)
As discussed in the Introduction, achieving 13C hyperpolarization via
cross-polarization (CP) from DNP-polarized 1H spins is the key for
metabolomics application. The pulse sequence implemented to polarize
13C nuclei is presented in Fig. B1b. The optimization of CP
parameters and the methodological developments ensuring efficient CP have
been described thoroughly in previous studies (Elliott et
al., 2021). Here, we followed a similar procedure of optimization and
implemented these developments for our study.
Dissolution (c)
After the completion of 13C hyperpolarization at 1.2 K, the
hyperpolarized sample is rapidly dissolved in a hot, pressurized solvent,
followed by a rapid transfer to the liquid-state spectrometer through a
magnetic tunnel to minimize polarization losses due to the nuclear spin
relaxation at room temperature during transfer. There are a number of
developments aiming for a rapid and robust dissolution process, such as
the development of gas-driven and liquid-driven sample transfer systems
(Katsikis et al., 2015; Ceillier et
al., 2021; Bowen and Hilty, 2010), a built-in sample transfer system attached
to a cold sample cup (Kress et al., 2021),
and solid sample transfer (Kouřil et al., 2019). Each of
the methods have their own advantages and disadvantages which have been
reviewed in detail (Elliott et al., 2021). Here, we focus
on the optimization of the gas-driven dissolution system available on our
setup.
In our case, dissolution is achieved upon manual coupling of a fluid
transfer stick to the sample cup after it has been lifted (∼ 9 cm) just above the l-He level. The stick includes two parallel capillaries
(PEEK; 1.6 mm ID): an inlet for the preheated, pressurized dissolution
solvent and an outlet to carry hyperpolarized fluid via a sample transfer
line to a 5 mm NMR tube situated in the probe of the solution-state NMR
observation magnet. The hyperpolarized solid sample is dissolved in 5 mL of
hot solvent, and the helium gas drives the dissolved liquid inside the
transfer line to run through a 0.56 T magnetic tunnel (Milani
et al., 2015) (DNP Instrumentation, https://dnp-instrumentation.com, last access: 25 September 2022). Inside the liquid-state NMR
spectrometer, a passive receiver system (injector) accepts the turbulent
dissolution sample and then facilitates phase separation (liquid sample and
helium gas) and settling through gravity after introduction of the sample
into the NMR tube at ambient temperature and pressure.
In this section, we discuss the experimental parameters related to the
optimization of the dissolution, transfer, and relax steps. In our previous
study, the long duration of this process (time from the start of the
dissolution to the start of the signal acquisition = 11.3 s) significantly reduced the sensitivity of 13C metabolite
signals (Dey et al., 2020). Moreover, the dissolution step
contributes most to the signal unrepeatability as it involves a manual step.
Therefore, careful optimization of the dissolution is crucial to ensure the
maximum and repeatable amount of hyperpolarization before signal
acquisition. From a technical point of view, the dissolution process
consists of three main events: (i) flushing the pre-pressurized hot solvent
to the sample cup for certain duration (termed “flush”, driven by the
pressure difference between the pressure cooker and the sample space), (ii) pushing (using helium gas) the dissolved hyperpolarized sample for a fixed
period of time (termed “boost”) through the sample transfer line to
reach to the injector, and (iii) collecting the liquid and allowing
the pushing helium gas to be released (termed “relax” duration) before the
dissolved sample reaches the connected NMR tube. The relax time ensures
the liquid is filled at least up to the active RF coil length devoid of any
microbubble, and to limit the residual motion of liquid that would impact
the line shapes. A longer delay has a favorable impact in the improvement of
signal line shape and linewidth, however resulting in sensitivity losses due
to the polarization decay which impacts differently depending on the
relaxation of different 13C sites. The optimum value of the delay needs
to be decided upon balancing the two opposing effects mentioned above to
obtain better 13C signal sensitivity for the majority of metabolites.
Also, this delay depends on the physical properties (viscosity, surface
tension etc.) of each dissolution solvent. Note that the relax delays
contain a fixed duration delay (0.1 s trigger), which is required to
switch/trigger the automatic signal acquisition pulse sequence in
a liquid-state spectrometer.
The scheme indicating three different stages of dissolution process is shown
with different colors in Fig. 1. There are several parameters involved in
these three events which can be optimized to reduce the loss of
polarization. We focused on the optimization of the following parameters:
dissolution duration (duration of flushing, boosting, and relaxing) (c.2)
sample transfer line (length and inner diameter) (c.3).
It is important to note that all parameters listed above are correlated to
each other. Therefore, we have focused on optimizing the combination of
parameters instead of optimizing parameters one by one. Before presenting
our attempts to find the best combination of parameters, we introduce the
influence of these parameters in the context of maximizing the available
hyperpolarization in the liquid state.
Dissolution solvent (c.1)
The dissolution solvent has a significant impact on the efficiency of
dissolving the hyperpolarized solid at a 1.2 K temperature and on the speed of
sample transfer as well as on stabilizing the dissolved liquid inside the NMR
tube. D2O is widely accepted as a dissolution solvent due to its high
heat transfer coefficient, which leads to efficient dissolution of the
hyperpolarized solid sample. Also, higher solubility of metabolites or other
biological samples in D2O forms an extra advantage. However, owing to
its higher viscosity and surface tension, D2O is less efficient in
terms of sample transfer speed, and a longer stabilization delay is required
to avoid microbubble during signal acquisition. Methanol-d4 has been
known to be used as an alternative dissolution solvent to boost the sample
transfer rate and to reduce the stabilization delay as it is less viscous
and has lower surface tension compared to
D2O (Singh et al., 2021; Mishkovsky and
Frydman, 2008). Both dissolution solvents have their own advantages and
disadvantages. For example, the potential solubility of metabolites depends
on the heat transfer efficiency, which in turn depends on the specific heat
capacity and on the solvent temperature set at the dissolution box. Such
specific heat capacity could be smaller for methanol-d4 compared to
D2O (the specific heat capacity of water and methanol is about 4.18 and 2.53 kJ (kg K)-1 respectively; for CD3OD and D2O we set the temperature in the dissolution oven at 156 and 170 ∘C respectively). We decided to determine the best combination of dissolution parameters for both solvents. Note that the choice of solvent is limited to these two options, owing to the incompatibility of the sample
transfer material and the bad solubility of metabolites in other solvents.
The dissolution solvent volume influences the overall signal sensitivity in
the liquid state. Reducing the solvent volume decreases the dilution factor,
which may either increase or decrease of signal sensitivity depending on the
relative influence of two opposing effects originating from the higher
radical concentration in the dissolved sample vs. increase of sample spin
concentration. However, a sufficient amount of dissolution solvent is
necessary to efficiently dissolve the hyperpolarized solid inside the
polarizer at a temperature of ∼ 1 K. In our system, the dissolution solvent volume (5 mL) was already optimized by the instrument provider.
Dissolution temperature and pressure also play a role in efficiently
dissolving and transferring the dissolved liquid. The choice of temperature
is limited by the boiling point of the solvent at a particular pressure.
Also, the choice of pressure is limited by the integrity of dissolution
components. Therefore, these two parameters will be fixed as initial settings
considered a “safe” maximum value for our dissolution setup.
Dissolution duration (c.2)
The optimum combination of flush, boost, and relax durations is
essential to reduce the overall dissolution time. The flush duration mainly
impacts the sample melting process, the boost duration is responsible for
transferring the dissolved sample through the sample transfer line, and the
relax duration is required to release the propellant helium gas avoiding
microbubbles in the liquid-state sample before signal acquisition. Among all
three durations, the boost and relax duration have the highest impact on
fast sample transfer and improved signal line shape respectively. Therefore,
optimization of the boost time and relax time is of highest priority in the
optimization.
Sample transfer line (c.3)
The inner diameter (ID) and length of the sample transfer line influence the
speed of sample transfer from the polarizer to the signal acquisition
spectrometer and, also, influence the formation of bubbles in the dissolved
sample. In our present setup, two different IDs (1.575 and 2.375 mm) of
sample transfer line were available. We investigated the effect of ID of
sample transfer line on the liquid-state signal sensitivity.
Signal acquisition (d)
Upon completion of the dissolution process, the liquid sample is collected
in the NMR tube by gravity, and the pulse sequence automatically triggers to
start signal acquisition, after a relax delay (discussed above). All d-DNP-enhanced NMR experiments were recorded at room temperature on a 400 MHz
Bruker AVANCE NEO spectrometer equipped with a liquid-N2 cryogenically
cooled probe (5 mm CryoProbe™ Prodigy BBFO with ATMA and
Z-gradient from Bruker BioSpin) using standard optimized pulse sequence and
calibrated pulse parameters. The 13C spectra were recorded in a single
scan at a 90∘ flip angle using Waltz-16 1H decoupling during
acquisition. They were processed with 1 Hz Lorentzian line-broadening, zero-filled to 256 000 data points, Fourier-transformed, manually phase-corrected,
and automatically baseline-corrected with a polynomial of degree 5.
Pre-shimming and pre-tuning (d.1)
Due to the rapidly decaying and irreversible nature of hyperpolarization,
the method does not allow tuning and shimming to be performed before acquisition
of the signal on the hyperpolarized liquid sample. Therefore, the
hyperpolarized signal is acquired on pre-tuned and pre-shimmed condition.
Pre-tuning and pre-shimming are done using similar sample composition and
maintaining a similar sample height in the NMR tube as in the case of
hyperpolarized signal acquisition. Moreover, to achieved improved line shape,
it is desirable to perform pre-shimming at the similar temperature as the
temperature of hyperpolarized, dissolved liquid during signal acquisition.
Note that to optimize the quality of experiments in methanol, we have
acquired a 1H spectrum of the residual protonated methanol using the
same dissolution settings with methanol-d4 and calculated the
temperature of hyperpolarized liquid after injection (292 K) from the
1H chemical shift difference of the methyl and –OH groups. Then, the
pre-shimming of identical sample was done at 292 K, which helps to improve
the line shape of 13C signals. We noticed that DNP signal acquisition
at 292 K improves linewidth of 13C signals of metabolites by more than
11 % compared to the DNP signal acquired at 298 K.
Results and discussion
In this section, we describe the result of parameter optimization under each
subunit in the experimental sequence of events (sample preparation,
polarization, dissolution, acquisition). In this section, each parameter is
mentioned using the numbering defined in Table 1. Also, while comparing
liquid-state signals at different parameter optimization stages, we
analyzed only those signals which are above the limit of quantification (SNR > 10). Figure 2 shows d-DNP-enhanced 13C spectra of the
metabolite mixture along with the reference, before experimental
optimization of parameters as used in the previous
report (Dey et al., 2020). Note that apart for TSP which
is more concentrated, only quaternary carbons are visible due to their low
signal-to-noise ratio (SNR).
The d-DNP-enhanced 13C–{1H}
spectra of metabolites acquired before the optimization, indicating all the
relevant signals above the limit of detection.
Sample preparation (a)PA concentration (a.1)
We compared three potentially suitable concentrations of TEMPOL (i.e., 75,
50, and 25 mM). The 1H DNP polarization buildup time for 75, 50, and
25 mM of TEMPOL was 20, 53, and > 3600 s respectively (Fig. C1). Here, to maintain high-throughput conditions, we compared the DNP
polarization of 13C at different TEMPOL concentration with a fixed
13C DNP polarization time of about 20 min (using contact time of 15 ms,
which was found to be optimal for each radical concentration and 80 s of
μw irradiation per each cycle of polarization transfer 1H →13C (CP contact)). Figure 3 compares DNP-enhanced 1H and 13C
signal in the solid state, as well as liquid-state 13C signal integrals
at the same polarization duration. Note that the differences of signal integral
at different radical concentration do not quantitatively reflect the
polarization due to bleaching effect in solid
state (Stern et al., 2021). Nevertheless, at a 50 mM radical concentration, the solid-state as well as liquid-state signals
are particularly more sensitive than other TEMPOL concentrations at a fixed
experimental time. It is important to note that although the protonated
carbon of TSP shows slightly higher sensitivity at 25 mM TEMPOL due to
smaller relaxation losses during dissolution, the sensitivity of
other 13C signals is considerably lower compared to 50 mM TEMPOL. At 25 mM TEMPOL, it would be possible to achieve similar DNP polarization as at 50 mM TEMPOL but at the cost of an experimental time an order of magnitude higher.
Comparison of signal integral values of (a)1H and
13C signals in the solid state and (b)13C–{1H} liquid-state signal integrals of metabolites with a 25, 50, and 75 mM TEMPOL concentration.
DNP juice composition (a.2)
We have investigated the DNP efficiency of two different compositions of DNP
juice (5 : 4 : 1 and 6 : 3 : 1 glycerol-d8 : D2O : H2O, v/v), which have
been reported to be efficient conditions for polarization with nitroxide
based radicals. As noted in the previous section, further increase in
glycerol content would result in an insolubility of metabolites in the DNP
juice. It is worthwhile to note that in our previous DNP-based metabolomic
work, the composition of the DNP juice was 5 : 4 : 1. Figure 4a compares the 1H and 13C signal integral values at two different DNP juice
compositions, which shows that DNP juice composition of 6 : 3 : 1
(glycerol-d8 : D2O : H2O, v/v) offers higher polarization compared to the 5 : 4 : 1 (glycerol-d8 : D2O : H2O, v/v) with
similar repeatability. Further increase in glycerol content could reduce the
solubility of metabolites which may hinder the metabolomic application in
general. Therefore, the optimized DNP juice composition will be 6 : 3 : 1 for the rest of the study.
(a) Plot of solid-state 1H signal without μw as well as 1H and 13C DNP signal integrals at two different DNP juice
compositions with 50 mM TEMPOL using optimized μw parameters. (b) Plot
of solid-state 1H and 13C signal without μw as well as DNP
signal integral at different ripening time with same TEMPOL concentration
using optimized μw parameters. The standard deviation for every
average integral value is calculated from three identical samples and displayed
as an error bar.
Ripening time (a.3)
With our sample of choice and DNP juice, we did not find any considerable
change of polarization after 30 min of ripening time (defined as the sum of
time elapsed from sample preparation to insertion in the freezer and from
the freezer to insertion inside the polarizer), as reflected in Fig. 4b.
Therefore, we chose to systematically wait 30 min at room temperature
before vitrifying the sample inside the DNP polarizer and also to prepare
all the samples at the same room temperature to avoid unnecessary sources of
signal variation.
Polarization (b)Microwave optimization (b.1)
In Fig. A1, we show the evolution of the relative 1H signal integral
vs. μw frequency and power. From this plot, we have chosen 198.08 GHz
as an optimized frequency, which corresponds to the negative DNP polarization
and 50 % of available μw power for CP-based 13C polarization.
We also investigated the effect of the μw modulation bandwidth for
efficient μw excitation by observing the 1H signal integral at
different modulation bandwidths and frequencies. The optimum values (a
triangular frequency modulation with a bandwidth (Δfμw) of
±5 MHz and frequency of 10 kHz is used) remained unchanged from our
previously reported studies.
We measured the 1H buildup time of our sample at a 50 mM TEMPOL
concentration with optimum DNP juice composition at 1.2 K, leading to an
estimated value of 53 s with 5 % variation over successive experiments.
CP parameters (b.3)
After optimization, the 1H polarization is transferred to 13C by
16 CP contacts of 15 ms each at intervals of 80 s, with a RF power of 15 W on 1H (using rectangular pulse with constant RF
amplitudes of 21 kHz) and 60 W on 13C (using ramped up pulse with
linearly increasing RF amplitudes from 16 to 23.2 kHz). Adiabatic half-passage pulses (WURST) of 30 and 60 W (pulse duration of 175 µs,
sweep width of 100 kHz) were used on 1H and 13C channels
respectively before and after the CP contacts. The total duration of CP
experiment was 21 min.
Dissolution (c)Dissolution duration (c.2)
Before optimization, the duration of flush, boost, and relax
times were set to 0.2, 5, and 6.1 s. We have considered this total duration
of dissolution time (flush, boost, and relax, 11.3 s) as an upper limit with
the objective to reduce the duration in the optimization process. Among the
three durations, the boost time is the most critical duration for
optimization. Therefore, we first focused on comparing several boost
durations by analyzing the 13C signal of metabolites with a fixed set
of flush time (0.2 s) and relax time (2.1 s). The results presented in
Fig. 5 were obtained using a capillary transfer line with 1.575 mm inner
diameter and a length of 370 cm with D2O as a dissolution solvent.
Plot of liquid-state hyperpolarized 13C–{1H} signal integrals (a) and SNR (b) of the metabolites
at different boost times using optimized μw and solid-state DNP
parameters using a transfer line with 1.575 mm ID and D2O as
dissolution solvent. The optimum value of boost time is chosen to be 2 s.
The comparison shows improved signal integral values as the boost time is
reduced from 5 to 1 s. Due to the reduction of boost time, the
fast-relaxing protonated 13C signal (the protonated 13C signal of
TSP at 0 ppm) shows significant improvement compared to the quaternary
13C, but protonated 13C signals from other metabolites remain
invisible. However, SNR comparison in Fig. 5b indicates an optimum
boost time of 2 s corresponding to an improved line shape. A similar
comparison with methanol-d4 solvent exhibits the same boost time
duration of 2 s for optimal sample transfer.
We have compared the repeatability of the newly optimized boost time with
the repeatability before optimization (Fig. 6). Figure 6 shows improved
signal integrals (especially for the protonated 13C of TSP) while
retaining similar repeatability at the optimized dissolution duration.
Following the optimization of the boost time, we also tested different flush
times (data not shown) at a fixed boost duration and relax duration of 2 s
and 1.1 s respectively. Overall, it was found that the reduction of flush
durations did not improve signal sensitivity for both dissolution solvents.
Plot of liquid-state hyperpolarized 13C–{1H} signal integral repeatability before and after
optimized dissolution timings using 1.575 mm ID of transfer line and
D2O as dissolution solvent, keeping a flush and relax delay of 2.1 s.
As mentioned earlier in the experimental section, the relax time
optimization is crucial to have an improved spectral line shape of
hyperpolarized 13C signal with minimum loss of polarization. In the
following section, we show the relax time optimization result at a fixed
flush duration and boost duration of 0.2 and 2 s, respectively, for the
two dissolution solvents (methanol-d4, D2O) separately as this
optimization is solvent-specific.
Dissolution solvent (c.1)
With D2O as dissolution solvent, the protonated and the quaternary
13C signal of TSP at 2.1 s of relax time offers significant improvement
of sensitivity compared to other relax values (see Fig. 7a). Here,
considering a significant sensitivity improvement of 13C signals, we set
2.1 s as an optimum relax time. With CD3OD, we obtained optimum
sensitivity at 1.1 s of relax time despite the irregular behavior of the
quaternary 13C of alanine at 4.1 s and protonated 13C of TSP (see
Fig. 7b). Indeed, with CD3OD, the dissolved sample stabilizes more
quickly compared to D2O, owing to the lower viscosity and surface
tension of CD3OD. Although at relax time = 1.1 s we obtained optimum SNR with CD3OD, we experienced some random failures in signal
acquisition. Systematic investigation of this failure revealed that caution
needs to be taken at the connection point of the injector and NMR tube to avoid
failure in acquiring the signal at 1.1 s of relax time.
Sensitivity comparison of 13C–{1H} signal at different relax times using (a) D2O and (b) CD3OD as dissolution solvent. The optimum relax time with D2O
and CD3OD is chosen to be 2.1 and 1.1 s.
As indicated in Fig. E1, the NMR tube should be exactly connected to the
bottom end of the injector as the imperfect connection at the junction
between the injector and NMR tube causes inefficient filling of liquid in
the NMR tube before the start of signal acquisition. This often results in
failure of signal acquisition. We have designed a special gauge to ensure
proper positioning of the NMR tube in the injector, which completely solved
such a failure issue. In a nutshell, the optimum total duration of the
dissolution time (time from the start of the dissolution to the start of the
signal acquisition) was set to 4.3 and 3.3 s considering flush, boost, and
relax durations of 0.2, 2, and 2.1 s for D2O and 0.2, 2, and 1.1 s for
CD3OD respectively. To appreciate the impact of dissolution time
optimization, we have measured the T1 values of metabolites in the
presence (at final concentration of TEMPOL after dissolution) and absence of
TEMPOL, as presented in Table 2.
T1 values of metabolites in D2O in the presence and
absence of TEMPOL.
These T1 measurements were done by dissolving the metabolites (each at
500 mM concentration) in D2O, with similar composition of DNP juice
present in the post-dissolution solution. Table 2 helps to evaluate the role
of longitudinal relaxation in the effect of the optimization of the
dissolution time on the observed hyperpolarization in solution. Similar
T1 measurements in CD3OD could not be performed as metabolites at a
500 mM concentration and even at a 100 mM concentration (particularly
alanine and pyruvate) were not soluble enough in CD3OD. However,
relative differences in T1 relaxation values may be anticipated to
follow similar trends as in D2O. Overall, CD3OD as a dissolution
solvent compared to D2O showcases superior performance by offering
better line shape, which translates into improved signal sensitivity of our
model metabolite mixture sample. However, for the wide range of biological
samples, a lack of chemical shift database of metabolites and the inefficient
solubility of metabolites in CD3OD could impose additional challenges.
On the one hand, the chemical shift assignment challenge in CD3OD could
be overcome by “spiking” experiments. On the other hand, previous studies
showed that aqueous-solvent-based dissolution techniques can be improved
using back-pressure techniques (Kouřil
et al., 2021, 2019; Katsikis et al., 2015; Bowen and Hilty, 2010; Ceillier
et al., 2021). Therefore, in general, the choice of the dissolution solvent
between CD3OD and D2O should be weighed by considering such
factors.
Transfer line optimization (c.3)
First, we suitably adjusted the length (370 cm) of sample transfer line
according to the distance between the polarizer and NMR acquisition magnet by
reducing the extra length of the line that was present in the initial
setting. The effect of the transfer line inner diameter on the signal
integral values along with the repeatability is presented in Fig. D1,
which shows superior signal obtained with the small diameter ID (1.575 mm)
transfer line compared to the wider one (2.375 mm). A possible explanation
for such a difference in the signal integral would be better homogeneity and
smaller segregation of the liquid and gas mixture in the smaller ID of
sample transfer line compared to the wider ID, resulting in a faster sample
mass transfer. To maintain signal line shape repeatability, care should be
taken at the connection point of the sample transfer line to the dissolution
stick and injector.
Result of optimization
Finally, we have compared the metabolite signal integrals and sensitivity to
investigate the performance of the two dissolution solvents with the
optimized d-DNP setting and benchmarked the improvement of signal with
respect to the spectrum acquired before optimization of d-DNP settings (see
spectra in Fig. 8). Figure 9 showcases significant improvement in
sensitivity (about 5 times improvement on quaternary 13C and 50 times
improvement on protonated 13C) as well as improvement in the signal
integral, especially with CD3OD compared to signals obtained using the
initial parameters before DNP optimization. The main contributing factors of
this improvement are the shorter dissolution duration and faster
stabilization of the dissolved liquid inside the NMR tube. These factors
also contributed to improving the line shape and the linewidth of 13C
signals with CD3OD (see spectra in Fig. 8) significantly (at least 3 times sharper). We found that after optimization the improved sensitivity
with CD3OD enables the detection and analysis of the quaternary alanine
signal which was not detected before. Moreover, the overall optimization
improved the limit of detection, which enabled the observation of the
protonated 13C signals of metabolites at natural abundance (e.g., signals of acetate and pyruvate at 29 and 26 ppm).
Comparison of 13C–{1H} spectra of metabolites before and after optimization in the (a) quaternary 13C region and (b) protonated 13C region using two dissolution solvents.
Comparison of 13C–{1H} signals of metabolites with respect to (a) the average signal integral and (b) the average sensitivity along with the standard deviation, with and without systematically optimized parameters using two dissolution solvents.
Repeatability comparison of 13C–{1H} signal integrals of metabolites with and without
systematically optimized parameters and with two different dissolution
solvents.
a Normalized signal repeatability was obtained from the normalized signal integral with respect to TSP-d4 (188.6 ppm).
b Peak areas were not measured when the signals were below the limit of quantification (SNR < 10).
c Liquid-state polarization was calculated by comparing the
thermal signal integral at a 500 mM concentration of metabolites acquired
with similar acquisition parameters as d-DNP, and the polarization values are
rounded off suitably.
We have summarized the impact of optimization in Table 3, which showcases the
changes in spectral qualities of signals (signal integral repeatability,
linewidth, and liquid-state polarization) before and after optimization of the
d-DNP setting. Table 3 highlights the significant improvement of the
linewidth and liquid-state polarization (especially with CD3OD).
In order to evaluate the impact of optimization on repeatability, Table 3
also compares the repeatability of absolute and normalized signal integrals
(with respect to TSP signal at 188 ppm). The results demonstrate a
considerable improvement for the quaternary 13C signals in both
solvents after optimization compared to the signal obtained before
optimization. However, reduction of the dissolution time and stabilization
delay introduces additional challenges in the manual dissolution efficiency
to maintain the repeatability of the protonated 13C as the T1
relaxation value of the fast-relaxing protonated 13C spins in the presence
of 2 mM TEMPOL (final concentration of radical after dissolution) is about 6 s. We found that the TSP signal at 0 ppm and the protonated 13C signals
of metabolites showed much higher variability. The T1 value (Table 2) of
protonated TSP is the minimum among all the peaks of our interest, which can
be linked to the much higher associated signal variability. In future DNP-enhanced metabolomics studies, care should be taken when choosing the
reference signals. T1 measurements under DNP conditions could provide a
hint towards the choice of a reference, as presented in Table 2. The higher
variability of the protonated 13C signals of the metabolites may be
linked to their lower sensitivity, which occurs due to the combined effect of
shorter relaxation times compared to TSP-d4. Future optimization studies
will focus on further improving the repeatability of protonated 13C
signal by better controlling the repeatability of the dissolution step.
Conclusion
We have presented the detailed report of a fine, user-oriented optimization
of a semi-automated, prototype d-DNP experimental setting dedicated to
13C NMR of metabolite mixtures at natural abundance. The optimization
allows the scope of natural-abundance 13C metabolomics
studies to be extended with high repeatability. The optimized conditions make it possible
to identify the previously inaccessible protonated 13C signals of
metabolites with improved line shape. Still, it also opens the way to
further optimization. In the near future, with the present d-DNP setting, it
would be interesting to investigate the impact of a few parameters that
would require minor modifications of the instrumental setting, such as the effect
of the magnetic tunnel, the dissolution solvent volume, and the length and geometry of the
injector. Further improvement of the signal repeatability of 13C
signals (especially the protonated 13C spins) will probably require
more extensive instrumental developments, such as an automated dissolution
system and rapid sample transfer module. Further reduction of the
dissolution, transfer, and stabilization delays could even enable the
acquisition of DNP-enhanced 1H spectra of the metabolites. Also, recent
reports on the use of porous polarizing matrices could provide a tremendous
boost for metabolomic applications as it makes DNP highly independent on the
sample, and it removes paramagnetic relaxation in the
liquid state (Cavaillès et al.,
2018; El Daraï et al., 2021). Overall, we have established a series of
optimization guidelines which could be of general interest for analytical
applications of d-DNP NMR. We hope that such optimized d-DNP NMR setting
will pave the way to new applications of hyperpolarized 13C NMR of
complex mixtures at natural abundance.
<inline-formula><mml:math id="M314" display="inline"><mml:mrow><mml:mi mathvariant="italic">μ</mml:mi><mml:mi>w</mml:mi></mml:mrow></mml:math></inline-formula> frequency and power optimization
(a) Plot of relative 1H DNP signal integrals vs. μw frequency at 50 mM TEMPOL. Signals are normalized with respect to the signal at 197.69 GHz. (b) Plot of relative 1H DNP signal integrals vs. μw power (%). Signals are normalized with respect to the signal at 198.08 GHz with a triangular frequency modulation with a bandwidth (Δfμw) of ±5 MHz and frequency of 10 kHz.
Pulse program to monitor <inline-formula><mml:math id="M321" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">1</mml:mn></mml:msup></mml:math></inline-formula>H DNP buildups and solid-state DNP pulse sequence via cross-polarization (CP)
Pulse sequence used to monitor (a)1H DNP buildups and (b) solid-state DNP pulse sequence via cross-polarization (CP) for d-DNP. A train (k= 64) of π/2 pulses was applied to both RF channels for pre-saturation. For 1H buildup measurements, a small-flip-angle radio frequency (RF) pulse is employed after each μw irradiation of 10 s (depicted as “blue” block) to monitor the polarization level. Here CP
(depicted as “grey” blocks) is performed using adiabatic half-passage
pulses (tw= 175 µs) to convert longitudinal magnetization into transverse magnetization before the start of the contact pulse (tHH= 15 ms) and vice versa after the contact. Contact pulses use RF powers of 15 W on 1H (using rectangular pulse with constant RF amplitudes of 21 kHz) and 60 W on 13C (using ramped up pulse with linearly increasing RF amplitudes from 16 to 23.2 kHz). In total, 16 CP contacts (n= 16) are made. For each contact, a sequence of m= 4 pulses with low-flip-angle pulse (5∘) is applied on the 13C channel to
monitor the buildup of the polarization from 1H to 13C. Microwave
irradiation is selectively switched on after each CP contact for 80 s to
improve DNP polarization efficiency by avoiding the significant contribution
of electron spin in the nuclear spin relaxation rate.
<inline-formula><mml:math id="M343" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">1</mml:mn></mml:msup></mml:math></inline-formula>H DNP polarization buildup plot
at different radical concentrations
1H DNP polarization buildup plot at different TEMPOL
concentrations (75, 50, and 25 mM). The data points are fitted
monoexponentially to obtain the DNP buildup rate.
Sample transfer line optimization
Comparative plot of liquid-state 13C signal integrals between
two different sample transfer line inner diameters at the same dissolution
delay.
Injector and NMR tube connection
Schematic presentation of correct and incorrect connections
between the top part of the NMR tube and injector.
Data availability
The NMR data shown in Figs. 2 to 9 are available for download in TopSpin format from 10.5281/zenodo.6810794 (Dey et al., 2022).
Author contributions
AD, BC, and VR designed experiments with the suggestions of all co-authors. AD, BC, and KL performed all the experiments. AD, BC, KL, and VR did the formal analysis of the data which were then critically discussed and validated by all the co-authors. AD did data curation and wrote the original draft. PG and JND conceptualized the research goal, analyzed the data, and edited the manuscript. All co-authors edited the manuscript and approved the final version.
Competing interests
Dmitry Eshchenko, Marc Schnell, Roberto Melzi, and James G. Kempf are employees of Bruker Biospin, which supplied the d-DNP polarizer. It is not a commercial instrument but a step in ongoing Bruker R&D.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors acknowledge the French National
Infrastructure for Metabolomics and Fluxomics MetaboHUB-ANR-11-INBS-0010
(http://www.metabohub.fr, last access: 25 September 2022) and the Corsaire metabolomics core facility
(Biogenouest). This work includes NMR experiments carried out on the
CEISAM NMR platform.
Financial support
This research has been supported by the European Research Council, H2020 European Research Council (grant nos. SUMMIT (814747), DINAMIX (801774), HP4all (714519)), the Marie Skłodowska-Curie grant (ZULF (grant no. 766402)), the Agence Nationale de la Recherche (grant no. MetaboHUB-ANR-11-INBS-0010), the Conseil Régional des Pays de la Loire (Connect Talent/HPNMR), the French CNRS, Nantes Université, and Lyon 1 University.
Review statement
This paper was edited by Geoffrey Bodenhausen and reviewed by Benno Meier and one anonymous referee.
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