Data acquisition approaches for single cell proteomics

Area of Science:

• Proteomics
• Systems Biology
• Cellular Biology

Background:

• Single-cell proteomics (SCP) provides high-resolution insights into cellular heterogeneity, crucial for understanding complex biological systems and disease mechanisms.
• Bulk proteome analysis often masks subtle yet significant variations present in distinct cellular populations.
• Characterizing individual cells is essential for developing targeted therapies and advancing precision medicine.

Purpose of the Study:

• To review advancements in mass spectrometry (MS)-based single-cell proteomics (SCP).
• To discuss strategies for overcoming key challenges in SCP, including limited sample material and low throughput.
• To highlight methods enhancing sensitivity, scalability, and efficiency in SCP experiments.

Main Methods:

• Utilizing data-dependent acquisition (DDA) and data-independent acquisition (DIA) mass spectrometry techniques for enhanced proteome coverage.
• Employing short liquid chromatography gradients to accelerate sample analysis and increase throughput.
• Implementing sample multiplexing strategies to improve the scalability and efficiency of single-cell measurements.

Main Results:

• MS advancements, including DDA and DIA, improve the sensitivity and depth of single-cell proteome characterization.
• Short gradients and sample multiplexing significantly increase sample throughput, addressing a major bottleneck in SCP.
• These combined methods enable more comprehensive analysis of cellular heterogeneity from individual cells.

Conclusions:

• Technological advancements in MS and experimental workflows are crucial for advancing single-cell proteomics.
• Improved sensitivity and throughput in SCP facilitate a deeper understanding of cellular heterogeneity.
• These developments hold significant promise for systems biology, disease research, and therapeutic development.