A New Stratage For Improving The Reliability Of Liquid Chromatography Tandem Mass Spectrometry-Based Proteomics

Proteomics was first reported in the?Electrophoresis?in 1995,then proteomics was pushed into the public view by the article“Proteomics in Genomeland”published on?Science?and the article“And Now for the Proteome”published on?Nature?in Feburary 2001.Ten years later,target proteomic technology was evaluated as“the best technology of the year”by the?Nature Methods?.In 2015,the National Center for Protein Sciences in China,dubbed as the PHOENIX Center was started to operation.With the booming development of biological technique and mass spectrometry,proteomics was applied to the research of life science such as cellular biology,neurobiology,signal transduction,cell differentiation,protein folding.However,due to the complexity of proteome samples,and with extremely wide dynamic ranges in concentrations,for example,more than the 10 orders of magnitude for protein concentrations in human plasma,only 16%of the peptide mixture can be detected,in which including a large number of redundant peptides.On the other hand,studies showed that if the yeast samples were repeated by six times LC-MS/MS runs,the detected proteins accounted for 83.4%of the total proteins.And if the analytical times up to nine,the experiment results came to the 95%saturation level under specific conditions.Thus,the random detection and poor repeatability of MS analysis make researchers sceptical of proteomics results.Furthermore,with further researches on life sciences,many biological samples are precious,such as human brain samples and space samples.If the experiments are repeated,it takes a lot of time and the samples can't meet the needs of the experiments.Alternatively,repeated experiments are necessary for getting reliable proteomics results.Therefore,the proteomics researches set a higher request to analysis methods.In order to solve the above questions,based on development and optimization of the target peptide screening method,and peptide chromatographic retention time prediction and correction method,a new strategy was established in this study,which can both satisfy to shotgun proteomics research and the target proteomics.This strategy aims to reduce randomness of MS detection,and to improve the efficiency and reliability of MS analysis.To achieve the goals above,from the methodological view,three technologies were developed:?1?According to the experimental requirements,select the target protein and digest the target protein theoretically.After evaluating the unique specificity,ionization efficiency,isoelectric point,segment length and hydrophobicity of these theoretical peptides,the target peptides were selected.This method makes the target peptides more clear and reduces the interference of high abundance peptides.?2?Predict and calibrate the chromatographic retention time of the target peptides,figure out the accurate retention time of target peptides;?3?Based on the corrected retention time,MS will detect the target peptides within retention time window,improving the detection probability,reliability and repeatability of proteomics.Above all,the application of this new strategy and technologies was studied.The results can be summarized as following:1.Optimization method based on the analysis of physical and chemical properties of the target peptides was established.Bioinformatics technology and tools were used to screen the target peptides.Firstly,we used the Skyline to theoretically digest the target protein,get the peptide of the 8-25 amino acids sequence.Then the BLAST was applied to detect the homology specificity of theoretical peptides,and all 100%homology peptides were selected as target peptides.Using GenePattern to predict the ionized effiency in ESI,the peptides which the ESP value greater than or equal to 0.3 were considered as target peptides.We used the PIPredictor to analyze the p I and keep the peptides whose p I were greater than or equal to 4.We used SSRCalc to analyze the HI of the peptides and keep the petides whose HI were between 3 and 25.Above all,we collected the target peptides.2.A new method for LC retention time prediction and correction was established.Based on their intensities and a widely distribution of the whole gradient,15 standard peptides were selected from a shotgun LC-MS/MS data.Then 15 peptides were detected by LC-MS and observed retention time was acquired.Then the retention time was used as time nodes and the elution gradient was divided into intervals,in which the predicted and observed retention time of the standard peptides were used to build regression curves.The regression curves were then used to calibrate the retention time of target peptides.Results showed:?1?four points calibration mode performed best,root mean square error of the four points calibration mode was minimal and determination coefficient was highest.?2?Its performance was testified in normal,microflow and nanoflow chromatography.For the results of BSA peptides,the calibration accurate was highest compared with ELUDE and SSRCal model for the same condition of liquid chromatography separation.The number of peptide which yielded 2 min error of corrected and observed retention time,accounted78.95%of the total peptide in normal-LC,68.42%in microflow-LC and 63.16%in nanoflow-LC,respectively.?3?Considering the application of biological samples and previous evaluation,the ELUDE and SSRCal model was compared with the calibration model in the microflow liquid chromatography.The results showed that the determination coefficient was 0.81 in calibrator,the peptides with errors of corrected and observed retention time under 2 min is 45%,both higher than that of ELUDE and SSRCalc predictors.3.A database on the basis of optimization method was developed.The database stored the selected target peptides.In the environment of Visual Studio 2010,the platform was designed and developed using Microsoft Access 2010.We designed database field,which including peptide sequence,peptide retention time,m/z,CE value and so on.After designing the database field,the 12 fields were respectively built into 12 dependent database report.Then the connection was built between the reports depending on the logic relationship and the reports were integrated into an entirety.Windows were built on each reports to make schematic interface.The database finally realized such functions as loading and outputting retention time,searching peptides and proteins and other functions.4.A software was developed on the basis of method of retention time calibration.The software can automatically and quickly calibrate the retention time of target peptides.In the environment of Visual Studio 2010,combining with.NET technique,the system was developed and analyzed.The software contained two functional modules and four subsystems,containing retention time calculation modules and display module.Calculation module is responsible for two tasks,one is to build calibration curve based on regression analysis between predicted and observed retention time of standard peptides,the other is to compute and calibrate the retention time of target peptide based on the former calibration curve.Display module is responsible for outputting calibration curve containing calibration retention time of target peptide.Subsystems are respectively entry and output system,selection of calibration mode system,data displaying system and operating system.The software we developed is stable and easy to handle,and it completely realized the algorithm of retention time calibration in this study.5.The new strategy was applied to the actual biological samples.?1?The standard proteins sample.The results showed that the determination coefficient of corrected and observed retention time was 0.94,the number of peptides with errors of corrected and observed retention time under 2 min is 78.95%of total peptides.?2?A unilateral 6-OHDA PD rat model was established,targeted the PD pathway proteins.First,with the optimized target peptide selection methods,the target peptides in rats of the PD pathway were choosen.Second,the theoretical retention time of the target peptides was calculated by the corrector.Finally,the“Preferred”model of MS was applied to get the MS/MS information about the target peptides.The results showed that the coefficient of determination between the corrected retention time and observed retention time was R²=0.88.Comparised with ELUDE and SSRCalc predictor,the percentage of peptides which errors under 2 min was higher in corrected model.Six repeat experiments of 69 peptides showed that 95.65%peptides can be detected.The same situation was observed in mitochondrial proteins.?3?The heavy ion irradiation of rat was used for this study.Firstly,comparative analysis based on ¹⁸O-labeling revealed that proteins involved in glycolysis were up-regulated in cortical proteome,indicating the energy metabolism disorder.These up-regulated proteins were thought to compensate for a decrease in ATP production.Based on this findings,target proteins were repeated analysis by using the new strategy,the repeatable detection rate was86%in six times.In summary,the new strategy can be used in form of simple sample and complex biological samples.On the one hand,this strategy reduces the redundant peptides which can interfere the target peptides,and within the specified time range to increase detection rate of target peptide;On the other hand,the physicochemical properties and/or tetention time of peptides could be used for protein identification,imporving the reliability of proteomics research.