Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies

Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz 1H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.     

Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study

Large-scale population phenotyping for molecular epidemiological studies is subject to all the usual criteria of analytical chemistry. As part of a major phenotyping investigation we have used high-resolution 1H NMR spectroscopy to characterize 24-h urine specimens obtained from population samples in Aito Town, Japan (n = 259), Chicago, IL (n = 315), and Guangxi, China (n = 278). We have investigated analytical reproducibility, urine specimen storage procedures, interinstrument variability, and split specimen detection. Our data show that the multivariate analytical reproducibility of the NMR screening platform was >98% and that most classification errors were due to urine specimen handling inhomogeneity. Differences in metabolite profiles were then assessed for Aito Town, Chicago, and Guangxi population samples; novel combinations of biomarkers were detected that separated the population samples. These cross-population differences in urinary metabolites could be related to genetic, dietary, and gut microbial factors.     

Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics

Because of the wide range of chemically and structurally diverse metabolites, efforts to survey the complete metabolome rely on the implementation of multiplatform approaches based on nuclear magnetic resonance (NMR) and mass spectrometry (MS). Sample preparation disparities between NMR and MS, however, may limit the analysis of the same samples by both platforms. Specifically, deuterated solvents used in NMR strategies can complicate LC/MS analysis as a result of potential mass shifts, whereas acidic solutions typically used in LC/MS methods to enhance ionization of metabolites can severely affect reproducibility of NMR measurements. These intrinsically different sample preparation requirements result in the application of different procedures for metabolite extraction, which involve additional sample and unwanted variability. To address this issue, we investigated 12 extraction protocols in liver tissue involving different aqueous/organic solvents and temperatures that may satisfy the requirements for both NMR and LC/MS simultaneously. We found that deuterium exchange did not affect LC/MS results, enabling the measurement of metabolites by NMR and, subsequently, the direct analysis of the same samples by using LC/MS with no need for solvent exchange. Moreover, our results show that the choice of solvents rather than the temperature determined the extraction efficiencies of metabolites, a combination of methanol/chloroform/water and methanol/water being the extraction methods that best complement NMR and LC/MS analysis for metabolomic studies.     

Virtual chromatographic resolution enhancement in cryoflow LC-NMR experiments via statistical total correlation spectroscopy

A new approach to enhancing information recovery from cryogenic probe "on-flow" LC-NMR spectroscopic analyses of complex biological mixtures is demonstrated using a variation on the statistical total correlation spectroscopy (STOCSY) method. Cryoflow probe technology enables sensitive and efficient NMR detection of metabolites on-flow, and the rapid spectral scanning allows multiple spectra to be collected over chromatographic peaks containing several species with similar, but nonidentical, retention times. This enables 1H NMR signal connectivities between close-eluting metabolites to be identified resulting in a "virtual" chromatographic resolution enhancement visualized directly in the NMR spectral projection. We demonstrate the applicability of the approach for structure assignment of drug and endogenous metabolites in urine. This approach is of wide general applicability to any complex mixture analysis problem involving chromatographic peak overlap and with particular application in metabolomics and metabonomics.     

Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe: application to the analysis of acetaminophen metabolites in urine

Cryogenic cooling of the NMR radio frequency coils and electronics to give greatly enhanced sensitivity is arguably the most significant recent advance in NMR spectroscopy. Here we report the first cryogenic probe built in flow configuration and demonstrate the application to LC-NMR-MS studies. This probe provides superior sensitivity over conventional noncryogenic flow NMR probes, allowing the use of 100 microL of untreated urine (40% less material than previous studies that required preconcentration) and yet revealing drug metabolites hitherto undetected by LC-NMR-MS at 500 MHz. Besides the known sulfate and glucuronide metabolites, previously undetected metabolites of acetaminophen were directly observable in a 15-min on-flow experiment. Simultaneous MS data also provided knowledge on the NMR-silent functional moieties. Further, stop-flow LC-NMR-MS experiments were conducted for greater signal-to-noise ratios on minor metabolites. The cryoflow probe enables the NMR analysis of lower concentrations of metabolites than was previously possible for untreated biofluids. This strategy is generally applicable for samples containing mass-limited analytes, such as those from drug metabolism studies, biomarker and toxicity profiling, impurity analysis, and natural product analysis.     

Integration of knowledge-based metabolic predictions with liquid chromatography data-dependent tandem mass spectrometry for drug metabolism studies: application to studies on the biotransformation of indinavir

Despite recent advances in the application of data-dependent liquid chromatography/tandem mass spectrometry (LC/MS/MS) to the identification of drug metabolites in complex biological matrixes, a prior knowledge of the likely routes of biotransformation of the therapeutic agent of interest greatly facilitates the detection and structural characterization of its metabolites. Thus, prediction of the [M + H]+ m/z values of expected metabolites allows for the construction of user-defined MS(n) protocols that frequently reveal the presence of minor drug metabolites, even in the presence of a vast excess of coeluting endogenous constituents. However, this approach suffers from inherent user bias, as a result of which additional "survey scans" (e.g., precursor ion and constant neutral loss scans) are required to ensure detection of as many drug-related components in the sample as possible. In the present study, a novel approach to this problem has been evaluated, in which knowledge-based predictions of metabolic pathways are first derived from a commercial database, the output from which is used to formulate a list-dependent LC/MS(n) data acquisition protocol. Using indinavir as a model drug, a substructure similarity search on the MDL metabolism database with a similarity index of 60% yielded 188 "hits", pointing to the possible operation of two hydrolytic, two N-dealkylation, three N-glucuronidation, one N-methylation, and several aromatic and aliphatic oxidation pathways. Integration of this information with data-dependent LC/MS(n) analysis using an ion trap mass spectrometer led to the identification of 18 metabolites of indinavir following incubation of the drug with human hepatic postmitochondrial preparations. This result was accomplished with only a single LC/MS(n) run, representing significant savings in instrument use and operator time, and afforded an accurate view of the complex in vitro metabolic profile of this drug.     

Heteronuclear 1H-31P statistical total correlation NMR spectroscopy of intact liver for metabolic biomarker assignment: application to galactosamine-induced hepatotoxicity

As part of our ongoing development of methods for enhanced biomarker information recovery from spectroscopic data we present the first example of a new hetero-nuclear statistical total correlation spectroscopy (HET-STOCSY) approach applied to intact tissue samples collected as part of a toxicological study. One-dimensional 1H and 31P-{1H} magic angle spinning (MAS) NMR spectra of intact liver samples after galactosamine (galN) treatment to rats and after cotreatment of galN plus uridine were collected at 275 K. Individual samples were also followed by 1H and 31P-{1H} MAS NMR through time generating time dependent modulations in metabolite signatures relating to toxicity. High-resolution 1H NMR spectra of urine and plasma and clinical chemical data were also collected to establish a biological framework in which to place these novel statistical heterospectroscopic data. In HET-STOCSY, calculation of the covariance between the 31P-{1H} and 1H NMR signals of phosphorus containing metabolites allows their molecular connectivities to be established and the construction of virtual two-dimensional heteronuclear correlation spectra that connect all protons on the molecule to the heteroatom. We show how HET-STOCSY applied to MAS NMR spectra of liver samples can be used to augment biomarker detection. This approach is generic and can be applied to correlate the covarying signals for any spin-active nuclei where there is parallel or serial collection of data.     

Metabolite identification using automated comparison of high-resolution multistage mass spectral trees

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.     

Monitoring diet effects via biofluids and their implications for metabolomics studies

The effect of diet on metabolites found in rat urine samples has been investigated using nuclear magnetic resonance (NMR) and a new ambient ionization mass spectrometry experiment, extractive electrospray ionization mass spectrometry (EESI-MS). Urine samples from rats with three different dietary regimens were readily distinguished using multivariate statistical analysis on metabolites detected by NMR and MS. To observe the effect of diet on metabolic pathways, metabolites related to specific pathways were also investigated using multivariate statistical analysis. Discrimination is increased by making observations on restricted compound sets. Changes in diet at 24-h intervals led to predictable changes in the spectral data. Principal component analysis was used to separate the rats into groups according to their different dietary regimens using the full NMR, EESI-MS data or restricted sets of peaks in the mass spectra corresponding only to metabolites found in the urea cycle and metabolism of amino groups pathway. By contrast, multivariate analysis of variance from the score plots showed that metabolites of purine metabolism obscure the classification relative to the full metabolite set. These results suggest that it may be possible to reduce the number of statistical variables used by monitoring the biochemical variability of particular pathways. It should also be possible by this procedure to reduce the effect of diet in the biofluid samples for such purposes as disease detection.     

Capillary LC-MS for high sensitivity metabolomic analysis of single islets of Langerhans

Reversed-phase, packed capillary liquid chromatography interfaced by electrospray ionization to mass spectrometry was explored as an analytical method for determination of metabolites in microscale tissue samples using single islets of Langerhans as a model system. With the use of a 75 microm inner diameter column coupled to a quadrupole ion trap mass spectrometer in full scan mode, detection limits of 0.1-33 fmol were achieved for glycoloytic and tricarboxylic acid cycle metabolites. Reproducible processing of islets for analysis with little loss of metabolites was performed by rapid freezing followed by methanol-water extraction. The method yielded 20 microL of extract of which just 15 nL was injected suggesting the potential for performing multiple assays on the same islet. Approximately 200 presumed metabolites could be detected, of which 22 were identified by matching retention times and MS/MS spectra to standards. Relative standard deviations for peak detection was from 7 to 18% and was unaffected by storage for up to 11 days. The method was used to detect changes in metabolism associated with increasing extracellular islet glucose concentration from 3 to 20 mM yielding results largely consistent with known metabolism of islets. Because most previous studies of islet metabolism have only observed a few compounds at once and require far more tissue, this measurement method represents a significant advance for studies of metabolism of islets and other microscale samples.     

Heteronuclear 19F-1H statistical total correlation spectroscopy as a tool in drug metabolism: study of flucloxacillin biotransformation

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.     

Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets

We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.     

Method for determining molar concentrations of metabolites in complex solutions from two-dimensional 1H-13C NMR spectra

One-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) (1)H-(13)C NMR strategy (fast metabolite quantification, FMQ, by NMR) for identifying and quantifying the approximately 40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae, and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 min using our quantitative 2D (1)H-(13)C NMR strategy. In contrast, traditional 1D (1)H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D (1)H NMR for metabolomics studies in which 50-mg (extract dry weight) samples can be obtained.     

A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS

In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.     

Comparison of drug distribution images from whole-body thin tissue sections obtained using desorption electrospray ionization tandem mass spectrometry and autoradiography

Desorption electrospray ionization tandem mass spectrometry (DESI-MS/MS) and whole-body autoradiography (WBA) were used for chemical imaging of whole-body thin tissue sections of mice intravenously dosed with propranolol (7.5 mg/kg). DESI-MS/MS imaging utilized selected reaction monitoring detection performed on an AB/MDS SCIEX 4000 QTRAP mass spectrometer equipped with a prototype extended length particle discriminator interface. Propranolol images of the tissue sections using DESI-MS/MS were obtained at surface scan rates of 0.1, 0.5, 2, and 7 mm/s. Although signal decreased with increasing scan rate, useful whole-body images for propranolol were obtained from the tissues even at 7 mm/s, which required just 79 min of analysis time. Attempts to detect and image the distribution of the known propranolol metabolites were unsuccessful. Regions of the tissue sections showing the most radioactivity from WBA sections were excised and analyzed by high-performance liquid chromatography (HPLC) with radiochemical detection to determine relative levels of propranolol and metabolites present. Comparison of the DESI-MS/MS signal for propranolol and the radioactivity attributed to propranolol from WBA sections indicated nominal agreement between the two techniques for the amount of propranolol in the brain, lung, and liver. Data from the kidney showed an unexplained disparity between the two techniques. The results of this study show the feasibility of using DESI-MS/MS to obtain useful chemical images of a drug in whole-body thin tissue sections following drug administration at a pharmacologically relevant level. Further optimization to improve sensitivity and enable detection of the drug metabolites will be among the requirements necessary to move DESI-MS/MS chemical imaging forward as a practical tool in drug discovery.     

Polyphenol identification based on systematic and robust high-resolution accurate mass spectrometry fragmentation

High-mass resolution multi-stage mass spectrometry (MS(n)) fragmentation was tested for differentiation and identification of metabolites, using a series of 121 polyphenolic molecules. The MS(n) fragmentation approach is based on the systematic breakdown of compounds, forming a so-called spectral tree. A chip-based nanoelectrospray ionization source was used combined with an ion-trap, providing reproducible fragmentation, and accurate mass read-out in an Orbitrap Fourier transform (FT) MS enabling rapid assignment of elemental formulas to the molecular ions and all fragment ions derived thereof. The used protocol resulted in reproducible MS(n) fragmentation trees up to MS(5). Obtained results were stable over a 5 month time period, a concentration change of 100-fold, and small changes in normalized collision energy, which is key to metabolite annotation and helpful in structure and substructure elucidation. Differences in the hydroxylation and methoxylation patterns of polyphenolic core structures were found to be reflected by the differential fragmentation of the entire molecule, while variation in a glycosylation site displayed reproducible differences in the relative intensities of fragments originating from the same aglycone fragment ion. Accurate MS(n)-based spectral tree data are therefore a powerful tool to distinguish metabolites with similar elemental formula, thereby assisting compound identification in complex biological samples such as crude plant extracts.     

Development of ultrahigh-throughput NMR spectroscopic analysis utilizing capillary flow NMR technology

An ultrahigh-throughput method for acquiring 1H NMR spectra is described. By constructing a continuous flow system utilizing an HPLC pump, autosampler, and a capillary flow NMR probe, it was possible to inject samples into the NMR spectrometer every 30 s using a continuous flow rate of 30 microL/min. 1H NMR spectroscopic data were acquired continuously into a pseudo-2D data file, with a 96-well-plate completed in <50 min. Spectra in continuous flow mode were readily obtained from approximately 3.4 mug (500 MHz), while the LOD was <850 ng. There was found to be little variation in either sample broadening within the flow system or signal intensities between multiple injections. This system offers several advantages over more conventional NMR spectroscopic analyses, notably the limited solvent required, high sensitivity, high speed, and improved spectral quality as a result of reduced spectral "dead" regions resulting from residual solvent levels.     

Ratio analysis nuclear magnetic resonance spectroscopy for selective metabolite identification in complex samples

Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.     

Analysis of complex protein mixtures with improved sequence coverage using (CE-MS/MS)n

Identification of proteins, in a complex protein mixture, using one-dimensional high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) analysis of its digest, usually suffers from low sequence coverage. There are several reasons for the low coverage including undersampling, wide concentration dynamic range of the proteins in a complex protein mixture, and wide range of electrospray ionization efficiency of peptides under each mobile-phase composition. To address this low sequence coverage, we introduce a novel technique, (CE-MS/MS)n, which utilizes the most significant advantages of CE-MS/MS, including economy of sample size, fast analysis time, and high separation efficiency, to increase the sequence coverage of a complex protein mixture. Based on these characteristics, (CE-MS/MS)n can be performed in which multiple CE-MS/MS subanalyses (injections followed by analyses) are analyzed and experimental variables are manipulated during each CE-MS/MS subanalysis in order to maximize sequence coverage. (CE-MS/MS)n is a practical technique since each CE-MS/MS subanalysis consumes <10 nL, and each CE-MS/MS subanalysis takes approximately 10 min; therefore, several subanalyses can be performed in approximately 1 h consuming only nanoliters of the sample. Two techniques have been introduced to address the undersampling: (1) (CE-MS/MS)n using dynamic exclusion. In this technique, several CE-MS/MS analyses (injection followed by separation) were performed in one run using the dynamic exclusion capability of the mass spectrometer until all peptide peaks were analyzed by MS/MS. (2) Gas-phase fractionation. In this technique, (CE-MS/MS)n is performed by scanning a narrow mass range (every approximately 100 m/z) during each CE-MS/MS subanalysis without using dynamic exclusion. Under this condition, in each subanalysis, the number of peptides available for MS/MS analysis is significantly reduced, and peptides with the same nominal masses are analyzed, thereby increasing sequence coverage. Additionally, to address the lack of detection of low-level peptides in a mixture containing a wide concentration dynamic range, the concentration of the sample was systematically increased in each subanalysis (while utilizing dynamic exclusion) so that low-intensity peptides would rise above the mass spectrometer threshold and, consequently, undergo MS/MS analysis. Moreover, to alter the ionization efficiency of peptides with low electrospray ionization efficiency, and to change the migration behavior of comigrating peptides under a specific liquid composition, the CE background electrolyte was modified in several subanalyses to further improve sequence coverage. The combination of the above-mentioned techniques was applied to the analysis of the tryptic digests of three well-characterized protein mixtures: a six-protein mixture with average MW of approximately 26,000 (standard I), a six-protein mixture with an average MW approximately 49,000 (standard II), and a more complex protein mixture containing 55 proteins (E. coli ribosomal proteins). In approximately 1 h, when the MS/MS of the peptides were manually checked, all peptides that produced peaks under electrospray ionization in the scanned range of the analysis (500-2000 m/z) and within the practical fragmentation capability of the MS (peptides with MW <3500) were identified for standard I by consuming only 200 fmol of each protein. When searched against a Swissprot database, the average sequence coverage for the standard I, II, and E. coli's ribosomal proteins were 57, 34, and 15%, respectively.