Surface-enhanced Laser Desorption And Ionization Time-of-flight Mass Spectrometry Research Articles

Modifications in Rat Plasma Proteome after Remote Ischemic Preconditioning (RIPC) Stimulus: Identification by a SELDI-TOF-MS Approach

Remote ischemic preconditioning’s (RIPC) ability to render the myocardium resistant to subsequent prolonged ischemia is now clearly established in different species, including humans. Strong evidence suggests that circulating humoral mediators play a key role in signal transduction, but their identities still need to be established. Our study sought to identify potential circulating RIPC mediators using a proteomic approach. Rats were exposed to 10-min limb ischemia followed by 5- (RIPC 5′) or 10-min (RIPC 10′) reperfusion prior to blood sampling. The control group only underwent blood sampling. Plasma samples were isolated for proteomic analysis using surface-enhanced laser desorption and ionization - time of flight - mass spectrometry (SELDI-TOF-MS). A total of seven proteins, including haptoglobin and transthyretin, were detected as up- or down-regulated in response to RIPC. These proteins had previously been identified as associated with organ protection, anti-inflammation, and various cellular and molecular responses to ischemia. In conclusion, this study indicates that RIPC results in significant modulations of plasma proteome.

Apolipoprotein A-I Is a Potential Mediator of Remote Ischemic Preconditioning

BackgroundRemote ischemic preconditioning (RIPC) has emerged as an attractive strategy in clinical settings. Despite convincing evidence of the critical role played by circulating humoral mediators, their actual identities remain unknown. In this study, we aimed to identify RIPC-induced humoral mediators using a proteomic approach.Methodsand Results Rats were exposed to 10-min limb ischemia followed by 5- (RIPC 5′) or 10-min (RIPC 10′) reperfusion prior to blood sampling. The control group only underwent blood sampling. Plasma samples were analyzed using surface-enhanced laser desorption and ionization - time of flight - mass spectrometry (SELDI-TOF-MS). Three protein peaks were selected for their significant increase in RIPC 10′. They were identified and confirmed as apolipoprotein A-I (ApoA-I). Additional rats were exposed to myocardial ischemia-reperfusion (I/R) and assigned to one of the following groups RIPC+myocardial infarction (MI) (10-min limb ischemia followed by 10-min reperfusion initiated 20 minutes prior to myocardial I/R), ApoA-I+MI (10 mg/kg ApoA-I injection 10 minutes before myocardial I/R), and MI (no further intervention). In comparison with untreated MI rats, RIPC reduced infarct size (52.2±3.7% in RIPC+MI vs. 64.9±2.6% in MI; p<0.05). Similarly, ApoA-I injection decreased infarct size (50.9±3.8%; p<0.05 vs. MI).ConclusionsRIPC was associated with a plasmatic increase in ApoA-I. Furthermore, ApoA-I injection before myocardial I/R recapitulated the cardioprotection offered by RIPC in rats. This data suggests that ApoA-I may be a protective blood-borne factor involved in the RIPC mechanism.

Computational modeling of the human serum proteome response to colon resection surgery

Principal component analysis (PCA) is much used in exploring time-course biological data sets, but does not distinguish variation between time and subjects. This study proposes a new integrated approach by combining analysis of variance (ANOVA) and three component modeling methods. The former was used to separate the between- and within-subject variation, and the latter represent modeling strategies on a scale moving from commonality to individuality. The proposed approach was applied to a surface-enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF-MS) data set of a serum protein expression time course before and after colon resection. Two common biological processes are identified and individual differences among patients were also detected, and the biological relevance of both is discussed.

Discovery of biomarkers from serum in hepatocellular carcinoma using protein chip technique

Objective To screen serum biomarkers in patients with hepatocelhlar carcinoma by using surface-enhanced laser desorption and ionization time-of-flight mass spectrometry(SELDI-TOF-MS)technique and to evaluate its clinical implication in the patients whose alpha-fetoprotein were negative in the sera. Methods Proteomic spectra were generated by mass spectroscopy in 112 cases, including 57 cases AFP-negative hepatocellular carcinoma,and 55 cases of healthy control. The consequence was analyzed and the characteristic preteomic peaks was selected by using Biomarker Wizard. Results Seven low expressed potential biomarker were indentified with the mass-to-charge ratio of 4.2×103, 4.1×103, 6.7×103, 5.7×103, 6.5×103, 6.9×103, 5.8×103. The sensitivity for diagnosing hepatic cancer were 88.23 % and specificity was 92.31%. These peaks were not correlated with age, sex, tumor mass size, pathology grading and cirrhosis in hepatocellular carcinoma. Conclusion SELDI-TOF-MS offers a unique platform for the proteomic detection of hepatocellular carcinoma.It also offers an auxiliary diagnosis method to the patients whose alpha-fetoprotein are negative in the serum. Key words: Carcinoma, hepatocelhlar; Tumor markers, biological; SELDI; Alpha-fetoproteins

Pancreatic cancer biomarkers discovery by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry

Surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF/MS), a laboratory-friendly technique, is used to identify biomarkers for cancer. The aim of the present study was to explore the application of SELDI proteomic patterns in serum for distinguishing between cases of pancreatic cancer, chronic pancreatitis, type 2 diabetes mellitus and healthy controls. Sera from 12 healthy controls, 24 patients with type 2 diabetes mellitus, 126 with pancreatic cancer, including 84 with diabetes, and 61 with chronic pancreatitis, 32 of which were diabetics, were analyzed using SELDI-TOF/MS. Spectra (IMAC-30) were clustered and classified using Biomarker Wizard and Biomarker Pattern software. Two decision tree classification algorithms, one with and one without CA 19-9, were constructed. In the absence of CA 19-9, the splitting protein peaks were: m/z 1526, 1211, and 3519; when CA 19-9 was used in the analysis, it replaced the m/z 3519 splitter. The two algorithms performed equally for classifying patients. A classification tree that considered diabetic patients only was constructed; the main splitters were: 1211, CA 19-9, 7903, 3359, 1802. With this algorithm, 100% of patients with type 2 diabetes mellitus, 97% with chronic pancreatitis and 77% of patients with pancreatic cancer were correctly classified. SELDI-TOF/MS features improved the diagnostic accuracy of CA 19-9 (AUC = 0.883 for CA 19-9; AUC = 0.935 for CA 19-9 and SELDI-TOF/MS features combined). SELDI-TOF/MS allows identification of new peptides which, in addition to CA 19-9, enable the correct classification of the vast majority of patients with pancreatic cancer, which can be distinguished from patients with chronic pancreatitis or type 2 diabetes mellitus.

Rapid Genotyping of SNPs Influencing Warfarin Drug Response by SELDI-TOF Mass Spectrometry

The anticoagulant drug Warfarin exhibits significant inter-individual variability in dosing requirements. Different responses to Warfarin therapy are partly attributed to the single nucleotide polymorphisms (SNPs) that influence either Warfarin drug action (VKORC1) or drug metabolism (CYP2C9). Rapid genotyping of these SNPs is essential for clinicians to choose appropriate initial doses in order to quickly achieve anticoagulation effects and to prevent the complications associated with Warfarin overdoses. In this study, we explore the utility of surface-enhanced laser desorption and ionization time-of- flight (SELDI-TOF) mass spectrometry in the rapid genotyping of SNPs that control Warfarin drug sensitivity. The DNA containing the targeted SNPs is first amplified by polymerase chain reactions and then underwent the single base extension to generate specific SNP product. Afterwards, genetic variants displaying different masses are bound to Q10 anionic proteinChips and genotyped using a SELDI-TOF mass spectrometer in a multiplexed fashion. SELDI-TOF mass spectrometer offers a unique property of on-chip sample enrichment and clean-ups. Therefore, this genotype method eliminated many tedious experimental steps, such as sample desalting and concentrating, that are required prior to detection by standard mass spectrometers. The turnaround time for genotyping three known Warfarin sensitivity SNPs, CYP2C9*2, CYP2C9*3, VKORC1, is less than five hours. The analytical accuracy of genotyping detected by SELDI mass spectrometer has been confirmed by DNA sequencing. In summary, we have devised a novel multiplex genotyping method using a SELDI-TOF mass spectrometer. This test is fast, accurate, and therefore provides a superb clinical laboratory platform to promote the personalized health care in Warfarin therapy.

Proteomic Evaluation of Urine from Renal Cell Carcinoma Using SELDI-TOF-MS and Tree Analysis Pattern

There is no useful marker in screening and early diagnosis for renal cell carcinomas (RCCs), especially in the urine. To screen for specific markers in the urine of RCCs patients, surface enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF-MS) was used and coupled with a tree analysis pattern to develop SELDI protein profiling of urine. Urine samples from 58 RCC patients, 45 healthy volunteers, and 56 patients with other urogenital diseases were analyzed using IMAC-Cu ProteinChip capable of specifically binding metal interesting proteins. Proteomic spectra were generated by mass spectrometry. Bioinformatic calculations were performed with Biomarker Wizard software 3.0 (Ciphergen). Four differentially expressed potential biomarkers from urine were identified with the relative molecular weights of 4020, 4637, 5070, and 5500. The discriminatory classifier with a panel of the four biomarkers determined in the training set could precisely detect 24 of 30 (sensitivity, 80.0%) RCC and 52 of 58 (specificity, 89.6%) non-RCC samples. Furthermore, a sensitivity of 67.8% (19/28) and a specificity of 81.4% (35/43) for the blinded test were obtained when comparing the RCC vs. non-RCC. So SELDI-TOF combined with a tree analysis pattern could potentially serve as a useful tool for diagnosis of RCC by urine samples.

Serum Protein Profile Analysis Following Definitive Treatment in Patients With Head and Neck Squamous Cell Carcinoma

To determine the sensitivity and specificity of surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) assay for head and neck squamous cell carcinoma (HNSCC) disease surveillance. The SELDI-TOF-MS serum protein profiles of patients with HNSCC were analyzed to determine the sensitivity and specificity of the SELDI assay for HNSCC detection following definitive treatment. Academic research. Thirty-two patients with previously untreated HNSCC. Serum samples were collected prospectively at 3-month intervals following treatment during a 24-month follow-up period. Ninety-three serum samples were analyzed. The SELDI-TOF-MS identified protein peaks in the range of 0 to 100 kDa. Classification tree analysis based on peak expression distinguished pretreatment from 6-month posttreatment samples with 75.0% sensitivity and 87.5% specificity. Samples collected at 3 months following treatment did not significantly differ from pretreatment samples. Serum samples from patients who were disease free at 6 months or longer following treatment differed from matched pretreatment samples by the overexpression of a protein peak at 6495 Da, while serum samples from patients with recurrence differed from matched pretreatment samples by the underexpression of a protein peak at 4493 Da. Proteomic analysis of serum protein profiles distinguishes pretreatment and posttreatment samples from patients with HNSCC with a high degree of sensitivity and specificity. After 6 months, serum protein profiles seem to have distinct differences in peak expression based on disease status. Further investigation of the clinical usefulness of this technology in HNSCC detection and surveillance is warranted.

Quantitative quality-assessment techniques to compare fractionation and depletion methods in SELDI-TOF mass spectrometry experiments

Mass spectrometry (MS), such as the surface-enhanced laser desorption and ionization time-of-flight (SELDI-TOF) MS, provides a potentially promising proteomic technology for biomarker discovery. An important matter for such a technology to be used routinely is its reproducibility. It is of significant interest to develop quantitative measures to evaluate the quality and reliability of different experimental methods. We compare the quality of SELDI-TOF MS data using unfractionated, fractionated plasma samples and abundant protein depletion methods in terms of the numbers of detected peaks and reliability. Several statistical quality-control and quality-assessment techniques are proposed, including the Graeco-Latin square design for the sample allocation on a Protein chip, the use of the pairwise Pearson correlation coefficient as the similarity measure between the spectra in conjunction with multi-dimensional scaling (MDS) for graphically evaluating similarity of replicates and assessing outlier samples; and the use of the reliability ratio for evaluating reproducibility. Our results show that the number of peaks detected is similar among the three sample preparation technologies, and the use of the Sigma multi-removal kit does not improve peak detection. Fractionation of plasma samples introduces more experimental variability. The peaks detected using the unfractionated plasma samples have the highest reproducibility as determined by the reliability ratio. Our algorithm for assessment of SELDI-TOF experiment quality is available at http://www.biostat.harvard.edu/~xlin. Supplementary data are available at Bioinformatics online.

Serum Protein Profile Analysis in Patients With Head and Neck Squamous Cell Carcinoma

To analyze surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein profiles of patients with head and neck squamous cell carcinoma (HNSCC) and healthy controls and to determine the sensitivity and specificity of SELDI assay for HNSCC detection before and after treatment. Proteomic analysis and comparison of serum samples. Tertiary care academic medical center. Seventy-eight patients with HNSCC and 68 healthy controls. Serum samples were prospectively collected from 78 patients with HNSCC and 68 healthy control volunteers. SELDI-TOF-MS was performed on serum samples to identify protein peaks in the range of 0 to 100 kDa. Classification analysis of the spectral data was performed and used to classify the disease status of the patients. The SELDI-TOF-MS assay generated serum protein profiles ranging from 0 to 100 kDa. After background subtraction, mass calibration, and normalization, 545 protein peaks were identified. Classification tree analysis based on peak expression correctly classified patients with HNSCC with 82% sensitivity and 76% specificity. Subgroup analysis correctly classified 83% of oral cavity tumors, 81% of oropharyngeal tumors, and 88% of laryngeal tumors. Pretreatment and posttreatment samples were available from 12 patients, and the posttreatment samples were correctly classified in 86% of the patients at 3 months and 75% of the patients at 6 months. Proteomic SELDI-TOF-MS analysis of serum protein profiles distinguishes patients with HNSCC from controls with a high degree of sensitivity and specificity. Further investigation into the clinical utility of this technology in HNSCC detection and surveillance is warranted.

A Serum Proteomic Pattern for the Detection of Colorectal Adenocarcinoma Using Surface Enhanced Laser Desorption and Ionization Mass Spectrometry

Purpose: New serum biomarkers are needed to improve the early detection of colorectal adenocarcinoma. We performed surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to screen for differentially expressed proteins in serum and build a proteomic diagnostic pattern for the detection of colorectal adenocarcinoma to improve the prognosis of patients with this disease. Experimental Design: In an attempt to improve current approaches to the serologic diagnosis of colorectal cancer, we analyzed serum samples from subjects with or without colorectal cancer using SELDI-MS. Using a case-control study design, SELDI-MS profile of serum samples from 74 colorectal adenocarcinoma patients were compared with 48 age-and sex-matched healthy subjects using a ProteinChip reader, PBSII-C. Proteomic MS spectra were generated using IMAC3 chips, and protein peaks clustering and classification analyses were performed to build a proteomic pattern that could differentiate patients with colorectal adenocarcinoma from healthy subjects utilizing Biomarker Wizard and Biomarker Patterns software packages, respectively. The constructed pattern was then used to test an independent set of masked serum samples from 60 colorectal cancer patients and 39 healthy subjects.Results: Among the differentially expressed protein peaks identified by SELDI-MS profiling that had the ability to distinguish between patients and healthy subjects, we determined a minimum set of two protein peaks for system training and for developing a decision classification pattern. Masked analysis of an independent set of serum samples showed the diagnostic pattern could differentiate patients with different stages of colorectal cancer from healthy subjects with a sensitivity of 95.00 percent and specificity of 94.87 percent. Conclusion: SELDI-TOF-MS profiling of serum proteins combined with bioinformatics tools can be applied to accurately differentiate patients with colorectal cancer from healthy subjects. The high sensitivity and specificity achieved by the constructed clustering analysis algorithm show great potential for the early detection of colorectal cancer.

Identification of hemoglobin‐α and ‐β subunits as potential serum biomarkers for the diagnosis and prognosis of ovarian cancer

The development of new biomarkers for ovarian cancer is clearly necessary for the improved detection and monitoring of the disease. Surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) can be employed in the identification of differentially expressed proteins in cancer cells. The objective of this study was, then, to identify potential diagnostic serological biomarkers for ovarian cancer. We performed protein expression difference analyses of 45 serum samples using SELDI protein chip array. Forty-five sera obtained from ovarian cancer patients (n=35) and normal healthy females (n=10), were profiled on the surface of SELDI protein chip. The candidate biomarkers were purified by CM-Sepharose, and their N-terminal amino sequence was determined. The amounts of hemoglobin (Hb) in cancer patient's sera versus that of normal sera were measured by ELISA. Nine sera proteins that were found to be significantly differentially expressed (P<0.05) between the sera of ovarian cancer patients and that of normal healthy females were selected using the WCX2 array. The most distinctive polypeptide peaks detected in the ovarian cancer samples were at 15.1 and 15.8 kDa and these two peaks were identified as the hemoglobin-alpha (Hb-alpha) and -beta (Hb-beta) chain, respectively. ELISA indicated that the sensitivity for intact Hb level was 77% in sera obtained from ovarian cancer patients, as compared with normal healthy female sera. In conclusion, two ovarian cancer biomarker proteins were discovered and identified as Hb-alpha and Hb-beta. Hb levels were significantly different in ovarian cancer serum samples and those obtained from normal healthy females, as determined by ELISA. Additional studies are required to further validate Hb-alpha and Hb-beta biomarkers.

Preliminary study on proteomics of gastric carcinoma and its clinical significance

To explore the preliminary identification of serum protein pattern models that may be novel potential biomarkers in the detection of gastric cancer. A total of 130 serum samples, including 70 from patients with gastric cancer and 60 from healthy adults, were detected by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by Biomarker Patterns Software (BPS). Thirty serum samples of gastric cancer patients and 30 serum samples of healthy adults were grouped into the training group to build models, and the other 70 samples were used to test and evaluate the models. The samples of the test group were judged only with their peaks' height and were separated into cancer group or healthy control group by BPS automatically and the judgments were checked with the histopathologic diagnosis of the samples. Sixteen mass peaks were found to be potential biomarkers with a significant level of P< 0.01. Among them, nine mass peaks showed increased expression in patients with gastric cancer. Analyzed by BPS, two peaks were chosen to build the model for gastric cancer detection. The sensitivity, specificity, and accuracy of the model were 90%, 36/40, 86.7%, 26/30, and 88.6%, 62/70, respectively, which were greatly higher than those of clinically used serum biomarkers CEA (carcinoembryonic antigen), CA19-9 and CA72-4. Stage I/II gastric cancer samples of the test group were all judged correctly. The novel biomarkers in serum and the established model could be potentially used in the detection of gastric cancer. However, large-scale studies should be carried on to further explore the clinical impact on the model.

Screening urine markers of renal cell carcinoma using SELDI-TOF-MS

To screen relatively specifical markers in urines from renal cell carcinoma patients using surface-enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF-MS) ProteinChip technology. Urine samples from 40 renal cell carcinoma (RCC) patients, 40 healthy volunteers and 40 patients with other urogenital diseases were analyzed using IMAC-Cu-3 PoteinChip, which can specifically bind the metal-combining-proteins. Proteomic spectra were generated by mass spectrometry. Four differentially expressed potentially biomarkers were identified with the relative molecular weights of 4020, 4637, 5070, and 5500. The sensitivity for diagnosing RCC was 57.5%, 66.7%, 63.7%, 65%, and specificity was 86.2%, 95%, 82.5% and 75% when the critical points were 2.0, 5.0, 5.0 and 5.0 respectively. SELDI-TOF-MS ProteinChip technology is a quickly, easy and convenient, and high-throughput analyzing method capable of screening several relatively specific, potential biomarkers from the urines of RCC patients and had better clinical value.

Measurement Reproducibility in the Early Stages of Biomarker Development

Biomarker discovery and development requires measurement reproducibility studies in addition to case-control studies. Parallel pursuit of reproducibility studies is especially important for emerging technologies such as protein biomarkers based on time-of-flight mass spectrometry, the case considered in this paper. For parallel studies, a way to improve reproducibility prior to identification of protein species is necessary. One approach is use of functional principal components analysis (PCA) as the basis for assessing measurement reproducibility. Reproducibility studies involve repeated measurement of a reference material such as a human serum standard. Measurement in our example is by SELDI-TOF (surface-enhanced laser desorption and ionization time-of-flight) mass spectrometry. Reproducibility is defined in reference to a source of variation, which in our example is associated with a type of commercially available protein biochip. We obtained spectra for 8 spots on each 11 chips. Two spectra are generally more alike when obtained from the same chip rather than different chips. Thus, our experiment indicates potential improvements from reducing variation in chip manufacture and chip handling during measurement. Our analysis involves careful registration of the spectra and characterization of the spectral differences. As shown by our example, a metrological analysis may enhance case-control studies by guiding optimization of the measurements underlying the biomarker.