Coefficient of Variation, Signal-to-Noise Ratio, and Effects of Normalization in Validation of Biomarkers from NMR-based Metabonomics Studies

NMR-Based Stable Isotope Tracing of Cancer Metabolism

NMR is widely used for metabolite profiling (metabolomics, metabonomics) particularly of various readily obtainable biofluids such as plasma and urine. It is especially valuable for stable isotope tracer studies to track metabolic pathways under control or perturbed conditions in a wide range of cell models as well as animal models and human subjects. NMR has unique properties for utilizing stable isotopes to edit or simplify otherwise complex spectra acquired in vitro and in vivo, while quantifying the level of enrichment at specific atomic positions in various metabolites (i.e., isotopomer distribution analysis).In this protocol, we give an overview with specific protocols for NMR-based stable isotope-resolved metabolomics, or SIRM, with a workflow from administration of isotope-enriched precursors, via sample preparation through to NMR data collection and reduction. We focus on indirect detection of common NMR-active stable isotopes including 13C, 15N, 31P, and 2H, using a variety of 1H-based two-dimensional experiments. We also include the application and analyses of multiplex tracer experiments.

NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows

INTRODUCTION

Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling.

OBJECTIVES

An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision.

METHODS

A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool.

RESULTS

For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables.

CONCLUSION

External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.

Novel candidate genes for lignin structure identified through genome-wide association study of naturally varying Populus trichocarpa

Populus is a promising lignocellulosic feedstock for biofuels and bioproducts. However, the cell wall biopolymer lignin is a major barrier in conversion of biomass to biofuels. To investigate the variability and underlying genetic basis of the complex structure of lignin, a population of 409 three-year-old, naturally varying Populus trichocarpa genotypes were characterized by heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR). A subsequent genome-wide association study (GWAS) was conducted using approximately 8.3 million single nucleotide polymorphisms (SNPs), which identified 756 genes that were significantly associated (-log₁₀(p-value)>6) with at least one lignin phenotype. Several promising candidate genes were identified, many of which have not previously been reported to be associated with lignin or cell wall biosynthesis. These results provide a resource for gaining insights into the molecular mechanisms of lignin biosynthesis and new targets for future genetic improvement in poplar.

Qualitative and Quantitative Mass Spectrometry in Salivary Metabolomics and Proteomics

The metabolomics and proteomics analysis of saliva, an excellent biofluid that is a rich source of biological compounds, allows for the safe and frequent screening of drugs, their metabolites, and molecular biomarkers of various diseases. One of the most frequently used analytical methods in saliva analysis is liquid chromatography coupled with mass spectrometry (LC-MS) and tandem mass spectrometry. The low ionisation efficiency of some compounds and a complex matrix makes their identification by MS difficult. Furthermore, quantitative analysis by LC-MS frequently cannot be performed without isotopically labelled standards, which usually have to be specially synthesised. This review presented reports on qualitative and quantitative approaches in salivary metabolomics and proteomics. The purpose of this manuscript was to present the challenges, advances, and future prospects of mass spectrometry, both in the analysis of salivary metabolites and proteins. The presented review should appeal to those interested in the recent advances and trends in qualitative and quantitative mass spectrometry in salivary metabolomics and proteomics, which may facilitate a diagnostic accuracy, the evaluation of treatment efficacy, the early diagnosis of disease, and a forensic investigation of some unapproved drugs for any medical or dietary administration.

Fine optimization of a dissolution dynamic nuclear polarization experimental setting for 13C NMR of metabolic samples

NMR-based analysis of metabolite mixtures provides crucial information on biological systems but mostly relies on 1D 1 H experiments for maximizing sensitivity. However, strong peak overlap of 1 H 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 13 C 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 13 C 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 13 C 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 13 C NMR at natural abundance and paves the way to a more general use of DNP-hyperpolarized NMR in metabolomics studies.

Metabolomic analysis of honey bee (Apis mellifera L.) response to glyphosate exposure

Glyphosate is among the world's most commonly used herbicides in agriculture and weed control. The use of this agrochemical has unintended consequences on non-target organisms, such as honey bees (Apis mellifera L.), the Earth's most prominent insect pollinator. However, detailed understanding of the biological effects in bees in response to sub-lethal glyphosate exposure is still limited. In this study, 1H NMR-based metabolomics was performed to investigate whether oral exposure to an environmentally realistic concentration (7.12 mg L-1) of glyphosate affects the regulation of honey bee metabolites in 2, 5, and 10 days. On Day 2 of glyphosate exposure, the honey bees showed significant downregulation of several essential amino acids, including leucine, lysine, valine, and isoleucine. This phenomenon indicates that glyphosate causes an obvious metabolic perturbation when the honey bees are subjected to the initial caging process. The mid-term (Day 5) results showed negligible metabolite-level perturbation, which indicated the low glyphosate impact on active honeybees. However, the long-term (Day 10) data showed evident separation between the control and experimental groups in the principal component analysis (PCA). This separation is the result of the combinatorial changes of essential amino acids such as threonine, histidine, and methionine, while the non-essential amino acids glutamine and proline as well as the carbohydrate sucrose were all downregulated. In summary, our study demonstrates that although no significant behavioral differences were observed in honey bees under sub-lethal doses of glyphosate, metabolomic level perturbation can be observed under short-term exposure when met with other environmental stressors or long-term exposure.

High-throughput drug screening models of mature adipose tissues which replicate the physiology of patients' Body Mass Index (BMI)

Obesity is a complex and incompletely understood disease, but current drug screening strategies mostly rely on immature in vitro adipose models which cannot recapitulate it properly. To address this issue, we developed a statistically validated high-throughput screening model by seeding human mature adipocytes from patients, encapsulated in physiological collagen microfibers. These drop tissues ensured the maintenance of adipocyte viability and functionality for controlling glucose and fatty acids uptake, as well as glycerol release. As such, patients' BMI and insulin sensitivity displayed a strong inverse correlation: the healthy adipocytes were associated with the highest insulin-induced glucose uptake, while insulin resistance was confirmed in the underweight and severely obese adipocytes. Insulin sensitivity recovery was possible with two type 2 diabetes treatments, rosiglitazone and melatonin. Finally, the addition of blood vasculature to the model seemed to more accurately recapitulate the in vivo physiology, with particular respect to leptin secretion metabolism.

A Monte Carlo Method for Analyzing Systematic and Random Uncertainty in Quantitative Nuclear Magnetic Resonance Measurements

Quantitative nuclear magnetic resonance (qNMR) is a powerful analytical technology that is capable of quantifying the concentration of any analyte with exquisite accuracy and precision so long as it contains at least one nonlabile nuclear magnetic resonance (NMR)-active nucleus. Unlike with traditional analytical technologies, the concentrations of analytes do not directly influence the uncertainty in the quantification of NMR signals because an ideal NMR response depends only on the nature and amount of the nucleus being observed. Rather, in the absence of spectral artifacts and under favorable experimental conditions, the measurement uncertainty may be influenced by the following factors: (1) spectroscopic parameters such as the spectral width, number of time domain points, and acquisition time; (2) postacquisition data processing, such as apodization and zero-filling; (3) the signal-to-noise ratios (SNRs) and lineshapes of the two signals being used in a qNMR measurement; and (4) the method of signal quantification employed, such as numerical integration or lineshape fitting (LF). Here, a general Monte Carlo (MC) method that considers these factors is presented, with which the random and systematic contributions to qNMR measurement uncertainty may be calculated. Autocorrelation analysis of synthetic and experimental noise is used in a fingerprint-like approach to demonstrate the validity of the simulations. The MC method allows for a general quantitative assessment of measurement uncertainty without the need to acquire spectral replicates and without reference to the molecular structures and concentrations of analytes. Representative examples of qNMR measurement uncertainty simulations are provided in which the metrological performances of integration and LF are contrasted for signal pairs obtained using various acquisition and processing schemes in the low-SNR regime-an area where application of the proposed MC method may prove to be particularly salient.

The Differential Metabolic Profiles Between Deltamethrin-Resistant and -Susceptible Strains of Aedes albopictus (Diptera: Culicidae) by 1H-NMR

Metabolomics can indicate the physiological and biochemical responses of mosquitoes to different stimulants, including insecticides, which allow them to adapt to different inhospitable environments. Though metabolic differences between insecticide-resistant and -susceptible strains have been established for other mosquito species, such as Anopheles and Culex, it is yet to be done for Aedes albopictus (Skuse). In this study, nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis performed on Ae. albopictus deltamethrin-resistant and -susceptible strains showed significant differences in amino acid, organic acid, and sugar metabolism. Concentrations of neutral amino acids and sugars tended to be lower in the deltamethrin-resistant strain than in the deltamethrin-suceptible strain, but the concentration of basic and acidic amino acids and organic acids increased. All these changes might accommodate biochemical and physiological needs in deltamethrin-resistant mosquitoes, such as enzyme synthesis and detoxification. This was further confirmed by the predictable draft metabolic map. This is the first report using NMR spectroscopy to investigate the metabolic differences between deltamethrin-resistant and -susceptible strains of Ae. albopictus. To a certain degree, this demonstrates how Ae. albopictus develop insecticide resistance by metabolic reprograming to survive under the insecticide pressure.

Reproducibility and accuracy of microscale thermophoresis in the NanoTemper Monolith: a multi laboratory benchmark study

Microscale thermophoresis (MST), and the closely related Temperature Related Intensity Change (TRIC), are synonyms for a recently developed measurement technique in the field of biophysics to quantify biomolecular interactions, using the (capillary-based) NanoTemper Monolith and (multiwell plate-based) Dianthus instruments. Although this technique has been extensively used within the scientific community due to its low sample consumption, ease of use, and ubiquitous applicability, MST/TRIC has not enjoyed the unambiguous acceptance from biophysicists afforded to other biophysical techniques like isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR). This might be attributed to several facts, e.g., that various (not fully understood) effects are contributing to the signal, that the technique is licensed to only a single instrument developer, NanoTemper Technology, and that its reliability and reproducibility have never been tested independently and systematically. Thus, a working group of ARBRE-MOBIEU has set up a benchmark study on MST/TRIC to assess this technique as a method to characterize biomolecular interactions. Here we present the results of this study involving 32 scientific groups within Europe and two groups from the US, carrying out experiments on 40 Monolith instruments, employing a standard operation procedure and centrally prepared samples. A protein-small molecule interaction, a newly developed protein-protein interaction system and a pure dye were used as test systems. We characterized the instrument properties and evaluated instrument performance, reproducibility, the effect of different analysis tools, the influence of the experimenter during data analysis, and thus the overall reliability of this method.

Differences in Human Milk Lactose and Citrate Concentrations Based on Gestational Diabetes Status

Background: Exclusive breastfeeding is the optimal manner of early infant nutrition but women with gestational diabetes mellitus (GDM) often experience challenges with lactation in the early postpartum period. Increases in the colostral metabolites of lactose and citrate have been found to indicate increased milk production. Materials and Methods: A follow-up study of 133 postpartum women with and without GDM was conducted to examine differences in specific colostral metabolite levels using enzymatic methods to determine transition to lactogenesis II during the first week postpartum. We used linear mixed models for repeated measures over time to examine the effect of GDM on colostral metabolite levels at baseline and follow-up with fixed effects of GDM status, time, covariates, and interactions between time and GDM, between time and time, and between time, time and GDM into the model allowing quadratic trends over time. Results: Over time, lactose and citrate levels increased for all mothers (p < 0.001 and p < 0.001, respectively), although mothers with GDM had consistently lower lactose and citrate levels compared with nondiabetic mothers (p = 0.004 and p = 0.014, respectively). Age, prepregnancy body mass index, mode of birth, and parity did not independently influence colostral concentrations of lactose and citrate. Conclusions: Findings suggest that the rate of change overtime in lactose and citrate concentrations differ by GDM status. Further research examining the trajectory of colostral metabolite levels by GDM status is warranted.

Evaluating line-broadening factors on a reference spectrum as a bucketing method for NMR based metabolomics

Metabolomics based nuclear magnetic resonance (NMR) is widely used in disease mechanism analysis and drug discovery. One of the most important factors in NMR based metabolomics study is the accuracy of spectra bucketing which plays a critical role in data interpretation. Though various methods have been developed for automatic bucketing, the most popular approach is still the traditional rectangular bucketing method which is mainly due to the requirement of user expertise for the automatic bucketing methods. In this study, we developed a new automatic bucketing method that not only efficiently increases peak bucketing accuracy but also allows the bucketing process to be conveniently visualized and adjusted by the end-users. This method applied the line broadening (lb) factor to the average spectrum for a study set which serves as the reference spectrum, and the peak width of the reference spectrum was then set as the peak bucketing pattern. The approach to pick the bucket boundaries is simple but powerful after the line broadening factor was applied. The line broadening factors from 0 to 2 lb were tested using mouse fecal samples and the 1 lb method showed similar peak patterns and data interpretation results compared with a careful manual bucketing pattern. Besides this, the new method generated bucketing patterns could be easily visualized using the Amix software and revised by general users without excessive data science and NMR instrumentation expertise. In summary, our study showed a powerful and convenient tool in NMR peak auto bucketing with flexible visualization and adjustment ability for metabolomics studies.

Introduction of a new method for two-dimensional NMR quantitative analysis in metabolomics studies

NMR is one of the most important platforms for metabolomic studies. Though 2D NMR has been applied in metabolomics, most applications have mainly focused on metabolite identification whilst limitations causing a bottle-neck for applying high-throughput 2D NMR data for quantity related statistical analysis lies on the data interpretation methods. In this study, instead of using the traditional methods of calculating the 2D NMR data to search for the important features, a new procedure, which applies the high-resolution 1D NMR metabolites chemical shift range to filter the 2D NMR data, was developed. This new method was demonstrated using both a mixture of standard metabolites and a case study on plant extracts using 2D non-uniform sampling (NUS) total correlation spectroscopy (TOCSY) data. As a result, our method successfully filtered out the important features with a high success rate, and the extracted peaks showed high linearity between the calculated intensities and the concentrations of metabolites from a range of 0.05 mM-2 mM. The method was successfully applied to a metabolomics case study which included 18 Begonia samples that showed excellent peak extractions. In summary, our study has provided a practical new 2D NMR data extraction method for use in future metabolomics studies.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Preparation of modified waterworks sludge particles as adsorbent to enhance coagulation of slightly polluted source water

Without treatment, waterworks sludge is ineffective as an adsorbent. In this study, raw waterworks sludge was used as the raw material to prepare modified sludge particles through high-temperature calcination and alkali modification. The feasibility of using a combination of modified particles and polyaluminum chloride (PAC) as a coagulant for treatment of slightly polluted source water was also investigated. The composition, structure, and surface properties of the modified particles were characterized, and their capabilities for removing ammonia nitrogen and turbidity were determined. The results indicate that the optimal preparation conditions for the modified sludge particles were achieved by preparing the particles with a roasting temperature of 483.12 °C, a roasting time of 3.32 h, and a lye concentration of 3.75%. Furthermore, enhanced coagulation is strengthened with the addition of modified sludge particles, which is reflected by reduction of the required PAC dose and enhancement of the removal efficiency of ammonia nitrogen and turbidity by over 80 and 93%, respectively. Additional factors such as pH, temperature, dose, and dosing sequence were also evaluated. The optimum doses of modified particles and PAC were 40 and 15 mg/L, respectively, and adding modified particles at the same time as or prior to adding PAC improves removal efficiency.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

Apparent Ultra-High b-Value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields

A promising, recently explored, alternative to ultra-high b-value diffusion weighted imaging (UHB-DWI) is apparent ultra-high b-value diffusion-weighted image reconstruction (AUHB-DWR), where a computational model is used to assist in the reconstruction of apparent DW images at ultra-high b -values. Firstly, we present a novel approach to AUHB-DWR that aims to improve image quality. We formulate the reconstruction of an apparent DW image as a hidden conditional random field (HCRF) in which tissue model diffusion parameters act as hidden states in this random field. The second contribution of this paper is a new generation of fully connected conditional random fields, called the hidden stochastically fully connected conditional random fields (HSFCRF) that allows for efficient inference with significantly reduced computational complexity via stochastic clique structures. The proposed AUHB-DWR algorithms, HCRF and HSFCRF, are evaluated quantitatively in nine different patient cases using Fisher's criteria, probability of error, and coefficient of variation metrics to validate its effectiveness for the purpose of improving intensity delineation between expert identified suspected cancerous and healthy tissue within the prostate gland. The proposed methods are also examined using a prostate phantom, where the apparent ultra-high b-value DW images reconstructed using the tested AUHB-DWR methods are compared with real captured UHB-DWI. The results illustrate that the proposed AUHB-DWR methods has improved reconstruction quality and improved intensity delineation compared with existing AUHB-DWR approaches.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Sample preparation issues in NMR-based plant metabolomics: optimisation for Vitis wood samples

INTRODUCTION

Nuclear magnetic resonance (NMR) is one of the most commonly used analytical techniques in plant metabolomics. Although this technique is very reproducible and simple to implement, sample preparation procedures have a great impact on the quality of the metabolomics data.

OBJECTIVE

Investigation of different sample preparation methods and establishment of an optimised protocol for untargeted NMR-based metabolomics of Vitis vinifera L. wood samples.

METHODS

Wood samples from two different cultivars of V. vinifera with well-defined phenotypes (Gamaret and 2091) were selected as reference materials. Different extraction solvents (successively, dichloromethane, methanol and water, as well as ethyl acetate and 7:3 methanol-water (v/v)) and deuterated solvents (methanol-d4, 7:3 chloroform-d-methanol-d4 (v/v), dimethylsulphoxide-d6 and 9:1 dimethylsulphoxide-d6-water-d2 (v/v)) were evaluated for NMR acquisition, and the spectral quality was compared. The optimal extract concentration, chemical shift stability and peak area repeatability were also investigated.

RESULTS

Ethyl acetate was found to be the most satisfactory solvent for the extraction of all representative chemical classes of secondary metabolites in V. vinifera wood. The optimal concentration of dried extract was 10 mg/mL and 7:3 chloroform-d-methanol-d4 (v/v) was the most suitable solvent system for NMR analysis. Multivariate data analysis was used to estimate the biological variation and clustering between different cultivars.

CONCLUSION

Close attention should be paid to all required procedures before NMR analysis, especially to the selection of an extraction solvent and a deuterated solvent system to perform an extensive metabolomic survey of the specific matrix.

Introduction of a new critical p value correction method for statistical significance analysis of metabonomics data

Nuclear magnetic resonance (NMR) spectroscopy-based metabonomics is of growing importance for discovery of human disease biomarkers. Identification and validation of disease biomarkers using statistical significance analysis (SSA) is critical for translation to clinical practice. SSA is performed by assessing a null hypothesis test using a derivative of the Student's t test, e.g., a Welch's t test. Choosing how to correct the significance level for rejecting null hypotheses in the case of multiple testing to maintain a constant family-wise type I error rate is a common problem in such tests. The multiple testing problem arises because the likelihood of falsely rejecting the null hypothesis, i.e., a false positive, grows as the number of tests applied to the same data set increases. Several methods have been introduced to address this problem. Bonferroni correction (BC) assumes all variables are independent and therefore sacrifices sensitivity for detecting true positives in partially dependent data sets. False discovery rate (FDR) methods are more sensitive than BC but uniformly ascribe highest stringency to lowest p value variables. Here, we introduce standard deviation step down (SDSD), which is more sensitive and appropriate than BC for partially dependent data sets. Sensitivity and type I error rate of SDSD can be adjusted based on the degree of variable dependency. SDSD generates fundamentally different profiles of critical p values compared with FDR methods potentially leading to reduced type II error rates. SDSD is increasingly sensitive for more concentrated metabolites. SDSD is demonstrated using NMR-based metabonomics data collected on three different breast cancer cell line extracts.