Project description:Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data showed that DIA-based LC-MS/MS related consensus QC metric is more sensitive than DDA-based QC in detecting MS status changes. We then optimized 15 DIA-QC metrics, and invited to manually assess the quality of 2638 DIA files generated by 21 mass spectrometers based on each metric. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2059 DIA files, and predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 523). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC
Project description:Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrated that DIA-based LC-MS/MS-related consensus QC metrics exhibit higher sensitivity compared to DDA-based QC metrics in detecting changes in LC-MS status. We then optimized 15 metrics and invited 21 experts to manually assess the quality of 2638 DIA files based on those metrics. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2110 DIA files. This model predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 528). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC.
Project description:Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrated that DIA-based LC-MS/MS-related consensus QC metric exhibit higher sensitivity compared to DDA-based QC metric in detecting changes in LC-MS status. We then optimized 15 metrics and invited 21 experts to manually assess the quality of 2638 DIA files based on those metrics. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2110 DIA files. This model predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 528). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC.
Project description:Glutaminyl cyclase (QC) activity in macrophage cells is correlated with the gene expression of MCP-2 and QC-catalyzed N-terminal pGlu formation of MCPs is required for macrophage migration and provide new insights into the role of QC in the inflammation process.
Project description:Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics.. Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.