Viewing zone enlargement method for holographic displays based on the slanted pixel arrangement on a spatial light modulator.

This study proposes a method for enlarging the viewing zone of holographic displays using the slanted arrangement of pixels on a spatial light modulator (SLM). The pixel arrangement equivalently reduces the horizontal pixel pitch, which enlarges the horizontal viewing zone of displays. Computer-generated holograms (CGHs) were calculated using an asymmetric band-limit filter corresponding to the asymmetric bandwidth of the SLM with slanted pixels. The proposed methods were evaluated through an optical reconstruction experiment using static holograms with a pixel size of 1×1µm, fabricated via electron-beam lithography. The enlarged horizontal viewing zone angle was found to be 41.6°.

Magneto-optical spatial light modulator driven by current-induced domain wall motion for holographic display applications.

We have developed a magneto-optical spatial light modulator (MO-SLM) with a 10 k × 5 k pixel layout and with a pixel pitch horizontally of 1 µm and vertically of 4 µm. An MO-SLM device pixel has a magnetic nanowire made of Gd-Fe magneto-optical material whose magnetization was reversed by current-induced magnetic domain wall motion. We successfully demonstrated the reconstruction of holographic images, showing large viewing zone angles as wide as 30 degrees and visualizing different depths of the objects. These characteristics are unique to holographic images, providing physiological depth cues which may play a vital role in three-dimensional (3D) perception.

Image quality assessment procedure for holographic displays based on exact numerical reconstruction of computer-generated holograms.

We proposed a technique for the computer-based reconstruction of computer-generated holograms and evaluation of the reconstructed 3D image quality. The proposed method mimics how the eye's lens works, thus allowing for viewing position and eye focus adjustments. The angular resolution of the eye was used to output reconstructed images with the requisite resolution, and a reference object was used to normalize the images. Such data processing enables the numerical analysis of image quality. By comparing the reconstructed images with the original image with incoherent illumination, the image quality was quantitatively evaluated.

GlycanAnalysis Plug-in: a database search tool for N-glycan structures using mass spectrometry.

UNLABELLED: Tandem mass spectrometry (MS/MS or MS(n)) is a potent technique for characterizing N-glycan structures. GlycanAnalysis searches a glycan database to support the identification of glycan structures from MS/MS spectra. It also calculates diagnostic ions of glycan structures registered in a glycan database (GlycomeDB or KEGG GLYCAN) and searches for MS/MS spectra of N-glycans that match diagnostic ions to determine the structures. This program functions as a plug-in for Mass++, a freeware mass spectrum visualization and analysis program. AVAILABILITY AND IMPLEMENTATION: The executable files of Mass++ are available for free at http://www.first-ms3d.jp/english/. The GlycanAnalysis plug-in is included in the standard package of Mass++ for Windows. CONTACT: k-morimt@shimadzu.co.jp or nishikaz@shimadzu.co.jp or acyshzw@shimadzu.co.jp SUPPLEMENTARY INFORMATION: Supplementary material are available at Bioinformatics online.

A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry.

BACKGROUND: Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study. RESULTS: We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools. CONCLUSIONS: AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Mass++: A Visualization and Analysis Tool for Mass Spectrometry.

We have developed Mass++, a plug-in style visualization and analysis tool for mass spectrometry. Its plug-in style enables users to customize it and to develop original functions. Mass++ has several kinds of plug-ins, including rich viewers and analysis methods for proteomics and metabolomics. Plug-ins for supporting vendors' raw data are currently available; hence, Mass++ can read several data formats. Mass++ is both a desktop tool and a software development platform. Original functions can be developed without editing the Mass++ source code. Here, we present this tool's capability to rapidly analyze MS data and develop functions by providing examples of label-free quantitation and implementing plug-ins or scripts. Mass++ is freely available at http://www.first-ms3d.jp/english/ .

Eribulin mesylate reduces tumor microenvironment abnormality by vascular remodeling in preclinical human breast cancer models.

Eribulin mesylate is a synthetic macrocyclic ketone analog of the marine sponge natural product halichondrin B and an inhibitor of microtubule dynamics. Some tubulin-binding drugs are known to have antivascular (antiangiogenesis or vascular-disrupting) activities that can target abnormal tumor vessels. Using dynamic contrast-enhanced MRI analyses, here we show that eribulin induces remodeling of tumor vasculature through a novel antivascular activity in MX-1 and MDA-MB-231 human breast cancer xenograft models. Vascular remodeling associated with improved perfusion was shown by Hoechst 33342 staining and by increased microvessel density together with decreased mean vascular areas and fewer branched vessels in tumor tissues, as determined by immunohistochemical staining for endothelial marker CD31. Quantitative RT-PCR analysis of normal host cells in the stroma of xenograft tumors showed that eribulin altered the expression of mouse (host) genes in angiogenesis signaling pathways controlling endothelial cell-pericyte interactions, and in the epithelial-mesenchymal transition pathway in the context of the tumor microenvironment. Eribulin also decreased hypoxia-associated protein expression of mouse (host) vascular endothelial growth factor by ELISA and human CA9 by immunohistochemical analysis. Prior treatment with eribulin enhanced the anti-tumor activity of capecitabine in the MDA-MB-231 xenograft model. These findings suggest that eribulin-induced remodeling of abnormal tumor vasculature leads to a more functional microenvironment that may reduce the aggressiveness of tumors due to elimination of inner tumor hypoxia. Because abnormal tumor microenvironments enhance both drug resistance and metastasis, the apparent ability of eribulin to reverse these aggressive characteristics may contribute to its clinical benefits.

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer's Disease Neuroimaging Initiative cohort.

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10-8 and 4.3 × 10-7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

Relationship between olfactory and gustatory functions: The Iwaki health promotion project 2019.

OBJECTIVE: Olfactory and gustatory functions are important sensory aspects in humans. Although they are believed to influence each other, their interrelationship is not well understood. In this study, we aimed to investigate the relationship between the olfactory and gustatory functions based on the results of a large-scale epidemiological study (Iwaki Health Promotion Project) of the general local population. METHODS: We analyzed 565 participants who underwent taste and olfactory tests in the 2019 Iwaki Project. Gustatory function was tested for four taste qualities (sweet, sour, salty, and bitter) using whole-mouth taste tests. Olfactory function was tested using the University of Pennsylvania Smell Identification Test modified for Japanese (UPSIT-J). We evaluated sex-related differences between olfactory and gustatory functions and the effects of various factors on olfactory identification using multivariate analysis. Furthermore, we compared the percentage of accurate UPSIT-J responses between the normal and hypogeusia groups. We also analyzed the effects of taste and olfactory functions on eating. RESULTS: Olfactory and gustatory functions were lower in men than in women. Among the four taste qualities, salty taste was the most closely associated with olfactory identification ability, with lower olfactory scores of salty taste in the hypogeusia group than in the normal group. Moreover, the hyposmia group had higher daily salt intake than the normal olfaction group in women. CONCLUSION: These results suggest that olfactory identification tests may be useful in predicting elevated salt cognitive thresholds, leading to a reduction in salt intake, which may contribute to hypertension prevention.

Prediction of relaxin-3-induced downstream pathway resulting in anxiolytic-like behaviors in rats based on a microarray and peptidome analysis.

The effect of the intracerebroventricular (i.c.v.) injection of relaxin-3 (RLX3) was evaluated using anxiety-related behavioral tests in rats. RLX3-injected animals showed normal locomotion activity in a habituated environment and declined anxiety cognition in the elevated plus maze test and the shock probe-burying test. The measurement of spontaneous locomotor activity in a novel environment also suggested that RLX3 reduced the stress response. To elucidate the regulatory mechanisms of the downstream signaling pathways underlying RLX3 activity and its relation to anxiolytic and hyperphagic behavior phenotypes, RLX3-i.c.v.-injected rat hypothalamic responses were examined using a microarray analysis. Ingenuity Pathway Analysis software listed the phenotype-relating genes and they showed characteristic expression patterns in the rat hypothalamus. When peptidome data sets for the same listed genes was analyzed using a semi-quantitative approach, the expressions of two neuropeptides were found to have increased. One of these neuropeptides, oxytocin (Oxt), exhibited increased expression in both the microarray and the peptidomic analysis, and a Western blot analysis validated the mass spectrometry results. A cross-omics data analysis is useful for predicting downstream signaling pathways, and the anxiolytic-like behavior of RLX3 may be mediated by an oxytocin signaling pathway in rats. These results suggest that RLX3 acts as an anxiolytic peptide and that the downstream pathways mediated by its receptors may be potential candidates for the treatment of anxieties in the future.

Nationwide Database Analysis of Risk Factors Associated with Decreased Activities of Daily Living in Patients with Alzheimer's Disease.

BACKGROUND: Preserving activities of daily living (ADL) is the key issue for Alzheimer's disease (AD) patients and their caregivers. OBJECTIVE: To clarify the ADL level of AD patients at diagnosis and the risk factors associated with decreased ADL during long-term care (≤3 years). METHODS: Medical records of AD patients in a Japanese health insurance claims database were analyzed retrospectively to determine ADL using the Barthel Index (BI) and identify the risk factors associated with decreased ADL. RESULTS: A total of 16,799 AD patients (mean age at diagnosis: 83.6 years, 61.5% female) were analyzed. Female patients were older (84.6 versus 81.9 years; p < 0.001) and had lower BI (46.8 versus 57.6; p < 0.001) and body mass index (BMI) (21.0 versus 21.7 kg/m2; p < 0.001) than male patients at diagnosis. Disability (BI≤60) increased at age≥80 years and was significantly higher in females. Complete disability was most frequent for bathing and grooming. Risk factors for decreased ADL were determined separately by sex through comparing the ADL-preserved and ADL-decreased groups using propensity score matching by age and BI and multivariable logistic regression analysis. In males, decreased ADL was significantly associated with BMI < 21.5 kg/m2, stroke, and hip fracture, and inversely associated with hyperlipidemia. In females, decreased ADL was significantly associated with BMI < 21.5 kg/m2 and vertebral and hip fractures, and inversely associated with lower back pain. CONCLUSION: AD patients with low BMI, stroke, and fractures had increased risks of decreased ADL; such patients should be identified early and managed appropriately, including rehabilitation to preserve ADL.

Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors.

BACKGROUND: Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer's disease or the clinical setting. We developed machine learning models using objectively measured lifestyle factors to predict elevated brain amyloid burden on positron emission tomography. METHODS: Our prospective cohort study of non-demented, community-dwelling older adults aged ≥ 65 years was conducted from August 2015 to September 2019 in Usuki, Oita Prefecture, Japan. One hundred and twenty-two individuals with mild cognitive impairment or subjective memory complaints (54 men and 68 women, median age: 75.50 years) wore wearable sensors and completed self-reported questionnaires, cognitive test, and positron emission tomography imaging at baseline. Moreover, 99 individuals in the second year and 61 individuals in the third year were followed up. In total, 282 eligible records with valid wearable sensors, cognitive test results, and amyloid imaging and data on demographic characteristics, living environments, and health behaviors were used in the machine learning models. Amyloid positivity was defined as a standardized uptake value ratio of ≥ 1.4. Models were constructed using kernel support vector machine, Elastic Net, and logistic regression for predicting amyloid positivity. The mean score among 10 times fivefold cross-validation repeats was utilized for evaluation. RESULTS: In Elastic Net, the mean area under the receiver operating characteristic curve of the model using objectively measured lifestyle factors alone was 0.70, whereas that of the models using wearable sensors in combination with demographic characteristics and health and life environment questionnaires was 0.79. Moreover, 22 variables were common to all machine learning models. CONCLUSION: Our machine learning models are useful for predicting elevated brain amyloid burden using readily-available and noninvasive variables without the need to visit a hospital. TRIAL REGISTRATION: This prospective study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee of Oita University Hospital (UMIN000017442). A written informed consent was obtained from all participants. This research was performed based on the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.

Identification of a new plasma biomarker of Alzheimer's disease using metabolomics technology.

We performed unbiased analysis of steroid-related compounds to identify novel Alzheimer's disease (AD) plasma biomarkers using liquid chromatography-atmospheric pressure chemical ionization-mass spectroscopy. The analysis revealed that desmosterol was found to be decreased in AD plasma versus controls. To precisely quantify variations in desmosterol, we established an analytical method to measure desmosterol and cholesterol. Using this LC-based method, we discovered that desmosterol and the desmosterol/cholesterol ratio are significantly decreased in AD. Finally, the validation of this assay using 109 clinical samples confirmed the decrease of desmosterol in AD as well as a change in the desmosterol/cholesterol ratio in AD. Interestingly, we could also observe a difference between mild cognitive impairment and control. In addition, the decrease of desmosterol was somewhat more significant in females. Receiver operating characteristic (ROC) analysis between controls and AD, using plasma desmosterol shows a score of 0.80, indicating a good discrimination power for this marker in the two reference populations and confirms the potential usefulness of measuring plasma desmosterol levels for diagnosing AD. Further analysis showed a significant correlation of plasma desmosterol with Mini-Mental State Examination scores. Although larger sample populations will be needed to confirm this diagnostic marker sensitivity, our studies demonstrate a sensitive and accurate method of detecting plasma desmosterol concentration and suggest that plasma desmosterol could become a powerful new specific biomarker for early and easy AD diagnosis.

Nationwide database analysis of insomnia, depression, and sleeping pill prescriptions in hepatocellular carcinoma patients.

OBJECTIVE: To clarify the status of insomnia and depression and the prescription of sleeping pills in hepatocellular carcinoma (HCC) patients before and after HCC diagnosis and treatment. METHODS: Patients' data from a Japanese health insurance claims database were analyzed retrospectively to determine the incidence of insomnia and depression and their association with sleeping pill prescriptions during the 6 months before and after HCC diagnosis and treatment. RESULTS: A total of 9,109 HCC patients (median age at diagnosis = 71.5 years, 68.1% male) were analyzed. The incidences of insomnia and depression increased significantly after HCC diagnosis. Insomnia was reported in 15.0% of patients before diagnosis, and it increased to 27.6% after diagnosis. Similarly, depression was reported in 6.3% and 11.3% before and after diagnosis, respectively. The incidences of insomnia and depression before diagnosis were higher in patients with concomitant liver diseases including hepatitis, cirrhosis, and hepatic encephalopathy. However, the rate of sleeping pill prescription was significantly lower in patients with concomitant liver diseases after diagnosis. The incidence of fracture was higher in insomnia or depression patients than others and in patients treated with sleeping pills than without before and after diagnosis. CONCLUSIONS: HCC patients had increased risks of insomnia and depression after diagnosis. The high risk of fracture in HCC patients with insomnia and depression and treated with sleeping pills suggests that it is difficult to optimize the management of HCC patients, especially those with concomitant liver diseases.

A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique.

Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose of base feature extraction, wake-sleep and rapid eye movement (REM) and non- rapid eye movement (NREM) models were developed by extracting defining features from mouse-derived electromyogram (EMG) and electroencephalogram (EEG) signals, respectively. The wake-sleep model and REM-NREM sleep model were integrated into three different algorithms including a rule-based integration approach, an ensemble stacking approach, and a multimodal with fine-tuning approach. The deep learning algorithm assessing sleep stages in animal experiments by the multimodal with fine-tuning approach showed high potential for increasing accuracy in sleep stage scoring in mice and promoting sleep research.

Real-world database analysis of the characteristics and treatment patterns of patients with endometrial cancer in Japan.

OBJECTIVE: The aim was to identify the characteristics and treatment patterns of early and advanced stage endometrial cancer patients using real-world data. METHODS: Patients' data extracted from a Japanese health insurance claims database were analyzed. RESULTS: Of the 12,449 endometrial cancer patients, 74.4% were in stage I, 5.1% in stage II, 12.0% in stage III, and 8.4% in stage IV. Their median age was 60.5 years, higher in advanced stages (III/IV) than in early stages (I/II). Overall, 11,055 patients (88.8%) underwent surgery, and 4977 patients (40.0%) received post-surgery treatment, including chemotherapy (4441: 35.7%), chemoradiation therapy (379: 3.0%), and radiation therapy (157 patients: 1.3%); 1394 patients (11.2%) were not treated by surgery, and 742 patients (6.0%) received other treatment, with chemotherapy (548: 4.4%), radiation therapy (105: 0.8%), and chemoradiation therapy (89: 0.7%). The rate of patients undergoing surgery decreased, and that receiving chemotherapy increased significantly as cancer stage progressed. Paclitaxel/carboplatin was the most frequent first-line regimen (85.4% of patients), whereas various combination and monotherapy regimens were used as second- and third-line regimens. The most frequent second-line monotherapy was paclitaxel. The rate of monotherapy increased as the treatment line progressed (first-line 3.5%, second-line 22.0%, and third-line 36.4%). CONCLUSIONS: The characteristics and treatment patterns of endometrial cancer patients differed between early and advanced stages, as did the chemotherapy regimens among first-, second-, and third-lines. Since various regimens were used for second- and third-line chemotherapies, development of appropriate second- and third-line chemotherapy regimens is warranted. A real-world analysis of cancer patients using a nationwide claims database may be a valuable approach to identifying unmet medical needs.

Quantitative and wide-ranging profiling of phospholipids in human plasma by two-dimensional liquid chromatography/mass spectrometry.

Normal-phase or reverse-phase liquid chromatography has been used in phospholipidomics for lipid separation prior to mass spectrometry analysis. However, separation using a single separation mode is often inadequate, as high-abundance phospholipids can mask large numbers of low-abundance lipids of interest. In order to detect and quantify low-abundance phospholipids, we present a novel two-dimensional (2D) approach for sensitive and quantitative global analysis of phospholipids. The methodology monitors individual glycerolipids and phospholipids through the use of a new quantitative normal-phase, solid-phase extraction procedure, followed by molecular characterization and relative quantification using an ion-trap Orbitrap equipped with a reverse-phase liquid chromatograph, with data processing by MS++ software. The CV (%) of the peak area of each lipid standard was less than 15% with this extraction method. When the method was applied to a liver sample, we could detect more phosphatidylserine (PS) compared to the previous method. Finally, our developed method was applied to Alzheimer's disease (AD) plasma samples. Several hundred peaks were detected from a 60 µL plasma sample. A partial-least-squares discriminant analysis (PLS-DA) plot using peak area ratio gave a unique group of PLS scores which could distinguish plasma samples of Alzheimer's disease (AD) patients from those of age-matched healthy controls.

Polar anionic metabolome analysis by nano-LC/MS with a metal chelating agent.

We have developed a practical method for the comprehensive analysis of polar anionic metabolites in biological samples with the use of a nano-LC/MS system. A polyamine-bonded polymer-based apHera NH2 column, which is compatible with ammonium carbonate buffer, effectively retained anionic polar metabolites, such as organic acids, sulfates, and phosphates, but multiply phosphorylated or carboxylated compounds showed highly distorted peak shapes on chromatograms. We found that addition of a trace amount of the metal chelating reagent ethylenediaminetetraacetic acid (EDTA) to the sample solution dramatically improved peak shapes of multiply charged anionic compounds, even though the mass spectra showed no trace of adduct ions in the absence of EDTA. The detection limits of typical polar anionic metabolites in the full-scan mode were from 0.19 to 2.81 pmol. After optimization of all the procedures from sample preparation to nano-LC/MS analysis, we applied our method to real biological samples: Hela cells, mouse brain, human cerebrospinal fluid (CSF), and human plasma. Our results indicated that phosphorylated metabolites were abundant in Hela cells and brain, while plasma and cerebrospinal fluid (CSF) mostly contained organic acids. Phosphorylated compounds might not be secreted into CSF/plasma or might be unstable in CSF/plasma. Finally, the method was used to examine the mode of action of the anticancer drug methotrexate (MTX), which inhibits purine de novo biosynthesis and thymidine biosynthesis. In addition of the expected changes of metabolite levels, we found that a previously unreported metabolite, probably a methylated uridine 5'-triphosphate (UTP), was produced by MTX-treated Hela cells.

Quantitative profiling of polar cationic metabolites in human cerebrospinal fluid by reversed-phase nanoliquid chromatography/mass spectrometry.

Reversed-phase (RP) nanoliquid chromatography (LC)/mass spectrometry (MS) is widely used for proteome analysis, but hydrophilic metabolites are poorly retained on RP columns. We describe here the development and application of an efficient, robust, and quantitative nano-LC/MS method for cationic metabolome analysis in the positive ionization mode without any derivatization of analytes. Various stationary phases for nano-LC, coating of the internal wall of the capillary column, and various mobile phases were evaluated in terms of separation and peak shapes for 33 hydrophilic metabolites, including nonderivatized amino acids. Polar cationic compounds were strongly bound to mixed-functional RP with cation exchange mode resin, and the best separation was obtained with hydrophilic internal wall coating and a two-step trifluoroacetic acid (TFA) gradient in methanol as the mobile phase. Simple, but optimized, sample processing and the use of a high content of methanol allowed robust nano-LC/MS analysis. Our developed method was applied for biomarker discovery in Alzheimer's disease (AD). Several hundred peaks were detected from 10 microL of cerebrospinal fluid (CSF). In a principal component analysis (PCA) plot using peak intensities without normalization, peak separation depended on the experimental date, not disease state. Therefore, constant amounts of two stable isotope-labeled amino acids, Val and Lys, were added as internal standards (ISs) to each sample before processing. These ISs were eluted in different gradient slopes in the two-step gradient, and the normalized peak ratios using the corresponding ISs gave a unique group of PCA scores which could distinguish AD CSF samples from age-matched control CSF samples.

Quantitative phosphorus metabolomics using nanoflow liquid chromatography-tandem mass spectrometry and culture-derived comprehensive global internal standards.

Highly sensitive and quantitative analytical methods are essential for metabolomics. In this report, we introduce an analytical method focused on endogenous phosphorus metabolites, using nanoflow liquid chromatography-electrospray ionization tandem mass spectrometry (nanoLC-ESI-MS/MS) and culture-derived isotope-tagged metabolites as global internal standards for quantitative metabolomics. The nanoLC-ESI-MS/MS method employing a stone-arch microcolumn with amino propyl silica gel achieved good separation of phosphorus metabolites with forty- to hundred-fold increase of sensitivity compared with semimicro flow LC-ESI-MS. The quantitative reproducibility of the nanoLC-ESI-MS has been improved to the point where it is useful for studies of cellular metabolism. Focused metabolomics using culture-derived internal standards was employed to monitor 184 phosphorus-related metabolic changes in cancer cells treated with metabolic enzyme inhibitors, methotrexate, fluorouracil, and gemcitabine. We found marked perturbations of cellular metabolism, of which many, though not all, were in line with the known biological activities of these drugs.