Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent...

Area of Science:

• Proteomics
• Cellular Biology
• Mass Spectrometry

Background:

• Single-cell resolution analysis is crucial for understanding cellular heterogeneity and signaling.
• Analyzing limited material from single cells presents significant technical challenges for molecular profiling.
• Single-cell proteomics by Mass Spectrometry (scp-MS) is a powerful but developing tool for ultra-low input proteome profiling.

Purpose of the Study:

• To comprehensively analyze orbitrap-based data-independent acquisition (DIA) for limited material proteomics.
• To develop and optimize an ultra-low input DIA method for enhanced sensitivity and robust quantification.
• To demonstrate the method's capability in profiling complex biological samples like stem cell cultures.

Main Methods:

• Comprehensive analysis of orbitrap-based data-independent acquisition (DIA) parameters for low-input samples.
• Improvement of DIA method by incorporating high-resolution MS1 quantification for enhanced sensitivity.
• Tailoring DIA method for ultra-low input by optimizing injection times, resolution, and scan cycle time.
• Application of the developed method to profile mouse embryonic stem cell cultures.

Main Results:

• Identified fundamental differences in optimal DIA methods for high- and low-load samples.
• Enhanced sensitivity in low-input DIA by utilizing high-resolution MS1 quantification and efficient mass analyzer time.
• Developed an ultra-low input DIA method accommodating long injection times, high resolution, and low scan cycle times for robust quantification.
• Successfully profiled mouse embryonic stem cell cultures, revealing global proteome heterogeneity and distinct metabolic enzyme expression in subclusters.

Conclusions:

• The optimized ultra-low input DIA method significantly enhances sensitivity and robustness for single-cell proteomics.
• This approach effectively captures cellular heterogeneity and differential protein expression in complex biological systems.
• The method provides valuable insights into cell-state variations and signaling patterns at single-cell resolution.