Related Experiment Video
Updated: Jun 6, 2026
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
Published on: February 15, 2022
Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for
Robert J Milewski1, Yutaro Kumagai, Katsumasa Fujita
1Laboratory of Systems Immunology, WPI Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita, Osaka 565-0871, Japan. standley@ifrec.osaka-u.ac.jp.
Background:
Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively.
Results:
We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest.
Conclusions:
The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.
Insights
A new hybrid z-score normalization and standard regression (Z-LSR) method enables label-free imaging of macrophage cells. This automated technique efficiently resolves subcellular features in Raman microscopy images, overcoming noise and signal challenges.
Area of Science:
- Label-free imaging
- Cellular and molecular imaging
- Immunology
Background:
- Macrophages are key immune cells, but imaging them presents challenges.
- Labeling techniques can perturb cells, necessitating label-free methods like Raman microscopy.
- Raman microscopy requires advanced image processing to extract molecular and organelle information from noisy data.
Purpose of the Study:
- To develop an efficient, automated algorithm for resolving subcellular features in noisy Raman microscopy images.
- To overcome limitations of existing image processing techniques for label-free cellular imaging.
Main Methods:
- Hybrid z-score normalization and standard regression (Z-LSR) algorithm.
- Application to Raman microscopy images of mouse macrophage cells.
- Comparison with multivariate analysis methods like single value decomposition (SVD).
Main Results:
- Z-LSR effectively highlights spectral differences and provides contrast based on spectral content.
- The Z-LSR method operates in near real-time, automatically removing background and bias.
- Z-LSR automatically resolved subcellular architectures in macrophage images, unlike SVD which often requires manual intervention.
Conclusions:
- Z-LSR offers computational efficiency for automated subcellular feature resolution in large Raman microscopy datasets.
- The method maintains image quality and spectral information integrity.
- Results support the use of Z-LSR with label-free microscopy for real-time live immune cell imaging.
