Automatic cleaning in acoustic ejection mass spectrometry: Enhancing the system robustness for large-scale high-throughput analysis of complex samples

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS SLAS Technology Pub Date : 2024-11-23 DOI:10.1016/j.slast.2024.100227
Heguang Ji , Xuejiao Yin , Wan Ee Ang , Abdullah Bin Rawshan , Susan Gay , Jing Ma , Chiu Cheong Aw , Chang Liu
{"title":"Automatic cleaning in acoustic ejection mass spectrometry: Enhancing the system robustness for large-scale high-throughput analysis of complex samples","authors":"Heguang Ji ,&nbsp;Xuejiao Yin ,&nbsp;Wan Ee Ang ,&nbsp;Abdullah Bin Rawshan ,&nbsp;Susan Gay ,&nbsp;Jing Ma ,&nbsp;Chiu Cheong Aw ,&nbsp;Chang Liu","doi":"10.1016/j.slast.2024.100227","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid evolution of high-throughput mass spectrometry (HT-MS) technologies has positioned MS as a pivotal analytical tool across diverse disciplines. Its significance is particularly pronounced in high-throughput drug discovery and development, where MS plays a critical role throughout various phases. Acoustic ejection mass spectrometry (AEMS) is a recent addition to the HT-MS landscape, showcasing a balanced performance high analytical throughput and high data quality. Particularly, AEMS's in-line dilution feature allows the direct analysis of large-scale, complex reaction solutions without the need for sample cleanup, making it a popular choice for large-scale high-throughput screenings. However, the substantial volume of complex matrix introduces concerns about system robustness, specifically regarding the potential clogging of the sample transfer line. This study addresses this challenge by introducing an integrated automatic washing feature to the AEMS system. This enhancement significantly improves system robustness without imposing any additional demands on assay execution time. Demonstrating an extended electrode lifetime, the cleaning approach proves effective in maintaining system performance over prolonged periods, showcasing its potential for continuous large-sample-scale high-throughput analysis applications.</div></div>","PeriodicalId":54248,"journal":{"name":"SLAS Technology","volume":"29 6","pages":"Article 100227"},"PeriodicalIF":2.5000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SLAS Technology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2472630324001092","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

The rapid evolution of high-throughput mass spectrometry (HT-MS) technologies has positioned MS as a pivotal analytical tool across diverse disciplines. Its significance is particularly pronounced in high-throughput drug discovery and development, where MS plays a critical role throughout various phases. Acoustic ejection mass spectrometry (AEMS) is a recent addition to the HT-MS landscape, showcasing a balanced performance high analytical throughput and high data quality. Particularly, AEMS's in-line dilution feature allows the direct analysis of large-scale, complex reaction solutions without the need for sample cleanup, making it a popular choice for large-scale high-throughput screenings. However, the substantial volume of complex matrix introduces concerns about system robustness, specifically regarding the potential clogging of the sample transfer line. This study addresses this challenge by introducing an integrated automatic washing feature to the AEMS system. This enhancement significantly improves system robustness without imposing any additional demands on assay execution time. Demonstrating an extended electrode lifetime, the cleaning approach proves effective in maintaining system performance over prolonged periods, showcasing its potential for continuous large-sample-scale high-throughput analysis applications.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
声发射质谱仪中的自动清洁:增强系统鲁棒性,实现复杂样品的大规模高通量分析
高通量质谱(HT-MS)技术的飞速发展使 MS 成为各学科中举足轻重的分析工具。在高通量药物发现和开发领域,质谱仪在各个阶段都发挥着至关重要的作用,其意义尤为突出。声发射质谱(AEMS)是最近加入 HT-MS 领域的一种新技术,它在高分析通量和高数据质量之间实现了平衡。尤其是 AEMS 的在线稀释功能可直接分析大规模的复杂反应溶液,而无需进行样品清理,因此成为大规模高通量筛选的热门选择。然而,大量的复杂基质会引起对系统稳健性的担忧,特别是样品传输线的潜在堵塞。本研究通过在 AEMS 系统中引入集成自动清洗功能来应对这一挑战。这一改进大大提高了系统的稳健性,而不会对检测执行时间造成额外要求。清洗方法延长了电极的使用寿命,证明它能有效地长时间保持系统性能,展示了它在连续大样本高通量分析应用中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
期刊最新文献
Model-Based Interactive Visualization for Complex Systems Requirements and Design in Joint Tests. Implementing enclosed sterile integrated robotic platforms to improve cell-based screening for drug discovery. Life Sciences Discovery and Technology Highlights. Notes on AEMS methods development for high throughput experimentation in drug discovery. Prosthesis repair of oral implants based on artificial intelligenc`e finite element analysis.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1