pr0c00310_si_003.xlsx (770.27 kB)
Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling
dataset
posted on 2020-05-22, 15:42 authored by Tong Zhang, Matthew J. Gaffrey, Matthew E. Monroe, Dennis G. Thomas, Karl K. Weitz, Paul D. Piehowski, Vladislav A. Petyuk, Ronald J. Moore, Brian D. Thrall, Wei-Jun QianLabel-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.