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Updated: May 7, 2026

A Label-free Technique for the Spatio-temporal Imaging of Single Cell Secretions
Published on: November 23, 2015
S2Map: a novel computational platform for identifying secretio-types through cell secretion-signal map
Zongliang Yue1, Lang Zhou2, Peizhen Sun2
1Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, Auburn, AL, 36849, United States.
Motivation:
Cell communication is predominantly governed by secreted proteins, whose diverse secretion patterns often signify underlying physiological irregularities. Understanding these secreted signals at an individual cell level is crucial for gaining insights into regulatory mechanisms involving various molecular agents. To elucidate the array of cell secretion signals, which encompass different types of biomolecular secretion cues from individual immune cells, we introduce the secretion-signal map (S2Map).
Results:
S2Map is an online interactive analytical platform designed to explore and interpret distinct cell secretion-signal patterns visually. It incorporates two innovative qualitative metrics, the signal inequality index and the signal coverage index, which are exquisitely sensitive in measuring dissymmetry and diffusion of signals in temporal data. S2Map's innovation lies in its depiction of signals through time-series analysis with multi-layer visualization. We tested the SII and SCI performance in distinguishing the simulated signal diffusion models. S2Map hosts a repository for the single-cell's secretion-signal data for exploring cell secretio-types, a new cell phenotyping based on the cell secretion signal pattern. We anticipate that S2Map will be a powerful tool to delve into the complexities of physiological systems, providing insights into the regulation of protein production, such as cytokines at the remarkable resolution of single cells.
Availability And Implementation:
The S2Map server is publicly accessible via https://au-s2map.streamlit.app/.

