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快速 QC-MS:用于同步质谱数据采集质量控制的交互式仪表板。

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

机构信息

Chan Zuckerberg Biohub, San Francisco, California 94158, United States.

出版信息

Anal Chem. 2024 Nov 5;96(44):17465-17470. doi: 10.1021/acs.analchem.4c00786. Epub 2024 Oct 25.


DOI:10.1021/acs.analchem.4c00786
PMID:39454023
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11541893/
Abstract

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

摘要

一致地收集高质量的液相色谱-串联质谱(LC-MS/MS)数据是一个耗时的障碍,对于非靶向工作流程而言。分析控制,如内标和生物标准品,通常包含在高通量工作流程中,帮助研究人员识别低完整性的样本,而不管其生物来源如何。然而,在收集数据时评估这些标准品一直是一个相当大的瓶颈——无论是在人力方面还是在准确性方面。在这里,我们介绍 Rapid QC-MS,这是一个用于评估 LC-MS/MS 数据质量的自动化、交互式仪表板。在新数据文件写入后几分钟内,一个浏览器可见的仪表板就会更新,其中包含多个性能维度的质量控制结果,如仪器灵敏度、运行中保留时间偏移和质量准确性漂移。Rapid QC-MS 提供了交互式可视化功能,帮助用户识别这些性能指标的急性偏差,以及在数小时、数天、数月或数年内的逐渐漂移。Rapid QC-MS 是开源的,易于安装,并且高度可配置。通过集成开源 Python 库和广泛使用的 MS 分析软件,它可以适应任何 LC-MS/MS 工作流程。Rapid QC-MS 可以在本地运行,并通过与 Google Drive 同步提供可选的远程质量控制。此外,Rapid QC-MS 可以以半自主的方式运行,通过常用的消息传递应用程序向用户发出具有潜在不良分析完整性的样本警报。最初是为与 Thermo Orbitrap 工作流程集成而开发的,Rapid QC-MS 提供了一种快速、直接的方法,通过消除手动数据管理中许多最耗时的步骤,帮助用户收集高质量的非靶向 LC-MS/MS 数据。免费下载:https://github.com/czbiohub-sf/Rapid-QC-MS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f027/11541893/7101f4e71290/ac4c00786_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f027/11541893/71a0bc3c446b/ac4c00786_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f027/11541893/4cb8f1b3fb1b/ac4c00786_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f027/11541893/7101f4e71290/ac4c00786_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f027/11541893/71a0bc3c446b/ac4c00786_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f027/11541893/4cb8f1b3fb1b/ac4c00786_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f027/11541893/7101f4e71290/ac4c00786_0003.jpg

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引用本文的文献

[1]
Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer.

J Biomol Tech. 2024-9-12

[2]
Part A: Implementing an Analyte Panel and Sampling Protocol for Quality Control in Mass Spectrometry Imaging.

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[3]
A Framework for Quality Control in Quantitative Proteomics.

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[4]
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[5]
A framework for quality control in quantitative proteomics.

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本文引用的文献

[1]
A Framework for Quality Control in Quantitative Proteomics.

J Proteome Res. 2024-10-4

[2]
Communicating Mass Spectrometry Quality Information in mzQC with Python, R, and Java.

J Am Soc Mass Spectrom. 2024-8-7

[3]
Resource competition predicts assembly of gut bacterial communities in vitro.

Nat Microbiol. 2024-4

[4]
Trackable and scalable LC-MS metabolomics data processing using asari.

Nat Commun. 2023-7-11

[5]
TidyMass an object-oriented reproducible analysis framework for LC-MS data.

Nat Commun. 2022-7-28

[6]
Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

Metabolomics. 2022-4-9

[7]
OpenCell: Endogenous tagging for the cartography of human cellular organization.

Science. 2022-3-11

[8]
RawHummus: an R Shiny app for automated raw data quality control in metabolomics.

Bioinformatics. 2022-3-28

[9]
Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners.

Metabolomics. 2020-10-12

[10]
A lipidome atlas in MS-DIAL 4.

Nat Biotechnol. 2020-6-15

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