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Urine sample storage after collection at ultra-low-temperature (e.g., -80 °C) is normally required for comparative metabolome analysis of many samples, and therefore, freeze-thaw cycles (FTCs) are unavoidable. However, the reported effects of FTCs on the urine metabolome are controversial. Moreover, there is no report on the study of how urine FTCs affect biomarker discovery. Herein, we present our study of the FTC effects on the urine metabolome and biomarker discovery using a high-coverage quantitative metabolomics platform. Our study involved two centers located in Hangzhou, China, and Edmonton, Canada, to perform metabolome analysis of two separate cohorts of urine samples. The same workflow of sample preparation and dansylation isotope labeling LC-MS was used for in-depth analysis of the amine/phenol submetabolome. The analysis of 320 samples from the Hangzhou cohort consisting of 80 healthy subjects with each urine being subjected to four FTCs resulted in relative quantification of 3682 metabolites with 3307 identified or mass-matched. The analysis of 176 samples from the Edmonton cohort of 44 subjects with four FTCs quantified 3516 metabolites with 3166 identified or mass-matched. Multivariate and univariate analyses indicated that significant variations (fold change ≥ 1.5 with

We report a segmented spectrum scan method using Orbitrap MS in chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) for improving the metabolite detection efficiency. In this method, the full

The outbreak of novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. To meet the urgent and massive demand for the screening and diagnosis of infected individuals, many in vitro diagnostic assays using nucleic acid tests (NATs) have been urgently authorized by regulators worldwide. A reference standard with a well-characterized concentration or titer is of the utmost importance for the study of limit of detection (LoD), which is a crucial feature for a diagnostic assay. Although several reference standards of plasmids or synthetic RNA have already been announced, a reference standard for inactivated virus particles with an accurate concentration is still needed to evaluate the complete procedure. Here, we performed a collaborative study to estimate the NAT-detectable units as a viral genomic equivalent quantity (GEQ) of an inactivated whole-virus SARS-CoV-2 reference standard candidate using digital PCR (dPCR) on multiple commercialized platforms. The median of the quantification results (4.6 × 105 ± 6.5 × 104 GEQ/mL) was treated as the consensus true value of GEQ of virus particles in the reference standard. This reference standard was then used to challenge the LoDs of six officially approved diagnostic assays. Our study demonstrates that an inactivated whole virus quantified by dPCR can serve as a reference standard and provides a unified solution for assay development, quality control, and regulatory surveillance.

Blood metabolomics has been widely used for discovering potential metabolite biomarkers of various diseases. In this study, we report our investigation of the effects of freeze-thaw cycles (FTCs) of human serum samples on quantitative metabolomics using a differential chemical isotope labeling (CIL) LC-MS method. A total of 99 serum samples collected from healthy individuals (47 females and 52 males) were subjected to five FTCs, followed by 12C-/13C-dansylation labeling LC-MS analysis. A total of 2790 peak pairs or metabolites were relatively quantified among the 495 comparative samples, including 150 positively identified metabolites, 235 high-confident putatively identified metabolites and 1949 mass-matched metabolites from database searches. Multivariate analysis of the metabolome data showed a clustering of the third to fifth FTC samples in contrast to the separation of the first and second FTC samples, indicating that the extent of FTC-induced metabolome changes became smaller after the third cycle. The changing patterns among the FTC-effected metabolites were found to be complex. Using sex as a biological factor for grouping, we observed a clear separation of males and females when the samples were subjected to the same number of FTCs. However, when the male- and female-samples with different numbers of FTCs were compared, the number of significant metabolites found in male-female comparison increased dramatically, indicating that FTC effects could lead to a large number of false positives in biomarker discovery. Finally, we proposed a method of detecting the FTC effects by reanalyzing the original samples after subjecting them to an additional FTC.

High-performance chemical isotope labeling (CIL) LC-MS is an important tool for profiling chemical-group-based submetabolomes using different labeling chemistries for quantitative metabolomics. Metabolites containing carboxylic acid groups are a class of molecules playing diverse and significant roles in many metabolic pathways. We report a relatively simple and convenient method for analyzing the carboxyl submetabolome with high coverage. Dansylhydrazine (DnsHz) labeling of the carboxylic acid group in metabolites is used to improve both separation and ionization in LC-MS. Using differential 12C- and 13C-DnsHz reagents for labeling individual samples and a reference sample (e.g., a pooled sample), accurate relative quantification of individual metabolites among comparative samples can be achieved. DnsHz labeling of carboxylic acids could be carried out at room temperature in water-containing solution in 2 h. A labeled-carboxyl-standard library consisting of 193 endogenous human metabolites was constructe...

Blood is widely used for discovery metabolomics to search for disease biomarkers. However, blood sample matrix can have a profound effect on metabolome analysis, which can impose an undesirable restriction on the type of blood collection tubes that can be used for blood metabolomics. We investigated the effect of blood sample matrix on metabolome analysis using a high-coverage and quantitative metabolome profiling technique based on differential chemical isotope labeling (CIL) LC-MS. We used 12C-/13C-dansylation LC-MS to perform relative quantification of the amine/phenol submetabolomes of four types of samples (i.e., serum, EDTA plasma, heparin plasma, and citrate plasma) collected from healthy individuals and compare their metabolomic results. From the analysis of 80 plasma and serum samples in experimental triplicate, we detected a total of 3651 metabolites with an average of 1818 metabolites per run (n = 240). The number of metabolites detected and the precision and accuracy of relative quantification were found to be independent of the sample type. Within each sample type, the metabolome data set could reveal biological variation (e.g., sex separation). Although the relative concentrations of some individual metabolites might be different in the four types of samples, for sex separation, all 66 significant metabolites with larger fold-changes (FC ≥ 2 and p < 0.05) found in at least one sample type could be found in the other types of samples with similar or somewhat reduced, but still significant, fold-changes. Our results indicate that CIL LC-MS could overcome the sample matrix effect, thereby greatly broadening the scope of blood metabolomics; any blood samples properly collected in routine clinical settings, including those in biobanks originally used for other purposes, can potentially be used for discovery metabolomics.

Metabolites containing a carbonyl group represent several important classes of molecules including various forms of ketones and aldehydes such as steroids and sugars. We report a high-performance chemical isotope labeling (CIL) LC–MS method for profiling the carbonyl submetabolome with high coverage and high accuracy and precision of relative quantification. This method is based on the use of dansylhydrazine (DnsHz) labeling of carbonyl metabolites to change their chemical and physical properties to such an extent that the labeled metabolites can be efficiently separated by reversed phase LC and ionized by electrospray ionization MS. In the analysis of six standards representing different carbonyl classes, acetaldehyde could be ionized only after labeling and MS signals were significantly increased for other 5 standards with an enhancement factor ranging from ∼15-fold for androsterone to ∼940-fold for 2-butanone. Differential 12C- and 13C-DnsHz labeling was developed for quantifying metabolic differences ...

Compared to conventional MS and NMR techniques, high-performance chemical isotope labeling (CIL) LC-MS provides accurate relative quantification of many more metabolites in biological samples. However, to apply this technique for urine and fecal metabolomics studies of animal models, the entire workflow, including the preanalytical process, needs to be strictly controlled to avoid or minimize quantitative errors. In this study, we report our investigation of the effects of mouse urine and fecal sample collection methods on CIL LC-MS metabolome analysis. Metabolic-cage collection and spot-sample collection of urine and feces were compared in a mouse model of CCl

A key step in metabolomics is to perform accurate relative quantification of the metabolomes in comparative samples with high coverage. Hydroxyl-containing metabolites are an important class of the metabolome with diverse structures and physical/chemical properties; however, many of them are difficult to detect with high sensitivity. We present a high-performance chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) technique for in-depth profiling of the hydroxyl submetabolome, which involves the use of acidic liquid–liquid extraction to enrich hydroxyl metabolites into ethyl acetate from an aqueous sample. After drying and then redissolving in acetonitrile, the metabolite extract is labeled using a base-activated 12C- or 13C-dansylation reaction. A fast step-gradient LC-UV method is used to determine the total concentration of labeled metabolites. On the basis of the concentration information, a 12C-labeled individual sample is mixed with an equal mole amount of a 13C-labeled pool or...

Metabolomics requires quantitative comparison of individual metabolites present in an entire sample set. Unfortunately, missing intensity values in one or more samples are very common. Because missing values can have a profound influence on metabolomic results, the extent of missing values found in a metabolomic data set should be treated as an important parameter for measuring the analytical performance of a technique. In this work, we report a study on the scope of missing values and a robust method of filling the missing values in a chemical isotope labeling (CIL) LC-MS metabolomics platform. Unlike conventional LC-MS, CIL LC-MS quantifies the concentration differences of individual metabolites in two comparative samples based on the mass spectral peak intensity ratio of a peak pair from a mixture of differentially labeled samples. We show that this peak-pair feature can be explored as a unique means of extracting metabolite intensity information from raw mass spectra. In our approach, a peak-pair peaking algorithm, IsoMS, is initially used to process the LC-MS data set to generate a CSV file or table that contains metabolite ID and peak ratio information (i.e., metabolite-intensity table). A zero-fill program, freely available from MyCompoundID.org , is developed to automatically find a missing value in the CSV file and go back to the raw LC-MS data to find the peak pair and, then, calculate the intensity ratio and enter the ratio value into the table. Most of the missing values are found to be low abundance peak pairs. We demonstrate the performance of this method in analyzing an experimental and technical replicate data set of human urine metabolome. Furthermore, we propose a standardized approach of counting missing values in a replicate data set as a way of gauging the extent of missing values in a metabolomics platform. Finally, we illustrate that applying the zero-fill program, in conjunction with dansylation CIL LC-MS, can lead to a marked improvement in finding significant metabolites that differentiate bladder cancer patients and their controls in a metabolomics study of 109 subjects.

Human fecal samples contain endogenous human metabolites, gut microbiota metabolites, and other compounds. Profiling the fecal metabolome can produce metabolic information that may be used not only for disease biomarker discovery, but also for providing an insight about the relationship of the gut microbiome and human health. In this work, we report a chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) method for comprehensive and quantitative analysis of the amine- and phenol-containing metabolites in fecal samples. Differential (13)C2/(12)C2-dansyl labeling of the amines and phenols was used to improve LC separation efficiency and MS detection sensitivity. Water, methanol, and acetonitrile were examined as an extraction solvent, and a sequential water-acetonitrile extraction method was found to be optimal. A step-gradient LC-UV setup and a fast LC-MS method were evaluated for measuring the total concentration of dansyl labeled metabolites that could be used for normalizing the sample amounts of individual samples for quantitative metabolomics. Knowing the total concentration was also useful for optimizing the sample injection amount into LC-MS to maximize the number of metabolites detectable while avoiding sample overloading. For the first time, dansylation isotope labeling LC-MS was performed in a simple time-of-flight mass spectrometer, instead of high-end equipment, demonstrating the feasibility of using a low-cost instrument for chemical isotope labeling metabolomics. The developed method was applied for profiling the amine/phenol submetabolome of fecal samples collected from three families. An average of 1785 peak pairs or putative metabolites were found from a 30 min LC-MS run. From 243 LC-MS runs of all the fecal samples, a total of 6200 peak pairs were detected. Among them, 67 could be positively identified based on the mass and retention time match to a dansyl standard library, while 581 and 3197 peak pairs could be putatively identified based on mass match using MyCompoundID against a Human Metabolome Database and an Evidence-based Metabolome Library, respectively. This represents the most comprehensive profile of the amine/phenol submetabolome ever detected in human fecal samples. The quantitative metabolome profiles of individual samples were shown to be useful to separate different groups of samples, illustrating the possibility of using this method for fecal metabolomics studies.

In cellular metabolomics, it is desirable to carry out metabolomic profiling using a small number of cells in order to save time and cost. In some applications (e.g., working with circulating tumor cells in blood), only a limited number of cells are available for analysis. In this report, we describe a method based on high-performance chemical isotope labeling (CIL) nanoflow liquid chromatography mass spectrometry (nanoLC-MS) for high-coverage metabolomic analysis of small numbers of cells (i.e., ≤10000 cells). As an example, 12C-/13C-dansyl labeling of the metabolites in lysates of 100, 1000, and 10000 MCF-7 breast cancer cells was carried out using a new labeling protocol tailored to handle small amounts of metabolites. Chemical-vapor-assisted ionization in a captivespray interface was optimized for improving metabolite ionization and increasing robustness of nanoLC-MS. Compared to microflow LC-MS, the nanoflow system provided much improved metabolite detectability with a significantly reduced sample am...

Human sweat is an excellent biofluid candidate for metabolomics due to its noninvasive sample collection and relatively simple matrix. We report a simple and inexpensive method for sweat collection over a defined period (e.g., 24 h) based on the use of a nonocclusive style sweat patch adhered to a skin. This method was combined with differential chemical isotope labeling (CIL) LC-MS for mapping the metabolome profiles of sweat samples collected from skins of the left forearm, lower back, and neck of 20 healthy volunteers. Three 24-h sweat samples were collected at three different days from each subject for examining day-to-day metabolome variations. A total of 342 LC-MS runs were carried out (two runs were discarded due to instrumental issue), resulting in the detection and relative quantification of 3140 sweat metabolites with 84 metabolites identified and 2716 metabolites mass-matched to metabolome databases. Multivariate and univariate analyses of the metabolome data revealed a location-dependence characteristic of the sweat metabolome, offering a possibility of mapping the sweat metabolic differences according to skin locations. Significant differences in male and female sweat metabolomes could be detected, demonstrating the possibility of using the sweat metabolome to reveal biological variations among different comparative groups. Thus, the combination of noninvasive sweat collection and CIL LC-MS is a robust analytical tool for sweat metabolomics with potential applications including daily monitoring of the sweat metabolome as health indicators, discovering sweat-based disease biomarkers, and metabolomic mapping of sweat collected from different areas of skin with and without injuries or diseases.

Milk is a complex sample containing a variety of proteins, lipids, and metabolites. Studying the milk metabolome represents an important application of metabolomics in the general area of nutritional research. However, comprehensive and quantitative analysis of milk metabolites is a challenging task due to the wide range of variations in chemical/physical properties and concentrations of these metabolites. We report an analytical workflow for in-depth profiling of the milk metabolome based on chemical isotope labeling (CIL) and liquid chromatography mass spectrometry (LC-MS) with a focus of using dansylation labeling to target the amine/phenol submetabolome. An optimal sample preparation method, including the use of methanol at a 3:1 ratio of solvent to milk for protein precipitation and dichloromethane for lipid removal, was developed to detect and quantify as many metabolites as possible. This workflow was found to be generally applicable to profile milk metabolomes of different species (cow, goat, and human) and types. Results from experimental replicate analysis (n = 5) of 1:1, 2:1, and 1:2 12C-/13C-labeled cow milk samples showed that 95.7%, 94.3%, and 93.2% of peak pairs, respectively, had ratio values within ±50% accuracy range and 90.7%, 92.6%, and 90.8% peak pairs had RSD values of less than 20%. In the metabolomic analysis of 36 samples from different categories of cow milk (brands, batches, and fat percentages) with experimental triplicates, a total of 7104 peak pairs or metabolites could be detected with an average of 4573 ± 505 (n = 108) pairs detected per LC-MS run. Among them, 3820 peak pairs were commonly detected in over 80% of the samples with 70 metabolites positively identified by mass and retention time matches to the dansyl standard library and 2988 pairs with their masses matched to the human metabolome libraries. This unprecedentedly high coverage of the amine/phenol submetabolome illustrates the complexity of the milk metabolome. Since milk and milk products are consumed in large quantities on a daily basis, the intake of these milk metabolites even at low concentrations can be cumulatively high. The high-coverage analysis of the milk metabolome using CIL LC-MS should be very useful in future research involving the study of the effects of these metabolites on human health. It should also be useful in the dairy industry in areas such as improving milk production, developing new processing technologies, developing improved nutritional products, quality control, and milk product authentication.

Metabolomics involves the comparison of the metabolomes of individual samples from two or more groups to reveal the metabolic differences. In order to measure the metabolite concentration differences accurately, using the same amount of starting materials is essential. In this work, we describe a simple and rapid method for sample amount normalization. It is based on dansylation labeling of the amine and phenol submetabolome of an individual sample, followed by solvent extraction of the labeled metabolites and ultraviolet (UV) absorbance measurement using a microplate reader. A calibration curve of a mixture of 17 dansyl-labeled amino acid standards is used to determine the total concentration of the labeled metabolites in a sample. According to the measured concentrations of individual samples, the volume of an aliquot taken from each sample is adjusted so that the same sample amount is taken for subsequent metabolome comparison. As an example of applications, this dansylation metabolite assay method is shown to be useful in comparative metabolome analysis of two different E. coli strains using a differential chemical isotope labeling LC-MS platform. Because of the low cost of equipment and reagents and the simple procedure used in the assay, this method can be readily implemented. We envisage that, this assay, which is analogous to the bicinchoninic acid (BCA) protein assay widely used in proteomics, will be applicable to many types of samples for quantitative metabolomics.

Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and discovering potential metabolite biomarkers for diagnosis of bladder cancer.

We report a chemical-vapor-assisted electrospray ionization (ESI) technique to improve the detection sensitivity of ESI mass spectrometry (MS). This simple technique involves introducing a chemical vapor into the sheath gas around the nano-ESI spray tip or through a tubing with its outlet placed close to the spray tip. A variety of chemical vapors were tested and found to have varying degrees of effects on analyte signal intensities. The use of benzyl alcohol vapors in ESI was found to increase signal intensities of standard peptides by up to 4-fold. When this technique was combined with capillary liquid chromatography tandem MS (LC-MS/MS), the number of unique peptides identified in the acid hydrolysate of alpha casein increased by 45% and the number of peptides and proteins identified in a tryptic digest of E. coli cell lysate increased by 13% and 14%, respectively, along with an increased average match score. This technique could also increase the analyte signals for some small molecules, such as phenylephrine, by up to 3-fold. The increased analyte signals observed in the chemical-vapor-assisted ESI process is related to the enhancement of the ionization efficiency in ESI. The method can be readily implemented to an existing ESI mass spectrometer at minimum cost for improving detection sensitivity.

We report an enabling method for mapping the protein sequence with high sequence coverage. This method combines the high separation power of gel electrophoresis for protein separation with the high sequence coverage capability of microwave-assisted acid hydrolysis (MAAH) mass spectrometry (MS). In-gel MAAH using 25% trifluoroacetic acid was developed and optimized for degrading the gel-separated protein into small peptides suitable for tandem MS sequencing. For bovine serum albumin (BSA) (∼67 kDa), with 4 μg of protein loading onto a gel for separation, followed by excising the protein gel band for in-gel MAAH and then injecting ∼2 μg of the resultant peptides into a liquid chromatography quadrupole time-of-flight mass spectrometer for analysis, 689 ± 54 (n = 3) unique peptides were identified with a protein sequence coverage of 99 ± 1%. Both the number of peptides detected and sequence coverage decreased as the sample amount decreased, mainly due to background interference: 316 ± 59 peptides and 94 ± 3% coverage for 2 μg loading, 136 ± 19 and 76 ± 5% for 1 μg loading, and 30 ± 2 and 32 ± 2% for 0.5 μg loading. To demonstrate the general applicability of the method, 10 gel bands from gel electrophoresis of an albumin-depleted human plasma sample were excised for in-gel MAAH LC-MS analysis. In total, 19 relatively high abundance proteins with molecular weights ranging from ∼8 to ∼160 kD could be mapped with coverage of 100% for six proteins (MW 8759 to 68 425 Da), 96-98% for five proteins (MW 11 458 to 36 431 Da), 92% for three proteins (MW 15 971 to 36 431 Da), 80-87% for four proteins (MW 42 287 to 162 134 Da), and 56% for one protein (MW 51 358 Da). Finally, to demonstrate the applicability of the method for more detailed analysis of complex protein mixtures, two-dimensional (2D) gel electrophoresis was combined with in-gel MAAH, affinity purification, and LC-MS/MS to characterize six bovine alpha-S1-casein phosphoprotein isoforms. Full sequence coverage was achieved for each protein, and six new modification sites were found.

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool ( http://www.mycompoundid.org ) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8,021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries.

Human sweat can be noninvasively collected and used as a media for diagnosis of certain diseases as well as for drug detection. However, because of very low concentrations of endogenous metabolites present in sweat, metabolomic analysis of sweat with high coverage is difficult, making it less widely used for metabolomics research. In this work, a high-performance method for profiling the human sweat submetabolome based on chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is reported. Sweat was collected using a gauze sponge style patch, extracted from the gauze by centrifugation, and then derivatized using CIL. Differential (12)C- and (13)C-dansylation labeling was used to target the amine/phenol submetabolome. Because of large variations in the total amount of sweat metabolites in individual samples, sample amount normalization was first performed using liquid chromatography with UV detection (LC-UV) after dansylation. The (12)C-labeled individual sample was then mixed with an equal amount of (13)C-labeled pooled sample. The mixture was subjected to LC-MS analysis. Over 2707 unique metabolites were detected across 54 sweat samples collected from six individuals with an average of 2002 ± 165 metabolites detected per sample from a total of 108 LC-MS runs. Using a dansyl standard library, we were able to identify 83 metabolites with high confidence; many of them have never been reported to be present in sweat. Using accurate mass search against human metabolome libraries, we putatively identified an additional 2411 metabolites. Uni- and multivariate analyses of these metabolites showed significant differences in the sweat submetabolomes between male and female, as well as between early and late exercise. These results demonstrate that the CIL LC-MS method described can be used to profile the human sweat submetabolome with high metabolomic coverage and high quantification accuracy to reveal metabolic differences in different sweat samples, thereby allowing the use of sweat as another human biofluid for comprehensive and quantitative metabolomics research.

Preparation of raw, untreated biological samples remains a major challenge in microfluidics. We present a novel microfluidic device based on the integration of printed circuit boards and an isotachophoresis assay for sample preparation of nucleic acids from biological samples. The device has integrated resistive heaters and temperature sensors as well as a 70 μm × 300 μm × 3.7 cm microfluidic channel connecting two 15 μL reservoirs. We demonstrated this device by extracting pathogenic nucleic acids from 1 μL dispensed volume of whole blood spiked with Plasmodium falciparum. We dispensed whole blood directly onto an on-chip reservoir, and the system's integrated heaters simultaneously lysed and mixed the sample. We used isotachophoresis to extract the nucleic acids into a secondary buffer via isotachophoresis. We analyzed the convective mixing action with micro particle image velocimetry (micro-PIV) and verified the purity and amount of extracted nucleic acids using off-chip quantitative polymerase chain reaction (PCR). We achieved a clinically relevant limit of detection of 500 parasites per microliter. The system has no moving parts, and the process is potentially compatible with a wide range of on-chip hybridization or amplification assays.

We have developed a new isotope labeling method, based on the use of isotope-coded p-dimethylaminophenacyl (DmPA) bromide as a reagent, combined with liquid chromatography-mass spectrometry (LC-MS) for high-performance metabolome analysis with a focus on profiling carboxylic acid-containing metabolites. Derivatization is simple, fast (1 h plus 30 min for quenching the reaction), and applicable to a wide range of carboxylic acids with a high yield and little or no side reaction products. This labeling method is demonstrated to be not only effective in introducing an isotope tag for accurate metabolite quantification but also improving the chromatographic retention of the metabolites in reversed-phase (RP) LC, enhancing ESI efficiency by 2-4 orders of magnitude, and facilitating the identification of metabolite peaks in LC-MS. In triplicate experiments of a 1:1 ratio of (13)C-/(12)C-DmPA labeled human urine, we were able to detect 2671, 2546, and 2820 ion pairs from metabolites containing one or more carboxylic acid groups.

A shotgun proteome analysis method and its performance for protein identification from 500 to 5000 cells are described. Sample preparation, which was done in one tube, involved the use of a surfactant (NP-40) for cell lysis, followed by acetone precipitation of the proteins. The resulting protein pellet was washed with cold acetone to remove remaining surfactant, and the pellet was then solubilized in NH(4)HCO(3). After trypsin digestion of the proteins, the digest was analyzed by the use of nanoflow liquid chromatography (LC) quadrupole time-of-flight mass spectrometry (QTOF MS). Sample injection and gradient speed in running LC QTOF MS were optimized. It was shown that this method could identify an average (n = 3) of 167 +/- 21, 237 +/- 30, 491 +/- 63, and 619 +/- 59 proteins from 500, 1000, 2500, and 5000 MCF-7 breast cancer cells, respectively. To demonstrate the potential use of this method for generating proteome profile from circulating tumor cells (CTCs) isolated from human blood, a healthy human blood sample was spiked with MCF-7 cells, and this mixture was processed and then subjected to antibody tagging of the MCF-7 cells. The tagged cells were sorted and collected using flow cytometry. The proteome profiles of small numbers of cells isolated in this way were found to be similar to those of the original MCF-7 cells, suggesting the possibility of the use of this method for cell typing of CTCs.

We demonstrate a strategy of maximizing the performance of reversed-phase (RP) liquid chromatography (LC) tandem mass spectrometry (MS/MS) for efficient shotgun proteome analysis by optimizing the sample loading to the instrument in an off-line two-dimensional (2D) LC tandem MS platform. To determine the quantity of peptides present in a proteome digest or fractionated peptides from strong-cation exchange (SCX) separation, an automated system based on RPLC with a rapid step solvent gradient for peptide elution and ultraviolet (UV) detection was developed. This system also allowed the purification of the peptides by removing salts and other impurities present in a sample. It was found that controlling the amount of peptides injected into a RPLC MS/MS system was critical to achieve the maximum efficiency in peptide and protein identification. With the use of off-line 2D-LC-MS/MS, peptide fractions from the first dimension of separation were desalted and quantified, followed by injecting the optimal amount of the sample into RPLC-MS/MS for peptide sequencing. The application of this strategy was demonstrated in the proteome profiling of breast cancer MCF-7 cells. From the analysis of 28 SCX fractions with each injecting 1 microg of sample into a 75 mum x 100 mm C18 column interfaced to a quadrupole/time-of-flight mass spectrometer, a total of 2362 unique proteins or protein groups were identified with a false positive peptide identification rate of 0.19%, as determined by target-decoy proteome sequence searches. Replicate 2 h runs of individual fractions with the exclusion of precursor ions of peptides already identified in the first runs resulted in the identification of an additional 549 unique proteins or protein groups with a false positive identification rate of 0.60%. This example illustrated that off-line 2D-LC-MS/MS, with maximal sample injection to the RPLC-MS, is an effective method for shotgun proteome analysis. Finally, the advantages and limitations of this method, compared to other methods, are discussed.

In shotgun proteome analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS), not all coeluting peptides at a given retention time are subjected to MS/MS due to the limitation of spectral acquisition speed of a mass spectrometer. In this work, precursor ion exclusion (PIE) in an electrospray ionization (ESI) quadrupole time-of-flight (QTOF) mass spectrometer was explored as a means of mitigating the undersampling problem. This strategy is based on running replicates of the sample where the precursor ions detected in the initial run(s) are excluded for MS/MS in the subsequent run. Four PIE methods as well as running replicates without PIE were investigated and compared for their effectiveness in identifying peptides and proteins. In the analysis of an MCF-7 breast cancer cell lysate digest by three replicate 2 h gradient LC-ESI runs, the first PIE method used a list of precursor ions detected in the initial run(s) for exclusion and identified a total of 572 proteins from the three runs combined with an average of 3.59 peptides matched to a protein. The second PIE method involved in the generation of a list of m/ z values of precursor ions along with their retention time information from the initial run(s), followed by entering these ions with retention times into the ion exclusion program of the QTOF control software for exclusion at a predefined retention time window (i.e., +/-150 s). In comparison to the first PIE method, this method reduced the possibility of excluding different peptide ions of the same m/ z (within a mass tolerance window) eluted at different retention windows. A total of 657 proteins were identified with an average of 3.75 peptides matched to a protein. The third PIE method studied relied on the exclusion of the precursor ions of peptides identified through database search of the MS/MS spectra generated in the initial run(s). This selective PIE method identified a total of 681 proteins with an average of 3.68 peptides matched to a protein. The final PIE method investigated involves the expansion of the selective PIE list by including nonidentifiable peptide ions found in the database search. This complete PIE method identified a total of 726 proteins with an average of 3.66 peptides per protein. In the case of three replicate runs without PIE, a total of 460 proteins were identified with an average of 3.51 peptides matched to a protein. Thus, the use of an optimal PIE strategy significantly increased the number of proteins identified from replicate runs (i.e., 726 vs 460 or a 58% increase). It is further demonstrated that this PIE strategy also improves protein identification efficiency in the analysis of a yeast whole cell lysate digesta less complex proteome digest. A total of 533 proteins identified from five replicate runs with complete PIE, compared to 353 proteins identified from the five replicate runs without PIE, representing a 51% increase in the number of proteins identified.

One of the challenges associated with metabolome profiling in complex biological samples is to generate quantitative information on the metabolites of interest. In this work, a targeted metabolome analysis strategy is presented for the quantification of amine-containing metabolites. A dimethylation reaction is used to introduce a stable isotopic tag onto amine-containing metabolites followed by LC-ESI MS analysis. This labeling reaction employs a common reagent, formaldehyde, to label globally the amine groups through reductive amination. The performance of this strategy was investigated in the analysis of 20 amino acids and 15 amines by LC-ESI MS. It is shown that the labeling chemistry is simple, fast (10-min reaction time), specific, and provides high yields under mild reaction conditions. The issue of isotopic effects of the labeled amines on reversed-phase (RP) and hydrophilic interaction (HILIC) LC separations was examined. It was found that deuterium labeling causes an isotope effect on the elution of labeled amines on RPLC but has no effect on HILIC LC. However, 13C-dimethylation does not show any isotope effect on either RPLC or HILIC LC, indicating that 13C-labeling is a preferred approach for relative quantification of amine-containing metabolites in different samples. The isotopically labeled 35 amine-containing analogues were found to be stable and proved to be effective in overcoming matrix effects in both relative and absolute quantification of these analytes present in a complicated sample, human urine. Finally, the characteristic mass difference provides additional structural information that reveals the existence of primary or secondary amine functional groups in amine-containing metabolites. As an example, for a human urine sample, a total of 438 pairs of different amine-containing metabolites were detected, at signal-to-noise ratios of greater than 10, by using the labeling strategy in conjunction with RP LC-ESI Fourier-transform ion cyclotron resonance MS.

Nanoflow liquid chromatography mass spectrometry (nLC-MS) is frequently used in the proteomics field to analyze a small amount of protein and peptide samples. However, this technique is currently not widespread in the metabolomics field. We report a detailed investigation on the development of an nLC-MS system equipped with a trap column for high-performance chemical isotope labeling (CIL) metabolomic profiling with deep coverage and high sensitivity. Experimental conditions were optimized for profiling the amine/phenol submetabolome with (13)C-/(12)C-dansylation labeling. Comparison of analytical results from nLC-MS and microbore LC-MS (mLC-MS) was made in the analysis of metabolite standards and labeled human urine and sweat samples. It is shown that, with a 5-μL loop injection, 7 labeled amino acid standards could be detected with S/N ranging from 7 to 150 by nLC-MS with an injection of 5 nM solution containing a total of 25 fmol labeled analyte. For urine metabolome profiling where the sample amount was not limited, nLC-MS detected 13% more metabolites than mLC-MS under optimal conditions (i.e., 4524 ± 37 peak pairs from 26 nmol injection in triplicate vs 4019 ± 40 peak pairs from 52 nmol injection). This gain was attributed to the increased dynamic range of peak detection in nLC-MS. In the analysis of human sweat where the sample amount could be limited, nLC-MS offered the advantage of providing much higher coverage than mLC-MS. Injecting 5 nmol of dansylated sweat, 3908 ± 62 peak pairs or metabolites were detected by nLC-MS, while only 1064 ± 6 peak pairs were detected by mLC-MS. Because dansyl labeled metabolites can be captured on a reversed phase (RP) trap column for large volume injection and are well separated by RPLC, the CIL platform can be readily implemented in existing nLC-MS instruments such as those widely used in shotgun proteomics.

High-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC-MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC-MS.

Generating precise and accurate quantitative information on metabolomic changes in comparative samples is important for metabolomics research where technical variations in the metabolomic data should be minimized in order to reveal biological changes. We report a method and software program, IsoMS-Quant, for extracting quantitative information from a metabolomic data set generated by chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). Unlike previous work of relying on mass spectral peak ratio of the highest intensity peak pair to measure relative quantity difference of a differentially labeled metabolite, this new program reconstructs the chromatographic peaks of the light- and heavy-labeled metabolite pair and then calculates the ratio of their peak areas to represent the relative concentration difference in two comparative samples. Using chromatographic peaks to perform relative quantification is shown to be more precise and accurate. IsoMS-Quant is integrated with IsoMS for picking peak pairs and Zero-fill for retrieving missing peak pairs in the initial peak pairs table generated by IsoMS to form a complete tool for processing CIL LC-MS data. This program can be freely downloaded from the www.MyCompoundID.org web site for noncommercial use.

An automated system has been developed for protein identification using mass spectrometry that incorporates sample cleanup, preconcentration, and protein digestion in a single stage. The procedure involves the adsorption of a protein or a protein mixture from solution onto a hydrophobic medium that is contained within a microcolumn. The protein is digested while still bound to the hydrophobic support. The peptides are then eluted from surface digestion to an electrospray ionization mass spectrometer for detection and sequencing. The entire system is fully automated wherein the mass spectrometer is collecting data continuously. We demonstrate that this system is capable of identifying standard protein samples at concentrations down to 100 nM. Further development of this technique may offer a potential solution for proteomics applications that require unattended operation, such as on-line monitoring and identification of microorganisms on the basis of the detection of their protein biomarkers.

A protein mass mapping approach using mass spectrometry (MS) combined with an experimentally derived protein mass database is presented for rapid and effective identification of bacterial species. A prototype mass database from the protein extracts of nine bacterial species has been created by off-line high-performance liquid chromatography (HPLC) matrix-assisted laser desorption/ionization (MALDI) MS, in which the microbiological parameter of bacterial growth time is considered. A numerical method using a statistical weight factor algorithm is devised for matching the protein masses of an unknown bacterial sample against the database. The sum of these weight factors produces a corresponding summed weight factor score for each bacterial species listed in the database, and the database species producing the highest score represents the identity of the respective unknown bacterium. The applicability and reliability of this protein mass mapping approach has been tested with seven bacterial species in a single-blind study by both direct MALDI MS and HPLC electrospray ionization MS methods, and identification results with 100% accuracy are obtained. Our studies have demonstrated that the protein mass database can be rapidly established and readily adopted with relatively less dependency on experimental factors. Furthermore, it is shown that a number of proteins can be detected using a protein sample amount equivalent to an extract of less than 1000 cells, demonstrating that this protein mass mapping approach can potentially be highly sensitive for rapid bacterial identification.

Protein phosphorylation is one of the most important known posttranslational modifications. Tandem mass spectrometry has become an important tool for mapping out the phosphorylation sites. However, when a peptide generated from the enzymatic or chemical digestion of a phosphoprotein is highly phosphorylated or contains many potential phosphorylation residues, phosphorylation site assignment becomes difficult. Separation and enrichment of phosphopeptides from a digest mixture is desirable and often a critical step for MS/MS-based site determination. In this work, we present a novel open tubular immobilized metal ion affinity chromatography (OT-IMAC) method, which is found to be more effective and reproducible for phosphopeptide enrichment, compared to a commonly used commercial product, Ziptip from Millipore. A strategy based on a combination of OT-IMAC, sequential dual-enzyme digestion, and matrix-assisted laser desorption/ionization (MALDI) quadrupole time-of-flight tandem mass spectrometry for phosphoprotein characterization is presented. It is shown that MALDI MS/MS with collision-induced dissociation can be very effective in generating fragment ion spectra containing rich structural information, which enables the identification of phosphorylation sites even from highly phosphorylated peptides. The applicability of this method for real world applications is demonstrated in the characterization and identification of phosphorylation sites of a Na(+)/H(+) exchanger fusion protein, His182, which was phosphorylated in vitro using the kinase Erk2.

Detection and identification of pathogenic bacteria and their protein toxins play a crucial role in a proper response to natural or terrorist-caused outbreaks of infectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens new diagnostic possibilities for identification of bacteria by retrieving their genomic or proteomic information. We describe a method for identification of bacteria based on tandem mass spectrometric (MS/MS) analysis of peptides derived from bacterial proteins. This method involves bacterial cell protein extraction, trypsin digestion, liquid chromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MS spectral matching results for bacterial identification. To facilitate spectral data searching, a proteome database was constructed by translating genomes of bacteria of interest with fully or partially determined sequences. In this work, a prototype database was constructed by the automated analysis of 87 publicly available, fully sequenced bacterial genomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done by SEQUEST. To gauge the relative significance of the SEQUEST-generated matching parameters for correct peptide assignment, discriminant function (DF) analysis of these parameters was applied and DF scores were used to calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. The peptides with DF scores exceeding a threshold value determined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filtering or removal of degenerate peptides matched with multiple bacteria was then performed to further improve identification. It is demonstrated that using a preset criterion with known distributions of discriminant function scores and probabilities of correct peptide sequence assignments, a test bacterium within the 87 database microorganisms can be unambiguously identified.

The availability of a suitable database is critical in a proteomic approach for bacterial identification by mass spectrometry (MS). The major limitation of the present public proteome database is the lack of extensive low-mass bacterial protein entries with masses experimentally verified for most bacteria. Here, we present a method based on mass spectrometry to create protein mass tables specifically tailored for bacterial identification. Several issues related to the detection of bacterial proteins for the purpose of database creation are addressed. Three species of bacteria, namely, Escherichia coli, Bacillus megaterium, and Citrobacter freundii, which can be found in the ambient environment, were chosen for this study. Direct matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of each bacterial extract reveals 20-29 protein components in the mass range from 2000 to 20,000 Da. HPLC fractionation of bacterial extracts followed by off-line MALDI-TOF analysis of individual fractions detects 156-423 components. Analysis of the extracts by HPLC/electrospray ionization MS shows the number of detectable proteins in the range of 46-59. Although a number of components were common to the three detection schemes employed, some unique components were found using each of these techniques. In addition, for E. coli where a large proteome database exists in the public domain, a number of masses detected by the mass spectrometric methods do not match with the proteome database. Compared to the public proteome database, the mass tables generated in this work are demonstrated to be more useful for bacterial identification in an application where the bacteria of interest have limited protein entries in the public database. The implication of this work for future development of a comprehensive mass database is discussed.

Sodium dodecyl sulfate (SDS) is a strong surfactant that is widely used in protein sample preparation. While protein and peptide samples containing up to approximately 1% SDS can be analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) using a two-layer matrix/sample deposition method, the presence of SDS in a protein sample generally degrades mass resolution and mass measurement accuracy. This degradation in performance is found to be related to the formation of sodium-protein adducts in the MALDI process. If the instrument resolving power is insufficient to separate these adduct peaks from the protonated molecular ion peak, peak broadening is observed in the protein molecular ion region, and as a result, the peak centroid shifts to a higher mass. In this work, we present a method using ammonium dodecyl sulfate as a viable alternative to SDS for protein sample preparation with much improved MALDI MS performance. Three non-sodium-based dodecyl sulfate surfactants, ammonium dodecyl sulfate (ADS), hydrogen dodecyl sulfate, and tris(hydroxymethyl)aminomethane dodecyl sulfate were investigated. Of the three surfactants tested, it is found that ADS gives the best performance in MALDI. For proteins with moderate molecular masses (i.e., up to approximately 25 kDa), the presence of ADS in a protein sample does not result in significant degradation in mass resolution and accuracy, and the protonated molecular ion peak is the dominant peak in the MALDI spectrum. The ammonium adduct ions dominate the MALDI spectra when the protein mass exceeds approximately 25 kDa; however, ADS still gives better results than SDS. The behavior of ADS in gel electrophoresis was also investigated. It is shown that cell extracts dissolved in ADS can be separated by normal SDS-polyacrylamide gel electrophoresis by simply mixing them with the SDS sample buffer. The application of ADS as the surfactant for protein solubilization with improved performance in MALDI analysis is demonstrated in the study of a detergent insoluble fraction from a Raji/CD9 B-cell lymphocyte lysate.

A chemical isotope labeling or isotope coded derivatization (ICD) metabolomics platform uses a chemical derivatization method to introduce a mass tag to all of the metabolites having a common functional group (e.g., amine), followed by LC-MS analysis of the labeled metabolites. To apply this platform to metabolomics studies involving quantitative analysis of different groups of samples, automated data processing is required. Herein, we report a data processing method based on the use of a mass spectral feature unique to the chemical labeling approach, i.e., any differential-isotope-labeled metabolites are detected as peak pairs with a fixed mass difference in a mass spectrum. A software tool, IsoMS, has been developed to process the raw data generated from one or multiple LC-MS runs by peak picking, peak pairing, peak-pair filtering, and peak-pair intensity ratio calculation. The same peak pairs detected from multiple samples are then aligned to produce a CSV file that contains the metabolite information and peak ratios relative to a control (e.g., a pooled sample). This file can be readily exported for further data and statistical analysis, which is illustrated in an example of comparing the metabolomes of human urine samples collected before and after drinking coffee. To demonstrate that this method is reliable for data processing, five (13)C2-/(12)C2-dansyl labeled metabolite standards were analyzed by LC-MS. IsoMS was able to detect these metabolites correctly. In addition, in the analysis of a (13)C2-/(12)C2-dansyl labeled human urine, IsoMS detected 2044 peak pairs, and manual inspection of these peak pairs found 90 false peak pairs, representing a false positive rate of 4.4%. IsoMS for Windows running R is freely available for noncommercial use from www.mycompoundid.org/IsoMS.

Small peptides, such as dipeptides and tripeptides, are naturally present in many biological samples (e.g., human biofluids and cell extracts). They have attracted great attention in many research fields because of their important biological functions as well as potential roles as disease biomarkers. Tandem mass spectrometry (MS/MS) can be used to profile these small peptides. However, the type and number of fragment ions generated in MS/MS are often limited for unambiguous identification. Herein we report a novel database-search strategy based on the use of MS/MS spectra of both unlabeled and dimethyl labeled peptides to identify and confirm amino acid sequences of di/tripeptides that are separated using hydrophilic interaction (HILIC) liquid chromatography (LC). To facilitate the di/tripeptide identification, a database consisting of all the predicted MS/MS spectra from 400 dipeptides and 8000 tripeptides was created, and a search tool, PEP Search, was developed and housed at the MyCompoundID website ( www.mycompoundid.org/PEP). To evaluate the identification specificity of this method, we used acid hydrolysis to degrade a standard protein, cytochrome c, to produce many di/tripeptides with known sequences for LC/MS/MS. The resultant MS/MS spectra were searched against the database to generate a list of matches which were compared to the known sequences. We correctly identified the di/tripeptides in the protein hydrolysate. We then applied this method to detect and identify di/tripeptides naturally present in human urine samples with high confidence. We envisage the use of this method as a complementary tool to various LC/MS techniques currently available for small molecule or metabolome profiling with an added benefit of covering all di/tripeptide chemical space.

Sodium dodecyl sulfate (SDS) is widely used in protein sample workup. However, many mass spectrometric methods cannot tolerate the presence of this strong surfactant in a protein sample. We present a practical and robust technique based on a two-layer matrix/sample deposition method for the analysis of protein and peptide samples containing SDS by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). The two-layer method involves the deposition of a mixture of sample and matrix on top of a thin layer of matrix crystals. It was found that for SDS-containing samples, the intensity of the MALDI signals can be affected by the conditions of sample preparation: on-probe washing, choice of matrix, deposition method, solvent system, and protein-to-SDS ratio. However, we found that, under appropriate conditions, the two-layer method gave reliable MALDI signals for samples with levels of SDS up to approximately 1%. The applications of this method are demonstrated for MALDI analysis of hydrophobic membrane proteins as well as bacterial extracts. We envision that this two-layer method capable of handling impure samples including those containing SDS will play an important role in protein molecular weight analysis as well as in proteome identification by MALDI-MS and MS/MS.

Chemical structures of polymer end groups play an important role in determining the functional properties of a polymeric system. We present a mass spectrometric method for determining end group structures. Polymeric ions are produced by electrospray ionization (ESI), and they are subject to source fragmentation in the ESI interface region to produce low-mass fragment ions. A series of source-fragment ions containing various numbers of monomer units are selected for high-energy collision-induced dissociation (CID) in a sector/time-of-flight tandem mass spectrometer. It is shown that high-energy CID spectra of source-induced fragment ions are very informative for end group structure characterization. By comparing the CID spectra of fragment ions with those of known chemicals, it is possible to unambiguously identify the end group structures. The utility of this technique is illustrated for the analysis of two poly(ethylene glycol)-based slow-releasing drugs where detailed structural characterization is of significance for drug formulation, quality control, and regulatory approval. Practical issues related to the application of this method are discussed.

A method for generating peptide mass maps from dilute protein samples is presented. The method involves the concentration of proteins from aqueous solution by adsorption onto reversed-phase polymeric microbeads. These beads are then washed extensively to remove contaminants, after which the bound proteins are digested with trypsin. Analysis of the digestion products is performed by MALDI-TOF mass spectrometry following direct deposition of the beads on a MALDI target, along with the matrix solution. The procedure is demonstrated using solutions of cytochrome c, lysozyme, and bovine serum albumin. The results of these digests are compared to trypsin digestions of the protein samples without sample preconcentration. Comparative results are also presented for protein solutions contaminated with 2 M NaCl, 2 M urea, or sodium dodecyl sulfate at concentrations up to 0.02%. These results reveal that, with the microbead preconcentration procedure, peptide mass maps can routinely be generated from highly contaminated samples with a protein concentration of only 100 nM.

The analytical performance of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for direct analysis of peptide and protein mixtures is strongly dependent on the sample and matrix preparation. A two-layer sample preparation method is demonstrated to be very effective for analyzing complex mixtures. In this method, the first layer on the MALDI probe is the densely packed matrix microcrystals formed by fast solvent evaporation of a matrix solution. A mixture solution containing both matrix and sample is then deposited onto the first layer to form uniform analyte/matrix micrococrystals. It is found that the addition of matrix to the second-layer sample solution proves to be critical in analyzing mixtures of peptides and proteins covering a broad mass range. The effect of solvent conditions for preparing the second-layer solution is discussed. The application of this method is demonstrated for the analysis of cow's milk where milk proteins as well as peptide fragments produced from proteins by indigenous proteinases are detected. Direct analyses of peptides and proteins from a bacteria extract and crude egg white are also illustrated.

A nanoliter-chemistry station combined with matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry was developed to characterize proteins at the attomole level. Chemical reactions including protein digestion were carried out in nanoliter or subnanoliter volumes, followed by microspot sample deposition of the digest to a MALDI-TOF mass spectrometer. Accurate mass determination of the peptides from the enzyme digest, in conjunction with protein database searching, allowed the identification of the proteins in the protein database. This method is particularly useful for handling small-volume samples such as in single-cell analysis. The high sensitivity and specificity of this method were demonstrated by peptide mapping and identifying hemoglobin variants of sickle cell disease from a single red blood cell. The approach of combining nanoliter chemistry with highly sensitive mass spectrometric analysis should find general use in characterizing proteins from biological systems where only a limited amount of material is available for interrogation.