Peroxisome deficiency underlies failures in hepatic immune cell development and antigen presentation in a severe Zellweger disease model.

Peroxisome biogenesis disorders (PBDs) represent a group of metabolic conditions that cause severe developmental defects. Peroxisomes are essential metabolic organelles, present in virtually every eukaryotic cell and mediating key processes in immunometabolism. To date, the full spectrum of PBDs remains to be identified, and the impact PBDs have on immune function is unexplored. This study presents a characterization of the hepatic immune compartment of a neonatal PBD mouse model at single-cell resolution to establish the importance and function of peroxisomes in developmental hematopoiesis. We report that hematopoietic defects are a feature in a severe PBD murine model. Finally, we identify a role for peroxisomes in the regulation of the major histocompatibility class II expression and antigen presentation to CD4+ T cells in dendritic cells. This study adds to our understanding of the mechanisms of PBDs and expands our knowledge of the role of peroxisomes in immunometabolism.

CD8+ T cell metabolic rewiring defined by scRNA-seq identifies a critical role of ASNS expression dynamics in T cell differentiation.

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.

Tumor vessel co-option probed by single-cell analysis.

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.

Integrating Proteomes for Lung Tissues and Lavage Reveals Pathways That Link Responses in Allergen-Challenged Mice.

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.

Integrator restrains paraspeckles assembly by promoting isoform switching of the lncRNA NEAT1.

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.

BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization.

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.

Single-Cell RNA Sequencing Maps Endothelial Metabolic Plasticity in Pathological Angiogenesis.

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 cell-cycle 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.

Single-Cell Transcriptome Atlas of Murine Endothelial Cells.

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.

An Integrated Gene Expression Landscape Profiling Approach to Identify Lung Tumor Endothelial Cell Heterogeneity and Angiogenic Candidates.

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.

Single-Cell RNA Sequencing Reveals Renal Endothelium Heterogeneity and Metabolic Adaptation to Water Deprivation.

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.

Fluorescence-based real time monitoring and diagnostics of recombinant Pichia pastoris cultivations in a bioreactor.

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.

Distinguishing Vaccinium species by chemical fingerprinting based on NMR spectra, validated with spectra collected in different laboratories.

A method was developed to distinguish Vaccinium species based on leaf extracts using nuclear magnetic resonance spectroscopy. Reference spectra were measured on leaf extracts from several species, including lowbush blueberry (Vaccinium angustifolium), oval leaf huckleberry (Vaccinium ovalifolium), and cranberry (Vaccinium macrocarpon). Using principal component analysis, these leaf extracts were resolved in the scores plot. Analysis of variance statistical tests demonstrated 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 discrimination between species. To demonstrate the robustness 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), different sample diameters (1.7 mm versus 5 mm), and different consoles (Avance I versus Avance III). Each laboratory independently demonstrated the linearity of their NMR measurements by acquiring a standard curve for chlorogenic acid (R(2) = 0.9782 to 0.9998). Spectra acquired on different spectrometers at different sites classifed into the expected group for the Vaccinium spp., confirming the utility of the method to distinguish Vaccinium species and demonstrating nuclear magnetic resonance fingerprinting for material validation of a natural health product.

Pre-symptomatic activation of antioxidant responses and alterations in glucose and pyruvate metabolism in Niemann-Pick Type C1-deficient murine brain.

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 (1)H-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 pre-symptomatic 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.

t-10, c-12 CLA dietary supplementation inhibits atherosclerotic lesion development despite adverse cardiovascular and hepatic metabolic marker profiles.

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<0.05, n = 11) and LDLr(-/-) mice (p<0.01, n = 10). Principal component analysis was utilized to delineate the influence of multiple plasma and tissue metabolites on the development of atherosclerosis. Group clustering by dietary supplementation was evident, with the t-10, c-12 CLA supplemented animals having distinct patterns, suggestive of hepatic insulin resistance, regardless of mouse strain. The effect of CLA supplementation on hepatic lipid and fatty acid composition was explored in the LDLr(-/-) strain. Dietary supplementation with t-10, c-12 CLA significantly increased liver weight (p<0.05, n = 10), triglyceride (p<0.01, n = 10) and cholesterol ester content (p<0.01, n = 10). Furthermore, t-10, c-12 CLA also increased the ratio of 18∶1 to 18∶0 fatty acid in the liver suggesting an increase in the activity of stearoyl-CoA desaturase. Changes in plasma adiponectin and liver weight with t-10, c-12 CLA supplementation were evident within 3 weeks of initiation of the diet. These observations provide evidence that the individual CLA isomers have divergent mechanisms of action and that t-10, c-12 CLA rapidly changes plasma and liver markers of metabolic syndrome, despite evidence of reduction in atherosclerosis.

Molecular characterization of zebrafish embryogenesis via DNA microarrays and multiplatform time course metabolomics studies.

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, (1)H nuclear magnetic resonance ((1)H 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.

Alterations in urinary metabolites due to unilateral ureteral obstruction in a rodent model.

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.

Analysis of time course 1H NMR metabolomics data by multivariate curve resolution.

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.

NMR metabolic analysis of samples using fuzzy K-means clustering.

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.

Characterization of the measurement error structure in 1D 1H NMR data for metabolomics studies.

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.