Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics

Reproducible quantification of metabolites in tissue samples is of high importance for characterization of animal models and identification of metabolic changes that occur in different tissue types in specific diseases. However, the extraction of metabolites from tissue is often the most labor-intensive and error-prone step in metabolomics studies. Here, we report the development of a standardized high-throughput method for rapid and reproducible extraction of metabolites from multiple tissue samples from different organs of several species. The method involves a bead-based homogenizer in combination with a simple extraction protocol and is compatible with state-of-the-art metabolomics kit technology for quantitative and targeted flow injection tandem mass spectrometry. We analyzed different extraction solvents for both reproducibility as well as suppression effects for a range of different animal tissue types including liver, kidney, muscle, brain, and fat tissue from mouse and bovine. In this study, we show that for most metabolites a simple methanolic extraction is best suited for reliable results. An additional extraction step with phosphate buffer can be used to improve the extraction yields for a few more polar metabolites. We provide a verified tissue extraction setup to be used with different indications. Our results demonstrate that this high-throughput procedure provides a basis for metabolomic assays with a wide spectrum of metabolites. The developed method can be used for tissue extraction setup for different indications like studies of metabolic syndrome, obesity, diabetes or cardiovascular disorders and nutrient transformation in livestock.     

A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics

Metabolomics - Understanding the interaction between organisms and the environment is important for predicting and mitigating the effects of global phenomena such as climate change, and the fate,...     

Optimization for peptide sample preparation for urine peptidomics

Analysis of native or endogenous peptides in biofluids can provide valuable insights into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for non-invasive monitoring of human diseases. The non-invasive nature of urine collection and the abundance of peptides in the urine makes analysis by high-throughput ‘peptidomics’ methods , an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regards to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements that it hampers both the identification of peptides and the depth of the peptidomics read when utilizing LC-MS based peptidome analysis. We report on a novel adaptation of the standard solid phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS based peptidomics in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. Expense and time requirements were comparable for both SPE and mSPE, but more interfering contaminants from the urine matrix were evident in the SPE preparations (e.g., clogging of the LC-MS columns, yellowish background coloration of prepared samples due to retained urobilin, lower peptide yields) when compared to the mSPE method. When we compared data from technical replicates of 4 runs, the mSPE method provided significantly improved efficiencies for the preparation of samples from urine (e.g., mSPE peptide identification 82% versus 18% with SPE; p = 8.92E-05). Additionally, peptide identifications, when applying the mSPE method, highlighted the biology of differential activation of urine peptidases during acute renal transplant rejection with distinct laddering of specific peptides, which was obscured for most proteins when utilizing the conventional SPE method. In conclusion, the mSPE method was found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis due to the optimized sample clean up that provided improved experimental inference from the confidently identified peptides.     

基于非靶代谢组学多刺绿绒蒿不同器官代谢物差异分析

为了进一步探究传统藏药植物多刺绿绒蒿（Meconopsis horridula）中代谢物成分以及不同器官差异情况,采用UPLC-MS技术对多刺绿绒蒿的叶、根和花三个不同器官代谢物进行分析与鉴定。并利用主成分分析（PCA）、聚类热图分析、正交偏最小二乘-判别分析（OPLS-DA）和KEGG通路富集分析等方法进行不同器官差异代谢产物筛选与通路分析。结果显示,在ESI⁺和ESI^-模式下,共检测注释到947种代谢物,不同器官间差异代谢物进行分析,叶和根差异代谢物有301个,叶和花中差异代谢物有170个,根和花中差异代谢物有244个。通过聚类热图可以看出,大多数代谢物在根中含量较低;KEGG通路富集分析显示,差异代谢物大多富集在氨基酸代谢、花青素生物合成、黄酮类生物合成和生物碱合成等代谢途径。各器官优势黄酮类、萜类和生物碱类代谢物的分析为进一步探究多刺绿绒蒿的不同器官药用特征成分和开发利用提供一定的帮助。     

Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis

Introduction

Raspberries are becoming increasingly popular due to their reported health beneficial properties. Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries.

Objectives

The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties.

Methods

The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC–TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC–MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333).

Results

Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections.

Conclusions

In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers.

     

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

Introduction

For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks.

Objectives

The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS).

Methods

Using experimental DBS from the blood of an adult volunteer, we optimized extraction of small molecules and integrated measurement of potassium as a proxy for blood hematocrit. We then applied this extraction method to 4.7-mm punches from 106 control DBS samples from the CCLS. Sample extracts were analyzed with liquid chromatography—high resolution mass spectrometry (LC-HRMS) and an untargeted workflow was used to screen for metabolites that discriminate population characteristics such as sex, ethnicity, and birth weight.

Results

Thousands of small molecules were measured in extracts of archived DBS. Normalizing for potassium levels removed variability related to varying hematocrit across DBS punches. Of the roughly 1000 prevalent small molecules that were tested, multivariate linear regression detected significant associations with ethnicity (three metabolites) and birth weight (15 metabolites) after adjusting for multiple testing.

Conclusions

This untargeted workflow can be used for analysis of small molecules in archived DBS to discover novel biomarkers, to provide insights into the initiation and progression of diseases, and to provide guidance for disease prevention.

     

高水分、富含淀粉植物组织的扫描电镜制备技术优化

应用常规高真空扫描电子显微镜观察生物样品必须经过脱水和干燥处理,但无论采用临界点干燥还是冷冻干燥方法,都存在样品表面不同程度失真的问题。植物高水分、富含淀粉组织样品经处理后,容易出现淀粉流失、细胞壁变形等现象,从而造成扫描图像粗糙,无法获得真实的细胞内部结构。本文通过对CO_2临界点干燥、化学固定样品冷冻干燥和新鲜样品冷冻干燥3种扫描电镜样品制备技术中后期制样进行机械断裂和液氮脆断改进,优化出两种植物高水分、富含淀粉组织的扫描电镜样品制备方法:(1)样品首先进行FAA化学固定,经冷冻干燥后用液氮脆断,对断面喷金镀膜和扫描电镜观察。利用该方法所得细胞结构完整,细胞壁整齐,淀粉粒和蛋白轮廓明确,可用于分析淀粉粒和蛋白颗粒在细胞内的分布。(2)新鲜样品直接进行冷冻干燥,经液氮脆断后对断面喷金镀膜和扫描电镜观察。利用该方法所得细胞壁整齐,淀粉粒轮廓更清晰,并且无蛋白颗粒干扰,用于分析淀粉粒在细胞内的分布更加理想。     

Optimization of human dendritic cell sample preparation for mass spectrometry-based proteomic studies

Dendritic cells (DCs) are specialized leukocytes that orchestrate the adaptive immune response. Mass spectrometry (MS)-based proteomic study of these cells presents technical challenges, especially when the DCs are human in origin due to the paucity of available biological material. Here, to maximize MS coverage of the global human DC proteome, different cell disruption methods, lysis conditions, protein precipitation, and protein pellet solubilization and denaturation methods were compared. Mechanical disruption of DC cell pellets under cryogenic conditions, coupled with the use of RIPA (radioimmunoprecipitation assay) buffer, was shown to be the method of choice based on total protein extraction and on the solubilization and identification of nuclear proteins. Precipitation by acetone was found to be more efficient than that by 10% trichloroacetic acid (TCA)/acetone, allowing in excess of 28% more protein identifications. Although being an effective strategy to eliminate the detergent residue, the acetone wash step caused a loss of protein identifications. However, this potential drawback was overcome by adding 1% sodium deoxycholate into the dissolution buffer, which enhanced both solubility of the precipitated proteins and digestion efficiency. This in turn resulted in 6 to 11% more distinct peptides and 14 to 19% more total proteins identified than using 0.5 M triethylammonium bicarbonate alone, with the greatest increase (34%) for hydrophobic proteins.     

A skew in poo: Biases in primate fecal isotope analysis and recommendations for standardized sample preparation

Feces are a treasure trove in the study of animal behavior and ecology. Stable carbon and nitrogen isotope analysis allows to assess the dietary niches of elusive primate species and primate breastfeeding behavior. However, some fecal isotope data may unwillingly be biased toward the isotope ratios of undigested plant matter, requiring more consistent sample preparation protocols. We assess the impact of this potential data skew in 114 fecal samples of wild bonobos (Pan paniscus) by measuring the isotope differences (Δ¹³C, Δ¹⁵N) between bulk fecal samples containing larger particles (>1 mm) and filtered samples containing only small particles (<1 mm). We assess the influence of fecal carbon and nitrogen content (ΔC:N) and sample donor age (subadult, adult) on the resulting Δ¹³C, Δ¹⁵N values (n = 228). Additionally, we measure the isotope ratios in three systematically sieved fecal samples of chimpanzees (Pan troglodytes verus), with particle sizes ranging from 20 μm to 8 mm (n = 30). We found differences in fecal carbon and nitrogen content, with the smaller fecal fraction containing more nitrogen on average. While the Δ¹³C values were small and not affected by age or ΔC:N, the Δ¹⁵N values were significantly influenced by fecal ΔC:N, possibly resulting from the differing proportions of undigested plant macroparticles. Significant relationships between carbon stable isotope ratios (δ¹³C) values and %C in large fecal fractions of both age groups corroborated this assessment. Δ¹⁵N values were significantly larger in adults than subadults, which should be of concern in isotope studies comparing adult females with infants to assess breastfeeding. We found a random variation of up to 3.0‰ in δ¹³C and 2.0‰ in nitrogen stable isotope ratios within the chimpanzee fecal samples separated by particle sizes. We show that particle size influences isotope ratios and propose a simple, cost-effective filtration method for primate feces to exclude larger undigested food particles from the analysis, which can easily be adopted by labs worldwide.     

Optimization of a sample preparation method for the metabolomic analysis of clinically relevant bacteria

Metabolomics, or metabolite profiling, is an approach that is increasingly used to study the metabolism of diverse organisms, elucidate biological processes and/or find characteristic biomarkers of physiological states. Here, we describe the optimization of a method for global metabolomic analysis of bacterial cultures, with the following steps. Cells are grown to log-phase, starting from an overnight culture and bacterial concentrations are monitored by measuring the optical density of the cultures at 600 nm. At an appropriate density they are harvested by centrifugation, washed three times with NaCl solution and metabolites are extracted using methanol and a bead-mill. Dried extracts are methoxymated and derivatized with methyltrimethylsilyltrifluoroacetamide (MSTFA) then analyzed using gas chromatography coupled to time-of-flight mass spectrometry (GC-MS/TOF). Finally, patterns in the acquired data are examined by multivariate data modeling. This method enabled us to obtain reproducible metabolite profiles of Yersinia pseudotuberculosis, with about 25% compound identification, based on comparison with entries in available GC-MS libraries. To assess the potential utility of the method for comparative analysis of other bacterial species we analyzed cultures of Pseudomonas aeruginosa, Salmonella typhimurium, Escherichia coli and methicillin-sensitive Staphylococcus aureus (MSSA). Multivariate analysis of the acquired data showed that it was possible to differentiate the species according to their metabolic profiles. Our results show that the presented procedure can be used for metabolomic analysis of a wide range of bacterial species of clinical interest.     

基于非靶代谢组学的多花黄精沙藏种子萌发过程差异代谢物分析

低温沙藏处理可以有效解除多花黄精种子休眠,为此本实验利用非靶向气相色谱-质谱法（GC-MS）技术鉴定并分析了沙藏后的多花黄精种子不同萌发时期的代谢产物及其变化规律。共鉴定出初级代谢产物116种,对多花黄精种子萌发0 d vs. 7 d,7 d vs. 14 d,14 d vs. 21 d,21 d vs. 28 d和28 d vs. 35 d等比较组进行成对比较筛选,分别筛选鉴定出各比较组中发生显著变化的差异代谢物（VIP>1.0,P<0.05）17,27,9,7和1种。多花黄精种子萌发初期的0 d、7 d和14 d,种子内的二糖（海藻糖,麦芽糖,蔗糖等）以及TCA循环中苹果酸,琥珀酸,柠檬酸等含量显著增加,而在14 d后整体呈现下降趋势。γ-氨基丁酸等氨基酸含量在萌发0 d时最高。HCA分析表明,大部分的代谢物含量在沙藏的多花黄精种子萌发7 d时上升,14 d后下降,表明在14 d前后初级代谢产物被快速消耗。KEGG分析表明,种子萌发过程中TCA循环,精氨酸与脯氨酸代谢,淀粉和蔗糖代谢等多条代谢通路表现活跃。在萌发初期,氨基酸、糖类和有机酸类物质含量三者间显著相关。本研究...     

Discovery of exposure markers in urine for Brassica-containing meals served with different protein sources by UPLC-qTOF-MS untargeted metabolomics

An untargeted metabolomics approach has been applied to discover and identify exposure markers in urine for nine Nordic meals. A cross-over meal study was carried out in 17 subjects. The meals included a Pie, a Soup and a Barleyotto (pearl barley based risotto), each prepared with three protein sources; meat, fish or vegetarian. Urine samples were collected in different time intervals before and after intake of the test meals, covering a total of 24 h. The samples were analyzed by UPLC-qTOF-MS. Discriminating features for meals and protein sources were selected by use of double cross-validated partial least squares discriminant analysis and two additional validation steps: (1) time-course of excretion and (2) analysis of sensitivity and specificity. In addition, eight meal studies with single foods were carried out to investigate the food sources of the markers. In total 31 potential exposure markers (PEMs) of foods were found for the meals and protein sources. Fifteen of the 31 PEMs were also found in studies with single foods. Ten PEMs were identified or putatively annotated. Among the PEMs were a range of conjugated isothiocyanates from the Brassica oleracea species. Trimethylamine N-oxide was found as a fish marker. Additional unknown PEMs were found for chicory salad, parsley and fava beans, while other PEMs were dependent on the meal matrix rather than individual foods. The study demonstrates that it is possible to find PEMs in 24 h urine samples even when foods are given as part of a complex meal.     

Effect of cheese and butter intake on metabolites in urine using an untargeted metabolomics approach

Cheese intake has been shown to decrease total cholesterol and LDL cholesterol concentrations when compared to butter of equal fat content. An untargeted metabolite profiling may reveal exposure markers of cheese but may also contribute with markers which can help explain how the intake of cheese affects cholesterol concentrations. Twenty-three subjects collected 2 × 24 h urine samples after 6 weeks of cheese and 6 weeks of butter intake with equal amounts of fat in a cross-over intervention study. The samples were analyzed by UPLC-QTOF/MS. A two-step univariate data analysis approach using linear mixed model was applied separately for positive and negative ionization mode: In the first step a total of 44 features related to treatment were identified and in the second step 36 of these features were related to total cholesterol concentrations. Cheese intake resulted in increased urinary indoxyl sulfate, xanthurenic acid, tyramine sulfate, 4-hydroxyphenylacetic acid, isovalerylglutamic acid and several acylglycines including isovalerylglycine, tiglylglycine and isobutyrylglycine when compared to butter intake of equal fat content. The biological mechanisms of action linking the metabolites to cholesterol concentrations need to be further explored.     

Novel aspects of grape berry ripening and post-harvest withering revealed by untargeted LC-ESI-MS metabolomics analysis

We established a step-by-step, experiment-guided metabolomics procedure, based on LC-ESI-MS analysis, to generate a detailed picture of the changing metabolic profiles during late berry development in the important Italian grapevine cultivar Corvina. We sampled berries from four developmental time points and three post-harvest time points during the withering process, and used chromatograms of methanolic extracts to test the performance of the MetAlign and MZmine data mining programs. MZmine achieved a better resolution and therefore generated a more useful data matrix. Then both the quantitative performance of the analytical platform and the matrix effect were assessed, and the final dataset was investigated by multivariate data analysis. Our analysis confirmed the results of previous studies but also revealed some novel findings, including the prevalence of two specific flavonoids in unripe berries and important differences between the developmental profiles of flavones and flavanones, suggesting that specific individual metabolites could have different functions, and that flavones and flavanones probably play quite distinct biological roles. Moreover, the hypothesis-free multivariate analysis of subsets of the wide data matrix evidentiated the relationships between the various classes of metabolites, such as those between anthocyanins and hydroxycinnamic acids and between flavan-3-ols and anthocyanins.     

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

Background

Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.

Methods

We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n?=?1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.

Results

Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.

Conclusion

Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.

     

Metabolic derangements identified through untargeted metabolomics in a cross-sectional study of Nigerian children with severe acute malnutrition

Introduction

Severe acute malnutrition (SAM) is a major cause of child mortality worldwide, however the pathogenesis of SAM remains poorly understood. Recent studies have uncovered an altered gut microbiota composition in children with SAM, suggesting a role for microbes in the pathogenesis of malnutrition.

Objectives

To elucidate the metabolic consequences of SAM and whether these changes are associated with changes in gut microbiota composition.

Methods

We applied an untargeted multi-platform metabolomics approach [gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS)] to stool and plasma samples from 47 Nigerian children with SAM and 11 control children. The composition of the stool microbiota was assessed by 16S rRNA gene sequencing.

Results

The plasma metabolome discriminated children with SAM from controls, while no significant differences were observed in the microbial or small molecule composition of stool. The abundance of 585 features in plasma were significantly altered in malnourished children (Wilcoxon test, FDR corrected P?<?0.1), representing approximately 15% of the metabolome. Consistent with previous studies, children with SAM exhibited a marked reduction in amino acids/dipeptides and phospholipids, and an increase in acylcarnitines. We also identified numerous metabolic perturbations which have not been reported previously, including increased disaccharides, truncated fibrinopeptides, angiotensin I, dihydroxybutyrate, lactate, and heme, and decreased bioactive lipids belonging to the eicosanoid and docosanoid family.

Conclusion

Our findings provide a deeper understanding of the metabolic consequences of malnutrition. Further research is required to determine if specific metabolites may guide improved management, and/or act as novel biomarkers for assessing response to treatment.

     

A combination of untargeted and targeted metabolomics approaches unveils changes in the kynurenine pathway following cardiopulmonary resuscitation

The mechanisms responsible for post-resuscitation myocardial and cerebral dysfunction are not well understood, especially in the early post-resuscitation phases. In this investigation, we first adopted unbiased mass spectrometry-based metabolomic profiling to identify perturbations in circulating metabolites in a rat model of cardiac arrest and cardiopulmonary resuscitation. Our findings strongly indicated early alterations in a major route of the tryptophan catabolism, namely the kynurenines pathway, after resuscitation. Specific metabolites involved in the tryptophan catabolism were quantified absolutely using liquid chromatography-multiple reaction monitoring-mass spectrometry. Tryptophan plasma concentration fell significantly very early in the post-resuscitation phase, while its metabolites, l-kynurenine, kynurenic acid, 3-hydroxyanthranilic acid and 5-hydroxyindoleacetic acid, rose significantly. Changes in their concentration reflected changes in rat post-resuscitation myocardial dysfunction. Elevated plasma level of kynurenic acid, 3-hydroxyanthranilic acid were associated with significant decrease in ejection fraction and stroke volume. It is well known that kynurenines pathway is involved in the pathogenesis of numerous central nervous system disorders. By implication, altered levels of tryptophan metabolites in the early post resuscitation phase might contribute to the degree of cognitive recovery. Our results suggest that kynurenine pathway is activated early following resuscitation from cardiac arrest and might account for the severity of post-resuscitation syndrome. Our explorative investigation indicate that metabolomics can help to clarify unexplored biochemical pathways in cardiopulmonary resuscitation.