[论文解读] Comprehensive Lipidomic Automation Workflow using Large Language Models

Connor Beveridge, Sanjay Iyer+6 more|PubMed|Mar 22, 2024

Metabolomics and Mass Spectrometry Studies被引用 7

一句话总结

描述 CLAW，一种结合了基于 MRM 的解析、统计分析，以及使用大语言模型的 AI 驱动用户界面的自动化脂质组学工作流。

ABSTRACT

Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statistical and bioinformatics tools. We have developed the Comprehensive Lipidomic Automated Workflow (CLAW) platform with integrated workflow for parsing, detailed statistical analysis and lipid annotations based on custom multiple reaction monitoring (MRM) precursor and product ion pair transitions. CLAW contains several modules including identification of carbon-carbon double bond position(s) in unsaturated lipids when combined with ozone electrospray ionization (OzESI)-MRM methodology. To demonstrate the utility of the automated workflow in CLAW, large-scale lipidomics data was collected with traditional and OzESI-MRM profiling on biological and non-biological samples. Specifically, a total of 1497 transitions organized into 10 MRM-based mass spectrometry methods were used to profile lipid droplets isolated from different brain regions of 18-24 month-old Alzheimer's disease mice and age-matched wild-type controls. Additionally, triacyclglycerols (TGs) profiles with carbon-carbon double bond specificity were generated from canola oil samples using OzESI-MRM profiling. We also developed an integrated language user interface with large language models using artificially intelligent (AI) agents that permits users to interact with the CLAW platform using a chatbot terminal to perform statistical and bioinformatic analyses. We envision CLAW pipeline to be used in high-throughput lipid structural identification tasks aiding users to generate automated lipidomics workflows ranging from data acquisition to AI agent-based bioinformatic analysis.

研究动机与目标

• 为脂质组学数据中的解析、统计与脂质注释开发自动化工作流。
• 将多条 MRM 转换整合为一个连贯的 CLAW 平台。
• 通过 OzESI-MRM 启用不饱和脂质中双键位置的识别。
• 提供一个 AI 驱动的用户界面，以与 CLAW 进行统计和生物信息学分析的交互。
• 在来自生物和非生物样本的大规模脂质组数据集上展示该工作流。

提出的方法

• 创建一个 CLAW 平台，具备从 MRM 转换中解析和注释脂质的模块。
• 结合 OzESI-MRM 方法论以确定不饱和脂质中的碳–碳双键位置。
• 使用 1497 条转变，组织成 10 种 MRM 方法，分析来自小鼠脑脂滴（Alzheimer’s model vs 对照组）。
• 在 CLAW 工作流中应用整合的统计和生物信息分析。
• 开发一个基于 AI 代理的语言界面，以通过聊天机器人交互驱动分析。

实验结果

研究问题

• RQ1 CLA W 能否自动从大型 MRM 基础数据集中解析并注释脂质？
• RQ2 在 CLAW 中，OzESI-MRM 对不饱和脂质中双键位置的识别有多大程度的贡献？
• RQ3 CLAW 在生物和非生物样品中进行高通量脂质组学分析的可扩展性如何？
• RQ4 基于 AI 代理的界面能否实现脂质组学工作流程中的统计与生物信息分析的高效化？

主要发现

• CLAW 处理来自老龄 Alzheimer’s model 小鼠脑脂质组的 1497 条转变，分布在 10 种 MRM 方法。
• OzESI-MRM 通过识别不饱和脂质中的 C=C 位点提供结构特异性。
• 菜籽油 TG 分析表明碳–碳双键特异性在 CLAW 工作流中是可实现的。
• 一个由 AI 代理驱动的语言 UI 使得与 CLAW 进行统计和生物信息分析的交互成为可能。
• 该工作流支持从数据采集到分析的高通量脂质结构识别。