SpinCouple
1 · Ingest archive
.zip hereor click to choose — needs
fid.bin,
acqus/acqus2, procs/procs2,
xppar, udic2 · Process raw spectrum
noise_factor is the live coarse gate
(threshold = noise_factor × median of positive cells); the
legacy rel_threshold is inert unless this is blank. Measured on
stdmix20/1021: 8→7, 5→8, 3→10, 2→29, 1→2697 peaks.
tilt=auto should equal 0 on a correctly straightened JRES.
3 · Review calibrated contour & picks
4 · Edit the peak selection (controller)
|F1| pick, and
matching reaches the signed db_master rows on |F1|.
Quantification does NOT consume this table: it measures the selected
compound’s calibrated library peaks directly (stage 6), each at
its fixed ppm ± f2_half × F1 ± f1_half
window with the library’s own J lines — so nothing here can distort
a concentration (user picks only steer identification). Tick a row to send
it to Match (auto-detected peaks start ticked) or untick it to drop a
peak from identification -- the cross disappears from the plot. Rows tagged
lib come from Match’s add peaks and show where the
identified compound’s standard peaks sit; they start ticked off
(reference only) and never reach quantification.
5 · Identify what is in the tube
6 · Quantify
C = a · (I / IDSS · 15)b),
and the per-peak concentrations merge by the log-space geometric median.
The dispersion across peaks and the number of clean peaks drive the
confidence band — no single spectrum peak is chosen by hand.
Match preloads its hits here; edit the list freely before quantifying.
7 · Calibration library
Release Notes
July 10, 2018. InterSpin update.
SpinCouple was completely re-implemented and connected to the new SpinLIMS database to run within InterSpin.
About SpinCouple
SpinCouple provides batch-annotations of a large number of metabolites against user NMR peaks based on our original 1H chemical shift and 1H-1H spin coupling database. The database collected chemical shifts J-values and intensities under accurately standardized measurement conditions, thus adequate for batch-annotations and quantification of metabolite concentrations.
Introduction
Although many techniques exist for studying the metabolome, the annotation and quantification of metabolites remains problematic. NMR spectroscopy is a particularly effective, nondestructive method for detecting the components of biochemical mixtures. Advancements in NMR spectroscopy have been accompanied by rapid detection of metabolic biomarkers for various organisms based on nontargeted analysis of relative variations of metabolites. For these reasons, NMR spectroscopy is used in the medical and pharmacological sciences, the evaluation of agricultural and fishery products, environmental sciences, and basic biology. However, the spectra typically used in metabolomic studies, namely, one-dimensional 1H NMR spectra, often contain overlapped peaks, which are difficult to deconvolve. To overcome this obstacle, the indirect axis is expanded along the second dimension. Two-dimensional (2D) 1H-1H J-resolved (Jres) NMR spectroscopy is an alternative tool used when the 13C and 15N isotopic labeling becomes arduous. Most importantly, databases enable subsequent researchers to identify metabolites from complex spectra and evaluate the functions of those metabolites. Various databases have been developed for NMR-based metabolomics research, including SpinAssign, BMRB, HMDB, TOCCATA, and BML. These databases enable highly accurate metabolic studies. In particular, SpinAssign is developed similarly to BMRB, and its properties are inherited by our new database, SpinCouple. In SpinCouple, we have accumulated 598 standards of 2D-Jres NMR spectra and have made them freely available on the web. Therefore, we expect that our numerous databases will enable annotation of diverse samples and that many users will be attracted to the advantages of the 2D-Jres approach, which includes short acquisition time and applicability to numerous NMR instruments. Moreover, metabolic changes in time-course samples can be quantitatively analyzed. Such time-varying quantification should reveal the homeostatic dynamics not only of human beings but also of their surrounding environments such as external ecosystems. For this purpose, we developed a web tool that performs quantitative analysis, enabling the absolute quantification of commonly observed major metabolites.
Method
Figure 1 Scheme for metabolite annotation.
Figure 1. (A) Standard chemical compounds in metabolic pathways are selected for database construction. (B) A 2D-Jres spectrum is recorded for each of the selected chemical compounds. (C) Peaks on 2D-Jres spectrum (d1H, J) are accumulated in a standard database. (D) A 2D-Jres experiment is conducted and obtained peak-pick lists for a user biological sample. (E) The 2D-Jres peaks from user biological samples are queried within user defined tolerances in F2 and F1 axis by SpinCouple, then metabolite annotation can be computed. (The image in (A) was obtained from ExPASy Biochemical Pathways.)
Figure 2 Scheme for metabolite quantification.
Figure 2. (A) Standard chemical compound solutions with different concentration are prepared. (B) A 2D-Jres spectrum is recorded for each of the differently diluted standard solutions and the peak intensity information are accumulated in a standard database, as calibration curve for quantification. (C) A 2D-Jres experiment is conducted and obtained peak-pick lists for a user biological sample. (D) The 2D-Jres peaks from user biological samples with intensity information are queried within user defined tolerances in F2 and F1 axis by SpinCouple, then metabolite annotation can be computed, as well as calibration curve-based quantification relative with DSS intensity.