Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input...

Area of Science:

• Proteomics
• Mass Spectrometry
• Biochemistry

Background:

• Data-independent acquisition mass spectrometry (DIA-MS) offers comprehensive proteome profiling but struggles with very low-input samples.
• Conventional DIA libraries and acquisition methods are suboptimal for micro-nanogram samples due to proteome complexity and peptide ion abundance variations.

Purpose of the Study:

• To develop and validate a sample size-comparable library-based DIA approach for enhanced proteome coverage in low-input nanoscale samples.
• To demonstrate the superiority of small-size libraries for identifying proteins in samples as small as nanogram quantities (approximately 5-50 cells).

Main Methods:

• Construction of sample size-comparable spectral libraries tailored for low-input samples.
• Application of DIA-MS with optimized libraries for proteome profiling of nanogram-level cell samples.
• Comparative analysis of protein identification using small, medium, and large-size libraries, and a resource spectral library.

Main Results:

• Identification of 2380 and 3586 protein groups from 0.75 ng (approx. 5 cells) and 1.5 ng (approx. 10 cells) samples, respectively, with high reproducibility (86%-99%).
• Small-size library-based DIA significantly outperformed other library sizes and a resource library for the 0.75 ng sample, identifying 2380 proteins.
• The small-size library uniquely identified 518 (22%) low-abundant proteins and covered a 5-order dynamic range, with spectral similarity proving crucial for mapping.

Conclusions:

• Sample size-comparable libraries are essential for maximizing proteome coverage in low-input nanoscale samples using DIA-MS.
• The developed DIA strategy and freely available spectral libraries advance quantitative proteomics for mass-limited biological samples.
• This method provides a robust solution for analyzing minute biological samples, enabling deeper proteomic insights.