Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.

Area of Science:

• Single-cell biology and omics
• Computational biology and bioinformatics
• Proteomics and data analysis

Background:

• Cellular heterogeneity research has rapidly advanced single-cell omics technologies.
• High-dimensional single-cell data necessitates novel theoretical and analytical frameworks for interpretation.
• Existing analytical methods require scrutiny regarding assumptions, features, and limitations.

Purpose of the Study:

• To review state-of-the-art single-cell proteomic tools, focusing on data acquisition and quantification.
• To elaborate on statistical and computational approaches for dissecting high-dimensional single-cell data.
• To discuss the biological questions addressed by various analytical methods and their limitations.

Main Methods:

• Survey of single-cell proteomic technologies.
• Elaboration of statistical and computational analytical approaches.
• Focus on information-theoretical methods grounded in physics principles.

Main Results:

• Identification of key challenges in single-cell data acquisition and quantification.
• Detailed discussion of diverse analytical methods for high-dimensional single-cell data interpretation.
• Highlighting the predictive power of information-theoretical approaches.

Conclusions:

• Effective interpretation of single-cell omics data relies on advanced analytical strategies.
• Information-theoretical approaches offer a robust framework for understanding cellular heterogeneity.
• Further development of computational tools is crucial for advancing single-cell biology.