NMRProcFlow provides a complete set of tools for processing 1D NMR data, the whole embedded within an interface allowing to interact with a spectra viewer.

Our criterion for the choice of methods for each spectra processing stage (baseline, alignment, binning ...) to implement in our application were those that:

• were evaluated as effective according to our tests,
• can be applied to both a small zone ( <0.5 ppm) or a large zone (several ppm),
• when applied on several dozen of spectra require only few seconds in their execution (implying an appropriate implementation) in order to allow a fluid use of the application.

When these methods were already implemented within a R package, then we integrated it. Otherwise, we implemented them ourselves in R / C ++.

Moreover, the expert's eyes are crucial to select the parameters, and to validate the treatments. Apart for very well-mastered and very reproducible use cases (see Batch mode execution), the implementation of NMR spectra processing workflows executed in batch mode (regarding as a black-box) seems to us very hazardous, and can produce output aberrations. So, it is crucial to proceed in an interactive way with a NMR spectra viewer to allow the expert eye to disentangle the intertwined peaks. Being the heart of our approach, this aspect of interactive visualization is central to our method choices.

1 - Spectra pre-processing

The spectral preprocessing for 1D NMR can be automatically applied in case the input raw data are FID, either from a Bruker or Varian spectrometer. The term pre-processing designates here the transformation of the NMR spectrum from time domain to frequency domain, including the phase correction and the fast Fourier-transform (FFT).
From the work of Hans de Brouwer et al (2009), the adapted then implemented algorithm follows the steps (i.e. from FID to the corresponding real spectrum) depicted by the figure below:

(1) In the case of Bruker spectra a death time or group delay can be observed in the FID.

Note: You can optimize the parameters to apply on your own raw spectra from the small application online at the URL https://pmb-bordeaux.fr/nmrspec/.

References

Hans de Brouwer et al (2009) Evaluation of algorithms for automated phase correction of NMR spectra doi:10.1016/j.jmr.2009.09.017, Journal of Magnetic Resonance 201(2):230-8

2 - Baseline correction

Two types of Baseline correction were implemented: Global and Local. To be more efficient, both methods need to estimate the noise level, and by default the spectral or ppm range considered, is between 10.2 and 10.5 ppm. But users can choose another spectral range if some signal is present in this area.

2.1 - Global baseline correction

The global baseline correction was based on [Bao et al., 2012], but only two phases were implemented: i) Continuous Wavelet Transform (CWT), and ii) the sliding window algorithm. The user must choose the correction level, from 'soft' up to 'high'.

2.2 - Local baseline correction

The airPLS (adaptive iteratively reweighted penalized least squares) algorithm based on [Zhang et al., 2010] is a baseline correction algorithm that works completely on its own, and that only requires a “detail” parameter for the algorithm, called Lambda. Because this Lambda parameter can vary within a very large range (from 10 up to 1.e+06), we converted this parameter within a more convenient scale for the user, called 'level correction factor' chosen by the user from '1' (soft) up to '6' (high). The lower this level correction factor is set, the smoother baseline will be. Conversely, the higher this level correction factor is set, the more baseline will be corrected in details. To be more efficient, the algorithm needs to estimate the noise level; by default the spectral or ppm range considered is between 10.2 and 10.5 ppm. However, users can choose another spectral range if some signal is present in this area.

2.3 - q-HNMR baseline correction

To quantify target compounds, a simple and efficient method is to fit the baseline of a single resonance or a resonance pattern of the corresponding compounds and integrate these selected resonances. These resonance patterns or parts of them are chosen such that their peaks are not or little affected (i.e. in a smoothly way) by a possible overlap with a neighboring peak. In the figure below, the two targeted compounds (asparagine and aspartate) exhibit each a doublet of which only their baseline are affected by the trailing signal of a high intensity nearby lorentzian.

Note: This approach based on a local baseline correction applied on a specific pattern (singlet, doublet and triplet) works well when patterns are not overlapped (Moing et al. 2004). To address the issue in crowded regions with heavy peak overlap, a quantification method based on a local signal deconvolution seems more appropriate (Zheng et al. 2011). The implementation of such a method is planned in a future version of NMRprocFlow in order to cover these types of more complex needs but nevertheless widespread.

Which method to choose for baseline correction?

The spectra processing step consists in preserving as much as possible the variance between samples relative to the chemical compound signature ("useful" variance, i.e. informative) contained in the NMR spectra while reducing other types of variance induced by different sources of bias such as baseline ("unuseful" variance, i.e. non informative).
Nevertheless in the Metabolic Fingerprinting approach, we can accept a loss of "useful" variance, provided that each spectrum is equally affected (in %) by this loss (i.e. this does not affect the between-sample variances), if as a counterpart we can suppress all non-informative variances that unequally affect each spectrum (the variances being additional). The 'Local' baseline method is very efficient in that case, and this method can be applied to a large ppm range. In the figure below, the area ‘1’ is depending on the correction level.

However, in the Targeted Metabolomics approach, a loss of "useful" variance even if it is slight cannot be tolerated; otherwise it also implies the loss of the 'absolute' feature of the quantification. In that case, the 'q-NMR' method is more relevant but it is to be applied to a small ppm range, i.e. a compound pattern (singlet, doublet or triplet).

References

Bao, Q., Feng, J., Chen, F., Mao, W., Liu, Z., Liu, K., et al. (2012).A new automatic baseline correction method based on iterative method. Journal of Magnetic Resonance, 218, 35–43. doi: 10.1016/j.jmr.2012.03.010.

Moing, A., Maucourt, M., Renaud, C., Gaudillere, M., Brouquisse, R., Lebouteiller, B., Gousset-Dupont, A., Vidal, J., Granot, D., Denoyes-Rothan, B., Lerceteau-Kohler, E., Rolin, D. (2004) Quantitative metabolic profiling by 1-dimensional H-1-NMR analyses: application to plant genetics and functional genomics. Functional Plant Biology 31, 889-902. doi: 10.1071/FP04066

Zhang Z, Chen S, and Liang Y-Z (2010) Baseline correction using adaptive iteratively reweighted penalized least squares, Analyst, 2010,135, 1138-1146. doi:10.1039/B922045C

Zheng, C., Zhang, S., Ragg, S., Raftery, D. and Vitek, O. (2011) Identification and quantification of metabolites in 1H NMR spectra by Bayesian model selection. Bioinformatics Vol. 27 (12) 1637:1644, doi:10.1093/bioinformatics/btr118

3 - Spectra alignment

The alignment step is undoubtedly one of the most tedious to solve. The misalignments are the results of changes in chemical shifts of NMR peaks largely due to differences in pH, ionic strength and other physicochemical interactions. To solve this prickly problem, we implemented three alignment methods, one based on a Least- Squares algorithm, one based on Cluster-based Peak Alignment and the other based on a Parametric Time Warping (PTW).

3.1 - Least- Squares algorithm for alignment

To align a set of spectra, we need to choose or to define a reference spectrum. The user can align spectra either based on a particular spectrum chosen within the spectra set, or based on the average spectrum. In this latter case, the re-alignment procedure is executed three times, the average spectrum being recalculated at each time.
In order to limit the relative ppm shift between the spectra to be realigned and the reference spectrum, the user can set this limit by adjusting the parameter 'Relative shift max.', that sets the maximum shift between spectra and the reference. The range goes from 0 (no ppm shift allowed) up to 1 (maximum ppm shift equal to 100% of the selected ppm range).

3.2 - Cluster-based Peak Alignment

A novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA) is proposed by Vu et al (2011). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation.

3.3 - Parametric Time Warping for alignment

The implementation is based on the R package 'ptw' (Bloemberg et al. 2010). In this method, the spectra alignment consists in approximating the ‘time’ (ppm in our case) axis of the reference signal by applying a polynomial transformation of the ‘time’ (i.e. ppm) axis of the sample to be aligned.

where w() is the warping function. For more details, refer on the valuable explanations in Wehrens (2011).

Warnings: Wehrens R. (2011) highlights the fact that (§ 3.3.2) "alignment methods that are too flexible (such as PTW) may be led astray by the presence [so by the absence] of extra peaks, especially when these are of high intensity".

Note: In order to solve the different alignment cases, we have planned to include some other methods such as CluPA (Vu et al. 2013) or RUNAS (Alonso et al. 2014). We are currently testing these methods in order to evaluate their strengths and weaknesses compared to those we already implemented.

Which alignment method to choose?

Compliant with the NMRProcFlow philosophy and due to the diversity of problems encountered, for spectra alignment we chose the interactive approach. It means interval by interval, each interval being chosen by the user. We preconize to prioritize the CluPA method to align some large ppm areas (up to 2 or 3 ppm but no more). The larger the ppm zone, the higher the resolution should be. When the misalign zones are small, we recommand to choose the Least-Square method applied on spectra segments where intensities decrease to zero on both sides. It means that a baseline correction is also greatly preconized before attempting any alignment. Because the Least-Square method just applies a shift between spectra segments, this does not alter the lineshape of peaks. Regarding Targeted Metabolomics, this point is crucial for an absolute quantification. Nevertheless, when in presence of highly overlapped peaks and/or a highly misaligned zone, it could be valuable to apply the PTW method even if some peaks are altered in order to have at least an estimation, either of the concentration (Targeted Metabolomics) or of the relative integration value (Metabolic Fingerprinting).

References

Alonso, A., Rodríguez, M., Vinaixa, M., Tortosa, R., Correig, X., Julià, A., Marsal, S. (2014) Focus: a robust workflow for one-dimensional NMR spectral analysis. Anal Chem. 21;86(2):1160-9. doi: 10.1021/ac403110u

Bloemberg, T.G., Gerretzen, J., Wouters, H.J.P., Gloerich, J., van Dael, M., Wessels, H.J.C.T., et al. (2010). Improved parametric time warping for proteomics. Chemometrics and Intelligent Laboratory Systems, 104(1), 65-74. doi:10.1016/j.chemolab.2010.04.008

Vu T.N., Valkenborg D., Smets K., Verwaest K.A., Dommisse R., Lemière F., Verschoren A., Goethals B., Laukens K. An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data. BMC Bioinformatics. 2011;12:405 doi: 10.1186/1471-2105-12-405

Wehrens R. (2011) Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences, Ed Springer-Verlag Berlin Heidelberg doi: 10.1007/978-3-642-17841-2

4 - Binning

An NMR spectrum may contain several thousands of points, and therefore of variables. In order to reduce the data dimensionality binning or bucketing is commonly used. In binning, the spectra are divided into bins (so called buckets) and the total area (sum of each resonance intensity) within each bin is calculated to represent the original spectrum. The more simple approach (Uniform bucketing) consists in dividing all spectra with uniform spectral width (typically 0.01 to 0.04 ppm).

Due to the arbitrary division of spectrum, one bin may contain pieces from two or more resonances and one resonance may appear in two bins which may affect the data analysis. We have chosen to implement the Adaptive, Intelligent Binning method [De Meyer et al. 2008] that attempts to split the spectra so that each area common to all spectra contains the same single resonance, i.e. belonging to the same metabolite (Intelligent bucketing). In such methods, the width of each bucket is then determined by the maximum difference of chemical shifts among all spectra.

Such as AIBIN method, a method called ERVA (Extraction of Relevant Variables for Analysis) (Jacob et al, 2013) attempts to split the spectra so that each area common to all spectra contains the same single resonance, i.e. belonging to the same metabolite. This mathematical method is based on a convolution product between a spectrum (S) and the second-order derivative of the Lorentzian function (SDL). This products buckets that are exactly centered on each resonance having their width equal to twice the half-width of the resonance.

Another way for obtaining buckets is to choose the ppm ranges the user wants to integrate (Variable size bucketing). This method is typically used for the Targeted Metabolomics approach where only few resonances corresponding to targeted compounds are selected with their size depending on the signal pattern.

Note: The novel binning method called ERVA (Extraction of Relevant Variables for Analysis) will be soon available within NMRProcFlow, which that will greatly help the identification task of potential biomarker. (Jacob et al 2013) - See presentation online for more details on this approach.

Which binning method to choose?

Regarding the Targeted Metabolomics approach, clearly the ‘Variable size bucketing’ has to be chosen for reasons given above. Regarding the Metabolic Fingerprinting approach, the choice is depending if the alignment has been done or not. If yes and even in a imperfect way, the Intelligent bucketing or the ERVA method are clearly the most efficient. In case the alignment remains a too tedious task to be accomplished, one can choose the uniform bucketing as the most fast and simple (given that 'intelligence' will change nothing in the matter). But in this latter case, we strongly recommend after any subsequent statistical analyses to check the quality of the buckets especially those highlighted from Discriminant Analysis (e.g PLS-DA, OSC-PLS-DA ...). This has to be done with a critical eye by visualizing the overlaid spectra for these buckets, and thus ensuring that it is not due to a local misalignment.

References

De Meyer, T., Sinnaeve, D., Van Gasse, B., Tsiporkova, E., Rietzschel, E. R., De Buyzere, M. L., et al. (2008). NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Analytical Chemistry, 80(10), 3783–3790.doi: 10.1021/ac7025964

Jacob D., Deborde C. and Moing A. (2013). An efficient spectra processing method for metabolite identification from 1H-NMR metabolomics data. Analytical and Bioanalytical Chemistry 405(15) 5049–5061 doi: 10.1007/s00216-013-6852-y

5 - Buckets integration

The integration of buckets or zones of interest for targeted analysis is carried out according to the traditional trapezoid method. Knowing that a NMR spectrum is discretized at constant step, the formula is straightforward and described as follows.

6 - Bucket normalization

Before bucket data export, in order to make all spectra comparable with each other, the variations of the overall concentrations of samples have to be taken into account. We propose three normalization methods. In NMR metabolomics, the total intensity normalization (called the Constant Sum Normalization) is often used so that all spectra correspond to the same overall concentration. It simply consists in normalizing the total intensity of each individual spectrum to a same value.
Other methods such as Probabilistic Quotient Normalization [Dieterle et al. 2006] assume that biologically interesting concentration changes influence only parts of the NMR spectrum, while dilution effects will affect all metabolite signals. Probabilistic Quotient Normalization (PQN) starts by the calculation of a reference spectrum based on the median spectrum. Next, for each variable of interest the quotient of a given test spectrum and reference spectrum is calculated and the median of all quotients is estimated. Finally, all variables of the test spectrum are divided by the median quotient. An internal reference can be used to normalize the data. For example, an electronic reference (ERETIC, (see Akoka et al. 1999), or ERETIC2 generated with TopSpin software) can be used for this purpose. The integral value of each bucket will be divided by the integral value of the ppm range given as reference.

Which normalization method to choose?

We suggest the reference [Kohl et al. 2012] as a good review that could be read with great profit.

References

Akoka S1, Barantin L, Trierweiler M. (1999) Concentration Measurement by Proton NMR Using the ERETIC Method, Anal. Chem 71(13):2554-7. doi: 10.1021/ac981422i.

Dieterle F., Ross A., Schlotterbeck G. and Senn H. (2006). Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1H NMR Metabonomics. Analytical Chemistry, 78:4281-4290.doi: 10.1021/ac051632c

Kohl SM, Klein MS, Hochrein J, Oefner PJ, Spang R, Gronwald W. (2012) State-of-the art data normalization methods improve NMR-based metabolomic analysis, Metabolomics 146-160, doi: 10.1007/s11306-011-0350-z

Regarding Bruker, two types of raw data are accepted: Free Induction Decay (fid) and pre-processed raw spectra (1r). In both cases, the folder structure must follow that of the Bruker TopSpin software. "Pre-processed" means that it assumes that Fourier transform and phase correction have been applied on all spectra so that their corresponding processing directory (under 'pdata') exists along with their real spectrum (i.e 1r file). In the case where the input raw data are FID, the spectral pre-processing is automatically performed. See the Spectral pre-processing for 1D NMR section.

To ease the preparation phase, simply zip the entire directory including all spectra of the experiment. This means that it is useless to perform any prior selection or having to rename the numbers of experiment and processing. The figure below shows an example of ZIP file along with its corresponding samples file.

Supported directory structures for Bruker NMR spectra

(A): Each sample has its own directory (e.g MMBBI_15P07-F3-001) containing the different acquisition spectra ( 1, 10, 99999), the whole being contained under a root directory (i.e. NMRFRIM3-4). (B): Same as (A), but without the root directory. (C) is the sample file corresponding to both directory structures.

A root directory contains all acquisition spectra ( one for each sample). Acquisition spectra names can be a number (A), or a string (B). (C) and (D) are the sample files corresponding to the directory structures. Both directory structures shown in (E) are not supported.

Information provided within the samples file must correspond to the directories contained in the ZIP file. (The colored boxes show the correspondences)

• The 'rawdata' column may include all directories or just a subset contained in the ZIP file.
• The 'Samplecode' column can be filled with the biological sample name or can be just a copy-paste of the 'Rawdata' column.
• The 'expno' and 'procno' columns correspond to the experiment number (i.e. FID) and the processing number (i.e. 1r) respectively.
• Several factor columns can be added which will allow spectra to be visualized according to theirs factor levels (see example online). The only constraints on factor names are that they must be alphanumeric characters [0..9, a..z, A..Z]. The underscore '_' is also allowed.

In this way, it becomes easy to select each NMR spectrum that we want to include into the spectra serial in order to be processed together. In the absence of the file of samples provided as an input, NMRProcFlow will consider all of the root directories in the zip file by default, looking for the smallest FID identifier (expno) and the smallest processing identifier (procno) for each of them.

Warnings: In the case where the input raw data are FID, the 'procno' column has to be kept (e.g. filled with zero) so that the samples file is thus compliant with both type of input data (fid & 1r)

To facilitate the generation of the samples file, it is possible to proceed as follows:

• Upload the ZIP file only, and then NMRProcFlow will produce a text file containing the acquisition and processing parameters for each spectrum taken into account by default,
• Download the parameters file ('Export Parameters'),
• Edit this file to serve as a starting template for generating the samples file.

Once uploaded files, you can click on 'Launch' to start the pretreatment.

Once completed, the list of spectra considered with their acquisition and pre-processing parameters is provided

To switch to the processing steps, you must click on the 'Processing' tab at the top of the screen.

Regarding Agigent/Varian, only the Free Induction Delay are accepted, given that there is no normalized folder structure for pre-processed raw data provided by the OpenVnmrJ software. See the Spectral pre-processing for 1D NMR section to know how to choose parameters.

To ease the preparation phase, simply zip the entire directory including all spectra of the experiment. This means that it is useless to perform any prior selection or having to rename the numbers of experiment and processing. The figure below shows an example of ZIP file along with its corresponding samples file.

Information provided within the samples file must correspond to the directories contained in the ZIP file. (The colored boxes show the correspondences)

• The 'rawdata' column may include all directories or just a subset contained in the ZIP file.
• The 'Samplecode' column can be filled with the biological sample name or can be just a copy-paste of the 'Rawdata' column.
• Several factor columns can be added which will allow spectra to be visualized according to theirs factor levels. The only constraints on factor names are that they must be alphanumeric characters [0..9, a..z, A..Z]. The underscore '_' is also allowed.

In this way, it becomes easy to select each NMR spectrum that we want to include into the spectra serial in order to be processed together. In the absence of the file of samples provided as an input, NMRProcFlow will consider all of the root directories in the zip file by default, looking for all FID files.

To facilitate the generation of the samples file, see the corresponding section in the 'Bruker' tab

Once uploaded files, you can click on 'Launch' to start the pretreatment.

Once completed, the list of spectra considered with their acquisition and pre-processing parameters is provided

To switch to the processing steps, you must click on the 'Processing' tab at the top of the screen.

Since March 2018, the NMR Jeol spectra are supported (only the Free Induction Delay) within the JDF format created from the Jeol Delta software.

• See the Spectral pre-processing for 1D NMR section.
• See a complete example with a JEOL spectra set (JDF Format) - (PDF online)

Since March 2019, the NMR RS2D spectra are supported (both Free Induction Delay and Spectrum in frequency domain) within the SPINit format created from the SPINit software.

• See the Spectral pre-processing for 1D NMR section.
• See a complete example with a RS2D spectra set (SPINit Format) - (PDF online)

Since January 2018, the nmrML format are supported (only the Free Induction Delay).

• See the Spectral pre-processing for 1D NMR section.
• See an example of nmrML convertion of a JEOL spectra set (JDF Format) before uploading within NMRProcFlow - (PDF online)

References

Schober D, Jacob D, Wilson M, Cruz JA, Marcu A, Grant JR, Moing A, Deborde C, de Figueiredo LF, Haug K, Rocca-Serra P, Easton J, Ebbels TMD, Hao J, Ludwig C, Günther UL, Rosato A, Klein MS, Lewis IA, Luchinat C, Jones AR, Grauslys A, Larralde M, Yokochi M, Kobayashi N, Porzel A, Griffin JL, Viant MR, Wishart DS, Steinbeck C, Salek RM, Neumann S. (2018) Anal Chem doi: 10.1021/acs.analchem.7b02795

The calibration of the scale of PPM is to adjust the chemical shifts according to a known reference. The reference compound used for chemical shift (δ=0.00) is usually the sodium salt of 3-trimethylsilylpropionic acid-d4 (TSP-d4) with deuterated methylene groups. Other references standards are 2,2- 23 dimethyl-2-silapentane-5-sulfonate sodium salt (DSS) or for organic solvent trimethylsilane (TMS). But it may be any other compound such as creatinine (4.06ppm), α-glucose (5.23ppm), alanine (a doublet along with a peak at 1.488ppm), etc...

How to proceed (see fig below)

• Capture the reference peak into the ‘Range of the PPM reference’ box
• Specify the ppm value corresponding to the highest intensity of the reference peak
• Then launch

Because on the one hand the peak integration is very sensitive to baseline distortions and on the other hand the baseline distortions may not affect in the same way each spectrum, we have to apply a baseline correction.

Two types of Baseline correction were implemented: Global and Local. (For more details, see Processing methods). To be more efficient, both methods need to estimate the noise level, and by default the ppm range included between 10.2 and 10.5 ppm is taken. But you can choose another one if some signal is present in this area.

1 - Global

The global baseline correction was based on [Bao et al, 2012], but only two phases were implemented: i) Continuous Wavelet Transform (CWT) and ii) the sliding window algorithm. The user must choose the correction level, from 'soft' up to 'high'.

How to proceed (see fig below)

• Choose the correction level, from 'Soft correction' up to 'High correction'
• Capture the ppm range in order to estimate the noise level
• Then launch

Bao et al (2012) A new baseline correction method based on iterative method, Journal of Magnetic Resonance 218 (2012) 35-43

2 - Local

The airPLS (adaptive iteratively reweighted penalized least squares) algorithm based on [Zhang et al, 2010] is a baseline correction algorithm which works completely on its own, and that does only require a detail parameter for the algorithm, called Lambda. Because this Lambda parameter can vary within a very large range (from 10 up to 1.e+06), we converted this parameter within a more convenient scale for the user, called 'level correction factor' chosen by the user from '1' (soft) up to '6' (high). The lower this level correction factor is set the smoother baseline will be. Conversely, the higher this level correction factor is set, the more baseline will be corrected in details. To be more efficient, the algorithm needs to estimate the noise level, and it takes by default the ppm range included between 10.2 and 10.5 ppm. But you can choose another one if some signal is present in this area

How to proceed (see fig below)

• Capture the ‘Range of the PPM’ to be corrected
• Choose the correction level, from '1' up to '7'
• Capture the ppm range in order to estimate the noise level
• Then launch

Zhang Z, Chen S, and Liang Y-Z (2010) Baseline correction using adaptive iteratively reweighted penalized least squares, Analyst, 2010,135, 1138-1146. doi:10.1039/B922045C

The alignment step is undoubtedly one of the most tedious to solve. The misalignments are the results of changes in chemical shifts of NMR peaks largely due to differences in pH and other physicochemical interactions. To solve this prickly problem, we implement two alignment methods, one based on a Least- Squares algorithm and the other based on a Parametric Time Warping (PTW). Compliant with the NMRProcFlow philosophy and due to the diversity of problems encountered we chose for spectra alignment, the interactive approach. It means interval by interval, each interval being chosen by the user. (For more details, see Processing methods)

1 - Least- Squares algorithm

To align a set of spectra, we need to choose or to define a reference spectrum. You can align spectra either based on a particular spectrum chosen within the spectra set, or based on the average spectrum. In this latter case, the re-alignment procedure is executed three times, the average spectrum being recalculated at each time

In order to limit the relative ppm shift between the spectra to be realigned and the reference spectrum, you can set this limit by adjusting the parameter 'Relative shift max.', that sets the maximum shift between spectra and the reference. The range goes from 0 (no ppm shift allowed) up to 1 (maximum ppm shift equal to 100% of the selected ppm range)

1.1 - How to proceed (see fig below)

• Capture the ‘Range of the PPM’ to be aligned
• Set the 'Relative max/ shift'
• Choose the 'Reference Spectrum'
• Then launch

1.2 - Example showing the importance of the "Relative max. shift" parameter (see fig below)

Consider that we want to align spectra within the ppm range defined by the window as shown below:

With no way to limit the shifts between spectra, (ie. this corresponds to a relative maximum shift equal to 100%), we have the following result:

Clearly, it is not the right result. So, we set now a relative maximum shift equal to 5% (ie 0.05), and we have the right result, as shown below:

2 - Parametric Time Warping

The modus operandi for this method is very similar to the previous one, apart the "Relative max. shift" parameter that is not needed. The implementation is based on the R package 'ptw' (Bloemberg et al. 2010) and on the valuable explanations in Wehrens R. (2011).

2.1 - Example of comparison between the Least-Square and PTW methods (see fig below)

Clearly, in this example, PTW method is more efficient than a simple Least-Square approach.

Warnings: Wehrens R. (2011) highlights the fact that (§ 3.3.2) "alignment methods that are too flexible (such as PTW) may be led astray by the presence [so by the absence] of extra peaks, especially when these are of high intensity". Therefore, a "very esthetic alignment" must not be the only quality criterion.

A good example is shown in the figure below. We first align the spectra in two steps using the Least-Square approach. The orange arrows show the absence of one peak for one stage (J03). Then, we undo the second alignment of the zone that included the problematic peak and we align again this zone using the PTW approach. All spectra are seem well-aligned apart those corresponding to the J03 stage. The red arrows show the zones where the PTW algorithm has been "astray".

T.G. Bloemberg, J. Gerretzen, H.J.P. Wouters, J. Gloerich, M. van Dael, H.J.C.T. Wessels, L.P. van den Heuvel, P.H.C. Eilers, L.M.C. Buydens, and R. Wehrens. Improved parametric time warping for proteomics. Chemom. Intell. Lab. Systems, 2010.

Wehrens R. (2011) Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences, Ed Springer-Verlag Berlin Heidelberg

An NMR spectrum may contain several thousands of points, and therefore of variables. In order to reduce the data dimensionality binning is commonly used. In binning the spectra are divided into bins (so called buckets) and the total area within each bin is calculated to represent the original spectrum. The more simple approach consists to divide all the spectra with uniform areas width (typically 0.04 ppm). Due to the arbitrary division of peaks, one bin may contain pieces from two or more peaks which may affect the data analysis. We have chosen to implement the Adaptive, Intelligent Binning method [De Meyer et al. 2008] that attempt to split the spectra so that each area common to all spectra contains the same resonance, i.e. belonging to the same metabolite. In such methods, the width of each area is then determined by the maximum difference of chemical shift among all spectra.

How to proceed (see fig below)

• Specify a relevant zone in order to estimate the noise level.
• Choose a resolution factor between 0.1 and 0.6 (0.5 is the default value); the smaller value the greater resolution
• Select one or more PPM zones for applying the binning and put them in the box of ‘PPM ranges’
• Choose the threshold of the Signal/Noise Ratio (SNR) so that the buckets having a lower average integration will be excluded.
• Then launch

Warnings:Do not choose a resolution parameter too small in an area where alignment has not been made. NMRProcFlow makes it possible to adapt area by area the right resolution.

de Meyer T, Sinnaeve D, van Gasse B, Tsiporkova E, Rietzschel E, de Buyzere M, Gillebert T, Bekaert S, Martins J, van Criekinge W (2008) NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem 80:3783–3790

Then it is always possible to make some adjustments by merging or resetting one or more buckets.

Another way for obtain buckets is to choose yourself ppm ranges you want to integrate. This method is typically used for the Targeted metabolomics approach where only few peaks corresponding to targeted compounds are selected whose their size is depending of the signal pattern.

An exported buckets table can be imported into NMRProcFlow in order to retrieve the same bucketing obtained in a previous work session

How to proceed (see fig below)

• Choose the format corresponding of the imported file
• Check if the imported file have an header line or no
• Choose the corresponding columns for the lower and upper ppm bounds of the buckets.
• Check if imported buckets will be append to those already defined or not. If not, all buckets previously defined will be erased.
• Then launch

Before exporting, in order to make all spectra comparable each other, we have to account for variations of the overall concentrations of samples. In NMR metabolomics, the total intensity normalization (called the Constant Sum Normalization) is often used so that all spectra correspond to the same overall concentration. It simply consists to normalize the total intensity of each individual spectrum to a same value. But other methods such as Probabilistic Quotient Normalization [Dieterle et al. 2006] assumes that biologically interesting concentration changes influence only parts of the NMR spectrum, while dilution effects will affect all metabolite signals. Probabilistic Quotient Normalization (PQN) starts by the calculation of a reference spectrum based on the median spectrum. Next, for each variable of interest the quotient of a given test spectrum and reference spectrum is calculated and the median of all quotients is estimated. Finally, all variables of the test spectrum are divided by the median quotient. We suggest the reference [Kohl et al. 2012] as a good review that could be read with great profit.

An internal reference can be used to normalize the data. Typically, an Electronic reference (ERETIC) can be used for that (see Akoka et al. 1999). Integral value of each bucket will be divided by the integral value of the PPM range given as reference..

How to proceed (see fig below)

• Choose a Normalization Method
• Choose an Export Format
• Choose the threshold of the Signal/Noise Ratio (SNR) so that the buckets having a lower average integration will be excluded.
• Specify if necessary, the PPM range of the internal reference signal (typically, the ERETIC signal). Otherwise, leave empty this box.
• Then, click on Export

After exporting, the data matrix is formatted so that we can subsequently perform statistical analysis using BioStatFlow web application. Thus the data file manipulations are minimized. See Metabolic Fingerprinting

Dieterle F., Ross A., Schlotterbeck G. and Senn H. (2006). Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1H NMR Metabonomics. Analytical Chemistry, 78:4281-4290.
Kohl SM, Klein MS, Hochrein J, Oefner PJ, Spang R, Gronwald W. (2012) State-of-the art data normalization methods improve NMR-based metabolomic analysis, Metabolomics 146-160, DOI:10.1007/s11306-011-0350-z
Akoka S1, Barantin L, Trierweiler M. (1999) Concentration Measurement by Proton NMR Using the ERETIC Method., Anal. Chem 71(13):2554-7. doi: 10.1021/ac981422i.

The buckets table can be exported in order to be saved on your local space disk. Then it can be used as an association file along with the data matrix within BioStatFlow, or be imported into NMRProcFlow (see below)

The exported buckets table can be imported into NMRProcFlow in order to retrieve the same bucketing obtained in a previous work session. See Import Buckets

The Signal-Noise Ratio (SNR) matrix can be exported, as shown below

See online some slides about the SNR Exporting

After the processing and bucketing steps, NMRProcFlow allows users to export all data needed for the quantification in a same XLSX workbook. Two workbook templates were currently available: A simple one and a template dedicated for the quantification.

• The simple template just aggregates the buckets table, the SNR matrix and the data matrix, each data type being within a separate tab.
• The 'qHNMR' template, in the same way as the simple template aggregates information like the samples table, the buckets table, the SNR matrix and the data matrix within separate tabs, but also includes another tab with the pre-calculated quantifications according to a formula from data provided in the others tabs. Some information are set by default in both 'samples' and 'buckets' tabs. Just adjust them with the appropriate values and the quantifications within the eponymous tab will be automatically updated.

How to proceed (see fig below)

• Choose a template type
• Choose a Normalization Method
• Choose the threshold of the Signal/Noise Ratio (SNR) so that the buckets having a lower average integration will be excluded.
• Specify if necessary, the PPM range of the internal reference signal (typically, the ERETIC signal). Otherwise, leave empty this box.
• Then, click on Export

See Targeted Metabolomics for further information

1) Bookmark the URL corresponding to your session as shown in the figure below (Chrome browser). Instead of the URL itself, the chosen name is based on the ZIP name you have upload in your session, giving an easy mnemonic way to retrieve the bookmark in your list (sometimes crowded)

2) Recovering your working session in the same state as you left after few hours or days, depending on the period of the automatic cleaning process.

NMRProcFlow does not manage sessions in a medium or long term period.

• An automatic cleaning process has been implemented to periodically purge the working sessions with no activities over the past few days

The choice was to make a processing tool on the fly

• No need for large disk space and no need to backup

NMRProcFlow allows users to save on their own space a minimal set of small files ...

... in order to recover / replay their session

How to regenerate a session with the same treatment ?

1) Before existing your session, just export a file of macro-commands

To save the processing commands in order to replay them later on the same or similar NMR spectra, just click on the CMD button

As shown in the figure below, all processing commands previously launched are logged into a macro-command file which can be saved on your local disk.

2) Replay the same processing workflow ... few months later

An option allows advanced users to submit a file of macro-commands along with the ZIP file of the raw spectra so that we can process the 1D NMR spectra in the same way as we proceeded them in a previous work session.

To assess the influence of the environment on fruit metabolism, tomato (Solanum lycopersicum 'Moneymaker') plants were grown under contrasting conditions (optimal for commercial, shaded production) and locations. Samples were harvested at nine stages of development, and 36 enzyme activities of central metabolism were measured as well as protein, starch, and major metabolites, such as hexoses, sucrose, organic acids, and amino acids.

For more details, see FRIM - Fruit Integrative Modelling, an ERASysBio+ projet, and the references given below.

The available dataset for download can be simply depicted and summarized by the following figure:

References

Bénard C., Bernillon S., Biais B., Osorio S., Maucourt M., Ballias P., Deborde C., Colombié S., Cabasson C., Jacob D., Vercambre G., Gautier H., Rolin D., Génard M., Fernie A., Gibon Y., Moing A. 2015 Metabolomic profiling in tomato reveals diel compositional changes in fruit affected by source-sink relationships. Journal of Experimental Botany Vol. 66, No. 11 pp. 3391–3404 doi:10.1093/jxb/erv151

Beauvoit B., Colombié S., Monier A., Andrieu M.-H., Biais B., Bénard C., Chéniclet C., Dieuaide-Noubhani M., Nazaret C., Mazat J.-P., Gibon Y. (2014). Model-assisted analysis of sugar metabolism throughout tomato fruit development reveals enzyme and carrier properties in relation to vacuole expansion. Plant Cell 26: 3224–3242

Biais B, Bénard C, Beauvoit B, Colombié S, Prodhomme D, Ménard G, Bernillon S, Gehl B, Gautier H, Ballias P, Mazat J-P, Sweetlove L, Génard M, Gibon Y. 2014. Remarkable reproducibility of enzyme activity profiles in tomato fruits grown under contrasting environments provides a roadmap for studies of fruit metabolism. Plant Physiology 164, 1204-1221.

Colombié S, Nazaret, Bénard C, Biais B, Mengin V, Solé M, Fouillen L, Dieuaide- Noubhani M, Mazat J, Beauvoit B, Gibon Y. 2015. Modelling central metabolic fluxes by constraint-based optimization reveals metabolic reprogramming of developing tomato fruit. The Plant Journal 81, 24-39.

For 1H-NMR profiling, polar metabolites, were extracted from the ground lyophilised samples as previously described (Biais et al., 2009; Moing et al., 2004) with minor modifications. Frozen powdered samples were lyophilised and polar metabolites were extracted from 20 mg of lyophilised powder with an ethanol-water series at 80°C. The supernatants were combined, dried under vacuum and lyophilised. Two technical replicates were prepared for each fruit sample. Each lyophilised extract was solubilised in 500 µL of 250 mM potassium phosphate buffer solution at apparent pH 6.0, 5 mM ethylene diamine tetraacetic acid disodium salt (EDTA), in D2O, titrated with KOD solution to pH 6.00 ±0.02 when necessary, and lyophilised again. The lyophilised titrated extracts were stored in darkness under vacuum at room temperature, before 1H-NMR analysis was completed within one week.

Before 1H-NMR analysis, 500 µL of D2O with sodium trimethylsilyl [2,2,3,3-d4] propionate (TSP, 0.01% final concentration for chemical shift calibration) were added to the lyophilised titrated extracts. The mixture was centrifuged at 10,000 g for 5 min at room temperature. The supernatant was then transferred 1 into a 5 mm NMR tube for acquisition. Quantitative 1H-NMR spectra were recorded at 500.162 MHz and 300 K on a Bruker Avance III spectrometer (Wissembourg, France) using a 5-mm broadband inverse probe, a 90° pulse angle and an electronic reference for quantification (Biais et al., 2009; Mounet et al., 2007).

The assignments of metabolites in the NMR spectra were made by comparing the proton chemical shifts with literature (Fan, 1996; Mounet et al., 2007) or database values (MeRy-B, HMDB), by comparison with spectra of authentic compounds recorded in the same solvent conditions (in-house library) and by spiking the samples. 1H-1H COSY NMR experiments were acquired for selected samples for assignment verification.

For absolute quantification of metabolites, four calibration curves (glucose and fructose: 1.25 to 50 mM, glutamate and glutamine: 0 to 15 mM) were prepared and analysed under the same conditions. The glucose calibration was used for the absolute quantification of all compounds, as a function of the number of protons of selected resonances except fructose, glutamate and glutamine quantified using their respective calibration curve.

References

Biais B, Allwood JW, Deborde C, Xu Y, Maucourt M, Beauvoit B, Dunn WB, Jacob D, Goodacre R, Rolin D, Moing A. 2009. H-1 NMR, GC-EI-TOFMS, and Data Set Correlation for Fruit Metabolomics: Application to Spatial Metabolite Analysis in Melon. Analytical Chemistry 81, 2884-2894.

Fan TWM. 1996. Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures. Progress in Nuclear Magnetic Resonance Spectroscopy 28, 161- 219.

Moing A, Maucourt M, Renaud C, Gaudillere M, Brouquisse R, Lebouteiller B, Gousset-Dupont A, Vidal J, Granot D, Denoyes-Rothan B, Lerceteau-Kohler E, Rolin D. 2004. Quantitative metabolic profiling by 1-dimensional H-1-NMR analyses: application to plant genetics and functional genomics. Functional Plant Biology 31, 889-902.

Mounet F, Lemaire-Chamley M, Maucourt M, Cabasson C, Giraudel JL, Deborde C, Lessire R, Gallusci P, Bertrand A, Gaudillere M, Rothan C, Rolin D, Moing A. 2007. Quantitative metabolic profiles of tomato flesh and seeds during fruit development: complementary analysis with ANN and PCA. Metabolomics 3, 273-288.

Input files

Calibration files

Output files

Step 1: Load the dataset

• Download files in the 'input files' list, namely: 'NMRFRIM3-4.zip', 'samples_p1.txt' and 'NP_macro_cmd_NMRFRIM3-4.txt'
• Start NMRProcFlow from your web browser.
• Choose 'Bruker' as Instrument/Vendor/Format, and '1r spectrum' as Spectra type.
• Then load the ZIP file and the sample file as described in the 'Data preparation phase' section
• Before launchning, you have also to load the macro-command file in the form for advanced user as described in the 'Replay the same processing workflow' section
• Finaly, click on the 'Launch' button'

Step 2: Import the bucket table

• Download the bucket table file 'buckets_FRIM3-4.txt'.
• Import the bucket table as described in the 'Bucketing' section. Don't forget to select the 'Tabular Separator Value' as the import format.

Step 3: Export the XLSX workbook for quantification

• Download the XLSX workbook file 'wb_NMRFRIM3-4.xlsx'.
• Export the XLSX workbook for quantification as described in the 'Data Export' section