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Updated: Aug 4, 2025
Proteomic Profiling of Macrophages by 2D Electrophoresis
Published on: November 4, 2014
Physicochemical Profiling of Macrophage Heterogeneity Using Deep Learning Integrated Nanosensor Cytometry
Seunghee Han1, Yullim Lee2, Jihan Kim1
1Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
Insights
This study introduces a deep learning integrated nanosensor chemical cytometry (DI-NCC) platform for analyzing immune cell activation. The DI-NCC platform enables high-resolution profiling of macrophage activation and heterogeneity, advancing single-cell analytics.
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.
Abstract:
Label-free single-cell analytics have been developed for understanding the collective immune response mechanism of immune cells. However, it remains difficult to analyze the physicochemical properties of a single cell in high spatiotemporal resolution for an immune cell having dynamic morphological changes and significant molecular heterogeneities. It is deemed due to the absence of a sensitive molecular sensing construct and single-cell imaging analytic program. In this study, we developed a deep learning integrated nanosensor chemical cytometry (DI-NCC) platform, which combines a fluorescent nanosensor array in microfluidics and a deep learning model for cell feature analysis. The DI-NCC platform possesses the capability to collect rich, multivariate data sets for each individual immune cell (e.g., macrophage) within the population. We obtained LPS+ (n = 25) and LPS- (n = 61) near-infrared images and analyzed 250 cells/mm2 in 1 μm spatial resolution and 0 to 1.0 confidence level even with overlapped or adhered cell configurations. This enables automatic quantification of the activation and nonactivation levels of a single macrophage upon instantaneous immune stimulations. Furthermore, we support the activation level quantified by deep learning with heterogeneities analysis of both biophysical (cell size) and biochemical (nitric oxide efflux) properties. The DI-NCC platform can be promising for activation profiling of dynamic heterogeneity variations of cell populations.
