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.
Yang et al. (2026) studied this question.