Systematic Evaluation of Data-independent Acquisition Workflows for High-Throughput and Low-Input Proteomics Analysis with an Astral Mass Spectrometer

There
is a growing interest in developing high-throughput and high-sensitivity
mass spectrometry methods for proteomic profiling of low-input samples,
such as sorted cells or spatially resolved tissue samples. Data-independent
acquisition mass spectrometry (DIA-MS) coupled with short-gradient
liquid chromatography (LC) is gaining significant attention for providing
deep proteome coverage in low-input samples, particularly with the
recent release of high-speed mass spectrometers. However, the quantification
performance of existing DIA workflows for low-input samples has not
been extensively evaluated, and there is no consensus on optimal informatics
workflows to obtain high-quality quantitative data. As such, we systematically
evaluated multiple factors in low-input DIA workflows on an Astral
MS, including MS acquisition parameters, data analysis software (DIA-NN,
Spectronaut, and FragPipe), LC separation gradient lengths, database
searching algorithms, and protein quantification approaches. Using
three-species proteome samples (human, yeast, and Escherichia
coli) with total input ranging from 0.1 ng to 10 ng
and predefined quantity ratios, we focused on proteome coverage, quantification
accuracy, and precision, which are the most important considerations
when applying these methods in biological applications. Our evaluation
suggested a preferred DIA workflow for low-input samples, which involves
using a FAIMS interface, DIA-NN-based library-free database search
with the enabled match between runs (MBR) function, and MS1-level
protein quantification with the maxLFQ algorithm.