Reproducibility of serum protein profiling by systematic assessment using solid-phase extraction and matrix-assisted laser desorption/ionization mass spectrometry

Evaluation of the reliability of MS1-based approach to profile naturally occurring peptides with clinical relevance in urine samples

RATIONALE

The profiling of natural urinary peptides is a valuable indicator of kidney condition. While front-end separation limits the speed of peptidomic profiling, MS1-based results suffer from limited peptide coverage and specificity. Clinical studies on chronic kidney disease require an effective strategy to balance the trade-off between identification depth and throughput.

METHODS

CKD273, a urinary proteome classifier associated with chronic kidney disease, in samples from diabetic nephropathy patients was profiled in parallel using capillary electrophoresis-mass spectrometry (CE-MS), liquid chromatography with mass spectrometry (LC-MS), and matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). Through cross-comparison of results from MS1 of unfractionated peptides and elution-time-resolved MS1 as well as MS/MS in LC- and CE-MS approaches, we evaluated the contribution of false-positive identification to MS1-based identification and quantitation, and analyzed the benefit of front-end separation in terms of accuracy and efficiency.

RESULTS

In LC- and CE-MS, although MS1 data resulted in higher number of identifications than MS/MS, elution-time-dependent analysis revealed extensive interference by non-CKD273 peptides, which would contribute up to 50% to quantitation if they are not separated from genuine CKD273 peptides. In the absence of separation, MS1 data resulted in lower numbers of identifications and abundance pattern that significantly deviated from those by liquid chromatography with tandem mass spectrometry (LC-MS/MS) or capillary electrophoresis with tandem mass spectrometry (CE-MS/MS). CE showed higher identification efficiency even when less sample was used or achieved faster separation.

CONCLUSIONS

To ensure the reliability of MS1-based urinary peptide profiling, front-end separation should not be omitted, and elution time should be used in addition to intact mass for identification. Including MS/MS in data acquisition does not compromise the speed or identification number, while benefiting data reliability by providing real-time sequence verification.

Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry

Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min.

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

Applications of MALDI-TOF mass spectrometry in clinical proteomics

INTRODUCTION

The development of precision medicine requires advanced technologies to address the multifactorial disease stratification and to support personalized treatments. Among omics techniques, proteomics based on Mass Spectrometry (MS) is becoming increasingly relevant in clinical practice allowing a phenotypic characterization of the dynamic functional status of the organism. From this perspective, Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) MS is a suitable platform for providing a high-throughput support to clinics. Areas covered: This review aims to provide an updated overview of MALDI-TOF MS applications in clinical proteomics. The most relevant features of this analysis have been discussed, highlighting both pre-analytical and analytical factors that are crucial in proteomics studies. Particular emphasis is placed on biofluids proteomics for biomarkers discovery and on recent progresses in clinical microbiology, drug monitoring, and minimal residual disease (MRD). Expert commentary: Despite some analytical limitations, the latest technological advances together with the easiness of use, the low time and low cost consuming and the high throughput are making MALDI-TOF MS instruments very attractive for the clinical practice. These features offer a significant potential for the routine of the clinical laboratory and ultimately for personalized medicine.

Multicentric study of the effect of pre-analytical variables in the quality of plasma samples stored in biobanks using different complementary proteomic methods

Analytical proteomics has experienced exponential progress in the last decade and can be expected to lead research studies on diagnostic and therapeutic biomarkers in the near future. Because the development of this type of analysis requires the use of a large number of human samples with a minimum of quality requirements, our objective was to identify appropriate indicators for quality control of plasma samples stored in biobanks for research in proteomics. To accomplish this, plasma samples from 100 healthy donors were obtained and processed according to the pre-analytical variables of: a) time delay for the first centrifugation of the original blood sample (4 or 24h) and b) number of freeze/thaw cycles (1, 2 or 3) of the processed plasma samples. The analyses of samples were performed by different and complementary methods such as SPE MALDI-TOF, DIGE, shotgun (iTRAQ, nLC MALDI TOF/TOF) and targeted nLC MS/MS proteomic techniques (SRM). In general, because the distribution of proteins in all samples was found to be very similar, the results shown that delayed processing of blood samples and the number of freeze/thaw cycles has little or no effect on the integrity of proteins in the plasma samples.

SIGNIFICANCE

The results of the present work indicate that blood proteins in plasma are broadly insensitive to such preanalytical variables as delayed processing or freeze/thaw cycles when analyzed at the peptide level. Although there are other studies related to protein stability of clinical samples with similar results, what is remarkable about our work is the large number of plasma samples examined and that our analyses assessed protein integrity by combining a wide set of complementary proteomic approaches performed at different proteomic platform participating laboratories that all yielded similar results. We believe our study is the most comprehensive performed to date to determine the changes in proteins induced by delayed sample processing and plasma freeze/thaw cycles.

Tissue biomarkers of breast cancer and their association with conventional pathologic features

Background:

Tissue protein expression profiling has the potential to detect new biomarkers to improve breast cancer (BC) diagnosis, staging, and prognostication. This study aimed to identify tissue proteins that differentiate breast cancer tissue from healthy breast tissue using protein chip mass spectrometry and to examine associations with conventional pathological features.

Methods:

To develop a training model, 82 BC and 82 adjacent unaffected tissue (AT) samples were analysed on cation-exchange protein chips by time-of-flight mass spectrometry. For validation, 89 independent BC and AT sample pairs were analysed.

Results:

From the protein peaks that were differentially expressed between BC and AT by univariate analysis, binary logistic regression yielded two peaks that together classified BC and AT with a ROC area under the curve of 0.92. Two proteins, ubiquitin and S100P (in a novel truncated form), were identified by liquid chromatography/tandem mass spectrometry and validated by immunoblotting and reactive-surface protein chip immunocapture. The combined marker panel was positively associated with high histologic grade, larger tumour size, lymphovascular invasion, ER and PR positivity, and HER2 overexpression, suggesting that it may be associated with a HER2-enriched molecular subtype of breast cancer.

Conclusion:

This independently validated protein panel may be valuable in the classification and prognostication of breast cancer patients.

An automated plasma protein fractionation design: high-throughput perspectives for proteomic analysis

Background

Human plasma, representing the most complete record of the individual phenotype, is an appealing sample for proteomics analysis in clinical applications. Up to today, the major obstacle in a proteomics study of plasma is the large dynamic range of protein concentration and the efforts of many researchers focused on the resolution of this important drawback.

Findings

In this study, proteins from pooled plasma samples were fractionated according to their chemical characteristics on a home-designed SPE automated platform. The resulting fractions were digested and further resolved by reversed-phase liquid chromatography coupled with MALDI TOF/TOF mass spectrometry. A total of 712 proteins were successfully identified until a concentration level of ng/mL. Pearson correlation coefficient was used to test reproducibility.

Conclusions

Our multidimensional fractionation approach reduced the analysis time (2 days are enough to process 16 plasma samples filling a 96-well plate) over the conventional gel-electrophoresis or multi-LC column based methods. The robotic processing, avoiding contaminants or lack of sample handling skill, promises highly reproducible specimen analyses (more than 85% Pearson correlation). The automated platform here presented is flexible and easily modulated changing fractioning elements or detectors.

The proteomics big challenge for biomarkers and new drug-targets discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.

Comparative proteomic profiles indicating genetic factors may involve in hepatocellular carcinoma familial aggregation

Familial aggregation of hepatocellular carcinoma (HCC), the third leading cause of cancer death worldwide, has shown to be a common phenomenon. We investigated the association between the genetic background and HCC familial aggregation. Serum samples were collected from HCC family members and normal control family members for screening the differentially expressed protein peaks with the approach of surface-enhanced laser desorption ionization time-of-flight mass spectrometry. Potential genetically associated protein peaks were selected and further identified by matrix assisted laser desorption ionization-time of flight mass spectrometry. A panel of six protein peaks (m/z 6432.94, 8478.35, 9381.91, 17284.67, 17418.34, and 18111.04) were speculated to reflect the genetic susceptibility of HCC familial aggregation. Three of them (m/z 6432.94, 8478.35, and 9381.91) were selected to identify as the candidate proteins. Nine identified proteins, including mostly apolipoprotein family (ApoA1, ApoA2, ApoC3, ApoE) and serum amyloid A protein (SAA), were found overexpressed in the multiple HCC cases family members. The comparative proteomic profiles have suggested that genetic factors ought to be taken into account for familial aggregation of HCC.

Detection and measurement of carbohydrate deficient transferrin in serum using immuno-capture mass spectrometry: diagnostic applications for annual ryegrass toxicity and corynetoxin exposure

The neurological livestock disease annual ryegrass toxicity (ARGT) is caused by the ingestion of the naturally occurring glycolipid toxins - the corynetoxins. Corynetoxins also threaten human health as potential contaminants of the food supply. Presently, there are no routine diagnostic tests for corynetoxins-exposure in humans or livestock. Chronic ingestion of corynetoxins has been modeled in rats exposed to dietary tunicamycins for 12 months and carbohydrate deficient transferrin (CDT) has been previously identified as a candidate disease biomarker. Here, the technique of immuno-capture mass spectrometry (icMS) was used to evaluate serum levels of CDT, discriminating between control and tunicamycins-exposed rats with 85% accuracy. The icMS approach is based on the combination of specific transferrin enrichment with functionalized magnetic beads and automated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). With no other clinically-relevant diagnostic tests available icMS could be readily adapted for high-throughput clinical assessment of corynetoxins-exposure in humans or livestock.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Multifunctional nanoparticles composite for MALDI-MS: Cd2+-doped carbon nanotubes with CdS nanoparticles as the matrix, preconcentrating and accelerating probes of microwave enzymatic digestion of peptides and proteins for direct MALDI-MS analysis

For the first time, we utilized multifunctional nanoparticles composite (NPs composite) for matrix-assisted laser desorption/ionization mass spectrometric (MALDI-MS) analysis of peptides and proteins. Multiwalled carbon nanotubes doped with Cd(2+) ions and modified with cadmium sulfide NPs were synthesized by a chemical reduction method at room temperature. The multifunctional NPs composite applied for the analysis of peptides and microwave-digested proteins in the atmospheric pressure matrix-assisted laser desorption/ionization ion-trap and MALDI time-of-flight (TOF) mass spectrometry (MS) was successfully demonstrated. The maximum detection sensitivity for peptides in MALDI-MS was achieved by the adsorption of negatively charged peptides onto the surfaces of NP composite through electrostatic interactions. The optimal conditions of peptide mixtures were obtained at 20 min of incubation time using 1 mg of NPs composite when the pH of the sample solution was kept higher than the pI values of peptides. The potentiality of the NP composite in the preconcentration of peptides was compared with that of the individual NP by calculating the preconcentration factors (PF) and found that the NPs composite showed a 4-6 times of PF than the other NPs. In addition, the NPs composite was also applied as heat-absorbing materials for efficient microwave tryptic digestion of cytochrome c and lysozyme from milk protein in MALDI-TOF-MS analysis. We believe that the use of NPs composite technique would be an efficient and powerful preconcentrating tool for MALDI-MS for the study of proteome research.

Standardized peptidome profiling of human serum for the detection of pancreatic cancer

OBJECTIVE

To establish new biomarkers for accurate diagnosis of pancreatic cancer (PC) using a standardized serum peptidome profiling and compare the results with those from the tumor marker, CA 19-9.

METHODS

Serum samples from 102 patients (55 with chronic pancreatitis and 47 with PC) and 56 healthy controls were collected and analyzed following a protocol that was rigorously designed to prevent preanalytical variation. Serum peptides were extracted using immobilized copper ion chromatography on a robotic platform. Mass spectra were acquired by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry on an Autoflex II spectrometer (Bruker Daltonics, Bremen, Germany). Statistical analysis was performed using the Clinprotools 2.2 software (Bruker Daltonics) and the SPSS 15.0 software (SPSS Inc, Chicago, Ill).

RESULTS

Standardized peptidome profiling showed a median coefficient of variation of 11.6% calculated using all the extracted peptides and negligible influence of sex and age on peptidome profiles. The diagnostic sensitivity was 89.9%, and the diagnostic specificity was 92.7%, using 2 serum features and CA 19-9 serum concentration. Healthy controls were differentiated from patients with PC and chronic pancreatitis, with the use of 3 features of the peptidome (diagnostic sensitivity, 98.2%; diagnostic specificity, 97.1%).

CONCLUSIONS

Standardized serum peptidome profiling could be a useful tool to improve biochemical diagnosis of PC in combination with the classic tumor marker, CA 19-9.

Quality control based on isotopic distributions for high-throughput MALDI-TOF and MALDI-FTICR serum peptide profiling

In this study, we have implemented a new quality control (QC) parameter for peptide profiling based on isotopic distributions. This QC parameter is an objective measure and facilitates automatic sorting of large numbers of peptide spectra. Peptides in human serum samples were enriched using reversed-phase C(18)-functionalized magnetic beads using a high-throughput robotic platform. High-resolution MALDI-TOF and ultrahigh resolution MALDI-FTICR mass spectra were obtained and a workflow was developed for automated analysis and evaluation of these profiles. To this end, the isotopic distributions of multiple peptides were quantified from both MALDI-TOF and MALDI-FTICR spectra. Odd peptide isotope distributions in TOF spectra could be rationalized from ultrahigh resolution FTICR spectra that showed overlap of different peptides. The comparison of isotope patterns with estimated polyaveragine distributions was used to calculate a QC value for each single mass spectrum. Sorting these QC values enabled the best MALDI spectrum to be selected from replicate spots. Moreover, using this approach spectra containing high intensities of polymers or other contaminants and lacking peptides of interest can be efficiently removed from a clinical dataset. In general, this method simplifies the exclusion of low quality spectra from further statistical analysis.

Precipitation and selective extraction of human serum endogenous peptides with analysis by quadrupole time-of-flight mass spectrometry reveals posttranslational modifications and low-abundance peptides

The endogenous peptides of human serum may have regulatory functions, have been associated with physiological states, and their modifications may reveal some mechanisms of disease. In order to correlate levels of specific peptides with disease alongside internal standards, the polypeptides must first be reliably extracted and identified. Endogenous blood peptides can be effectively enriched by precipitation of the serum with organic solvents followed by selective extraction of peptides using aqueous solutions modified with organic solvents. Polypeptides on filter paper were assayed with Coomasie brilliant blue binding. The polypeptides were resolved by detergent tricine polyacrylamide electrophoresis and visualized by diamine silver staining. Peptides in the extracts were collected by C18 and analyzed by matrix-assisted laser desorption/ionization and liquid chromatography-electrospray ionization-tandem mass spectrometry (MS/MS) quadrupole time-of-flight MS/MS. Peptides were resolved as multiple isotopic peaks in MS mode with mass deviation of 0.1 Da or less and similar accuracy for fragments. The sensitivity of MS and MS/MS analysis was estimated to be in the picomolar range or less. The peptide composition of the extracts was dependent on solvent formulation. Multiple peptides from apolipoproteins, complement proteins, coagulation factors, and many others were identified by X!Tandem with high mass accuracy of peptide ions and fragments from collision-induced dissociation. Many previously unreported posttranslational modifications of peptides including phosphorylations, oxidations, glycosylations, and others were detected with high mass accuracy and may be of clinical importance. About 4,630 redundant peptides were identified with 99% confidence separately, and together some 1,251 distinct proteins were identified with 99% confidence or greater using the Paragon algorithm.

MALDI profiling of human lung cancer subtypes

BACKGROUND

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.

METHODOLOGY/PRINCIPAL FINDINGS

In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree-based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non-small cell lung cancer histological subtypes.

CONCLUSIONS/SIGNIFICANCE

A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.

Optimal preparation methods for automated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling of low molecular weight proteins and peptides

Mass spectrometry (MS) profiling of the proteome and peptidome for disease-associated patterns is a new concept in clinical diagnostics. The technique, however, is highly sensitive to external sources of variation leading to potentially unacceptable numbers of false positive and false negative results. Before MS profiling can be confidently implemented in a medical setting, standard experimental methods must be developed that minimize technical variance. Past studies of variance have focused largely on pre-analytical variation (i.e., sample collection, handling, etc.). Here, we examined how factors at the analytical stage including the matrix and solid-phase extraction influence MS profiling. Firstly, a standard peptide/protein sample was measured automatically by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS across five consecutive days using two different preparation methods, dried droplet and sample/matrix, of four types of matrix: alpha-cyano-4-hydroxycinnamic acid (HCCA), sinapinic acid (SA), 2,5-dihydroxybenzoic acid (DHB) and 2,5-dihydroxyacetophenone (DHAP). The results indicated that the matrix preparation greatly influenced a number of key parameters of the spectra including repeatability (within-day variability), reproducibility (inter-day variability), resolution, signal strength, background intensity and detectability. Secondly, an investigation into the variance associated with C8 magnetic bead extraction of the standard sample prior to automated MS profiling demonstrated that the process did not adversely affect these same parameters. In fact, the spectra were generally more robust following extraction. Thirdly, the best performing matrix preparations were evaluated using C8 magnetic bead extracted human plasma. We conclude that the DHAP prepared according to the dried-droplet method is the most appropriate matrix to use when performing automated MS profiling.

Pharmacokinetics and pharmacogenomics in esophageal cancer chemoradiotherapy

Esophageal cancer is one of the most lethal malignancies. Surgical resection of the tumor from the primary site has been the standard treatment, especially for localized squamous cell carcinoma, but considerable clinical efforts during the last decade have resulted in novel courses of treatment. These options include chemoradiotherapy, consisting of a continuous infusion of 5-fluorouracil (5-FU), cisplatin (CDDP), and concurrent radiation. Given the substantial inter- and/or intra-individual variation in clinical outcome, future improvements will likely require the incorporation of a novel anticancer drug, pharmacokinetically guided administration of CDDP or 5-FU, and identification of potential responders by patient genetic profiling prior to treatment. In this review, the latest information on incidence, risk factors, biomarkers, therapeutic strategies, and the pharmacokinetically guided or genotype-guided administration of CDDP and 5-FU is summarized for future individualization of esophageal cancer treatment.

An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies

Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

Analysis of the peptide profile of milk and its changes during thermal treatment and storage

In this study a new method was developed for analysis of the low molecular weight protein fraction of milk, allowing a simple and fast overview of the peptide profile of various milk samples. For this purpose, immobilized metal affinity chromatography (IMAC) was coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). By this technique, two major peptides in milk could be identified as fragments of alpha-s1-casein. During heat treatment of raw milk, five new peptides were generated, the origin of which could be assigned to the casein fraction. Storage experiments with extended shelf life milk at 4 degrees C did not show any changes in the peptide profile, whereas in ultra high temperature milk stored at room temperature, one peptide increased significantly, which was identified as the N-terminus of alpha-s1-casein. The peptide was assumed to be formed in an enzymatic reaction, which was confirmed in a storage experiment with sterilized milk. Analyses of different commercially available milk samples confirmed the results obtained with the heated and stored milk. Furthermore, differences in the peptide profiles of the samples, probably due to different cow breeds or lactation stages, were observed. These results establish IMAC prior to MALDI-TOF-MS as a valid tool for the rapid analysis of the peptide profile of milk.