LFQ Benchmark Dataset - Generation Beta: Assessing Modern Proteomics Instruments and Acquisition Workflows with High-Throughput LC Gradients

Recent advancements in liquid chromatography-mass spectrometry (LC-MS) have increasingly focused on high-throughput workflows, leveraging rapid chromatographic gradients and minimal sample input to maximize proteome coverage from limited material. This shift is particularly driven by the rise of single-cell proteomics, where sensitivity and reproducibility are critical. Building on our previous benchmark dataset (PXD028735), we now present an expanded study utilizing the latest generation of LC-MS platforms optimized for high-throughput proteomics. This study features shorter LC gradients and lower sample input to address the growing need for rapid and sensitive proteome analysis. Using a standardized hybrid proteome mixture with defined ratios of Human, Yeast, and E. coli, we generated a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset across multiple state-of-the-art LC-MS platforms. The updated dataset incorporates the latest acquisition methodologies and extends coverage across an even broader range of data formats, including enhanced ion mobility-enabled and scanning quadrupole-based acquisitions. Our results providea detailed assessment of the impact of technological advancements and demonstrate how shortening LC gradients influence proteome coverage, quantitative precision, and data consistency across instruments近年来，液相色谱-质谱联用仪（liquid chromatography-mass spectrometry，LC-MS）领域的最新研究愈发聚焦于高通量工作流程，通过采用快速液相色谱梯度与微量样本上样，实现在有限样本量下最大化蛋白质组覆盖度。这一发展趋势主要由单细胞蛋白质组学的兴起所驱动，该领域对检测灵敏度与实验重复性均有着严苛要求。本研究基于此前发布的基准数据集（PXD028735），针对当前最先进的高通量蛋白质组学LC-MS平台开展了扩展性研究。本研究采用更短的液相色谱梯度与更低的样本上样量，以满足日益增长的快速、高灵敏度蛋白质组分析需求。研究使用包含固定比例人、酵母菌与大肠杆菌的标准化混合蛋白质组样本，在多台当前最先进的LC-MS平台上，生成了涵盖数据依赖性采集（Data-Dependent Acquisition，DDA）与数据非依赖性采集（Data-Independent Acquisition，DIA）的完整数据集。本次更新的数据集纳入了最新的采集方法，并拓展了数据格式覆盖范围，包括增强型离子淌度采集与扫描型四极杆采集方案。本研究结果详细评估了技术进步带来的影响，并阐明了缩短液相色谱梯度如何影响不同仪器间的蛋白质组覆盖度、定量精度与数据一致性。