BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics

Data-dependent acquisition (DDA)
methods are the current standard
for quantitative proteomics in many biological systems. However, DDA
preferentially measures highly abundant proteins and generates data
that is plagued with missing values, requiring extensive imputation.
Here, we demonstrate that library-free BoxCarDIA acquisition, combining
MS1-level BoxCar acquisition with MS2-level data-independent acquisition
(DIA) analysis, outperforms conventional DDA and other library-free
DIA (directDIA) approaches. Using a combination of low- (HeLa cells)
and high- (Arabidopsis thaliana cell culture) dynamic
range sample types, we demonstrate that BoxCarDIA can achieve a 40%
increase in protein quantification over DDA without offline fractionation
or an increase in mass-spectrometer acquisition time. Further, we
provide empirical evidence for substantial gains in dynamic range
sampling that translates to deeper quantification of low-abundance
protein classes under-represented in DDA and directDIA data. Unlike
both DDA and directDIA, our new BoxCarDIA method does not require
full MS1 scans while offering reproducible protein quantification
between replicate injections and providing more robust biological
inferences. Overall, our results advance the BoxCarDIA technique and
establish it as the new method of choice for label-free quantitative
proteomics across diverse sample types.