Comprehensive Lipidomic Automation Workflow using Large Language Models

📄 arXiv: 2403.15076v1 📥 PDF

作者: Connor Beveridge, Sanjay Iyer, Caitlin E. Randolph, Matthew Muhoberac, Palak Manchanda, Amy C. Clingenpeel, Shane Tichy, Gaurav Chopra

分类: q-bio.QM, cs.AI, q-bio.BM, q-bio.SC

发布日期: 2024-03-22

备注: 53 pages, 4 main figures, 23 Supporting figures, 10 Supporting Tables

💡 一句话要点

提出综合脂质组学自动化工作流程以解决数据注释难题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 脂质组学 自动化工作流程 生物信息学 多重反应监测 人工智能 数据分析 OzESI-MRM

📋 核心要点

  1. 现有脂质组学方法在数据注释和解释上面临巨大挑战，尤其是结构异构体的复杂性。
  2. 本文提出的CLAW平台通过集成自动化工作流程，简化了脂质的注释和统计分析过程。
  3. 实验结果显示，CLAW能够有效处理来自阿尔茨海默病小鼠和油菜籽样本的脂质数据，提升了分析效率。

📝 摘要（中文）

脂质组学生成的大量数据使得手动注释和解释变得困难，尤其是脂质的化学和结构多样性以及结构异构体的存在进一步增加了注释的复杂性。尽管已有多种商业和开源软件用于靶向脂质识别，但缺乏自动化方法生成工作流程及与统计和生物信息学工具的集成。为此，本文开发了综合脂质组学自动化工作流程（CLAW）平台，集成了解析、详细统计分析和基于自定义多重反应监测（MRM）前体和产物离子对转移的脂质注释的工作流程。CLAW包含多个模块，包括在不饱和脂质中识别碳-碳双键位置的OzESI-MRM方法。通过对生物和非生物样本进行大规模脂质组学数据收集，展示了CLAW的实用性。

🔬 方法详解

问题定义：本文旨在解决脂质组学中手动注释和解释数据的困难，尤其是由于脂质的化学和结构多样性以及结构异构体的存在，现有方法缺乏自动化和集成化的工作流程。

核心思路：CLAW平台通过集成多种模块，提供自动化的脂质注释和统计分析，允许用户通过聊天机器人与平台交互，简化数据处理过程。

技术框架：CLAW的整体架构包括数据解析模块、统计分析模块和脂质注释模块，结合OzESI-MRM方法，能够识别不饱和脂质中的碳-碳双键位置。

关键创新：CLAW的主要创新在于其自动化工作流程和与大型语言模型的集成，使得用户能够通过自然语言与系统交互，显著提高了脂质组学分析的效率和准确性。

关键设计：在OzESI-MRM方法中，设计了1497个转移，组织成10种基于MRM的质谱方法，确保了对不同样本的全面分析。

📊 实验亮点

实验结果表明，CLAW平台能够有效处理来自阿尔茨海默病小鼠的脂质数据，使用1497个转移进行分析，显著提高了数据处理的效率和准确性，展示了其在脂质组学领域的应用潜力。

🎯 应用场景

CLAW平台在高通量脂质结构识别任务中具有广泛的应用潜力，能够帮助研究人员快速生成自动化的脂质组学工作流程，涵盖从数据采集到基于AI的生物信息学分析，推动脂质组学研究的发展。

📄 摘要（原文）

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.