Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major limitation is the potential performance drift of the LC–MS platform over time, which, in turn, limits its utility for analyzing large-scale sample sets. To address this, we introduce an experimental and data analysis scheme based on a block design with common references within each block for enabling large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller and more manageable blocks, minimizing instrument drift and variability within individual blocks. Each designated block also contains common reference samples (e.g., controls) for normalization across all blocks. We demonstrated the robustness of this approach by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. Following a common reference-based correction, 2537 proteins were quantified with high reproducibility without any imputation of missing values from 116 data sets. The data revealed the consistent quantification of proteins across all six blocks, as illustrated by the highly consistent abundances of house-keeping proteins in all samples and the high levels of correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

中文翻译：

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具有通用参考样本的块设计可实现强大的大规模无标签定量蛋白质组分析。

无标记定量蛋白质组学已成为分析全球蛋白质丰度的日益流行的工具。然而，一个主要限制是 LC-MS 平台随时间的潜在性能漂移，这反过来又限制了其在分析大规模样品组中的实用性。为了解决这个问题，我们引入了一种基于块设计的实验和数据分析方案，每个块中都有共同的参考，以实现大规模的无标签量化。在该方案中，大量样本（例如，>100 个样本）在更小且更易于管理的模块中进行分析，从而最大限度地减少仪器漂移和各个模块内的可变性。每个指定块还包含用于跨所有块标准化的公共参考样本（例如，对照）。我们通过分析人类巨噬细胞 THP-1 细胞对两种不同剂量的 11 种工程纳米材料的蛋白质组反应，证明了这种方法的稳健性。在六个区块中分析了总共 116 个样品，每个样品平均覆盖 4500 个蛋白质。在基于参考的共同校正之后，以高重现性对 2537 种蛋白质进行了量化，而没有对 116 个数据集中的缺失值进行任何插补。数据揭示了所有六个块中蛋白质的一致量化，如所有样品中管家蛋白质的高度一致丰度以及来自不同块的样品之间的高度相关性所示。数据还表明，无标记量化足够稳健和准确，足以量化非常细微的丰度变化以及大的倍数变化。