Micro-Data-Independent Acquisition for High-Throughput Proteomics and Sensitive Peptide Mass Spectrum Identification

Area of Science:

• Proteomics
• Mass Spectrometry
• Biochemistry

Background:

• Current proteomics strategies struggle with rapid, sensitive analysis of complex peptide mixtures.
• Untargeted interrogation of peptides requires high specificity in tandem mass spectrometry (MS/MS).

Purpose of the Study:

• To introduce micro data-independent acquisition (μDIA), a novel approach for enhanced proteomics.
• To improve peptide and protein identification rates in complex biological samples.

Main Methods:

• Development of μDIA, a data-independent acquisition technique.
• Application of a novel MS/MS spectral deconvolution method within μDIA.
• Utilizing the PROTALIZER software for automated deconvolution and library-free database searching.

Main Results:

• μDIA detected 3.1-fold more HeLa proteins compared to data-dependent acquisition (DDA) using a 10 min gradient.
• The MS/MS deconvolution is crucial for resolving modified peptides with small mass shifts.
• Benchmarking showed μDIA yielded 24% more true positives than DDA at an equivalent false positive rate.

Conclusions:

• μDIA significantly enhances the sensitivity and specificity of peptide and protein identification in proteomics.
• The integrated PROTALIZER software automates complex data analysis, enabling confident identification of multiple peptides per spectrum.
• μDIA represents a state-of-the-art strategy for interrogating complex peptide mixtures.