Dataset Information

QCMAP Brain proteomics data - QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems

ABSTRACT: we developed a web-based application (QCMAP) for interactive diagnosis and prediction of the per-formance of LC-MS systems across different biological sample types. Leveraging on a standardized HeLa sample run in Sydney MS core facility, we trained predictive models on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. Next, we demonstrated that the learned model can be applied to predict LC-MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to supply their own samples to benchmark the performance of their LC-MS systems and identify key factors for instrument opti-misation.. To demonstrate this, we obtained 10 datasets generated on a QECl instrument from mouse brain samples with different levels of quality.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Benjamin Parker

LAB HEAD: Benjamin Parker

PROVIDER: PXD010307 | Pride | 2019-05-06

REPOSITORIES: Pride

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			Action	DRS
	20180327_QECl_BenP_nLCII_100ngBrain_10.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_15.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_16.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_17.raw	Raw
	20180327_QECl_BenP_nLCII_100ngBrain_18.raw	Raw

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Project description:All large-scale LC-MS/MS post-translational methylation site discovery experiments require methylpeptide spectrum matches (methyl-PSMs) to be identified at acceptably low false discovery rates (FDRs). To meet estimated methyl-PSM FDRs, methyl-PSM filtering criteria are often determined using the target-decoy approach. The efficacy of this methyl-PSM filtering approach has, however, yet to be thoroughly evaluated. Here we conduct a systematic analysis of methyl-PSM FDRs across a range of sample preparation workflows (each differing in their exposure to the alcohols methanol and isopropanol) and mass spectrometric instrument platforms (each employing a different mode of MS/MS dissociation). Through 13CD3-methionine labeling (heavy-methyl SILAC) of S. cerevisiae cells and in-depth manual data inspection, accurate lists of true positive methyl-PSMs were determined, allowing methyl-PSM FDRs to be compared to target-decoy approach-derived methyl-PSM FDR estimates. These results show that global FDR estimates produce extremely unreliable methyl-PSM filtering criteria; we demonstrate that this is an unavoidable consequence of the high number of amino acid combinations capable of producing peptide sequences that are isobaric to methylated peptides of a different sequence. Separate methyl-PSM FDR estimates were also found to be unreliable due to prevalent sources of false positive methyl-PSMs that produce high peptide identity score distributions. Incorrect methylation site localizations, peptides containing cysteinyl-S-β-propionamide, and methylated glutamic or aspartic acid residues can partially, but not wholly, account for these false positive methyl-PSMs. Together these results indicate that the target-decoy approach is an unreliable means of estimating methyl-PSM FDRs and methyl-PSM filtering criteria. We suggest that orthogonal methylpeptide validation (e.g. heavy-methyl SILAC or its offshoots) should be considered a prerequisite for obtaining high confidence methyl-PSMs in large-scale LC-MS/MS methylation site discovery experiments, and make recommendations on how to reduce methyl-PSM FDRs in samples not amenable to heavy isotope labeling.

Project description:In order to ascertain the potential for histone deacetylase (HDAC) inhibitor-based treatment in non-small cell lung cancer (NSCLC), we analyzed the anti-tumour effects of Trichostatin A (TSA) and suberoylanilide hydroxamic acid (vorinostat) in a panel of 16 NSCLC cell lines via MTT assay. TSA and vorinostat both displayed strong anti-tumor activities in a proportion of NSCLC cell lines, and suggesting the need for the use of predictive markers to select patients receiving this treatment. There was a strong correlation between the responsiveness to TSA and vorinostat (P < 0.0001). To identify a molecular model of sensitivity to HDAC inhibitor treatment in NSCLC, we conducted a gene expression profiling study using cDNA arrays on the same set of cell lines and related the cytotoxic activity of TSA to corresponding gene expression pattern using a modified NCI program. In addition, pathway analysis was performed with Pathway Architect software. We used nine genes, which were identified by gene-drug sensitivity correlation and pathway analysis, to build a support vector machine (SVM) algorithm model by which sensitive cell lines were distinguished from resistant cell lines. The prediction performance of the SVM model was validated by an additional seven cell lines, resulting in a prediction value of 100% in respect to determining response to TSA. Our results suggested that [1] HDAC inhibitors may be promising anticancer drugs to NSCLC, and [2] the nine gene classifer is useful in predicting drug sensitivity to HDAC inhibitors and may contribute to achieving individualized therapy for NSCLC patients. training sample set:; GSM94303 PC9; GSM94304 PC7; GSM94305 PC14; GSM94306 A549; GSM94308 LK2; GSM94313 RERF LC-KJ; GSM94314 RERF LC-MS; GSM94315 RERF-LC-AI; GSM94316 PC-1; GSM94317 PC-3; GSM94319 PC-10; GSM94323 ABC-1; GSM94324 EBC-1; GSM94325 LC2/ad; GSM94328 SQ-5; GSM94329 QG-56; test sample set:; GSM94307 LU65; GSM94326 LC1/sq; GSM94327 LC-1F; GSM254967 LCOK; GSM254968 LCD; GSM254969 H1650; GSM254970 H1975 Experiment Overall Design: gene expression analyis in lung cancer cell lines

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QCMAP Brain proteomics data - QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems

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