Mass Spectral Profiles Research Articles

A non-target chemometric strategy applied to UPLC-MS sphingolipid analysis of a cell line exposed to chlorpyrifos pesticide: A feasibility study

A non-target chemometrics study based on the application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method to a data set obtained by ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) has been applied to the study of human prostate cancer (DU145) cell line samples treated with the organophosphate pesticide chlorpyrifos (CPF). Full scan UPLC-MS data sets were segmented in 17 different chromatographic windows and submitted to a non-target detailed study. Every one of these chromatographic windows of the different analyzed samples (treated and non-treated with CPF) was column-wise augmented in a new data matrix with their m/z values in the common column mode to preserve the fulfillment of the assumed spectral bilinear model. MCR-ALS was used to recover the elution and mass spectral profiles of the pure components present in each of the analyzed chromatographic windows. ANOVA (p≤0.05) was then applied to compare the areas under the concentration profiles of the MCR-ALS resolved components in the CPS treated and control samples. This analysis allowed the detection of those sphingolipids having their concentration in cells modified by the presence of CPS compared to control samples where this contaminant was absent. Positively identified sphingolipids included sphingomyelins, dihydrosphingomyelin and C16 ceramide. The strategy described in this work is proposed for a general non-target UPLC-MS MCR-ALS analysis of the effect of environmental contaminants in cells in lipidomic and metabonomic studies.

Plume composition and evolution in multicomponent ices using resonant two-step laser ablation and ionization mass spectrometry.

The composition and evolution of plumes generated in a resonant infrared (IR) laser desorption of low-temperature ices is investigated via a recently developed two-step laser desorption and ionization mass spectrometry (2S-LAIMS) technique where a neutral plume is ejected by an IR laser pulse and ionized by a UV laser pulse for analysis via time-of-flight mass spectrometry. By varying the delay between the lasers, we can construct a complete time-resolved model of the ejected plume components. We found that water ices containing mixtures of polar and nonpolar analytes displayed complex mass spectral profiles that varied as the plume evolved. In these samples, the low-volatility polar analytes and clusters were restricted to the early part of the plume, whereas volatile or nonpolar analytes were spread throughout the plume. The distributions of low-volatility polar species, clusters, and impurities from the copper substrate were well-represented by single Maxwell-like distributions centered at high velocities (600-800 m s(-1)), while nonpolar, volatile species contained two distinct components, indicating both ablation and thermal desorption processes. Characterization of plume distributions can therefore provide new insight into an analyte's chemical identity and can aid in assignment of otherwise ambiguous signals in the mass spectra.

Novel use of faecal sterols to assess human faecal contamination in Antarctica: a likelihood assessment matrix for environmental monitoring

AbstractWastewater containing human sewage is often discharged with little or no treatment into the Antarctic marine environment. Faecal sterols (primarily coprostanol) in sediments have been used for assessment of human sewage contamination in this environment, butin situproduction and indigenous faunal inputs can confound such determinations. Using gas chromatography with mass spectral detection profiles of both C27and C29sterols, potential sources of faecal sterols were examined in nearshore marine sediments, encompassing sites proximal and distal to the wastewater outfall at Davis Station. Faeces from indigenous seals and penguins were also examined. Faeces from several indigenous species contained significant quantities of coprostanol but not 24-ethylcoprostanol, which is present in human faeces.In situcoprostanol and 24-ethylcoprostanol production was identified by co-production of their respectiveepi-isomers at sites remote from the wastewater source and in high total organic matter sediments. A C29sterols-based polyphasic likelihood assessment matrix for human sewage contamination is presented, which distinguishes human from local fauna faecal inputs andin situproduction in the Antarctic environment. Sewage contamination was detected up to 1.5 km from Davis Station.

Chemometrics-Assisted GC-MS Analysis of Volatile and Semi-Volatile Constituents of Elettaria cardamomum

Multivariate curve resolutions (MCR) along with other chemometric techniques are proposed to improve the analysis of Iranian Elettaria cardamomum (E. cardamom) essential oil with gas chromatography-mass spectrometry (GC-MS). In addition, multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure elution and mass spectral profiles for the components present in each chromatographic segment as well as their relative concentrations. This strategy was also used to overcome the problems of baseline offset, asymmetric peaks, retention time shifts, and overlapped and embedded peaks occurring during GC-MS analysis. The analysis of GC-MS data revealed that 42 components exist in the E. cardamom essential oil. However, with the help of MCR and different chemometric methods, this number was extended to 73 components. The most important components of the Iranian E. cardamom are α-terpineol acetate (11.78 %), linalool (10.15 %), nerolidol (8.82 %), α-pinene (8.11 %), geranyl acetate (3.47 %),1,8-cineole (4.25 %), and γ-terpinene (3.88 %).

Automation Highlights from the Literature

Automation Highlights from the Literature

Resolution of co-eluting compounds of Cannabis Sativa in comprehensive two-dimensional gas chromatography/mass spectrometry detection with Multivariate Curve Resolution-Alternating Least Squares

Comprehensive Two Dimensional Gas Chromatography - Mass Spectrometry (GC × GC/qMS) analysis of Cannabis sativa extracts shows a high complexity due to the large variety of terpenes and cannabinoids and to the fact that the complete resolution of the peaks is not straightforwardly achieved. In order to support the resolution of the co-eluted peaks in the sesquiterpene and the cannabinoid chromatographic region the combination of Multivariate Curve Resolution and Alternating Least Squares algorithms was satisfactorily applied. As a result, four co-eluting areas were totally resolved in the sesquiterpene region and one in the cannabinoid region in different samples of Cannabis sativa. The comparison of the mass spectral profiles obtained for each resolved peak with theoretical mass spectra allowed the identification of some of the co-eluted peaks. Finally, the classification of the studied samples was achieved based on the relative concentrations of the resolved peaks.

The use of multivariate curve resolution methods to improve the analysis of muramic acid as bacterial marker using gas chromatography–mass spectrometry: An alternative method to gas chromatography–tandem mass spectrometry

In analysis of muramic acid (MA) as bacterial marker, two dominant disturbing factors lead the researchers to use gas chromatography-tandem mass spectrometry (GC-MS/MS) technique instead of gas chromatography-mass spectrometry (GC-MS). These factors are the trace concentration of MA and fundamental disturbance of base line mass channels in GC-MS technique. This study aimed to utilize multivariate curve resolution (MCR) methods combined with GC-MS to improve the analysis of MA. First, the background and noise in GC-MS analysis were corrected and reduced using MCR methods. In addition, the MA overlapped peaks were resolved to its pure chromatographic and mass spectral profiles. Then the two-way response of each component was reconstructed by the outer product of the pure chromatographic and mass spectral profiles. The overall volume integration (OVI) method was used for quantitative determination. The MA peak area was decreased dramatically after the background correction and noise reduction. The findings severely ratify the appropriateness of using MCR techniques combined with GC-MS analysis as a simple, fast and inexpensive method for the analysis of MA in complex mixtures. The proposed method may be considered as an alternative method to GC-MS/MS for thorough analysis of the bacterial marker.

Derivative component analysis for mass spectral serum proteomic profiles.

BackgroundAs a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage.MethodsIn this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers.ResultsOur results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagnosis.ConclusionsOur findings demonstrate the feasibility and power of the proposed DCA-based profile biomarker diagnosis in achieving high sensitivity and conquering the data reproducibility issue in serum proteomics. Furthermore, our proposed derivative component analysis suggests the subtle data characteristics gleaning and de-noising are essential in separating true signals from red herrings for high-dimensional proteomic profiles, which can be more important than the conventional feature selection or dimension reduction. In particular, our profile biomarker diagnosis can be generalized to other omics data for derivative component analysis (DCA)'s nature of generic data analysis.

A new approach combining LC-MS and multivariate statistical analysis for revealing changes in histone modification levels.

While acting upon chromatin compaction, histone post-translational modifications (PTMs) are involved in modulating gene expression through histone-DNA affinity and protein-protein interactions. These dynamic and environment-sensitive modifications are constitutive of the histone code that reflects the transient transcriptional state of the chromatin. Here we describe a global screening approach for revealing epigenetic disruption at the histone level. This original approach enables fast and reliable relative abundance comparison of histone PTMs and variants in human cells within a single LC-MS experiment. As a proof of concept, we exposed BeWo human choriocarcinoma cells to sodium butyrate (SB), a universal histone deacetylase (HDAC) inhibitor. Histone acid-extracts (n = 45) equally representing 3 distinct classes, Control, 1 mM and 2.5 mM SB, were analysed using ultra-performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometer (UPLC-QTOF-MS). Multivariate statistics allowed us to discriminate control from treated samples based on differences in their mass spectral profiles. Several acetylated and methylated forms of core histones emerged as markers of sodium butyrate treatment. Indeed, this untargeted histonomic approach could be a useful exploratory tool in many cases of xenobiotic exposure when histone code disruption is suspected.

Changes in the Matrix Markedly Enhance the Resolution and Accurate Identification of Human Pathogens by MALDI-TOF MS

The mass spectral profiles of three major/emerging Gram positive pathogens belonging to the genera Clostridium, Listeria and Propionibacterium were investigated using four MALDI-TOF (Matrix-Assisted Laser Desorption/ Ionization- Time of Flight) Mass Spectrometers from Waters, Shimadzu, Bruker and Ciphergen Biosystems. These instruments have been extensively used for developing a diagnostic platform for high throughput, low cost, near sample-free preparation for microbial identification. The results demonstrate the marked effect of spectral quality obtained by altering the matrix and the added value of simple preparative extraction procedures. While microbial spectral data are generally collected in the mass range 2 to 20 kDa for diagnostic signatures, most of the significant mass ions are <10 kDa. The change from the traditional use of CMBT (5-chloro-2-mercaptobenzothiazole) to DHB (2-(2, 5-DiHydroxyBenzoic acid) enabled successful identification of C. difficile whereas the substitution of CHCA (alpha-cyano-4-hydroxycinnamic acid) for DHB (2,5-dihydroxybenzoic acid) improved the identification of Listeria species. By contrast, the use of SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization- Time of Flight- Mass Spectrometry), which employs affinity pre-capture of specific classes of molecules and analysed using sinapinic acid, showed a fivefold extension of the mass range of analytes, extending the potential for biomarker discovery and higher resolution strain typing of P. acnes.

A new method for the automated selection of the number of components for deconvolving overlapping chromatographic peaks

Mathematical deconvolution methods can separate co-eluting peaks in samples for which (chromatographic) separation fail. However, these methods often heavily rely on manual user-input and interpretation. This is not only time-consuming but also error-prone and automation is needed if such methods are to be applied in a routine manner.One major hurdle when automating deconvolution methods is the selection of the correct number of components used for building the model. We propose a new method for the automatic determination of the optimum number of components when applying multivariate curve resolution (MCR) to comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC–MS) data. It is based on a two-fold cross-validation scheme. The obtained overall cross-validation error decreases when adding components and increases again once over-fitting of the data starts to occur. The turning point indicates that the optimum number of components has been reached. Overall, the method is at least as good as and sometimes superior to the inspection of the eigenvalues when performing singular-value decomposition. However, its strong point is that it can be fully automated and it is thus more efficient and less prone to subjective interpretation. The developed method has been applied to two different-sized regions in a GC×GC–MS chromatogram. In both regions, the cross-validation scheme resulted in selecting the correct number of components for applying MCR. The pure concentration and mass spectral profiles obtained can then be used for identification and/or quantification of the compounds. While the method has been developed for applying MCR to GC×GC–MS data, a transfer to other deconvolution methods and other analytical systems should only require minor modifications.

Analysis of xyloglucans by ambient chloride attachment ionization tandem mass spectrometry

Xyloglucan oligomers obtained upon enzyme digestion from Hymenaea courbaril, Arabidopsis Columbia-0 and mur3 were ionized and analyzed by using chloride anion attachment electrospray ionization (ESI) and tandem mass spectrometry. MW determination and structural elucidation of several xyloglucan oligomers was performed directly from the mixture solutions without sample pretreatment or derivatization. Sodium cation attachment was used to determine the number of xyloglucans present in the mixtures and their MWs. However, tandem mass spectrometry results showed that structure elucidation based on the sodium adducts is ambiguous. Chloride anion also forms stable adducts with these xyloglucans upon ESI. These adducts can be readily identified due to the chlorine isotope pattern. The mass spectral profile of xyloglucans obtained for the mixtures matches the HPAEC results, thus validating this methodology for the determination of the xyloglucan composition and the MW of each xyloglucan. Upon collisional activation in MS2 experiments, the chloride anion adducts readily lose HCl, which helps verify the molecular weight of each xyloglucan. Isolating the resulting anion (deprotonated oligomer) and subjecting it to further collision-activated dissociation experiments (MSn; n=3–4) yields useful structural information that allows the differentiation between isomeric anions and hence determination of the sequence of the xyloglucan oligomers. The deprotonated oligomers fragment by a stepwise loss of sugar units from the reducing end.

Characterization of fungal antagonistic bacilli isolated from aerial roots of banyan (Ficus benghalensis) using intact-cell MALDI-TOF mass spectrometry (ICMS)

To characterize fungal antagonistic bacilli isolated from aerial roots of banyan tree and identify the metabolites responsible for their antifungal activity. Seven gram positive, endospore-forming, rod-shaped endophytic bacterial strains exhibiting a broad-spectrum antifungal activity were isolated from the surface-sterilized aerial roots of banyan tree. The isolates designated as K1, A2, A4 and A12 were identified as Bacillus subtilis, whereas isolates A11 and A13 were identified as Bacillus amyloliquefaciens using Biolog Microbial Identification System. The antifungal lipopeptides, surfactins, iturins and fengycins with masses varying in the range from m/z 900 to m/z 1550 could be detected using intact-cell MALDI-TOF mass spectrometry (ICMS). On the basis of mass spectral and carbon source utilization profile, all seven endophytes could be distinguished from each other. Furthermore, ICMS analysis revealed higher extent of heterogeneity among iturins and fengycins produced by B. subtilis K1, correlating well with its higher antifungal activity in comparison with other isolates. Seven fungal antagonistic bacilli were isolated from aerial roots of banyan tree, exhibiting broad spectrum of antifungal activity, among which B. subtilis K1 isolate was found to be most potent. The ICMS analysis revealed that all these isolates produced cyclic lipopeptides belonging to surfactin, iturin and fengycin families and exhibited varying degree of heterogeneity. The endophytes are considered as a potential source of novel bioactive metabolites, and this study describes the potent fungal antagonistic bacilli from aerial roots of banyan tree. The isolates described in this study have a prospective application as biocontrol agents. Also ICMS analysis described in this study for characterization of antifungal metabolites produced by banyan endophytic bacilli may be used as a high throughput tool for screening of microbes producing novel cyclic lipopeptides.

Biotyping Saccharomyces cerevisiae strains using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS), a powerful biotyping tool for the identification of bacteria and clinical yeast isolates, was investigated as a method to rapidly identify industrial S. cerevisiae strains. In this study, an optimized sample preparation protocol was devised for the biotyping of S. cerevisiae strains. The results demonstrate that ethanol/formic acid protein extraction, of a cell density of 1 × 108 cells co-crystallized with α-cyano-4-hydroxycinnmaic acid, is required to yield mass spectral profiles that are diagnostic of each strain type. Forty-four S. cerevisiae strains commonly employed in South African fermentation-based industries were biotyped in an attempt to create a yeast reference database within a local context. The data revealed that MALDI-TOF MS can be used for the rapid and accurate identification of laboratory and industrial S. cerevisiae strains.

Analysis of secondary organic aerosol formation and aging using positive matrix factorization of high-resolution aerosol mass spectra: application to the dodecane low-NO&amp;lt;sub&amp;gt;x&amp;lt;/sub&amp;gt; system

Abstract. Positive matrix factorization (PMF) of high-resolution laboratory chamber aerosol mass spectra is applied for the first time, the results of which are consistent with molecular level MOVI-HRToF-CIMS aerosol-phase and CIMS gas-phase measurements. Secondary organic aerosol was generated by photooxidation of dodecane under low-NOx conditions in the Caltech environmental chamber. The PMF results exhibit three factors representing a combination of gas-particle partitioning, chemical conversion in the aerosol, and wall deposition. The slope of the measured high-resolution aerosol mass spectrometer (HR-ToF-AMS) composition data on a Van Krevelen diagram is consistent with that of other low-NOx alkane systems in the same O : C range. Elemental analysis of the PMF factor mass spectral profiles elucidates the combinations of functionality that contribute to the slope on the Van Krevelen diagram.

Comparison of Sample Preparation Methods and Evaluation of Intra- and Intersample Reproducibility in Bacteria MALDI-MS Profiling

A study is presented on the reproducibility of mass spectral profiles of the whole bacterium E. coli resulting from laser sampling at different regions within and between matrix-assisted laser desorption ionization (MALDI) samples deposited onto the plate. Samples were prepared with different deposition methods and using different MALDI matrices. The three most common matrices used in MALDI-mass spectrometry (MS) bacteria profiling, α-cyano-4-hydroxycinnamic acid (CHCA), sinapinic acid (SA), and ferulic acid (FA), were compared in this study along with two pipet-based sample deposition methods, dried-droplet and premix. Sample variability was determined by analysis of variances (ANOVA), principal component analysis (PCA), and multivariate ANOVA (MANOVA). For the two pipet-based sample deposition methods tested in this study, the intrasample variability (most commonly referred to as "spot-to-spot" reproducibility) was of the same magnitude as the intersample variability for all MALDI matrices tested. By incorporating a spray nebulizer sample deposition method to produce uniform sample/matrix mixtures onto the MALDI plate, we demonstrate that the crystalline morphology of the MALDI sample greatly influences the intrasample reproducibility (i.e., spot-to-spot) of the resulting whole cell MALDI-MS profiles. Overall, for the pipet-based deposition methods, results showed that the smallest variability in bacteria MALDI mass spectral profiles was obtained from samples deposited using the premix method, regardless of the MALDI matrix used, with the best reproducibility obtained with the CHCA matrix. It is concluded that a sample preparation strategy that reduces or eliminates the MALDI matrix morphology heterogeneity can reduce variability (i.e., spot-to-spot) of the bacteria mass spectral profiles by up to 90%.

Chromatographic fingerprint analysis of secondary metabolites in citrus fruits peels using gas chromatography–mass spectrometry combined with advanced chemometric methods

Multivariate curve resolution (MCR) and multivariate clustering methods along with other chemometric methods are proposed to improve the analysis of gas chromatography–mass spectrometry (GC–MS) fingerprints of secondary metabolites in citrus fruits peels. In this way, chromatographic problems such as baseline/background contribution, low S/N peaks, asymmetric peaks, retention time shifts, and co-elution (overlapped and embedded peaks) occurred during GC–MS analysis of chromatographic fingerprints are solved using the proposed strategy. In this study, first, informative GC–MS fingerprints of citrus secondary metabolites are generated and then, whole data sets are segmented to some chromatographic regions. Each chromatographic segment for eighteen samples is column-wise augmented with m/z values as common mode to preserve bilinear model assumption needed for MCR analysis. Extended multivariate curve resolution alternating least squares (MCR-ALS) is used to obtain pure elution and mass spectral profiles for the components present in each chromatographic segment as well as their relative concentrations. After finding the best MCR-ALS model, the relative concentrations for resolved components are examined using principal component analysis (PCA) and k-nearest neighbor (KNN) clustering methods to explore similarities and dissimilarities among different citrus samples according to their secondary metabolites. In general, four clear-cut clusters are determined and the chemical markers (chemotypes) responsible to this differentiation are characterized by subsequent discriminate analysis using counter-propagation artificial neural network (CPANN) method. It is concluded that the use of proposed strategy is a more reliable and faster way for the analysis of large data sets like chromatographic fingerprints of natural products compared to conventional methods.

Detection of the Spermicide Nonoxynol-9 Via GC-MS

The spermicide nonoxynol-9 is actually a complex mixture of dozens of closely related amphiphilic compounds, and the chemical properties of this assortment significantly hamper its characterization by GC-MS. The inability to perform routine GC-MS testing on nonoxynol-9 has limited its evidentiary value in forensic casework, which relies heavily on this technique for analysis. A disturbing trend in sexual assault is the use of condoms by assailants, to avoid leaving behind DNA evidence that can connect a perpetrator to a victim. This observation necessitates the development of alternative methods for the analysis of trace evidence that can show causal links between a victim and a suspect. Detection of lubricants associated with sexual assault is one such way to establish this connection. The development of GC-MS methods that permit facile detection of both nonoxynol-9 alone and nonoxynol-9 extracted from other complex matrices that have potential as trace evidence in sexual assault is reported. A detection limit of 2.14 μg of nonoxynol-9 is demonstrated, and a detailed mass spectral profile that elaborates on what is known of its structure is provided.

Changes in Plasma Mass-Spectral Profile in Course of Treatment of Non-small Cell Lung Cancer Patients with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors

Our previous study showed that pretreatment serum or plasma Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry may predict clinical outcome of non-small cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). In this study, plasma proteomic profiles of NSCLC patients were evaluated in the course of EGFR TKIs therapy. Plasma samples were collected at baseline, in the course of gefitinib therapy and at treatment withdrawal. Samples were analyzed by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. Acquired spectra were classified by the VeriStrat test into "good" and "poor" profiles. The association between VeriStrat classification and progression-free survival (PFS) and overall survival (OS), and types of clinical progression, was analyzed. Plasma samples from 111 NSCLC patients treated with gefitinib were processed. VeriStrat "good" classification at baseline correlated with longer PFS (hazard ratio [HR], 0.54; 95% confidence interval, 0.35-0.83; p = 0.005) and OS (HR, 0.40; 95% confidence interval, 0.26-0.61; p < 0.0001), when compared with VeriStrat "poor." Multivariate analysis confirmed longer PFS (HR, 0.52; p = 0.025) and OS (HR, 0.44; p = 0.001) in patients classified as VeriStrat "good", when VeriStrat was considered as a time-dependent variable. About one-third of baseline "good" classifications had changed to "poor" at the time of treatment withdrawal; progression in these patients was associated with the development of new lesions. Our findings support the role of VeriStrat in the assistance in treatment selection of NSCLC patients for EGFR TKI therapy and its potential utility in treatment monitoring.

High-resolution MALDI-TOF mass spectrometry of bacterial proteins using a Tris-EDTA buffer approach

The performance of matrix assisted laser desorption-ionization time of flight mass spectrometry (MALDI-TOF) of bacterial proteins strongly depends on sample preparation. It is found that the mass spectral profiles obtained from direct MALDI-TOF MS of the protein extracts are much weaker for individual bacterial cells than compared to those prepared by the Tris-EDTA buffer approach (TEBA). Characteristic mass spectral peaks were observed in the mass range from 3,000 to 15,000 Da. The mass peaks reported earlier and claimed to serve as species-specific biomarkers are consistently found here as well. Mass peaks at m/z of 3636, 5466, 5750, 6315, 6547, 7274, 9192, and 9742 are found for Escherichia coli studied and assigned as specific biomarkers. Similarly, specific mass peaks have been identified at m/z 5443, 7270, 7724, and 9888 for Bacillus subtilis, and at 3603, 5496, 6800, 8858 and 9531 for Serratia marcescens. The detection limits for the three target bacteria range from 2.4 × 105 to 3.3 × 105 cfu·mL-1. We conclude that the TE buffer approach can produce reliable data for rapid classification, high-resolution and highly sensitive detection of bacteria.