Induced Pluripotent Stem Cells Show Metabolomic Differences to Embryonic Stem Cells in Polyunsaturated Phosphatidylcholines and Primary Metabolism

<div><p>Induced pluripotent stem cells are different from embryonic stem cells as shown by epigenetic and genomics analyses. Depending on cell types and culture conditions, such genetic alterations can lead to different metabolic phenotypes which may impact replication rates, membrane properties and cell differentiation. We here applied a comprehensive metabolomics strategy incorporating nanoelectrospray ion trap mass spectrometry (MS), gas chromatography-time of flight MS, and hydrophilic interaction- and reversed phase-liquid chromatography-quadrupole time-of-flight MS to examine the metabolome of induced pluripotent stem cells (iPSCs) compared to parental fibroblasts as well as to reference embryonic stem cells (ESCs). With over 250 identified metabolites and a range of structurally unknown compounds, quantitative and statistical metabolome data were mapped onto a metabolite networks describing the metabolic state of iPSCs relative to other cell types. Overall iPSCs exhibited a striking shift metabolically away from parental fibroblasts and toward ESCs, suggestive of near complete metabolic reprogramming. Differences between pluripotent cell types were not observed in carbohydrate or hydroxyl acid metabolism, pentose phosphate pathway metabolites, or free fatty acids. However, significant differences between iPSCs and ESCs were evident in phosphatidylcholine and phosphatidylethanolamine lipid structures, essential and non-essential amino acids, and metabolites involved in polyamine biosynthesis. Together our findings demonstrate that during cellular reprogramming, the metabolome of fibroblasts is also reprogrammed to take on an ESC-like profile, but there are select unique differences apparent in iPSCs. The identified metabolomics signatures of iPSCs and ESCs may have important implications for functional regulation of maintenance and induction of pluripotency.</p> </div

Lipidomic analysis of pluripotent cell lines (iPSC, mESC) and mouse embryonic fibroblasts (mEF) using nanoelectrospray-linear ion trap MS/MS.

<p><u>Upper panel</u>: unsupervised Principal Component analysis (left) and supervised Partial Least Square regression analysis (right). Multivariate vectors with percent total variance explained. <u>Lower panel</u>: examples of differentially regulated membrane lipids. PC = phosphatidylcholines, PE = phosphatidylethanolamine, with number of carbons followed by number of double bonds. Mean ion intensities ± standard errors (boxes) and non-outlier ranges (whiskers).</p

Annotation of complex lipids by tandem mass spectrometry (MS/MS) and the LipidBlast mass spectral library.

<p>LipidBlast MS/MS spectra were modeled from fragmentation spectra of authentic reference standards using computational scaffolds that altered acyl chain lengths and degree of unsaturation for each lipid class. Shown here as example is the annotation of the experimental MS/MS spectrum of arachidonyl-palmitoyl phosphatidylcholine (PC 36:4) by matching major precursor and fragment ions as well as low abundant fragments between m/z 478–599 using LipidBlast.</p

MetaMapp visualization of metabolic changes in stem cells relative to m15 fibroblast cells.

<p>Red nodes represent metabolites with increased signal intensity in stem cells; blue nodes represent metabolites with decreased signal intensity in stem cells (p<0.05). White nodes represent detected metabolites without statistically significant changes. Node sizes scale with fold change. Blue edges connect metabolites with Tanimoto chemical similarity >700; red edges connect reaction-pair metabolites from the KEGG RPAIR database. (<b>A</b>): MetaMapp network comparing mESCs to m15 fibroblasts. (<b>B</b>): MetaMapp network comparing mESCs to m15 fibroblasts.</p

MetaMapp visualization of metabolic changes in mouse embryonic stem cells relative to metabolite levels in induced pluripotent stem cells.

<p>Red nodes represent metabolites with increased signal intensity in stem cells; blue nodes represent metabolites with decreased signal intensity in stem cells (p<0.05). White nodes represent detected metabolites without statistically significant changes. Node sizes scale with fold change. Blue edges connect metabolites with Tanimoto chemical similarity >700; red edges connect reaction-pair metabolites from the KEGG RPAIR database.</p

compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC–MS Data Sets

A long-standing challenge of untargeted metabolomic profiling by ultrahigh-performance liquid chromatography–high-resolution mass spectrometry (UHPLC–HRMS) is efficient transition from unknown mass spectral features to confident metabolite annotations. The <i>compMS</i>²<i>Miner</i> (Comprehensive MS² Miner) package was developed in the R language to facilitate rapid, comprehensive feature annotation using a peak-picker-output and MS² data files as inputs. The number of MS² spectra that can be collected during a metabolomic profiling experiment far outweigh the amount of time required for pain-staking manual interpretation; therefore, a degree of software workflow autonomy is required for broad-scale metabolite annotation. <i>CompMS</i>²<i>Miner</i> integrates many useful tools in a single workflow for metabolite annotation and also provides a means to overview the MS² data with a Web application GUI <i>compMS</i>²<i>Explorer</i> (Comprehensive MS² Explorer) that also facilitates data-sharing and transparency. The automatable <i>compMS</i>²<i>Miner</i> workflow consists of the following steps: (i) matching unknown MS¹ features to precursor MS² scans, (ii) filtration of spectral noise (dynamic noise filter), (iii) generation of composite mass spectra by multiple similar spectrum signal summation and redundant/contaminant spectra removal, (iv) interpretation of possible fragment ion substructure using an internal database, (v) annotation of unknowns with chemical and spectral databases with prediction of mammalian biotransformation metabolites, wrapper functions for <i>in silico</i> fragmentation software, nearest neighbor chemical similarity scoring, random forest based retention time prediction, text-mining based false positive removal/true positive ranking, chemical taxonomic prediction and differential evolution based global annotation score optimization, and (vi) network graph visualizations, data curation, and sharing are made possible via the <i>compMS</i>²<i>Explorer</i> application. Metabolite identities and comments can also be recorded using an interactive table within <i>compMS</i>²<i>Explorer</i>. The utility of the package is illustrated with a data set of blood serum samples from 7 diet induced obese (DIO) and 7 nonobese (NO) C57BL/6J mice, which were also treated with an antibiotic (streptomycin) to knockdown the gut microbiota. The results of fully autonomous and objective usage of <i>compMS</i>²<i>Miner</i> are presented here. All automatically annotated spectra output by the workflow are provided in the Supporting Information and can alternatively be explored as publically available <i>compMS</i>²<i>Explorer</i> applications for both positive and negative modes (https://wmbedmands.shinyapps.io/compMS2_mouseSera_POS and https://wmbedmands.shinyapps.io/compMS2_mouseSera_NEG). The workflow provided rapid annotation of a diversity of endogenous and gut microbially derived metabolites affected by both diet and antibiotic treatment, which conformed to previously published reports. Composite spectra (<i>n</i> = 173) were autonomously matched to entries of the Massbank of North America (MoNA) spectral repository. These experimental and virtual (lipidBlast) spectra corresponded to 29 common endogenous compound classes (e.g., 51 lysophosphatidylcholines spectra) and were then used to calculate the ranking capability of 7 individual scoring metrics. It was found that an average of the 7 individual scoring metrics provided the most effective weighted average ranking ability of 3 for the MoNA matched spectra in spite of potential risk of false positive annotations emerging from automation. Minor structural differences such as relative carbon–carbon double bond positions were found in several cases to affect the correct rank of the MoNA annotated metabolite. The latest release and an example workflow is available in the package vignette (https://github.com/WMBEdmands/compMS2Miner) and a version of the published application is available on the shinyapps.io site (https://wmbedmands.shinyapps.io/compMS2Example)

Pharmacometabolomic Signature of Ataxia SCA1 Mouse Model and Lithium Effects

<div><p>We have shown that lithium treatment improves motor coordination in a spinocerebellar ataxia type 1 (SCA1) disease mouse model (<i>Sca1^154Q/+</i>). To learn more about disease pathogenesis and molecular contributions to the neuroprotective effects of lithium, we investigated metabolomic profiles of cerebellar tissue and plasma from SCA1-model treated and untreated mice. Metabolomic analyses of wild-type and <i>Sca1^154Q/+</i> mice, with and without lithium treatment, were performed using gas chromatography time-of-flight mass spectrometry and BinBase mass spectral annotations. We detected 416 metabolites, of which 130 were identified. We observed specific metabolic perturbations in <i>Sca1^154Q/+</i> mice and major effects of lithium on metabolism, centrally and peripherally. Compared to wild-type, <i>Sca1^154Q/+</i> cerebella metabolic profile revealed changes in glucose, lipids, and metabolites of the tricarboxylic acid cycle and purines. Fewer metabolic differences were noted in <i>Sca1^154Q/+</i> mouse plasma versus wild-type. In both genotypes, the major lithium responses in cerebellum involved energy metabolism, purines, unsaturated free fatty acids, and aromatic and sulphur-containing amino acids. The largest metabolic difference with lithium was a 10-fold increase in ascorbate levels in wild-type cerebella (p<0.002), with lower threonate levels, a major ascorbate catabolite. In contrast, <i>Sca1^154Q/+</i> mice that received lithium showed no elevated cerebellar ascorbate levels. Our data emphasize that lithium regulates a variety of metabolic pathways, including purine, oxidative stress and energy production pathways. The purine metabolite level, reduced in the <i>Sca1^154Q/+</i> mice and restored upon lithium treatment, might relate to lithium neuroprotective properties.</p></div

Box-and-whisker plots: genotype-dependent metabolites in cerebellum tissue with significant differences between lithium and controls (<i>p</i><0.05).

<p><b>A.</b> Box-whisker plots for selected significantly regulated metabolites. <b>B.</b> Box-whisker plots for significantly regulated metabolites of purine metabolism pathway. The whiskers encompass 1.5 of the interquartile range (IQR). Median value is indicated with a line in the box. Boxes are filled in color (dark grey: SCA1 knock-in; light grey: wild-type) when the samples are statistically different between the two lithium treatments. Abbreviations: Ctl, Control; KI, SCA1 knock-in; Li, Lithium; WT, Wild-type.</p

Effect of lithium treatment on cerebellum metabolome.

<p>Metabolic network of wild-type and <i>Sca1^154Q/+</i> cerebellum phenotypes. <b>A.</b> Wild-type mice. <b>B.</b> SCA1 knock-in mice. Red nodes: Increased metabolite levels under Lithium treatment; blue nodes: decreased levels. Node shades indicate ANOVA significance levels, node size reflect differences in magnitude of regulation. Red lines: reactant pair relationships obtained from the KEGG reaction pair database. Yellow solid lines: chemical similarity >0.5 Tanimoto score (Tanimoto scores range between 0 to 1, where 1 reflects identical structures). Yellow broken lines: chemically closest structure at <0.5 Tanimoto scores. Green circles group significant compounds that changed only in the Wild-type genotype. Orange circles group significant compounds that changed in both genotypes.</p

Effect of introducing the <i>Sca1^154Q/+</i> gene into the wild-type genetic background for plasma and cerebellum.

<p>Individual box-whisker plots for selected significantly regulated metabolites. The whiskers encompass 1.5 of the interquartile range (IQR). Median value is indicated with a line in the box. The confidence diamonds indicate average values when the two samples are statistically different (colored boxplots, red for blood and grey for brain). Abbreviations: KI, SCA1 knock-in; WT, Wild-type.</p