Adrien Le Guennec1, Fariba Tayyari1, Arthur S Edison1. 1. Complex Carbohydrate Research Center (CCRC), ‡Departments of Genetics and Biochemistry & Molecular Biology, and §Institute of Bioinformatics, University of Georgia , 315 Riverbend Road, Athens, Georgia 30602, United States.
Abstract
NMR metabolomics are primarily conducted with 1D nuclear Overhauser enhancement spectroscopy (NOESY) presat for water suppression and Carr-Purcell-Meiboom-Gill (CPMG) presat as a T2 filter to remove macromolecule signals. Others pulse sequences exist for these two objectives but are not often used in metabolomics studies, because they are less robust or unknown to the NMR metabolomics community. However, recent improvements on alternative pulse sequences provide attractive alternatives to 1D NOESY presat and CPMG presat. We focus this perspective on PURGE, a water suppression technique, and PROJECT presat, a T2 filter. These two pulse sequences, when optimized, performed at least on par with 1D NOESY presat and CPMG presat, if not better. These pulse sequences were tested on common samples for metabolomics, human plasma, and urine.
NMR metabolomics are primarily conducted with 1D nuclear Overhauser enhancement spectroscopy (NOESY) presat for water suppression and Carr-Purcell-Meiboom-Gill (CPMG) presat as a T2 filter to remove macromolecule signals. Others pulse sequences exist for these two objectives but are not often used in metabolomics studies, because they are less robust or unknown to the NMR metabolomics community. However, recent improvements on alternative pulse sequences provide attractive alternatives to 1D NOESY presat and CPMG presat. We focus this perspective on PURGE, a water suppression technique, and PROJECT presat, a T2 filter. These two pulse sequences, when optimized, performed at least on par with 1D NOESY presat and CPMG presat, if not better. These pulse sequences were tested on common samples for metabolomics, human plasma, and urine.
NMR-based
metabolomics has become
established over the past 2 decades, with a great amount of work done
to standardize the acquisition parameters and analysis of the data.[1−3] While 2D NMR metabolomics are sometimes used,[4−6] the majority
of NMR-based metabolomics are conducted using 1D pulse sequences,
with most of these studies relying on two pulse sequences: 1D nuclear
Overhauser enhancement spectroscopy (NOESY) presat[7,8] (solvent
suppression) and Carr–Purcell–Meiboom–Gill (CPMG)
presat[9,10] (T2 filter).Both pulse
sequences have become gold standards for NMR-based metabolomics,
thanks to their effectiveness at removing unwanted signals and general
robustness. Despite their utility, these pulse sequences have limitations,
as we will show below. For these cases, recently developed alternatives
are worth considering.For water suppression, several pulse
sequences have been already
designed, like presaturation,[11] composite
pulses,[12] WET,[13,14] WATERGATE,[15−18] or PURGE.[19] Despite all these variants,
the 1D NOESY presat pulse sequence (noesypr1d on Bruker spectrometers)
has emerged as the leading choice for NMR-based metabolomics. One
of the main reasons for the success of noesypr1d is its ability to
greatly suppress the water peak without intensity losses for most
of the other peaks (except those close to the water resonance, like
glucose and carnitine) and not needing gradients to have adequate
water suppression, so gradient imperfections do not affect the final
spectra, as seen in Figure . However, the 1D NOESY presat does have some shortcomings,
the main one being the noncomplete suppression of “faraway
water”,[20] i.e., water outside of
the homogeneous part of the NMR volume, which does not experience
the same frequency as the rest of the water.
Figure 1
Zoom of the 1D NOESY
presat spectra overlay of pooled urine without
(black) and with (red) gradients, along with the complete spectrum
shown above, to show the difference in residual water peak intensity.
Zoom of the 1D NOESY
presat spectra overlay of pooled urine without
(black) and with (red) gradients, along with the complete spectrum
shown above, to show the difference in residual water peak intensity.
Alternate Water Suppression Techniques and
Avoiding Some of
Their Shortcomings
Some of the water suppression techniques
cited above are more efficient
at suppressing faraway water, along with pulse sequence variants aimed
at suppressing faraway water, like PRESAT180[21] or WET180.[22] However, they either use
shaped pulses or gradients, which limits the robustness and thus reproducibility
of the pulse sequence, an important requirement for metabolomics applications.
Among these alternatives, PURGE[19] was developed
for applications requiring extensive solvent suppression (LC–NMR,
metabolomics, protein study, etc.).Since its first publication,
the PURGE pulse sequence has been
used on a regular basis by the team that created it,[23−35] along with several other groups,[36−46] and even suggested in a Nature protocol.[47] Despite this visibility, the PURGE pulse sequence has not become
routine in metabolomics. The relative lack of popularity may come
from a lack of familiarity by a majority of NMR-based metabolomics
researchers and/or an important shortcoming of the PURGE pulse sequence,
namely, sensitivity to gradient imperfections.This sensitivity
to gradient imperfections is apparent in our relatively
new 600 MHz AVIII-HD, as shown on a sample of glucose in water (Figure b). This spectrometer
is equipped with a cryoprobe and is currently the main spectrometer
for our high-throughput metabolomics studies, which means the PURGE
pulse sequence is not the best fit for most of our metabolomics studies
due to its poor line shape performance. This issue has also been mentioned
in at least one other paper.[44] However,
a recent study[48] showed that pulse sequences
sensitive to gradient imperfections could still give good line shape
by alternating gradient signs between scans. In that case, the WATERGATEwater suppression scheme was used, but as we show here the scheme
seems to be useful for other pulse sequences, including PURGE.
Figure 2
Pulse sequences
for the original PURGE and the optimized PURGE
(a), where gradient signs are inverted between every scan. The gradient
levels themselves are shown for the original PURGE. d20 and d21 are
the delays used within the pulse sequence for short presaturation
times and were set to 200 μs, the recommend values. 1D 1H spectra of glucose with the original PURGE (b) and the optimized
PURGE (c) pulse sequences, with the water peak framed in red, showing
the nonexistent impact of alternating gradients on the water peak.
1D 1H spectra of a urine sample with the original PURGE
(d) and the optimized PURGE (e) pulse sequences. An expansion of part
e is shown in part f, where both the 1D NOESY presat (red) and the
PURGE (black) spectra of 10 urine samples are superimposed. A more
global view of the water peak is shown in the framed inset (g).
Pulse sequences
for the original PURGE and the optimized PURGE
(a), where gradient signs are inverted between every scan. The gradient
levels themselves are shown for the original PURGE. d20 and d21 are
the delays used within the pulse sequence for short presaturation
times and were set to 200 μs, the recommend values. 1D 1H spectra of glucose with the original PURGE (b) and the optimized
PURGE (c) pulse sequences, with the water peak framed in red, showing
the nonexistent impact of alternating gradients on the water peak.
1D 1H spectra of a urine sample with the original PURGE
(d) and the optimized PURGE (e) pulse sequences. An expansion of part
e is shown in part f, where both the 1D NOESY presat (red) and the
PURGE (black) spectra of 10 urine samples are superimposed. A more
global view of the water peak is shown in the framed inset (g).We modified the standard PURGE
to an “optimized”
PURGE sequence by applying gradients that alternated in sign between
scans (Figure a),
similar to the ROBUST-5 approach of Aguilar and co-workers.[48] While we started by including both alternating
gradients and prefocusing gradients, we found an increase in the water
resonance using prefocusing gradients (data not shown). Therefore,
we kept only the alternating gradients. On a concentrated glucose
sample (100 mM), the original PURGE shows sensitivity to gradient
imperfections, which causes line shape distortions (Figure b), while the optimized PURGE
shows good line shape and baseline without modification of the water
peak (Figure c).After verifying the optimized PURGE on the glucose sample, we compared
it to 1D NOESY preset using 10 replicates of pooled human urine samples
containing a high concentration of salts and 90% H2O. In
these conditions, the optimized PURGE still showed good line shape
and baseline compared to the original PURGE (Figure d,e). Spectra from each pulse sequence are
shown in Figure f.
This figure shows that the water peak in 1D NOESY presat distorted
the baseline over a bigger chemical shift range than for PURGE. This
property is probably caused by an incomplete suppression of “faraway
water”, as seen in the framed expansion in Figure g. In addition, the resonances
closest to the water have a higher intensity in the PURGE spectrum,
despite using the same presaturation power for both experiments. Our
results confirm the utility of the PURGE pulse sequence, especially
with the rather simple solution to compensate for gradient imperfections.[48] It should be noted that it is possible to adjust
the pre-emphasis on most gradients. However, this is time-consuming
and also specific for each probe. Alternating the sign of the gradients
provides a much simpler and robust solution to the problem.
T2 Filter
For samples with a large quantity of macromolecules
(like proteins
or lipids), T2 filters are useful in order to remove the
broad unwanted resonances, as seen in Figure b. The pulse sequence mainly used for T2 filters is the CPMG,[10] which has
been used for decades. However, a variant called PROJECT has been
developed recently, which has some distinct advantages with no significant
downsides (Figure a).[49] This variant uses perfect echoes
instead of spin echoes, an approach developed in 1989[50] but recently reintroduced after it was shown that it can
avoid J-modulation for most spin systems, in contrast to CPMG.[49]
Figure 3
1D 1H spectrum of whole plasma with 1D NOESY
presat
and CPMG presat, using standardized parameters[3] (a), along with a close-up of the CPMG presat spectrum, showing
in green frame regions where there is still some significant signal
from macromolecules (b). The use of a longer T2 filter
allows reducing further signals from macromolecules (c), but the use
of longer spin echoes (without changing the length of the T2 filter) decreases the sensitivity of signals and even distorts the
line shape of some of them (d). The use of the PROJECT pulse sequence
as shown in (e) and compared to the CPMG pulse sequence, allows retaining
sensitivity when using longer spin echoes (f).
1D 1H spectrum of whole plasma with 1D NOESY
presat
and CPMG presat, using standardized parameters[3] (a), along with a close-up of the CPMG presat spectrum, showing
in green frame regions where there is still some significant signal
from macromolecules (b). The use of a longer T2 filter
allows reducing further signals from macromolecules (c), but the use
of longer spin echoes (without changing the length of the T2 filter) decreases the sensitivity of signals and even distorts the
line shape of some of them (d). The use of the PROJECT pulse sequence
as shown in (e) and compared to the CPMG pulse sequence, allows retaining
sensitivity when using longer spin echoes (f).Since the first publication of PROJECT, a few other papers
have
shown its utility for T2 measurements or as a T2 filter.[51−57] However, a large number of the references to PROJECT use it as an
alternative spin echo for other pulse sequences,[58−65] like WATERGATE, HSQC, INEPT, etc. To our knowledge there has been
no mention of PROJECT for metabolomics. For that reason, we were interested
to compare PROJECT with CPMG, especially for the quantification of
plasma or serum, along with intact tissues with HR-MAS, as shown elsewhere.[54] It should be noted that no new parameters had
to be introduced in the PROJECT pulse sequence, so it should be as
robust and simple as CPMG.We compared intact plasma with methanol/water
extracted plasma[66,67] using the same pooled Red Cross
plasma that is used for quality
control in the Southeast Center for Integrated Metabolomics (SECIM).
Intact plasma allows for the comparison between CPMG presat and PROJECT
presat, while extracted plasma was used as the standard in terms of
quantification.[67] It should be noted that,
as described by Gowda and co-workers, extracted plasma or serum produce
excellent results. However, it adds extra steps to the sample preparation
and also removes the lipoproteins, which can be analyzed directly
on the same sample to obtain additional information.[68−70] We first applied CPMG presat with standardized parameters for metabolomics
with plasma (Figure b).[3] While most of the macromolecular
signals are already suppressed, a close examination of the baseline
shows that some broad signal remains (Figure c). These residual signals can interfere
with quantification, so we first tried to increase the length of the
T2 filter.In order to increase the length of T2 and avoid heating
the sample, it is preferable to increase the length of each echo rather
than the number of echoes. This strategy allows a further reduction
of intensity of macromolecules compared to the standardized CPMG presat
(Figure d). However,
when compared to a T2 filter with the same total duration
with shorter spin echoes, the CPMG presat with longer spin echoes
leads to sensitivity losses for most of the peaks (from 10% to more
than 50% in our data set), along with line shape distortion for some
peaks that is caused by J-modulation that is not completely suppressed
by CPMG presat (Figure e). These shortcomings of CPMG presat can be resolved by using the
PROJECT presat pulse sequence, which produces spectra almost identical
to the one obtained with the CPMG presat using short spin echoes (Figure f).Along with
this qualitative overview of the differences in the
spectra, we also wanted to determine the effect of the changes in
parameters for quantification, results shown in Figure . Peaks from 5 different molecules were selected
for quantification. These had different properties: isolated peaks
(lactate H10 formate H4), peaks with some overlap with macromolecule
signals (valine H9-11 and H12-14, α-glucose H19) and peaks with
major overlap with macromolecules (lactateH7-9, phenylalanine H13,
H14-15, and H16-17). All atom labeling comes from the recently developed
ALATIS, which creates a unique and atom-specific InChI string.[71]
Figure 4
CPMG presat spectrum of methanol/water extracted plasma.
Assigned
peaks used for quantification are displayed. Ten replicate pooled
aliquots were prepared for intact and extracted samples (20 total),
and each reported pulse sequence was applied to each sample. The bar
plots are the mean concentration (in μM) and standard deviation
of 10 replicates of each condition, each measured from standard addition,
using 3 spectra: one of the extract alone and two after 2 consecutive
standard additions of each quantified metabolite. For this figure,
the different variants correspond to different parameters (designated
in the legend) and not different pulse sequences. CPMG-A, CPMG-B,
CPMG-C, and PROJECT-A were used on intact plasma, while NOESYPR1d,
PURGE, PROJECT-B, and CPMG-D were used on extracted plasma. τ,
spin echo delay between two pulses; τmax, total duration
of the spin echo. The parameters for CPMG-A are considered standard
for analysis of intact plasma. For each peak, t tests
were done, comparing the results of the CPMG presat of extracted plasma
(CPMG-D) to the 7 other spectra. *, p < 0.05;
**, p < 0.005; ***, p < 0.0005.
CPMG presat spectrum of methanol/water extracted plasma.
Assigned
peaks used for quantification are displayed. Ten replicate pooled
aliquots were prepared for intact and extracted samples (20 total),
and each reported pulse sequence was applied to each sample. The bar
plots are the mean concentration (in μM) and standard deviation
of 10 replicates of each condition, each measured from standard addition,
using 3 spectra: one of the extract alone and two after 2 consecutive
standard additions of each quantified metabolite. For this figure,
the different variants correspond to different parameters (designated
in the legend) and not different pulse sequences. CPMG-A, CPMG-B,
CPMG-C, and PROJECT-A were used on intact plasma, while NOESYPR1d,
PURGE, PROJECT-B, and CPMG-D were used on extracted plasma. τ,
spin echo delay between two pulses; τmax, total duration
of the spin echo. The parameters for CPMG-A are considered standard
for analysis of intact plasma. For each peak, t tests
were done, comparing the results of the CPMG presat of extracted plasma
(CPMG-D) to the 7 other spectra. *, p < 0.05;
**, p < 0.005; ***, p < 0.0005.For each selected resonance, quantification
was done on both intact
and extracted plasma[67] by integrating the
area under each resonance and by using standard addition to build
the calibration curves. The spiking buffer was identical to that used
for the original samples. The single spiking solution contained 55
mM lactate, 57 mM glucose, 10 mM valine, 11 mM phenylalanine, and
0.55 mM formate. We added 20 μL of this solution and recorded
all 1D experiments for each spike; this was done twice. For extracted
plasma, all pulse sequences mentioned in this paper (noesypr1d, PURGE,
cpmgpr1d, and PROJECTpr1d) were tested, using the standard parameters.[3,67]t tests for each selected resonance were
performed
to statistically evaluate the changes in quantification between the
different pulse sequences, using a threshold of 0.05 for the p-value. The concentrations from the CPMG presat spectra
of the extracted plasma were used as the reference standards. These
analyses show two trends. First, the pulse sequences that show the
greatest differences with the CPMG presat of extracted plasma are
the ones without T2 filters (noesypr1d, PURGE), even though
these all used the extracted plasma. This shows that the remaining
proteins after extraction are still concentrated enough to interfere
with 1D quantification, demonstrating the need for a T2 filter.[67] This difference can be mainly
explained by the influence of protein signals in the total area under
some of the peaks, especially lactateH7-9, both valine peaks, and
α-glucose H19. For phenylalanine, in addition to some interfering
protein signal, the different results can be explained by low signal-to-noise
(S/N) and the longer T2 filters that further reduce the
S/N.Second, increasing the length of the T2 filter
in intact
plasma tends toward the values from the CPMG of the extracted plasma.
The main exceptions are the phenylalanine peaks, as mentioned above.
It can also be noted that for quantification, the length of the T2 filters is more important than the length of the spin echoes
or the variant of T2 filter.Results show that while
both CPMG presat and PROJECT presat can
be used for quantification, samples with high amount of proteins need
long T2 filters, and PROJECT gave better results in these
conditions, due to better overall sensitivity and line shape.In summary, we have shown that there are useful alternatives to
the most commonly used 1D NMR metabolomics pulse sequences. Should
everyone adopt these in their metabolomics workflow? The answer to
this question is complicated by the opposing need for the field to
move toward standardization so that data can be compared across studies
and between different laboratories. One could argue that it is more
important to keep protocols like pulse sequences constant than to
further optimize, but this argument is countered by each new generation
of instrumentation that is available, which will undoubtedly have
a significant impact on the spectra or we would not be willing to
pay large amounts of money to buy them. The good news is that the
sequences described here produce similar results to their currently
used counterparts. In our hands, the optimized PURGE pulse sequence
improved the water region when compared with noesypr1d for all samples
tested, but other regions of the spectra were essentially unchanged.
For plasma or serum, things are more complicated, because every combination of pulse sequence and sample preparation
method gives different quantitative values. We think that we understand
these differences and have attempted to describe them here. Our recommendation
is to use PROJECT presat in place of CPMG presat for intact or extracted
samples, because there is no change in parameter optimization and
no change for most resonances. However, there is a great improvement
when longer T2 filters need to be applied, because PROJECT
does not cause J-coupling distortions that arise in CPMG. As the field
advances and new techniques are introduced, the best way to establish
back-compatibility with older data sets and methods will be to employ
a reference standard, such as the NIST plasma (SRM 1950).[72]
Authors: Ann-Kristin Petersen; Klaus Stark; Muntaser D Musameh; Christopher P Nelson; Werner Römisch-Margl; Werner Kremer; Johannes Raffler; Susanne Krug; Thomas Skurk; Manuela J Rist; Hannelore Daniel; Hans Hauner; Jerzy Adamski; Maciej Tomaszewski; Angela Döring; Annette Peters; H-Erich Wichmann; Bernhard M Kaess; Hans Robert Kalbitzer; Fritz Huber; Volker Pfahlert; Nilesh J Samani; Florian Kronenberg; Hans Dieplinger; Thomas Illig; Christian Hengstenberg; Karsten Suhre; Christian Gieger; Gabi Kastenmüller Journal: Hum Mol Genet Date: 2011-12-08 Impact factor: 6.150
Authors: Karen W Phinney; Guillaume Ballihaut; Mary Bedner; Brandi S Benford; Johanna E Camara; Steven J Christopher; W Clay Davis; Nathan G Dodder; Gauthier Eppe; Brian E Lang; Stephen E Long; Mark S Lowenthal; Elizabeth A McGaw; Karen E Murphy; Bryant C Nelson; Jocelyn L Prendergast; Jessica L Reiner; Catherine A Rimmer; Lane C Sander; Michele M Schantz; Katherine E Sharpless; Lorna T Sniegoski; Susan S-C Tai; Jeanice B Thomas; Thomas W Vetter; Michael J Welch; Stephen A Wise; Laura J Wood; William F Guthrie; Charles R Hagwood; Stefan D Leigh; James H Yen; Nien-Fan Zhang; Madhu Chaudhary-Webb; Huiping Chen; Zia Fazili; Donna J LaVoie; Leslie F McCoy; Shahzad S Momin; Neelima Paladugula; Elizabeth C Pendergrast; Christine M Pfeiffer; Carissa D Powers; Daniel Rabinowitz; Michael E Rybak; Rosemary L Schleicher; Bridgette M H Toombs; Mary Xu; Mindy Zhang; Arthur L Castle Journal: Anal Chem Date: 2013-12-03 Impact factor: 6.986
Authors: Ivanhoe K H Leung; Marina Demetriades; Adam P Hardy; Clarisse Lejeune; Tristan J Smart; Andrea Szöllössi; Akane Kawamura; Christopher J Schofield; Timothy D W Claridge Journal: J Med Chem Date: 2013-01-04 Impact factor: 7.446
Authors: H Nothaft; M E Perez-Muñoz; G J Gouveia; R M Duar; J J Wanford; L Lango-Scholey; C G Panagos; V Srithayakumar; G S Plastow; C Coros; C D Bayliss; A S Edison; J Walter; C M Szymanski Journal: Appl Environ Microbiol Date: 2017-11-16 Impact factor: 4.792
Authors: Ciara Myer; Jordan Perez; Leila Abdelrahman; Roberto Mendez; Ram B Khattri; Anna K Junk; Sanjoy K Bhattacharya Journal: Exp Eye Res Date: 2020-04-01 Impact factor: 3.467
Authors: Jasmohan S Bajaj; Nita Salzman; Chathur Acharya; Hajime Takei; Genta Kakiyama; Andrew Fagan; Melanie B White; Edith A Gavis; Mary L Holtz; Michael Hayward; Hiroshi Nittono; Phillip B Hylemon; I Jane Cox; Roger Williams; Simon D Taylor-Robinson; Richard K Sterling; Scott C Matherly; Michael Fuchs; Hannah Lee; Puneet Puri; R Todd Stravitz; Arun J Sanyal; Lola Ajayi; Adrien Le Guennec; R Andrew Atkinson; Mohammad S Siddiqui; Velimir Luketic; William M Pandak; Masoumeh Sikaroodi; Patrick M Gillevet Journal: JCI Insight Date: 2019-12-19
Authors: M Saladrigas-García; M D'Angelo; H L Ko; S Traserra; P Nolis; Y Ramayo-Caldas; J M Folch; P Vergara; P Llonch; J F Pérez; S M Martín-Orúe Journal: Sci Rep Date: 2021-03-17 Impact factor: 4.379