Lymphatic endothelial-like cells promote glioblastoma stem cell growth through cytokine-driven cholesterol metabolism.

Glioblastoma is the most lethal primary brain tumor with glioblastoma stem cells (GSCs) atop a cellular hierarchy. GSCs often reside in a perivascular niche, where they receive maintenance cues from endothelial cells, but the role of heterogeneous endothelial cell populations remains unresolved. Here, we show that lymphatic endothelial-like cells (LECs), while previously unrecognized in brain parenchyma, are present in glioblastomas and promote growth of CCR7-positive GSCs through CCL21 secretion. Disruption of CCL21-CCR7 paracrine communication between LECs and GSCs inhibited GSC proliferation and growth. LEC-derived CCL21 induced KAT5-mediated acetylation of HMGCS1 on K273 in GSCs to enhance HMGCS1 protein stability. HMGCS1 promoted cholesterol synthesis in GSCs, favorable for tumor growth. Expression of the CCL21-CCR7 axis correlated with KAT5 expression and HMGCS1K273 acetylation in glioblastoma specimens, informing patient outcome. Collectively, glioblastomas contain previously unrecognized LECs that promote the molecular crosstalk between endothelial and tumor cells, offering potentially alternative therapeutic strategies.

Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression.

Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.

Multi-omic profiling of the leukemic microenvironment shows bone marrow interstitial fluid is distinct from peripheral blood plasma.

BACKGROUND: The bone marrow is the place of hematopoiesis with a microenvironment that supports lifelong maintenance of stem cells and high proliferation. It is not surprising that this environment is also favourable for malignant cells emerging in the bone marrow or metastasizing to it. While the cellular composition of the bone marrow microenvironment has been extensively studied, the extracellular matrix and interstitial fluid components have received little attention. Since the sinusoids connect the bone marrow interstitial fluid to the circulation, it is often considered to have the same composition as peripheral blood plasma. Stark differences in the cellular composition of the bone marrow and peripheral blood with different secretory capacities would however suggest profound differences. METHODS: In this study we set out to better define if and how the bone marrow interstitial fluid (BMIF) compares to the peripheral blood plasma (PBP) and how both are remodeled during chemotherapy. We applied a multi-omic strategy to quantify the metabolite, lipid and protein components as well as the proteolytic modification of proteins to gain a comprehensive understanding of the two compartments. RESULTS: We found that the bone marrow interstitial fluid is clearly distinct from peripheral blood plasma, both during active pediatric acute lymphoblastic leukemia and following induction chemotherapy. Either compartment was shaped differently by active leukemia, with the bone marrow interstitial fluid being rich in extracellular vesicle components and showing protease dysregulation while the peripheral blood plasma showed elevation of immune regulatory proteins. Following chemotherapy, the BMIF showed signs of cellular remodeling and impaired innate immune activation while the peripheral blood plasma was characterized by restored lipid homeostasis. CONCLUSION: This study provides a comprehensive examination of the fluid portion of the acute lymphoblastic leukemia microenvironment and finds the contribution of either microenvironment to tumourigenesis.

Epigenetic aberrations of gene expression in a rat model of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is mostly triggered by environmental and life-style factors and may involve epigenetic aberrations. However, a comprehensive documentation of the link between the dysregulated epigenome, transcriptome, and liver carcinogenesis is lacking. In the present study, Fischer-344 rats were fed a choline-deficient (CDAA, cancer group) or choline-sufficient (CSAA, healthy group) L-amino acid-defined diet. At the end of 52 weeks, transcriptomic alterations in livers of rats with HCC tumours and healthy livers were investigated by RNA sequencing. DNA methylation and gene expression were assessed by pyrosequencing and quantitative reverse-transcription PCR (qRT-PCR), respectively. We discovered 1,848 genes that were significantly differentially expressed in livers of rats with HCC tumours (CDAA) as compared with healthy livers (CSAA). Upregulated genes in the CDAA group were associated with cancer-related functions, whereas macronutrient metabolic processes were enriched by downregulated genes. Changes of highest magnitude were detected in numerous upregulated genes that govern key oncogenic signalling pathways, including Notch, Wnt, Hedgehog, and extracellular matrix degradation. We further detected perturbations in DNA methylating and demethylating enzymes, which was reflected in decreased global DNA methylation and increased global DNA hydroxymethylation. Four selected upregulated candidates, Mmp12, Jag1, Wnt4, and Smo, demonstrated promoter hypomethylation with the most profound decrease in Mmp12. MMP12 was also strongly overexpressed and hypomethylated in human HCC HepG2 cells as compared with primary hepatocytes, which coincided with binding of Ten-eleven translocation 1 (TET1). Our findings provide comprehensive evidence for gene expression changes and dysregulated epigenome in HCC pathogenesis, potentially revealing novel targets for HCC prevention/treatment.

Serum integrative omics reveals the landscape of human diabetic kidney disease.

OBJECTIVE: Diabetic kidney disease (DKD) is the most common microvascular complication of type 2 diabetes mellitus (2-DM). Currently, urine and kidney biopsy specimens are the major clinical resources for DKD diagnosis. Our study proposes to evaluate the diagnostic value of blood in monitoring the onset of DKD and distinguishing its status in the clinic. METHODS: This study recruited 1,513 participants including healthy adults and patients diagnosed with 2-DM, early-stage DKD (DKD-E), and advanced-stage DKD (DKD-A) from 4 independent medical centers. One discovery and four testing cohorts were established. Sera were collected and subjected to training proteomics and large-scale metabolomics. RESULTS: Deep profiling of serum proteomes and metabolomes revealed several insights. First, the training proteomics revealed that the combination of α2-macroglobulin, cathepsin D, and CD324 could serve as a surrogate protein biomarker for monitoring DKD progression. Second, metabolomics demonstrated that galactose metabolism and glycerolipid metabolism are the major disturbed metabolic pathways in DKD, and serum metabolite glycerol-3-galactoside could be used as an independent marker to predict DKD. Third, integrating proteomics and metabolomics increased the diagnostic and predictive stability and accuracy for distinguishing DKD status. CONCLUSIONS: Serum integrative omics provide stable and accurate biomarkers for early warning and diagnosis of DKD. Our study provides a rich and open-access data resource for optimizing DKD management.

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics.

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.

DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data.

Existing data acquisition modes such as full-scan, data-dependent (DDA), and data-independent acquisition (DIA) often present limited capabilities in capturing metabolic information in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. In this work, we proposed a novel metabolomic data acquisition workflow that combines DDA and DIA analyses to achieve better metabolomic data quality, including enhanced metabolome coverage, tandem mass spectrometry (MS2) coverage, and MS2 quality. This workflow, named data-dependent-assisted data-independent acquisition (DaDIA), performs untargeted metabolomic analysis of individual biological samples using DIA mode and the pooled quality control (QC) samples using DDA mode. This combination takes advantage of the high-feature number and MS2 spectral coverage of the DIA data and the high MS2 spectral quality of the DDA data. To analyze the heterogeneous DDA and DIA data, we further developed a computational program, DaDIA.R, to automatically extract metabolic features and perform streamlined metabolite annotation of DaDIA data set. Using human urine samples, we demonstrated that the DaDIA workflow delivers remarkably improved data quality when compared to conventional DDA or DIA metabolomics. In particular, both the number of detected features and annotated metabolites were greatly increased. Further biological demonstration using a leukemia metabolomics study also proved that the DaDIA workflow can efficiently detect and annotate around 4 times more significant metabolites than DDA workflow with broad MS2 coverage and high MS2 spectral quality for downstream statistical analysis and biological interpretation. Overall, this work represents a critical development of data acquisition mode in untargeted metabolomics, which can greatly benefit untargeted metabolomics for a wide range of biological applications.

Evaluation of significant features discovered from different data acquisition modes in mass spectrometry-based untargeted metabolomics.

Despite the growing popularity of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics, no study has yet to systematically compare the performance of different data acquisition modes in the discovery of significantly altered metabolic features, which is an important task of untargeted metabolomics for identifying clinical biomarkers and elucidating disease mechanism in comparative samples. In this work, we performed a comprehensive comparison of three most commonly used data acquisition modes, including full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA), using a metabolomics study of human plasma samples from leukemia patients before and after one-month chemotherapy. After optimization of data processing parameters, we extracted and compared statistically significant metabolic features from the results of each data acquisition mode. We found that most significant features can be consistently found in all three data acquisition modes with similar statistical performance as evaluated by Pearson correlation and receiver operating characteristic (ROC) analysis. Upon comparison, DDA mode consistently generated fewer uniquely found significant features than full-scan and DIA modes. We then manually inspected over 2000 uniquely discovered significant features in each data acquisition mode and showed that these features can be generally categorized into four major types. Many significant features were missed in DDA mode, primarily due to its low capability of detecting or extracting these features from raw LC-MS data. We thus proposed a bioinformatic solution to rescue these missing significant features from the raw DDA data with good reproducibility and accuracy. Overall, our work asserts that data acquisition modes can influence metabolomics results, suggesting room for improvement of data acquisition modes for untargeted metabolomics.

Fold-Change Compression: An Unexplored But Correctable Quantitative Bias Caused by Nonlinear Electrospray Ionization Responses in Untargeted Metabolomics.

The nonlinear signal response of electrospray ionization (ESI) presents a critical limitation for mass spectrometry (MS)-based quantitative analysis. In the field of metabolomics research, this issue has largely remained unaddressed; MS signal intensities are usually directly used to calculate fold changes for quantitative comparison. In this work, we demonstrate that, due to the nonlinear ESI response, signal intensity ratios of a metabolic feature calculated between two samples may not reflect their real metabolic concentration ratios (i.e., fold-change compression), implying that conventional fold-change calculations directly using MS signal intensities can be misleading. In this regard, we developed a quality control (QC) sample-based signal calibration workflow to overcome the quantitative bias caused by the nonlinear ESI response. In this workflow, calibration curves for every metabolic feature are first established using a QC sample injected in serial injection volumes. The MS signals of each metabolic feature are then calibrated to their equivalent QC injection volumes for comparative analysis. We demonstrated this novel workflow in a targeted metabolite analysis, showing that the accuracy of fold-change calculations can be significantly improved. Furthermore, in a metabolomic comparison of the bone marrow interstitial fluid samples from leukemia patients before and after chemotherapy, an additional 59 significant metabolic features were found with fold changes larger than 1.5, and an additional 97 significant metabolic features had fold changes corrected by more than 0.1. This work enables high-quality quantitative analysis in untargeted metabolomics, thus providing more confident biological hypotheses generation.

Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population.

Triple negative breast cancer (TNBC) is a devastating cancer disease characterized by its poor prognosis, distinct metastatic patterns, and aggressive biological behavior. Research indicates that the prevalence and presentation of TNBC varies among races, with Asian TNBC patients more commonly presenting with large invasive tumors, high node positivity, and high histologic grade. In this work, we applied ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomics to discover metabolic signatures in Asian female TNBC patients. Serum samples from 31 TNBC patients and 31 healthy controls (CN) were involved in this study. A total of 2860 metabolic features were detected in the serum samples. Among them, 77 metabolites, whose levels were significantly different between TNBC with CN, were confirmed. Using multivariate statistical analysis, literature mining, metabolic network and pathway analysis, we performed an in-depth study of the metabolic alterations in the Asian TNBC population. In addition, we discovered a panel of metabolic signatures that are highly correlated with the 5-year survival rate of the TNBC patients. This metabolomic study provides a better understanding of the metabolic details of TNBC in the Asian population.

Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology.

Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer's disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.

Glioma Stem Cell-Specific Superenhancer Promotes Polyunsaturated Fatty-Acid Synthesis to Support EGFR Signaling.

Glioblastoma ranks among the most aggressive and lethal of all human cancers. Functionally defined glioma stem cells (GSC) contribute to this poor prognosis by driving therapeutic resistance and maintaining cellular heterogeneity. To understand the molecular processes essential for GSC maintenance and tumorigenicity, we interrogated the superenhancer landscapes of primary glioblastoma specimens and in vitro GSCs. GSCs epigenetically upregulated ELOVL2, a key polyunsaturated fatty-acid synthesis enzyme. Targeting ELOVL2 inhibited glioblastoma cell growth and tumor initiation. ELOVL2 depletion altered cellular membrane phospholipid composition, disrupted membrane structural properties, and diminished EGFR signaling through control of fatty-acid elongation. In support of the translational potential of these findings, dual targeting of polyunsaturated fatty-acid synthesis and EGFR signaling had a combinatorial cytotoxic effect on GSCs. SIGNIFICANCE: Glioblastoma remains a devastating disease despite extensive characterization. We profiled epigenomic landscapes of glioblastoma to pinpoint cell state-specific dependencies and therapeutic vulnerabilities. GSCs utilize polyunsaturated fatty-acid synthesis to support membrane architecture, inhibition of which impairs EGFR signaling and GSC proliferation. Combinatorial targeting of these networks represents a promising therapeutic strategy.See related commentary by Affronti and Wellen, p. 1161.This article is highlighted in the In This Issue feature, p. 1143.

[Efficacy and safety of Serenoa repens extract combined with α-receptor blocker in the treatment of benign prostatic hyperplasia].

OBJECTIVE: To evaluate the effect and safety of Serenoa repens extract (SR) combined with α-receptor blocker (αRB) in the treatment of BPH. METHODS: We included 7 published randomized controlled trials (RCT) studying the effect and safety of SR+αRB versus αRB monotherapy in the treatment of 1 009 BPH patients, and performed a meta-analysis on the data obtained using the RevMan 5.1.3 software. RESULTS: The baseline data from the RCTs were all comparable. Compared with the patients treated by αRB monotherapy, those of the SR+αRB group showed significant decreases in the total IPSS, sub-IPSS in the storage and voiding stages, quality of life score (QOL) and PSA level (all P < 0.05), an increase in the maximum urinary flow rate (Qmax) (P = 0.04), but no statistically significant differences in the prostate volume and postvoid residual urine volume (P > 0.05). CONCLUSIONS: Serenoa repens extract combined with α-receptor blocker is safe and effective, and even better than α-receptor blocker monotherapy, in the treatment of BPH.

Bretschneider solution-induced alterations in the urine metabolome in cardiac surgery patients.

The development of Bretschneider's histidine-tryptophan-ketoglutarate (HTK) cardioplegia solution represented a major advancement in cardiac surgery, offering significant myocardial protection. However, metabolic changes induced by this additive in the whole body have not been systematically investigated. Using an untargeted mass spectrometry-based method to deeply explore the urine metabolome, we sought to provide a holistic and systematic view of metabolic perturbations occurred in patients receiving HTK. Prospective urine samples were collected from 100 patients who had undergone cardiac surgery, and metabolomic changes were profiled using a high-performance chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) method. A total of 14,642 peak pairs or metabolites were quantified using differential 13C-/12C-dansyl labeling LC-MS, which targets the amine/phenol submetabolome from urine specimens. We identified 223 metabolites that showed significant concentration change (fold change > 5) and assembled several potential metabolic pathway maps derived from these dysregulated metabolites. Our data indicated upregulated histidine metabolism with subsequently increased glutamine/glutamate metabolism, altered purine and pyrimidine metabolism, and enhanced vitamin B6 metabolism in patients receiving HTK. Our findings provide solid evidence that HTK solution causes significant perturbations in several metabolic pathways and establish a basis for further study of key mechanisms underlying its organ-protective or potential harmful effects.

Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology.

Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics data sets. As a proof of concept, this multimodal analysis approach was applied to a colorectal cancer (CRC) study, in which complementary liquid chromatography-mass spectrometry (LC-MS) data were combined with proteomic and transcriptomic data. Our approach provided a highly resolved overview of colon cancer metabolic dysregulation, with an average 17% increase of detected dysregulated metabolites per pathway and an increase in metabolic pathway prediction confidence. Moreover, 95% of the altered metabolic pathways matched with the dysregulated genes and proteins, providing additional validation at a systems level. The analysis platform is currently available via the XCMS Online ( XCMSOnline.scripps.edu ).

Metabolomics Reveals that Dietary Xenoestrogens Alter Cellular Metabolism Induced by Palbociclib/Letrozole Combination Cancer Therapy.

Recently, the palbociclib/letrozole combination therapy was granted accelerated US FDA approval for the treatment of estrogen receptor (ER)-positive breast cancer. Since the underlying metabolic effects of these drugs are yet unknown, we investigated their synergism at the metabolome level in MCF-7 cells. As xenoestrogens interact with the ER, we additionally aimed at deciphering the impact of the phytoestrogen genistein and the estrogenic mycotoxin zearalenone. A global metabolomics approach was applied to unravel metabolite and pathway modifications. The results clearly showed that the combined effects of palbociclib and letrozole on cellular metabolism were far more pronounced than that of each agent alone and potently influenced by xenoestrogens. This behavior was confirmed in proliferation experiments and functional assays. Specifically, amino acids and central carbon metabolites were attenuated, while higher abundances were observed for fatty acids and most nucleic acid-related metabolites. Interestingly, exposure to model xenoestrogens appeared to counteract these effects.

Efficacy and safety of prostate artery embolization on lower urinary tract symptoms related to benign prostatic hyperplasia: a systematic review and meta-analysis.

BACKGROUND: Prostate artery embolization (PAE) is emerging and is a promising minimally invasive therapy that improves lower urinary tract symptoms (LUTS) related to benign prostatic hyperplasia (BPH). The purpose of this article was to evaluate the efficacy and safety of PAE on LUTS related to BPH. MATERIALS AND METHODS: A literature review was performed to identify all published articles of PAE for BPH. The sources included MEDLINE, EMBASE and Cochrane Library from 1980 to 2016. A systematic review and meta-analysis was conducted. The outcome measurements were combined by calculating the mean difference with 95% confidence interval. Statistical analysis was carried out using Review Manager 5.3.0. RESULTS: Twelve studies involving 840 participants were included. Compared with baseline, the International Index of Erectile Function (IIEF-5; International Prostate Symptom Score) scores, the quality of life scores, peak urinary flow rate (Qmax) and postvoid residual volume all had significant improvements during the 24-month follow-up (all P<0.00001). Both prostate volume (PV) and prostate-specific antigen had significant decrease during the 12-month follow-up (P<0.00001 and P=0.005, respectively), except postoperative 24 months (P=0.47 and P=0.32, respectively). The IIEF-5 short form scores had significant increase at postoperative 6 months (P=0.002) and 12 months (P<0.0001), except postoperative 1 month (P=0.23) and 24 months (P=0.21). For large volume (PV ≥80 mL) BPH, the results were similar. There were no life-threatening complications. CONCLUSION: PAE is an effective, safe and well-tolerable treatment for LUTS related to BPH, including large volume (PV ≥80 mL) BPH, with a good short-term follow-up. Studies with large number of cases and longer follow-up time are needed to validate our results.

Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer.

We report a method of metabolomic profiling of intact tissue based on molecular preservation by extraction and fixation (mPREF) and high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). mPREF extracts metabolites by aqueous methanol from tissue biopsies without altering tissue architecture and thus conventional histology can be performed on the same tissue. In a proof-of-principle study, we applied dansylation LC-MS to profile the amine/phenol submetabolome of prostate needle biopsies from 25 patient samples derived from 16 subjects. 2900 metabolites were consistently detected in more than 50% of the samples. This unprecedented coverage allowed us to identify significant metabolites for differentiating tumor and normal tissues. The panel of significant metabolites was refined using 36 additional samples from 18 subjects. Receiver Operating Characteristic (ROC) analysis showed area-under-the-curve (AUC) of 0.896 with sensitivity of 84.6% and specificity of 83.3% using 7 metabolites. A blind study of 24 additional validation samples gave a specificity of 90.9% at the same sensitivity of 84.6%. The mPREF extraction can be readily implemented into the existing clinical workflow. Our method of combining mPREF with CIL LC-MS offers a powerful and convenient means of performing histopathology and discovering or detecting metabolite biomarkers in the same tissue biopsy.

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses.

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.