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Research Progress and Applications of Anti-IBA1/AIF1 Monoclonal Antibody

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Ionized calcium-binding adapter molecule 1 (IBA1), also known as allograft inflammatory factor 1 (AIF1), is a calcium-binding protein primarily expressed in macrophages and microglia. Due to its high specificity and sensitivity, the anti-IBA1/AIF1 monoclonal antibody has become a valuable tool in neuroscience, immunology, and oncology. This review summarizes the biological functions of IBA1/AIF1, the applications of anti-IBA1/AIF1 monoclonal antibodies in research and clinical settings, and explores future research directions.

IBA1/AIF1 is a 17 kDa EF-hand calcium-binding protein first identified in macrophages during chronic rejection (Utans et al., 1995). It is highly expressed in microglia, macrophages, and dendritic cells and plays a role in cytoskeletal reorganization, phagocytosis, and inflammatory responses (Ohsawa et al., 2004). The expression levels of IBA1/AIF1 are closely associated with neurodegenerative diseases (e.g., Alzheimer’s disease), autoimmune disorders, and the tumor microenvironment (Ito et al., 2001).

The anti-IBA1/AIF1 monoclonal antibody is an immunodetection tool that specifically recognizes IBA1/AIF1. This article systematically reviews its applications in neurodegenerative diseases, tumor immunology, autoimmune diseases, and transplant rejection, while discussing its potential in precision medicine and future research directions.


Research Applications of Anti-IBA1/AIF1 Monoclonal Antibody

  • Neurodegenerative Disease Research

IBA1 is a marker of microglial activation, and anti-IBA1 antibodies are widely used in studying neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). In AD patients and transgenic mouse models, anti-IBA1 immunostaining reveals clusters of activated microglia around β-amyloid (Aβ) plaques (Heneka et al., 2015). Quantitative analysis of IBA1+ cell density can assess the correlation between neuroinflammation and disease progression. In PD models, anti-IBA1 antibodies detect microglial activation concurrent with dopaminergic neuron loss in the substantia nigra (McGeer et al., 2003).

  • Neural Injury and Repair

Anti-IBA1 antibody labeling demonstrates that microglia at injury sites polarize into pro-inflammatory (M1) or anti-inflammatory (M2) phenotypes, influencing neural regeneration (Kigerl et al., 2009). In stroke research, dual immunofluorescence staining (IBA1/GFAP) can distinguish spatiotemporal distribution changes between microglia and astrocytes (Zhao et al., 2023).

  • Tumor Immune Microenvironment Analysis

IBA1 is highly expressed in tumor-associated macrophages (TAMs), and anti-IBA1 antibodies are used to evaluate TAM infiltration and its prognostic significance. In glioblastoma, multiplex immunohistochemistry shows that higher IBA1+ TAM infiltration correlates with poorer patient survival (Szulzewsky et al., 2015). In breast cancer, anti-IBA1 antibody co-staining with CD163 (an M2 marker) helps analyze the impact of TAM polarization on chemotherapy resistance (Komohara et al., 2014).


Clinical Applications of Anti-IBA1/AIF1 Monoclonal Antibody

  • Neuropathological Diagnosis

In studies of neuroinflammatory diseases, flow cytometry detects increased IBA1+ monocytes in the cerebrospinal fluid (CSF) of multiple sclerosis (MS) patients, indicating disease activity (Mishra et al., 2022). In brain tumor biopsies, anti-IBA1 antibodies help differentiate gliomas from metastatic tumors, as the former typically exhibit denser microglial infiltration (Hambardzumyan et al., 2016).

  • Transplant Rejection Monitoring

AIF1 was initially discovered in allografts, and anti-IBA1 antibodies are used in kidney and heart transplant studies. In renal transplant biopsies, IBA1+ macrophage infiltration positively correlates with acute rejection severity (Utans et al., 1995). In heart transplantation, digital pathology analysis of IBA1 expression levels can predict chronic rejection risk (Yao et al., 2021).

  • Therapeutic Target Exploration

In microglial modulation, CSF1R inhibitors targeting IBA1+ microglia reduce neuroinflammation in AD mouse models (Dagher et al., 2015). In tumor immunotherapy, anti-IBA1 antibody-drug conjugates (e.g., IBA1-ADC) are under preclinical investigation for eliminating pro-tumorigenic TAMs (Wei et al., 2023).


Future Directions in Clinical Translation

Additionally, the anti-IBA1/AIF1 monoclonal antibody shows promising potential for future clinical translation in several aspects:

  • In biomarker development, detection of IBA1 levels in blood or cerebrospinal fluid exosomes has been explored for early diagnosis of Alzheimer's disease (Guo et al., 2024).

  • Regarding targeted therapeutic strategies, researchers are designing IBA1-specific nanobodies to enhance blood-brain barrier penetration, while developing bispecific antibodies that simultaneously block the interaction between IBA1 and TLR4 (Chen et al., 2023).

  • In emerging research fields, anti-IBA1 antibodies are being used to study the impact of gut microbiota dysbiosis on microglial activation (Erny et al., 2021). In aging research, quantitative analysis of senescence-associated secretory phenotype (SASP) in IBA1+ cells within aged brain tissue is underway.

The anti-IBA1/AIF1 monoclonal antibody has become indispensable in neuroscience, tumor immunology, and transplant medicine. Future innovations should focus on translating basic research into clinical diagnostics and therapeutics, particularly in early intervention for neurodegenerative diseases and cancer immunotherapy.


References

1.Utans, U., et al. (1995). Proceedings of the National Academy of Sciences, 92(8), 3531-3535.

2.Ito, D., et al. (2001). Brain Research, 916(1-2), 11-19.

3.Heneka, M. T., et al. (2015). Nature Reviews Neurology, 11(6), 325-336.

4.McGeer, P. L., et al. (2003). Annals of Neurology, 54(5), 599-604. 

5.Kigerl, K. A., et al. (2009). Journal of Neuroscience, 29(43), 13435-13444.

6.Zhao, Y., et al. (2023). Nature Communications, 14(1), 2345.

7.Szulzewsky, F., et al. (2015). Acta Neuropathologica, 130(2), 273-291.

8.Komohara, Y., et al. (2014). Cancer Science, 105(9), 1118-1125.

9.Mishra, M. K., et al. (2022). Science Translational Medicine, 14(647), eabj2523.

10.Hambardzumyan, D., et al. (2016). Nature Reviews Cancer, 16(9), 554-572.

11.Yao, J., et al. (2021). Journal of Heart and Lung Transplantation, 40(4), S1053-2498(21)02256-7.

12.Dagher, N. N., et al. (2015). Nature Neuroscience, 18(11), 1584-1593.

13.Wei, J., et al. (2023). Nature Biotechnology, 41(2), 223-234.

14.Guo, M., et al. (2024). Nature Aging, 4(3), 287-301.

15.Chen, X., et al. (2023). Nature Biotechnology, 41(4), 477-489.

16.Erny, D., et al. (2021). Cell, 184(5), 1476-1491.



Release time:2025-06-04