S2Map: a novel computational platform for identifying secretio-types through cell secretion-signal map.

Area of Science:

• Cellular biology
• Immunology
• Bioinformatics

Background:

• Cell communication relies on secreted proteins, with abnormal patterns indicating physiological issues.
• Understanding single-cell secretion is key to deciphering regulatory mechanisms.

Purpose of the Study:

• To introduce the secretion-signal map (S2Map), an analytical platform for visualizing and interpreting cell secretion signals.
• To enable the exploration of cell secretion patterns at a single-cell level.

Main Methods:

• Developed S2Map, an interactive online platform for analyzing cell secretion signals.
• Incorporated novel metrics: signal inequality index (SII) and signal coverage index (SCI) for temporal data analysis.
• Utilized time-series analysis and multi-layer visualization for signal depiction.

Main Results:

• S2Map visually represents distinct cell secretion-signal patterns.
• The SII and SCI effectively distinguish simulated signal diffusion models.
• A repository for single-cell secretion-signal data enables exploration of novel cell secretio-types.

Conclusions:

• S2Map provides a powerful tool for analyzing complex physiological systems at the single-cell level.
• The platform offers insights into protein production regulation, such as cytokines.
• S2Map facilitates a new paradigm of cell phenotyping based on secretion signals.