Single-Cell Multiomics of Macrophage Polarization in Inflammatory Microenvironments

Authors

  • Kyungsoo Song Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
  • Sandrine Charpentier Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

DOI:

https://doi.org/10.66238/fsrma79

Keywords:

Macrophage Polarization, Single-Cell Multiomics, Transcriptomics, Inflammatory Microenvironment

Abstract

Macrophages are highly plastic innate immune cells that adapt their functional phenotypes in response to environmental cues, a process traditionally conceptualized as polarization. However, the binary classification of classically activated and alternatively activated macrophages fails to capture the immense phenotypic diversity observed in vivo, particularly within complex inflammatory microenvironments. Recent advances in single-cell multiomics technologies have revolutionized our understanding of macrophage heterogeneity by enabling the simultaneous profiling of the transcriptome, epigenome, and surface proteome at single-cell resolution. This paper provides a comprehensive overview of how single-cell multiomics has redefined macrophage polarization from a distinct set of binary states to a multi-dimensional continuum of activation trajectories. We explore the limitations of conventional bulk sequencing approaches and discuss the fundamental principles of multiomic integration. Furthermore, we examine the computational frameworks required to delineate complex cellular trajectories and assess the regulatory gene networks governing macrophage plasticity in various pathological contexts, including the tumor microenvironment and chronic autoimmune diseases. By dissecting the epigenetic priming and transcriptomic execution of macrophage activation, this paper highlights the pivotal role of single-cell multiomics in identifying novel therapeutic targets for immune modulation. Ultimately, these high-resolution analytical frameworks pave the way for precision medicine strategies aimed at reprogramming dysregulated macrophage populations in severe inflammatory disorders.

References

1. Cai, Lie, et al. "Deep learning model for breast shear wave elastography to improve breast cancer diagnosis (INSPiRED 006): An international, multicenter analysis." Journal of Clinical Oncology 43.32 (2025): 3482-3493.

2. Lin, Q., Li, Y., Li, R., Hou, R., Wang, Y., & Xu, G. (2025). Correlation Between Bladder Debris and Abnormal Urinalysis: Health Warnings and Research Prospects in Elderly Patients. Medicine Bulletin, 1(2), 182-187.

3. Ding, S., Yu, X., Wang, Q., Luo, P., Li, H., Li, Z., ... & Zhang, C. (2025). Multifunctional hydrogel sensors with dynamic covalent networks for machine learning-assisted Parkinson's disease diagnosis and encrypted human-computer interaction. Materials Today Bio, 102524.

4. Yang, Y., Lin, Y. T., Li, G., Zhong, Y., Xu, Q., & Cai, J. J. (2023). Interpretable modeling of time-resolved single-cell gene–protein expression with CrossmodalNet. Briefings in Bioinformatics, 24(6), bbad342.

5. Cai, Y., Jing, T., Liu, C., Lian, Z., Huang, S. S., & Huang, H. (2025, June). Spatio-Temporally Consistent Depth Estimation for Dynamic Scenes using 3D Scene Flows. In 2025 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.

6. Li, Y., Liao, X., Tang, S., Wang, Q., Lin, H., Yu, X., ... & Zhong, T. (2025). Potential Therapeutic Targets for Androgenetic Alopecia (AGA) in Obese Individuals as Revealed by a Gut Microbiome Analysis: A Mendelian Randomization Study. Nutrients, 17(11), 1892.

7. Chu, Y. B., Li, J., Jia, P., Cui, J., Zhang, R., Kang, X., ... & Zhang, S. (2021). Irf1-and Egr1-activated transcription plays a key role in macrophage polarization: A multiomics sequencing study with partial validation. International Immunopharmacology, 99, 108072.

8. Banerjee, S., Kwaan, M. R., Wu, Y., Ren, Y., & Xirasagar, S. (2023). Ostomy Surgery for Patients with Large Bowel Obstruction in the Modern Era: a Nationwide Inpatient Sample Study. Journal of Gastrointestinal Surgery, 27(3), 585-589.

9. Sun, L., Wang, Y., Zhao, L., Lu, L., Hou, F., Yin, C., ... & Fu, J. (2026). New Technologies Empower Hemophilic Arthropathy Treatment: Focusing on Regenerative Medicine, Molecular Targeting, and Gene Therapy. Medicine Bulletin.

10. Peng, Q., Zheng, C., & Chen, C. (2024). A dual-augmentor framework for domain generalization in 3d human pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2240-2249).

11. Xiao, Y., Xiao‐yue, Z., Yue, W., Ruo‐tao, L., Xiang‐jie, L., Xing‐yuan, W., ... & Zhen‐yi, J. (2022). Use of computed tomography for the diagnosis of surgical sarcopenia: Review of recent research advances. Nutrition in Clinical Practice, 37(3), 583-593.

12. Kelley, D. R., Snoek, J., & Rinn, J. L. (2016). Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research, 26(7), 990–999.

13. Xu, X., Bowtell, J., Young, W. R., Fong, D. T., & Williams, G. K. (2025). Distinct patterns in neuromuscular adaptation to repeated perturbations in chronic ankle instability. Journal of NeuroEngineering and Rehabilitation.

14. Wolf, F. A., Angerer, P., & Theis, F. J. (2018). SCANPY: Large-scale single-cell gene expression data analysis. Genome Biology, 19, 15.

15. Mi, L., Wang, W., Tu, W., He, Q., Kong, R., Fang, X., ... & Liu, Y. (2025, March). Empower vision applications with LoRA LMM. In Proceedings of the Twentieth European Conference on Computer Systems (pp. 261-277).

16. Wan, G., Huang, J. D., Xu, R. H., & Jin, M. (2025). Advances and Challenges in Pluripotent Stem Cell‐Based Whole‐Cell Vaccines for Cancer Treatment. Medicine Bulletin, 1(1), 60-76.

17. Xie, C., Zhu, D., Wang, Z., Zhang, H., & Wei, Z. (2026). Compliance-Aware Discharge Agent for Auditable ICU Discharge Planning: A Pilot Feasibility Study Using Structured eICU Records. Available at SSRN 6429758.

18. Zhu, Di, et al. "RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction." arXiv preprint arXiv:2512.07848 (2025).

19. Xu, X., Bowtell, J., Fong, D. T., Young, W. R., & Williams, G. K. (2025). Kinematics of balance controls in people with chronic ankle instability during unilateral stance on a moving platform. Scientific Reports, 15(1), 1126.

20. Xie, C., Zhu, D., Wang, Z., Zhang, H., & Wei, Z. (2026). Explainable AI for Mental Health Detection from Social Media: A Comparative Study of Traditional Machine Learning and a Large Language Model. Available at SSRN 6429778.

21. Wei, Q., Xu, Y., Zhang, T., Xu, S., Wang, H., Wang, Y., ... & Liang, X. (2026). Nano Delivery Strategy Opens Effective Avenues for the Treatment of Colorectal Cancer. Medicine Bulletin, 2(2), 205-230.

22. Ren, Y., Wu, D., Tong, Y., López-DeFede, A., & Gareau, S. (2023). Issue of data imbalance on low birthweight baby outcomes prediction and associated risk factors identification: establishment of benchmarking key machine learning models with data rebalancing strategies. Journal of medical Internet research, 25, e44081.

23. Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577(7792), 706–710.

Toor, M. J., Shabbir, S., Rauf, A., Nisar, A., Safdar, S., & Zaib-Un-Nisa. (n.d.). The role of structured-communication-technique in developing pragmatic language capability among ASD-children. https://doi.org/10.63878/cjssr.v3i2.785

Toor, M. J., Mehmood, H., Yasmeen, A., Honey, Q., Waseem, M., & Wasif, M. (n.d.). Exploring the effect of verbal encouraging phrases (VEP) on Chinese language learners’ academic achievement at university level.

Toor, M. J., Arafat, M., Ayoub, M., Ali, M., & Alessi Longa, F. E. (n.d.). From coherent self to fragmented persona: Digital identity fragmentation and academic self-concept among Generation Z, with comparative insights from Generation Alpha. https://doi.org/10.63878/cjssr.v4i2.2318

Katsitadze, N., Kharadze, N., & Pirtskhalaishvili, D. (2025). The impact of social media on students’ effective time management. Baltic Journal of Economic Studies, 11(1), 200–210. https://doi.org/10.30525/2256-0742/2025-11-1-200-210

Toor, M. J., Zafar, S., Omer, F., Sarwar, M., & Hussain, A. K. (n.d.). Teachers’ perspectives on effectiveness of translanguaging in enhancing English language learning at secondary school level.

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Published

2026-05-01