Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine...

Area of Science:

• Immunology
• Single-cell analysis
• Biotechnology

Background:

• Immune responses involve complex cytokine cascades, leading to inflammation or sepsis-induced organ damage.
• Current diagnostic methods for immunological disorders, relying on serum cytokines, lack accuracy and struggle to differentiate normal inflammation from sepsis.
• There is a critical need for precise diagnostic tools to identify sepsis and other immunological conditions.

Purpose of the Study:

• To develop and validate a novel approach for detecting immunological disorders using single-cell analysis.
• To assess the capability of single-cell multiplex in situ tagging (scMIST) technology for ultrahigh-multiplex T cell analysis.
• To differentiate between normal inflammation and sepsis using T cell profiling.

Main Methods:

• Employed single-cell multiplex in situ tagging (scMIST) technology for simultaneous detection of 46 markers and cytokines from individual T cells.
• Utilized a cecal ligation and puncture (CLP) sepsis model in mice, collecting T cells from survivors and non-survivors.
• Applied a random forest machine learning model for T cell classification and group prediction.

Main Results:

• scMIST successfully captured T cell features and dynamics during sepsis progression and recovery.
• T cell markers exhibited distinct dynamics and cytokine levels compared to peripheral blood cytokines.
• The machine learning model achieved 94% accuracy in predicting mouse groups (sepsis vs. survival) based on single T cell data.

Conclusions:

• Single-cell multiplex in situ tagging (scMIST) offers a rapid and accurate method for analyzing T cell responses in immunological disorders.
• This T cell-based approach demonstrates superior potential in distinguishing sepsis from normal inflammation compared to traditional methods.
• The study pioneers single-cell omics for diagnostics and holds broad applicability for human disease diagnosis and research.