SYSTEMATIC REVIEW
Open Access

Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease

Hiroshi Nakamura1, Yuki Tanaka2, Aya Kobayashi3* ORCID 

¹ Department of Neurology, Graduate School of Medicine, Kyoto Medical University, Kyoto, Japan
² Department of Neuroscience, Faculty of Medicine, Osaka Biomedical University, Osaka, Japan
³ Laboratory of Neuroimmunology and Translational Medicine, Institute of Biomedical Research, Tokyo, Japan

DOI: 10.18081/ajbm.2026.2.99

Publication History: Received 04 January 2026, Revised 20 February 2026, Accepted 04 April 2026, Available online 22 April 2026
Copyright: © 2026 Kobayashi, et al. This is an open-access article under a Creative Commons license (CC BY 4.0).
Crossmark logo

ABSTRACT

Background

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and a leading cause of dementia worldwide. Increasing evidence indicates that immune dysregulation and chronic neuroinflammation are fundamental contributors to disease initiation and progression, preceding overt neuronal degeneration. Conventional molecular approaches have provided valuable insights into AD pathogenesis but are limited by their inability to resolve cellular heterogeneity. Recent advances in single-cell multiomic technologies have transformed the understanding of immune-cell diversity and molecular mechanisms underlying early Alzheimer's disease.

Objectivs

This review aims to summarize current evidence regarding the application of single-cell multiomic technologies in identifying novel immune-cell signatures associated with early Alzheimer's disease, highlighting emerging biomarkers, molecular pathways, computational approaches, and potential therapeutic targets.

Methods

A comprehensive literature review was conducted using PubMed/MEDLINE, Scopus, Web of Science Core Collection, Embase, ScienceDirect, SpringerLink, Wiley Online Library, and Google Scholar to identify peer-reviewed studies published between January 2018 and June 2025. Eligible publications investigating single-cell transcriptomics, epigenomics, spatial transcriptomics, proteomics, and integrated multiomic analyses in Alzheimer's disease were systematically evaluated. Evidence was synthesized narratively according to immune-cell populations, molecular pathways, biomarker discovery, artificial intelligence-assisted analyses, and translational applications.

Results

A total of 127 eligible studies were included in the qualitative synthesis. The reviewed evidence consistently demonstrated extensive immune-cell heterogeneity within the Alzheimer's disease microenvironment. Disease-associated microglia, reactive astrocytes, infiltrating monocytes, cytotoxic T lymphocytes, regulatory T cells, natural killer cells, and dendritic cells exhibited distinct molecular signatures involving inflammatory signaling, lipid metabolism, complement activation, interferon responses, mitochondrial dysfunction, and chromatin remodeling. Integrated single-cell multiomic platforms, including single-cell RNA sequencing, single-cell ATAC sequencing, spatial transcriptomics, and proteomic profiling, substantially improved the identification of disease-specific biomarkers and regulatory networks. Furthermore, artificial intelligence and machine-learning algorithms enhanced high-dimensional data integration, immune-cell classification, biomarker prediction, and precision disease stratification.

Conclusion

Single-cell multiomics has fundamentally reshaped the understanding of early Alzheimer's disease by revealing previously unrecognized immune-cell populations and complex neuroimmune interactions that drive disease pathogenesis. The integration of transcriptomic, epigenomic, spatial, proteomic, and computational analyses provide unprecedented opportunities for early diagnosis, biomarker discovery, therapeutic target identification, and personalized immunomodulatory strategies. Continued technological innovation, multicenter validation, and standardized analytical pipelines will be essential for translating these discoveries into routine clinical practice and precision medicine.

Keywords: Alzheimer's disease; Single-cell multiomics; Single-cell RNA sequencing; scRNA-seq; scATAC-seq; Spatial transcriptomics; Neuroinflammation; Disease-associated microglia

Recommended Citation

Nakamura H, Tanaka Y, Kobayashi A. Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease. Advanced Journal of Biomedicine & Medicine. 2026;14(2):99-118. doi:10.18081/ajbm.2026.2.99

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

References

  1. Mathys H, Davila-Velderrain J, Peng Z, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570(7761):332-337. doi:10.1038/s41586-019-1195-2
  2. Grubman A, Chew G, Ouyang JF, et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019;22(12):2087-2097. doi:10.1038/s41593-019-0539-4
  3. Zhou Y, Song WM, Andhey PS, et al. Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat Med. 2020;26(1):131-142. doi:10.1038/s41591-019-0695-9
  4. Leng K, Li E, Eser R, et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat Neurosci. 2021;24(2):276-287. doi:10.1038/s41593-020-00764-7
  5. Olah M, Menon V, Habib N, et al. Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease. Nat Commun. 2020;11(1):6129. doi:10.1038/s41467-020-19737-2
  6. Gerrits E, Brouwer N, Kooistra SM, et al. Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease. Acta Neuropathol. 2021;141(5):681-696. doi:10.1007/s00401-021-02263-w
  7. Nguyen AT, Wang K, Hu G, et al. APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease. Acta Neuropathol. 2020;140(4):477-493. doi:10.1007/s00401-020-02200-3
  8. Smith AM, Davey K, Tsartsalis S, et al. Diverse human astrocyte and microglial transcriptional responses to Alzheimer’s pathology. Acta Neuropathol. 2022;143(1):75-91. doi:10.1007/s00401-021-02372-6
  9. Prater KE, Green KJ, Mamde S, et al. Human microglia show unique transcriptional changes in Alzheimer’s disease. Nat Aging. 2023;3(7):894-907. doi:10.1038/s43587-023-00424-y
  10. Sadick JS, O’Dea MR, Hasel P, et al. Astrocytes and oligodendrocytes undergo subtype-specific transcriptional changes in Alzheimer’s disease. Neuron. 2022;110(11):1788-1805.e10. doi:10.1016/j.neuron.2022.03.008
  11. Habib N, McCabe C, Medina S, et al. Disease-associated astrocytes in Alzheimer’s disease and aging. Nat Neurosci. 2020;23(6):701-706. doi:10.1038/s41593-020-0624-8
  12. Keren-Shaul H, Spinrad A, Weiner A, et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell. 2017;169(7):1276-1290.e17. doi:10.1016/j.cell.2017.05.018
  13. Sala Frigerio C, Wolfs L, Fattorelli N, et al. The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to Aβ plaques. Cell Rep. 2019;27(4):1293-1306.e6. doi:10.1016/j.celrep.2019.03.099
  14. Krasemann S, Madore C, Cialic R, et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity. 2017;47(3):566-581.e9. doi:10.1016/j.immuni.2017.08.008
  15. Chen WT, Lu A, Craessaerts K, et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell. 2020;182(4):976-991.e19. doi:10.1016/j.cell.2020.06.037
  16. Navarro JF, Croteau DL, Jurek A, et al. Spatial transcriptomics reveals genes associated with dysregulated mitochondrial functions and stress signaling in Alzheimer disease. iScience. 2020;23(10):101556. doi:10.1016/j.isci.2020.101556
  17. Masuda T, Sankowski R, Staszewski O, et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature. 2019;566(7744):388-392. doi:10.1038/s41586-019-0924-x
  18. Hammond TR, Dufort C, Dissing-Olesen L, et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity. 2019;50(1):253-271.e6. doi:10.1016/j.immuni.2018.11.004
  19. Li Q, Cheng Z, Zhou L, et al. Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing. Neuron. 2019;101(2):207-223.e10. doi:10.1016/j.neuron.2018.12.006
  20. Hansen DV, Hanson JE, Sheng M. Microglia in Alzheimer’s disease. J Cell Biol. 2018;217(2):459-472. doi:10.1083/jcb.201709069
  21. Shi Y, Holtzman DM. Interplay between innate immunity and Alzheimer disease: APOE and TREM2 in the spotlight. Nat Rev Immunol. 2018;18(12):759-772. doi:10.1038/s41577-018-0071-2
  22. Heneka MT, Carson MJ, El Khoury J, et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 2015;14(4):388-405. doi:10.1016/S1474-4422(15)70016-5
  23. Deczkowska A, Keren-Shaul H, Weiner A, Colonna M, Schwartz M, Amit I. Disease-associated microglia: a universal immune sensor of neurodegeneration. Cell. 2018;173(5):1073-1081. doi:10.1016/j.cell.2018.05.003
  24. Ising C, Venegas C, Zhang S, et al. NLRP3 inflammasome activation drives tau pathology. Nature. 2019;575(7784):669-673. doi:10.1038/s41586-019-1769-z
  25. Venegas C, Kumar S, Franklin BS, et al. Microglia-derived ASC specks cross-seed amyloid-β in Alzheimer’s disease. Nature. 2017;552(7685):355-361. doi:10.1038/nature25158
  26. Ulland TK, Song WM, Huang SC, et al. TREM2 maintains microglial metabolic fitness in Alzheimer’s disease. Cell. 2017;170(4):649-663.e13. doi:10.1016/j.cell.2017.07.023
  27. Wang Y, Cella M, Mallinson K, et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell. 2015;160(6):1061-1071. doi:10.1016/j.cell.2015.01.049
  28. Yeh FL, Wang Y, Tom I, Gonzalez LC, Sheng M. TREM2 binds to apolipoproteins, including APOE and CLU/APOJ, and thereby facilitates uptake of amyloid-beta by microglia. Neuron. 2016;91(2):328-340. doi:10.1016/j.neuron.2016.06.015
  29. Jonsson T, Stefansson H, Steinberg S, et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N Engl J Med. 2013;368(2):107-116. doi:10.1056/NEJMoa1211103
  30. Guerreiro R, Wojtas A, Bras J, et al. TREM2 variants in Alzheimer’s disease. N Engl J Med. 2013;368(2):117-127. doi:10.1056/NEJMoa1211851
  31. Bellenguez C, Küçükali F, Jansen IE, et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat Genet. 2022;54(4):412-436. doi:10.1038/s41588-022-01024-z
  32. Lambert JC, Ibrahim-Verbaas CA, Harold D, et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet. 2013;45(12):1452-1458. doi:10.1038/ng.2802
  33. Ståhl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78-82. doi:10.1126/science.aaf2403
  34. Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363(6434):1463-1467. doi:10.1126/science.aaw1219
  35. Vickovic S, Eraslan G, Salmén F, et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods. 2019;16(10):987-990. doi:10.1038/s41592-019-0548-y
  36. Stickels RR, Murray E, Kumar P, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol. 2021;39(3):313-319. doi:10.1038/s41587-020-0739-1
  37. Eng CL, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature. 2019;568(7751):235-239. doi:10.1038/s41586-019-1049-y
  38. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348(6233):aaa6090. doi:10.1126/science.aaa6090
  39. Stoeckius M, Hafemeister C, Stephenson W, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865-868. doi:10.1038/nmeth.4380
  40. Cao J, Cusanovich DA, Ramani V, et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science. 2018;361(6409):1380-1385. doi:10.1126/science.aau0730
  41. Ma S, Zhang B, LaFave LM, et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell. 2020;183(4):1103-1116.e20. doi:10.1016/j.cell.2020.09.056
  42. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e21. doi:10.1016/j.cell.2019.05.031
  43. Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29. doi:10.1016/j.cell.2021.04.048
  44. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411-420. doi:10.1038/nbt.4096
  45. Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289-1296. doi:10.1038/s41592-019-0619-0
  46. Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko EZ. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell. 2019;177(7):1873-1887.e17. doi:10.1016/j.cell.2019.05.006
  47. Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15(12):1053-1058. doi:10.1038/s41592-018-0229-2
  48. Gayoso A, Lopez R, Xing G, et al. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol. 2022;40(2):163-166. doi:10.1038/s41587-021-01206-w
  49. Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746. doi:10.15252/msb.20188746
  50. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15(4):1484-1506. doi:10.1038/s41596-020-0292-x
  51. Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088. doi:10.1038/s41467-021-21246-9

2026 Vol 14, Issue 2 Pages 99-118

Download article

PDF Download: 252

PDF (458.1 KB) XML (1.7 KB)

Cite this article

Nakamura H, Tanaka Y, Kobayashi A (2026). Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> . Advanced Journal of Biomedicine & Medicine, 14(1), 57-81. https://doi.org/10.18081/ajbm.2026.2.82
Nakamura H, Tanaka Y, Kobayashi A. "Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> ." Advanced Journal of Biomedicine & Medicine, vol. 14, no. 1, 2026, pp. 57-81. DOI: 10.18081/ajbm.2026.2.82.
Nakamura H, Tanaka Y, Kobayashi A. Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> . AJBM. 2026;14(1):57-81. DOI: 10.18081/ajbm.2026.2.82. PMID: .
Nakamura H, Tanaka Y, Kobayashi A 2026, "Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> ", Advanced Journal of Biomedicine & Medicine, vol. 14, no. 1, pp. 57-81.
@article{nakamura2026, title={Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> }, author={Nakamura H, Tanaka Y, Kobayashi A}, journal={Advanced Journal of Biomedicine & Medicine}, volume={14}, number={1}, pages={57-81}, year={2026}, doi={10.18081/ajbm.2026.2.82} }
TY - JOUR AU - Nakamura H, Tanaka Y, Kobayashi A TI - Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> JO - American Journal of Biomedicine VL - 14 IS - 1 SP - 57-81 PY - 2026 DO - 10.18081/ajbm.2026.2.82 ER -
%0 Journal Article %A Nakamura H, Tanaka Y, Kobayashi A %T Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> %J American Journal of Biomedicine %V 14 %N 1 %P 57-81 %D 2026 %R 10.18081/ajbm.2026.2.82 %M
Nakamura H, Tanaka Y, Kobayashi A (2026). Single-Cell Multiomics Reveals Novel Immune Cell Signatures Associated with Early Alzheimer's Disease<br /> . Advanced Journal of Biomedicine & Medicine, 14(1), 57-81. https://doi.org/10.18081/ajbm.2026.2.82

PlumX Metrics