Physicochemical Profiling of Macrophage Heterogeneity Using Deep Learning Integrated Nanosensor Cytometry.

Area of Science:

• Single-cell analysis
• Immunology
• Biophysics

Background:

• Label-free single-cell analytics are crucial for understanding immune responses.
• Analyzing physicochemical properties of immune cells with dynamic changes and heterogeneity is challenging.
• Existing methods lack sensitive molecular sensing and advanced imaging analysis programs.

Purpose of the Study:

• To develop a novel platform for high spatiotemporal resolution analysis of single immune cells.
• To enable precise quantification of immune cell activation and heterogeneity.
• To overcome limitations in analyzing dynamic morphological and molecular variations in immune cells.

Main Methods:

• Development of a deep learning integrated nanosensor chemical cytometry (DI-NCC) platform.
• Integration of a fluorescent nanosensor array in microfluidics with a deep learning model.
• Acquisition and analysis of near-infrared images for macrophages (LPS+ and LPS-) at high spatial resolution.

Main Results:

• The DI-NCC platform successfully collected rich, multivariate data for individual immune cells.
• Automatic quantification of single macrophage activation and nonactivation levels was achieved.
• Deep learning-based activation levels were supported by biophysical (cell size) and biochemical (nitric oxide efflux) heterogeneity analysis.

Conclusions:

• The DI-NCC platform offers a promising approach for activation profiling of immune cell populations.
• It enables detailed analysis of dynamic heterogeneity variations at the single-cell level.
• This technology advances the understanding of collective immune response mechanisms.