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To capture interplay between biological pathways, we analyzed the proteome from matched lung tissues and bronchoalveolar lavage fluid (BALF) of individual allergen-naïve and house dust mite (HDM)-challenged BALB/c mice, a model of allergic asthma. Unbiased label-free liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis quantified 2675 proteins from tissues and BALF of allergen-naïve and HDM-exposed mice. In comparing the four datasets, we found significantly greater diversity in proteins between lung tissues and BALF than in the changes induced by HDM challenge. The biological pathways enriched after allergen exposure were compartment-dependent. Lung tissues featured innate immune responses and oxidative stress, while BALF most strongly revealed changes in metabolism. We combined lung tissues and BALF proteomes, which principally highlighted oxidation reduction (redox) pathways, a finding influenced chiefly by the lung tissue dataset. Integrating lung and BALF proteomes also uncovered new proteins and biological pathways that may mediate lung tissue and BALF interactions after allergen challenge, for example, B-cell receptor signaling. We demonstrate that enhanced insight is fostered when different biological compartments from the lung are investigated in parallel. Integration of proteomes from lung tissues and BALF compartments reveals new information about protein networks in response to environmental challenge and interaction between intracellular and extracellular processes.

Serial RNA-seq studies of bulk samples are widespread and provide an opportunity for improved understanding of gene regulation during e.g., development or response to an incremental dose of a pharmacotherapeutic. In addition, the widely popular single cell RNA-seq (scRNA-seq) data implicitly exhibit serial characteristics because measured gene expression values recapitulate cellular transitions. Unfortunately serial RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a principled framework for analyzing RNA-seq data that incorporates measurement uncertainty in the analysis, while introducing a special formulation for modelling data that are acquired as a function of time or other continuous variable. By incorporating uncertainties in the analysis, EMOGEA is specifically suited for RNA-seq studies in which low-count transcripts with small fold-changes lead to significant biological effects. Such transcripts include signaling mRNAs and non-coding RNAs (ncRNA) that are known to exhibit low levels of expression. Through this approach, missing values are handled by associating with them disproportionately large uncertainties which makes it particularly useful for single cell RNA-seq data. We demonstrate the utility of this framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and, a scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate for false negative discoveries compared to common approaches. Finally, we provide an R package (https://github.com/itikadi/EMOGEA) that is self-contained and easy to use. Graphical Abstract:Graphical representation of EMOGEA indicating the incorporation of measurement errors in modeling RNA-seq data to generate superior results in exploratory analysis, differential gene expression analyses and, scRNA-seq and Time Course analyses.

T cells dynamically rewire their metabolism during an immune response. We applied single-cell RNA sequencing to CD8+ T cells activated and differentiated in vitro in physiological medium to resolve these metabolic dynamics. We identify a differential time-dependent reliance of activating T cells on the synthesis versus uptake of various non-essential amino acids, which we corroborate with functional assays. We also identify metabolic genes that potentially dictate the outcome of T cell differentiation, by ranking them based on their expression dynamics. Among them, we find asparagine synthetase (Asns), whose expression peaks for effector T cells and decays toward memory formation. Disrupting these expression dynamics by ASNS overexpression promotes an effector phenotype, enhancing the anti-tumor response of adoptively transferred CD8+ T cells in a mouse melanoma model. We thus provide a resource of dynamic expression changes during CD8+ T cell activation and differentiation, and identify ASNS expression dynamics as a modulator of CD8+ T cell differentiation. ispartof: CELL REPORTS vol:41 issue:7 ispartof: location:United States status: published

T cells dynamically rewire their metabolism during an immune response. We applied single-cell RNA sequencing to CD8

Cytotoxic T cells dynamically rewire their metabolism during the course of an immune response. While T-cell metabolism has been extensively studied at phenotypic endpoints of activation and differentiation, the underlying dynamics remain largely elusive. Here, we leverage on single-cell RNA-sequencing (scRNA-seq) measurements of in vitro activated and differentiated CD8+ T cells cultured in physiological media to resolve these metabolic dynamics. We find that our scRNA-seq analysis identifies most metabolic changes previously defined in in vivo experiments, such as a rewiring from an oxidative to an anabolism-promoting metabolic program during activation to an effector state, which is later reverted upon memory polarization. Importantly, our scRNA-seq data further provide a dynamic description of these changes. In this sense, our data predict a differential time-dependent reliance of CD8+ T cells on the synthesis versus uptake of various non-essential amino acids during T-cell activation, which we corroborate with additional functional in vitro experiments. We further exploit our scRNA-seq data to identify metabolic genes that could potentially dictate the outcome of T-cell differentiation, by ranking them based on their expression dynamics. Among the highest-ranked hits, we find asparagine synthetase (Asns), whose expression sharply peaks for effector CD8+ T cells and further decays towards memory polarization. We then confirm that these in vitro Asns expression dynamics are representative of an in vivo situation in a mouse model of viral infection. Moreover, we find that disrupting these expression dynamics in vitro, by depleting asparagine from the culture media, delays central-memory polarization. Accordingly, we find that preventing the decay of ASNS by stable overexpression at the protein level in vivo leads to a significant increase in effector CD8+ T-cell expansion, and a concomitant decrease in central-memory formation, in a mouse model of viral infection. This shows that ASNS expression dynamics dictate the fate of CD8+ T-cell differentiation. In conclusion, we provide a resource of dynamic expression changes during CD8+ T-cell activation and differentiation that is expected to increase our understanding of the dynamic metabolic requirements of T cells progressing along the immune response cascade.

Tumor vessel co-option is poorly understood, yet it is a resistance mechanism against anti-angiogenic therapy (AAT). The heterogeneity of co-opted endothelial cells (ECs) and pericytes, co-opting cancer and myeloid cells in tumors growing via vessel co-option, has not been investigated at the single-cell level. Here, we use a murine AAT-resistant lung tumor model, in which VEGF-targeting induces vessel co-option for continued growth. Single-cell RNA sequencing (scRNA-seq) of 31,964 cells reveals, unexpectedly, a largely similar transcriptome of co-opted tumor ECs (TECs) and pericytes as their healthy counterparts. Notably, we identify cell types that might contribute to vessel co-option, i.e., an invasive cancer-cell subtype, possibly assisted by a matrix-remodeling macrophage population, and another M1-like macrophage subtype, possibly involved in keeping or rendering vascular cells quiescent. ispartof: CELL REPORTS vol:35 issue:11 ispartof: location:United States status: published

Summary Human induced pluripotent stem cells (hiPSCs) are used to study organogenesis and model disease as well as being developed for regenerative medicine. Endothelial cells are among the many cell types differentiated from hiPSCs, but their maturation and stabilization fall short of that in adult endothelium. We examined whether shear stress alone or in combination with pericyte co-culture would induce flow alignment and maturation of hiPSC-derived endothelial cells (hiPSC-ECs) but found no effects comparable with those in primary microvascular ECs. In addition, hiPSC-ECs lacked a luminal glycocalyx, critical for vasculature homeostasis, shear stress sensing, and signaling. We noted, however, that hiPSC-ECs have dysfunctional mitochondrial permeability transition pores, resulting in reduced mitochondrial function and increased reactive oxygen species. Closure of these pores by cyclosporine A improved EC mitochondrial function but also restored the glycocalyx such that alignment to flow took place. These results indicated that mitochondrial maturation is required for proper hiPSC-EC functionality., Graphical Abstract, Highlights • hiPSC-ECs lack a functional glycocalyx and fail to align to flow • hiPSC-ECs have reduced mitochondrial function and increased leakage of ROS • Closing the mPTP with cyclosporine A induces mitochondrial maturation • Improved mitochondrial function restores the glycocalyx and alignment to flow, Closure of the mitochondrial permeability transition pore by cyclosporine A improved mitochondrial function and maturation of hiPSC-ECs but also restored the glycocalyx such that alignment to flow took place. This functional glycocalyx, necessary for growth factor signaling and anticoagulation, is a prerequisite for future hiPSC-EC applications in tissue engineering, organoid vascularization and therapeutic use of hiPSC-ECs.

Integrator is a key regulator of NEAT1 isoform switching and, thereby, paraspeckles biogenesis and chemosensitivity., RNA 3′ end processing provides a source of transcriptome diversification which affects various (patho)-physiological processes. A prime example is the transcript isoform switch that leads to the read-through expression of the long non-coding RNA NEAT1_2, at the expense of the shorter polyadenylated transcript NEAT1_1. NEAT1_2 is required for assembly of paraspeckles (PS), nuclear bodies that protect cancer cells from oncogene-induced replication stress and chemotherapy. Searching for proteins that modulate this event, we identified factors involved in the 3′ end processing of polyadenylated RNA and components of the Integrator complex. Perturbation experiments established that, by promoting the cleavage of NEAT1_2, Integrator forces NEAT1_2 to NEAT1_1 isoform switching and, thereby, restrains PS assembly. Consistently, low levels of Integrator subunits correlated with poorer prognosis of cancer patients exposed to chemotherapeutics. Our study establishes that Integrator regulates PS biogenesis and a link between Integrator, cancer biology, and chemosensitivity, which may be exploited therapeutically.

BACKGROUND: Renal endothelial cells from glomerular, cortical, and medullary kidney compartments are exposed to different microenvironmental conditions and support specific kidney processes. However, the heterogeneous phenotypes of these cells remain incompletely inventoried. Osmotic homeostasis is vitally important for regulating cell volume and function, and in mammals, osmotic equilibrium is regulated through the countercurrent system in the renal medulla, where water exchange through endothelium occurs against an osmotic pressure gradient. Dehydration exposes medullary renal endothelial cells to extreme hyperosmolarity, and how these cells adapt to and survive in this hypertonic milieu is unknown. METHODS: We inventoried renal endothelial cell heterogeneity by single-cell RNA sequencing >40,000 mouse renal endothelial cells, and studied transcriptome changes during osmotic adaptation upon water deprivation. We validated our findings by immunostaining and functionally by targeting oxidative phosphorylation in a hyperosmolarity model in vitro and in dehydrated mice in vivo. RESULTS: We identified 24 renal endothelial cell phenotypes (of which eight were novel), highlighting extensive heterogeneity of these cells between and within the cortex, glomeruli, and medulla. In response to dehydration and hypertonicity, medullary renal endothelial cells upregulated the expression of genes involved in the hypoxia response, glycolysis, and-surprisingly-oxidative phosphorylation. Endothelial cells increased oxygen consumption when exposed to hyperosmolarity, whereas blocking oxidative phosphorylation compromised endothelial cell viability during hyperosmotic stress and impaired urine concentration during dehydration. CONCLUSIONS: This study provides a high-resolution atlas of the renal endothelium and highlights extensive renal endothelial cell phenotypic heterogeneity, as well as a previously unrecognized role of oxidative phosphorylation in the metabolic adaptation of medullary renal endothelial cells to water deprivation. ispartof: JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY vol:31 issue:1 pages:118-138 ispartof: location:United States status: published

The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex. ispartof: NUCLEIC ACIDS RESEARCH vol:48 issue:W1 pages:W385-W394 ispartof: location:England status: published

Summary Pancreatic cancer is a rare but fatal form of cancer, the fourth highest in absolute mortality. Known risk factors include obesity, diet, and type 2 diabetes; however, the low incidence rate and interconnection of these factors confound the isolation of individual effects. Here, we use epidemiological analysis of prospective human cohorts and parallel tracking of pancreatic cancer in mice to dissect the effects of obesity, diet, and diabetes on pancreatic cancer. Through longitudinal monitoring and multi-omics analysis in mice, we found distinct effects of protein, sugar, and fat dietary components, with dietary sugars increasing Mad2l1 expression and tumor proliferation. Using epidemiological approaches in humans, we find that dietary sugars give a MAD2L1 genotype-dependent increased susceptibility to pancreatic cancer. The translation of these results to a clinical setting could aid in the identification of the at-risk population for screening and potentially harness dietary modification as a therapeutic measure., Graphical Abstract, Highlights • Distinct roles for dietary fat, protein, and sugar on murine pancreatic cancer • Dietary glucose triggers Mad2l1 upregulation and tumor cell proliferation in mice • Gene-diet interaction identifies sugar-MAD2L1 link in human pancreatic cancer • Dietary plant fats were protective in human pancreatic cancer susceptibility, Dooley et al. used parallel analysis of a murine pancreatic cancer model and a human prospective cohort to study the interaction of diet and pancreatic cancer. Both systems identify complex effects with different dietary components, converging on a link between dietary sugar and the cell-cycle checkpoint gene MAD2L1.

This study describes the application of the multivariate curve resolution (MCR) analysis technique for real-time analysis of culture fluorescence during recombinant Pichia pastoris cultivation in a bioreactor. Fluorescence spectra were acquired with an on-line dual excitation wavelength fluorometer and then used to develop a real time MCR-based bioprocess monitoring and diagnostics tool. Initial bioreactor experiments using two similar recombinant antibody secreting P. pastoris cell lines showed significant differences in protein production. To distinguish between the contributions of operating conditions and the specific cell line's genetic composition to the observed differences in protein production, the bioreactor experiments were repeated and accompanied by real time MCR analysis. The tests demonstrated high sensitivity of MCR-derived "pure concentration" profiles to growth as well as to initial conditions, thus enabling real-time cultivation process trend diagnostics and fault detection. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2761, 2019.

Endothelial cell (EC) metabolism is an emerging target for anti-angiogenic therapy in tumor angiogenesis and choroidal neovascularization (CNV), but little is known about individual EC metabolic transcriptomes. By single-cell RNA sequencing 28,337 murine choroidal ECs (CECs) and sprouting CNV-ECs, we constructed a taxonomy to characterize their heterogeneity. Comparison with murine lung tumor ECs (TECs) revealed congruent marker gene expression by distinct EC phenotypes across tissues and diseases, suggesting similar angiogenic mechanisms. Trajectory inference predicted that differentiation of venous to angiogenic ECs was accompanied by metabolic transcriptome plasticity. ECs displayed metabolic transcriptome heterogeneity during cellcycle progression and in quiescence. Hypothesizing that conserved genes are important, we used an integrated analysis, based on congruent transcriptome analysis, CEC-tailored genome-scale metabolic modeling, and gene expression meta-analysis in cross-species datasets, followed by in vitro and in vivo validation, to identify SQLE and ALDH18A1 as previously unknown metabolic angiogenic targets. ispartof: Cell Metabolism vol:31 issue:4 pages:862-877 ispartof: location:United States status: published

The heterogeneity of endothelial cells (ECs) across tissues remains incompletely inventoried. We constructed an atlas of >32,000 single-EC transcriptomes from 11 mouse tissues and identified 78 EC subclusters, including Aqp7+ intestinal capillaries and angiogenic ECs in healthy tissues. ECs from brain/testis, liver/spleen, small intestine/colon, and skeletal muscle/heart pairwise expressed partially overlapping marker genes. Arterial, venous, and lymphatic ECs shared more markers in more tissues than did heterogeneous capillary ECs. ECs from different vascular beds (arteries, capillaries, veins, lymphatics) exhibited transcriptome similarity across tissues, but the tissue (rather than the vessel) type contributed to the EC heterogeneity. Metabolic transcriptome analysis revealed a similar tissue-grouping phenomenon of ECs and heterogeneous metabolic gene signatures in ECs between tissues and between vascular beds within a single tissue in a tissue-type-dependent pattern. The EC atlas taxonomy enabled identification of EC subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value. ispartof: CELL vol:180 issue:4 pages:764-+ ispartof: location:United States status: published

Heterogeneity of lung tumor endothelial cell (TEC) phenotypes across patients, species (human/mouse), and models (in vivo/in vitro) remains poorly inventoried at the single-cell level. We single-cell RNA (scRNA)-sequenced 56,771 endothelial cells from human/mouse (peri)-tumoral lung and cultured human lung TECs, and detected 17 known and 16 previously unrecognized phenotypes, including TECs putatively regulating immune surveillance. We resolved the canonical tip TECs into a known migratory tip and a putative basement-membrane remodeling breach phenotype. Tip TEC signatures correlated with patient survival, and tip/breach TECs were most sensitive to vascular endothelial growth factor blockade. Only tip TECs were congruent across species/models and shared conserved markers. Integrated analysis of the scRNA-sequenced data with orthogonal multi-omics and meta-analysis data across different human tumors, validated by functional analysis, identified collagen modification as a candidate angiogenic pathway.

Dual excitation wavelength fluorometry was used for real-time monitoring and diagnosis of the growth of the microalgae Scenedesmus AMDD in a 300-L continuous photobioreactor (PBR). Emission spectra were acquired at 1-min intervals using excitation lights at 365 and 540 nm. Real-time dry weight estimations were achieved using linear regression with the chlorophyll peak, while protein estimations required a more complex Principal Component Regression model, which takes advantage of the full emission spectrum. The resulting regression coefficients were 0.95 and 0.80, respectively. Furthermore, the spectra were analyzed using multivariate curve resolution technique. The proposed approach for fluorescence-based, real-time measurements of key algae cultivation parameters and culture state diagnosis was successfully demonstrated in a 42-day PBR validation test.

A method was developed to distinguish Vacci- nium species based on leaf extracts using nuclear magnetic resonance spectroscopy. Reference spectra were measured on leaf extracts from sev- eral species, including lowbush blueberry (Vacci- nium angustifolium), oval leaf huckleberry (Vacci- nium ovalifolium), and cranberry (Vaccinium mac- rocarpon). Using principal component analysis, these leaf extracts were resolved in the scores plot. Analysis of variance statistical tests demon- strated that the three groups differ significantly on PC2, establishing that the three species can be distinguished by nuclear magnetic resonance. Soft independent modeling of class analogies models for each species also showed discrimina- tion between species. To demonstrate the robust- ness of nuclear magnetic resonance spectroscopy for botanical identification, spectra of a sample of lowbush blueberry leaf extract were measured at five different sites, with different field strengths (600 versus 700 MHz), different probe types (cryogenic versus room temperature probes), dif- ferent sample diameters (1.7mm versus 5mm), and different consoles (Avance I versus Avance III). Each laboratory independently demonstrated the linearity of their NMR measurements by ac- quiring a standard curve for chlorogenic acid (R 2 =0.9782 to 0.9998). Spectra acquired on dif- ferent spectrometers atdifferent sites classifed in- to the expectedgroup for the Vaccinium spp., con- firming the utility of the method to distinguish Vaccinium species and demonstrating nuclear magnetic resonance fingerprinting for material validation of a natural health product. " NMR fingerprinting l " Vaccinium spp. l " Ericaceae

The interpretation of NMR spectroscopic information for structure elucidation involves decoding of complex resonance patterns that contain valuable molecular information (δ and J), which is not readily accessible otherwise. We introduce a new concept of 2D-NMR barcoding that uses clusters of fingerprint signals and their spatial relationships in the δ-δ coordinate space to facilitate the chemical identification of complex mixtures. Similar to widely used general barcoding technology, the structural information of individual compounds is encoded as a specifics pattern of their C,H correlation signals. Software-based recognition of these patterns enables the structural identification of the compounds and their discrimination in mixtures. Using the triterpenes from various Actaea (syn. Cimicifuga) species as a test case, heteronuclear multiple-bond correlation (HMBC) barcodes were generated on the basis of their structural subtypes from a statistical investigation of their δH and δC data in the literature. These reference barcodes allowed in silico identification of known triterpenes in enriched fractions obtained from an extract of A. racemosa (black cohosh). After dereplication, a differential analysis of heteronuclear single-quantum correlation (HSQC) spectra even allowed for the discovery of a new triterpene. The 2D barcoding concept has potential application in a natural product discovery project, allowing for the rapid dereplication of known compounds and as a tool in the search for structural novelty within compound classes with established barcodes. © 2014 American Chemical Society.

Modeling NMR-based metabolomics data often involves linear methods such as principal component analysis (PCA) and partial least squares (PLS). These methods have the objective of describing the main variance in the data and maximum covariance between the predictor variables and some response variable respectively. If the experiment is designed to investigate temporal biological fluctuations, however, the factors obtained become difficult to interpret in a biological context. Moreover, when these methods are applied to analyze data, an implicit assumption is made that the measurement errors exhibit an iid-normal distribution, often limiting the extent of the information recovered. A method for the linear decomposition of NMR-based metabolomics data by multivariate curve resolution (MCR), which has been used elsewhere for time course transcriptomics applications, is introduced and implemented via a weighted alternating least squares (ALS) approach. Measurement of error information is incorporated in the modeling process, allowing the least squares projections to be performed in a maximum likelihood fashion. As a result, noise heteroscedasticity resulting from pH-induced peak shifts can be modeled, eliminating the need for binning/bucketing. The utility of the method is demonstrated using two sets of temporal NMR metabolomics data, HgCl(2)-induced nephrotoxicity in rat, and fish (Japanese medaka, Oryzias latipes) embryogenesis. Profiles extracted for the nephrotoxicity data exhibit strong correlations with metabolites consistent with temporal fluctuations in glucosuria. The concentration of metabolites such as acetate, glucose, and alanine exhibit a steady increase, which peaks at Day 3 post dose and returns to basal levels at Day 8. Other metabolites including citrate and 2-oxoglutarate exhibit the opposite characteristics. Although the fish embryogenesis data are more complex, the profiles extracted by the algorithm display characteristics that depict temporal variation consistent with processes associated with embryogenesis.

The global analysis of metabolites can be used to define the phenotypes of cells, tissues or organisms. Classifying groups of samples based on their metabolic profile is one of the main topics of metabolomics research. Crisp clustering methods assign each feature to one cluster, thereby omitting information about the multiplicity of sample subtypes. Here, we present the application of fuzzy K-means clustering method for the classification of samples based on metabolomics 1D (1)H NMR fingerprints. The sample classification was performed on NMR spectra of cancer cell line extracts and of urine samples of type 2 diabetes patients and animal models. The cell line dataset included NMR spectra of lipophilic cell extracts for two normal and three cancer cell lines with cancer cell lines including two invasive and one non-invasive cancers. The second dataset included previously published NMR spectra of urine samples of human type 2 diabetics and healthy controls, mouse wild type and diabetes model and rat obese and lean phenotypes. The fuzzy K-means clustering method allowed more accurate sample classification in both datasets relative to the other tested methods including principal component analysis (PCA), hierarchical clustering (HCL) and K-means clustering. In the cell line samples, fuzzy clustering provided a clear separation of individual cell lines, groups of cancer and normal cell lines as well as non-invasive and invasive tumour cell lines. In the diabetes dataset, clear separation of healthy controls and diabetics in all three models was possible only by using the fuzzy clustering method.

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics. NMR data are quite complex and the use of such multivariate statistical methods as principal components analysis (PCA) for their analysis assumes that the data are multivariate normal with errors that are identical, independent and normally distributed (i.e. iid normal). There is a consensus that these assumptions are not always true for these data and, thus, several methods have been devised to transform the data or weight them prior to analysis by PCA. The structure of NMR measurement noise, or the extent to which violations of error homoscedasticity affect PCA results have neither been characterized nor investigated. A comprehensive characterization of measurement uncertainties in NMR based metabolomics was achieved in this work using an experiment designed to capture contributions of several sources of error to the total variance in the measurements. The noise structure was found to be heteroscedastic and highly correlated with spectral characteristics that are similar to the mean of the spectra and their standard deviation. A model was subsequently developed that potentially allows errors in NMR measurements to be accurately estimated without the need for extensive replication.

Several fundamental requirements must be met so that NMR-based metabolomics and the related technique of metabonomics can be formally adopted into environmental monitoring and chemical risk assessment. Here we report an intercomparison exercise which has evaluated the effectiveness of 1H NMR metabolomics to generate comparable data sets from environmentally derived samples. It focuses on laboratory practice that follows sample collection and metabolite extraction, specifically the final stages of sample preparation, NMR data collection (500, 600, and 800 MHz), data processing, and multivariate analysis. Seven laboratories have participated from the U.S.A., Canada, U.K., and Australia, generating a total of ten data sets. Phase 1 comprised the analysis of synthetic metabolite mixtures, while Phase 2 investigated European flounder (Platichthys flesus) liver extracts from clean and contaminated sites. Overall, the comparability of data sets from the participating laboratories was good. Principal components analyses (PCA) of the individual data sets yielded ten highly similar scores plots for the synthetic mixtures, with a comparable result for the liver extracts. Furthermore, the same metabolic biomarkers that discriminated fish from clean and contaminated sites were discovered by all the laboratories. PCA of the combined data sets showed excellent clustering of the multiple analyses. These results demonstrate that NMR-based metabolomics can generate data that are sufficiently comparable between laboratories to support its continued evaluation for regulatory environmental studies.

Stressors of various kinds constantly affect fish both in the wild and in culture, examples being acute water temperature and quality changes, predation, handling, and confinement. Known physiological responses of fish to stress such as increases in plasma cortisol and glucose levels, are considered to be adaptive, allowing the animal to cope in the short term. Prolonged exposure to stressors however, has the potential to affect growth, immune function, and survival. Nonetheless, little is known about the mechanisms underlying the long-term stress response. We have investigated the metabolic response of juvenile Atlantic salmon (Salmo salar) to long-term handling stress by analyzing fish plasma via 1H nuclear magnetic resonance spectroscopy and ultra high performance liquid chromatography–mass spectrometry (UPLC–MS), and comparing results with controls. Analysis of NMR data indicated a difference in the metabolic profiles of control and stressed fish after 1 week of stress with a maximum difference observed after 2 weeks. These differences were associated with stress-induced increases in phosphatidyl choline, lactate, carbohydrates, alanine, valine and trimethylamine-N-oxide, and decreases in low density lipoprotein, very low density lipoprotein, and lipid. UPLC-MS data showed differences at week 2, associated with another set of compounds, tentatively identified on the basis of their mass/charge. Overall the results provided a multi-faceted view of the response of fish to long-term handling stress, indicating that the metabolic disparity between the control and stress groups increased to week 2, but declined by weeks 3 and 4, and revealed several new molecular indicators of long-term stress.

The conceptual simplicity of DNA microarray technology often belies the complex nature of the measurement errors inherent in the methodology. As the technology has developed, the importance of understanding the sources of uncertainty in the measurements and developing ways to control their influence on the conclusions drawn has become apparent. In this review, strategies for modeling measurement errors and minimizing their effect on the outcome of experiments using a variety of techniques are discussed in the context of spotted, dual-color microarrays. First, methods designed to reduce the influence of random variability through data filtering, replication, and experimental design are introduced. This is followed by a review of data analysis methods that partition the variance into random effects and one or more systematic effects, specifically two-sample significance testing and analysis of variance (ANOVA) methods. Finally, the current state of measurement error models for spotted microarrays and their role in variance stabilizing transformations are discussed.

Background Gastrointestinal microbial communities are diverse and are composed of both beneficial and pathogenic groups. Prebiotics, such as digestion-resistant fibers, influence the composition of gut microbiota, and can contribute to the improvement of host health. The red seaweed Chondrus crispus is rich in dietary fiber and oligosaccharides, however its prebiotic potential has not been studied to date. Methods Prebiotic effects were investigated with weaning rats fed a cultivated C. crispus-supplemented diet. Comparison standards included a fructo-oligo-saccharide (FOS) diet and a basal diet. The colonic microbiome was profiled with a 16S rRNA sequencing-based Phylochip array. Concentrations of short chain fatty acids (SCFAs) in the feacal samples were determined by gas chromatography with a flame ionization detector (GC-FID) analysis. Immunoglobulin levels in the blood plasma were analyzed with an enzyme-linked immunosorbent assay (ELISA). Histo-morphological parameters of the proximal colon tissue were characterized by hematoxylin and eosin (H&E) staining. Results Phylochip array analysis indicated differing microbiome composition among the diet-supplemented and the control groups, with the C. crispus group (2.5 % supplementation) showing larger separation from the control than other treatment groups. In the 2.5 % C. crispus group, the population of beneficial bacteria such as Bifidobacterium breve increased (4.9-fold, p = 0.001), and the abundance of pathogenic species such as Clostridium septicum and Streptococcus pneumonia decreased. Higher concentrations of short chain fatty acids (i.e., gut microbial metabolites), including acetic, propionic and butyric acids, were found in faecal samples of the C. crispus-fed rats. Furthermore, both C. crispus and FOS supplemented rats showed significant improvements in proximal colon histo-morphology . Higher faecal moisture was noted in the 2.5 % C. crispus group, and elevated plasma immunoglobulin (IgA and IgG) levels were observed in the 0.5 % C. crispus group, as compared to the basal feed group. Conclusions The results suggest multiple prebiotic effects, such as influencing the composition of gut microbial communities, improvement of gut health and immune modulation in rats supplemented with cultivated C. crispus. Electronic supplementary material The online version of this article (doi:10.1186/s12906-015-0802-5) contains supplementary material, which is available to authorized users.

Since the discovery of Electron Paramagnetic Resonance (EPR) spectroscopy in 1944 by Za‐ voisky[1], and the realization of the Nuclear Magnetic Resonance (NMR) spectroscopic sig‐ nal in the mid 1940’s by Bloch and Purcell[2,3], the capabilities and applications of the technology have continued to advance at an enormous rate particularly after the implemen‐ tation of Fourier transform NMR in the mid 1960’s by Ernst[4]. Magnetic Resonance (MR) spectroscopy was initially utilized to characterize the structure of matter.[1,5] Through the early to mid 1970’s, the development of multidimensional (nD) methods and more powerful instruments opened the door for the detailed atomistic characterization of small molecules culminating in structural elucidation of proteins by the mid 1980’s.[6] At about the same time, it was proposed that a magnetic field gradient could be applied to obtain a 3-dimen‐ sional (3D) image leading to the invention of nuclear Magnetic Resonance Imaging (MRI) with, among other capabilities, the potential to monitor the bio-distribution and bio-accu‐ mulation of molecules in vivo.[7] Beyond the 1980’s MR technologies were mixing with other technologies and evolving to play an integral role for advancing pharmaceuticals and be‐ coming indispensable tools for drug discovery, design and diagnostics.

Niemann-Pick Type C (NPC) disease is an autosomal recessive neurodegenerative disorder caused in most cases by mutations in the NPC1 gene. NPC1-deficiency is characterized by late endosomal accumulation of cholesterol, impaired cholesterol homeostasis, and a broad range of other cellular abnormalities. Although neuronal abnormalities and glial activation are observed in nearly all areas of the brain, the most severe consequence of NPC1-deficiency is a near complete loss of Purkinje neurons in the cerebellum. The link between cholesterol trafficking and NPC pathogenesis is not yet clear; however, increased oxidative stress in symptomatic NPC disease, increases in mitochondrial cholesterol, and alterations in autophagy/mitophagy suggest that mitochondria play a role in NPC disease pathology. Alterations in mitochondrial function affect energy and neurotransmitter metabolism, and are particularly harmful to the central nervous system. To investigate early metabolic alterations that could affect NPC disease progression, we performed metabolomics analyses of different brain regions from age-matched wildtype and Npc1 -/- mice at pre-symptomatic, early symptomatic and late stage disease by 1H-NMR spectroscopy. Metabolic profiling revealed markedly increased lactate and decreased acetate/acetyl-CoA levels in Npc1-/- cerebellum and cerebral cortex at all ages. Protein and gene expression analyses indicated a presymptomatic deficiency in the oxidative decarboxylation of pyruvate to acetyl-CoA, and an upregulation of glycolytic gene expression at the early symptomatic stage. We also observed a pre-symptomatic increase in several indicators of oxidative stress and antioxidant response systems in Npc1 -/- cerebellum. Our findings suggest that energy metabolism and oxidative stress may present additional therapeutic targets in NPC disease, especially if intervention can be started at an early stage of the disease. © 2013 Kennedy et al.

Animal and human studies have indicated that fatty acids such as the conjugated linoleic acids (CLA) found in milk could potentially alter the risk of developing metabolic disorders including diabetes and cardiovascular disease (CVD). Using susceptible rodent models (apoE(-/-) and LDLr(-/-) mice) we investigated the interrelationship between mouse strain, dietary conjugated linoleic acids and metabolic markers of CVD. Despite an adverse metabolic risk profile, atherosclerosis (measured directly by lesion area), was significantly reduced with t-10, c-12 CLA and mixed isomer CLA (Mix) supplementation in both apoE(-/-) (p

The genus Actaea (including Cimicifuga) has been the source of ∼200 cycloartane triterpenes. While they are major bioactive constituents of complementary and alternative medicines, their structural similarity is a major dereplication problem. Moreover, their trivial names seldom indicate the actual structure. This project develops two new tools for Actaea triterpenes that enable rapid dereplication of more than 170 known triterpenes and facilitates elucidation of new compounds. A predictive computational model based on classification binary trees (CBTs) allows in silico determination of the aglycone type. This tool utilizes the Me (1)H NMR chemical shifts and has potential to be applicable to other natural products. Actaea triterpene dereplication is supported by a new systematic naming scheme. A combination of CBTs, (1)H NMR deconvolution, characteristic (1)H NMR signals, and quantitative (1)H NMR (qHNMR) led to the unambiguous identification of minor constituents in residually complex triterpene samples. Utilizing a 1.7 mm cryo-microprobe at 700 MHz, qHNMR enabled characterization of residual complexity at the 10-20 μg level in a 1-5 mg sample. The identification of five co-occurring minor constituents, belonging to four different triterpene skeleton types, in a repeatedly purified natural product emphasizes the critical need for the evaluation of residual complexity of reference materials, especially when used for biological assessment.

One of the greatest strengths of "-omics" technologies is their ability to capture a molecular snapshot of multiple cellular processes simultaneously. Transcriptomics, proteomics, and metabolomics have, individually, been used in wide-ranging studies involving cell lines, tissues, model organisms, and human subjects. Nonetheless, despite the fact that their power lies in the global acquisition of parallel data streams, these methods continue to be employed separately. We highlight work done to merge transcriptomics and metabolomics technologies to study zebrafish (Danio rerio) embryogenesis. We combine information from three bioanalytical platforms, that is, DNA microarrays, 1H nuclear magnetic resonance (1H NMR), and mass spectrometry (MS)-based metabolomics, to identify and provide insights into the organism's developmental regulators. We apply a customized approach to the analysis of such time-ordered measurements to provide temporal profiles that depict the modulation of metabolites and gene transcription. Initially, the three data sets were analyzed individually but later they were fused to highlight the advantages gained through such an integrated approach. Unique challenges posed by fusion of such data are discussed given differences in the measurement error structures, the wide dynamic range for the molecular species, and the analytical platforms used to measure them (i.e., fluorescence ratios, NMR, and MS intensities). Our data analysis reveals that changes in transcript levels at specific developmental stages correlate with previously published data with over 90% accuracy. In addition, transcript profiles exhibited trends that were similar to the accumulation of metabolites over time. Profiles for metabolites such as choline-like compounds (Trimethylamine-N- oxide, phosphocholine, betaine), creatinine/creatine, and other metabolites involved in energy metabolism exhibited a steady increase from 15 hours post fertilization (hpf) to 48 hpf. Other metabolite and transcript profiles were transiently rising and then falling back to baseline. The "house keeping" metabolites such as branched chain amino acids exhibited a steady presence throughout embryogenesis. Although the transcript profiling corresponds to only 16 384 genes, a subset of the total number of genes in the zebrafish genome, we identified examples where gene transcript and metabolite profiles correlate with one another, reflective of a relationship between gene and metabolite regulation over the course of embryogenesis. © Published 2011 by the American Chemical Society.

This tutorial presents a basic introduction to DNA microarrays as employed for gene expression analysis, approaching the subject from a chemometrics perspective. The emphasis is on describing the nature of the measurement process, from the platforms used to a few of the standard higher-level data analysis tools employed. Topics include experimental design, detection, image processing, measurement errors, ratio calculation, background correction, normalization, and higher-level data processing. The objective is to present the chemometrician with as clear a picture as possible of an evolving technology so that the strengths and limitations of DNA microarrays are appreciated. Although the focus is primarily on spotted, two-color microarrays, a significant discussion of single-channel, lithographic arrays is also included.

DNA microarrays permit the measurement of gene expression across the entire genome of an organism, but the quality of the thousands of measurements is highly variable. For spotted dual-color microarrays the situation is complicated by the use of ratio measurements. Studies have shown that measurement errors can be described by multiplicative and additive terms, with the latter dominating for low-intensity measurements. In this work, a measurement-error model is presented that partitions the variance into general experimental sources and sources associated with the calculation of the ratio from noisy pixel data. The former is described by a proportional (multiplicative) structure, while the latter is estimated using a statistical bootstrap method. The model is validated using simulations and three experimental data sets. Monte-Carlo fits of the model to data from duplicate experiments are excellent, but suggest that the bootstrap estimates, while proportionately correct, may be underestimated. The bootstrap standard error estimates are particularly useful in determining the reliability of individual microarray spots without the need for replicate spotting. This information can be used in screening or weighting the measurements.

DNA microarrays, or “DNA chips”, represent a relatively new technology that is having a profound impact on biology and medicine, yet analytical research into this area is somewhat sparse. This article presents an overview of DNA microarrays and their application to gene expression analysis from the perspective of analytical chemistry, treating aspects of array platforms, measurement, image analysis, experimental design, normalization, and data analysis. Typical approaches are described and unresolved issues are discussed, with a view to identifying some of the contributions that might be made by analytical chemists.

The response of Atlantic salmon, Salmo salar, to infection by the bacterial pathogen Aeromonas salmonicida (the causative agent of furunculosis), was investigated using a cohabitation model and a custom Atlantic salmon cDNA microarray consisting of over 4000 different amplicons. Pooled samples of each of three immune-relevant tissues (spleen, head kidney and liver) were obtained from fish exposed to infected salmon for 13 days. Reverse transcription-PCR assays were used to verify the differential expression of 12 candidate genes uncovered by microarray analysis. Among the differentially expressed genes were several previously revealed by suppression subtractive hybridization and EST surveys and that are recognized to encode humoral components of the innate immune system. Other genes identified in this study were not previously associated with infection. In addition, a number of genes with no known homologs were uncovered. Determination of their specific roles during infection may lead to a better understanding of innate immunity.

e21013 Background: Accurate risk stratification of pediatric renal tumors is essential in optimal management. Urine metabolic profiling may enhance accurate categorization. We determined if the uri...

Urinary tract obstruction (UTO) results in renal compensatory mechanisms and may progress to irrecoverable functional loss and histologic alterations. The pathophysiology of this progression is poorly understood. We identified urinary metabolite alterations in a rodent model of partial and complete UTO using (1)H nuclear magnetic resonance ((1)H-NMR) spectroscopy. Principal component analysis (PCA) was used for classification and discovery of differentiating metabolites. UTO was associated with elevated urinary levels of alanine, succinate, dimethylglycine (DMG), creatinine, taurine, choline-like compounds, hippurate, and lactate. Decreased urinary levels of 2-oxoglutarate and citrate were noted. The patterns of alteration in partial and complete UTO were similar except that an absence of elevated urinary osmolytes (DMG and hippurate) was noted in complete UTO. This pattern of metabolite alteration indicates impaired oxidative metabolism of the mitochondria in renal proximal tubules and production of renal protective osmolytes by the medulla. Decreased production of osmolytes in complete obstruction better elucidates the pathophysiology of progression from renal compensatory mechanisms to irrecoverable changes. Further confirmation of these potential biomarkers in children with UTO is necessary.

Background Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. Results In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. Conclusion Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.