Innovative approaches in immunology: single cell multi-omics and spatial transcriptomics

• Olga Yu. Perik-Zavodskaia
• Roman Yu. Perik-Zavodskiy
• Sergey V. Sennikov

Abstract

Modern omics technologies, combined with the latest bioinformatics approaches to data analysis, offer unique opportunities for accurate, detailed or unbiased study of immunological processes. Previously, the scientific community had access to standard mass methods for analyzing a limited number of markers of individual cell populations, entailing significant processes for their isolation, which have recently been replaced by modern technologies for analyzing single cells, which allow simultaneous analysis of hundreds and thousands of markers of traditional cell populations without the need to separate each of the individual cell population. In this review, we describe recent advances in single-cell omics and superplex spatial analysis technologies through their application in immunological research, providing valuable insights for further advances in basic immunology, new strategies, and personalized approaches to selected immune pathologies.

Keywords: omics technologies; bioinformatics; transcriptomics; genomics; proteomics; single cell RNA sequencing; scRNA-seq; spatial transcriptomics

References

1. Cossarizza A., Chang H.D., Radbruch A., Abrignani S., Addo R., Akdis M., Andrä I., Andreata F., Annunziato F., Arranz E., Bacher P. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur J Immunol. 2021; 51 (12): 2708–3145. DOI: https://doi.org/10.1002/eji.202170126

2. Miltenyi S., Müller W., Weichel W., Radbruch A. High gradient magnetic cell separation with MACS. Cytometry. 1990; 11 (2): 231–8. DOI: https://doi.org/10.1002/cyto.990110203

3. Rabien A., Kristiansen G. Tissue microdissection. Cancer Gene Profiling: Methods and Protocols. 2016: 39–52. DOI: https://doi.org/10.1007/978-1-4939-3204-7_2

4. Hernandez S., Lazcano R., Serrano A., Powell S., Kostousov L., Mehta J., Khan K., Lu W., Solis L.M. Challenges and opportunities for immunoprofiling using a spatial high-plex technology: the NanoString GeoMx^® digital spatial profiler. Frontiers Oncol. 2022; 12: 890410. DOI: https://doi.org/10.3389/fonc.2022.890410

5. Hao Y., Hao S., Andersen-Nissen E., Mauck W.M., Zheng S., Butler A., Lee M.J., Wilk A.J., Darby C., Zager M., Hoffman P. Integrated analysis of multimodal single-cell data. Cell. 2021; 184 (13): 3573–87. DOI: https://doi.org/10.1016/j.cell.2021.04.048

6. Udani S., Langerman J., Koo D., Baghdasarian S., Cheng B., Kang S., Soemardy C., de Rutte J., Plath K., Di Carlo D. Associating growth factor secretions and transcriptomes of single cells in nanovials using SEC-seq. Nat Nanotechnol. 2024; 19 (3): 354–63. DOI: https://doi.org/10.1038/s41565-023-01560-7

7. Wolf F.A., Angerer P., Theis F.J. SCANPY: large-scale single-cell gene expression data analysis. Genome biol. 2018; 19: 1–5. DOI: https://doi.org/10.1186/s13059-017-1382-0

8. Khozyainova A.A., Valyaeva A.A., Arbatsky M.S., Isaev S.V., Iamshchikov P.S., Volchkov E.V., Sabirov M.S., Zainullina V.R., Chechekhin V.I., Vorobev R.S., Menyailo M.E. Complex Analysis of Single-Cell RNA Sequencing Data. Biochemistry (Moscow). 2023; 88 (2): 231–52. DOI: https://doi.org/10.1134/S0006297923020074

9. Wu T., Hu E., Xu S., Chen M., Guo P., Dai Z., Feng T., Zhou L., Tang W., Zhan L.I., Fu X. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The innovation. 2021; 2 (3): 100141. DOI: http://dx.doi.org/10.1016/j.xinn.2021.100141

10. Fang Z., Liu X., Peltz G. GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics. 2023; 39 (1): btac757. DOI: https://doi.org/10.1093/bioinformatics/btac757

11. Salcher S., Heidegger I., Untergasser G., Fotakis G., Scheiber A., Martowicz A., Noureen A., Krogsdam A., Schatz C., Schäfer G., Trajanoski Z. Comparative analysis of 10X Chromium vs. BD Rhapsody whole transcriptome single-cell sequencing technologies in complex human tissues. Heliyon. 2024; 10 (7): E28358. DOI: https://doi.org/10.1016/j.heliyon.2024.e28358

12. Janesick A., Shelansky R., Gottscho A.D., Wagner F., Williams S.R., Rouault M., Beliakoff G., Morrison C.A., Oliveira M.F., Sicherman J.T., Kohlway A. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Communicat. 2023; 14 (1): 8353. DOI: https://doi.org/10.1038/s41467-023-43458-x

13. Cui A., Huang T., Li S., Ma A., Pérez J.L., Sander C., Keskin D.B., Wu C.J., Fraenkel E., Hacohen N. Dictionary of immune responses to cytokines at single-cell resolution. Nature. 2024; 625 (7994): 377–84. DOI: https://doi.org/10.1038/s41586-023-06816-9

14. Perik-Zavodskii R., Perik-Zavodskaia O., Shevchenko J., Denisova V., Alrhmoun S., Volynets M., Tereshchenko V., Zaitsev K., Sennikov S. Immune Transcriptome Study of Human Nucleated Erythroid Cells from Different Tissues by Single-Cell RNA-Sequencing. Cells. 2022; 11 (22): 3537. DOI: https://doi.org/10.3390%2Fcells11223537

15. Sennikov S.V., Silkov A.N., Kozlov V.A. The role of alternative splicing of cytokine genes in the formation of the polymorphic structure of the cytokine network. Meditsinskaya Immunologiya. 2001; 3 (3): 389–400. (in Russian)

16. Cadot S., Valle C., Tosolini M., Pont F., Largeaud L., Laurent C., Fournie J.J., Ysebaert L., Quillet-Mary A. Longitudinal CITE-Seq profiling of chronic lymphocytic leukemia during ibrutinib treatment: evolution of leukemic and immune cells at relapse. Biomarker research. 2020; 8: 1–3. DOI: https://doi.org/10.1186/s40364-020-00253-w

17. Perik-Zavodskii R., Perik-Zavodskaia O., Alrhmoun S., Volynets M., Shevchenko J., Nazarov K., Denisova V., Sennikov S. Single-cell multi-omics reveal stage of differentiation and trajectory-dependent immunity-related gene expression patterns in human erythroid cells. Front. Immunol. 2024; 15: 1431303. DOI: https://doi.org/10.3389/fimmu.2024.1431303

18. Kuznetsova M., Tereshchenko V., Shevchenko Yu.A., Fisher M., Kurilin V., Alsallum A., Alrhmun S., Akahori Ya., Shiku H., Sennikov S. Phenotypic and functional features of in vitro generated TCR T cells specific for tumor-associated antigen NY-ESO-1. Immunologiya. 2022; 43 (5): 536–47. DOI: https://doi.org/10.33029/0206-4952-2022-43-5-536-547 (in Russian)

19. Tereshchenko V., Kuznetsova M., Shevchenko Yu.A., Fisher M., Kurilin V., Alsallum A., Akahori Ya., Shiku H., Sennikov S. Characterization of lymphocytes with MAGE-A4-specific TCR-like CAR receptor in vitro. Immunology. 2022; 43 (4): 401–11. DOI: https://doi.org/10.33029/0206-4952-2022-43-4-401-411 (in Russian)

20. Jacobsen K., Inekci D., Brix L. 30 Single cell multiomic profiling of the antigen-specific immune response using antigen specific dCODE Dextramer^®(RiO) reagents and BD^® AbSeq Reagents on the BD Rhapsody™ single-cell analysis system. J Immunother Cancer 2022; 10 (Suppl 2): A1 –A1603. DOI: https://doi.org/10.1136/jitc-2022-SITC2022.0030

21. Iyer A., Hamers A.A., Pillai A.B. CyTOF^® for the Masses. Front Immunol. 2022; 13: 815828. DOI: https://doi.org/10.3389%2Ffimmu.2022.815828

22. Sharma S., Boyer J., Teyton L. A practitioner’s view of spectral flow cytometry. Nature Methods. 2023; 4: 740–3. DOI: https://doi.org/10.1038/s41592-023-02042-3

23. Geanon D., Lee B., Gonzalez-Kozlova E., Kelly G., Handler D., Upadhyaya B., Leech J., De Real R.M., Herbinet M., Magen A., Del Valle D. A streamlined whole blood CyTOF workflow defines a circulating immune cell signature of COVID-19. Cytometry Part A. 2021; 99 (5): 446–61. DOI: https://doi.org/10.1002/cyto.a.24317

24. Palla G., Spitzer H., Klein M., Fischer D., Schaar A.C., Kuemmerle L.B., Rybakov S., Ibarra I.L., Holmberg O., Virshup I., Lotfollahi M. Squidpy: a scalable framework for spatial omics analysis. Nature methods. 2022; 19 (2): 171–8. DOI: https://doi.org/10.1038/s41592-021-01358-2

25. Jin S., Guerrero-Juarez C.F., Zhang L., Chang I., Ramos R., Kuan C.H., Myung P., Plikus M.V., Nie Q. Inference and analysis of cell-cell communication using CellChat. Nature communications. 2021; 12 (1): 1088. DOI: https://doi.org/10.1038/s41467-021-21246-9

26. Tashireva L.A., Kalinchuk A.Y., Gerashchenko T.S., Menyailo M., Khozyainova A, Denisov E.V., Perelmuter V.M. Spatial profile of tumor microenvironment in PD-L1-negative and PD-L1-positive triple-negative breast cancer. International Journal of Molecular Sciences. 2023; 24 (2): 1433. DOI: https://doi.org/10.3390/ijms24021433

27. Chen A., Liao S., Cheng M., Ma K., Wu L., Lai Y., Qiu X., Yang J., Xu J., Hao S., Wang X. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022; 185 (10): 1777–92. DOI: https://doi.org/10.1016/j.cell.2022.04.003

28. Liu C., Li R., Li Y., Lin X., Zhao K., Liu Q., Wang S., Yang X., Shi X., Ma Y., Pei C. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev Cell. 2022; 57 (10): 1284–98. DOI: https://doi.org/10.1016/j.devcel.2022.04.009

29. Ben-Chetrit N., Niu X., Swett A.D., Sotelo J., Jiao M.S., Stewart C.M., Potenski C., Mielinis P., Roelli P., Stoeckius M., Landau D.A. Integration of whole transcriptome spatial profiling with protein markers. Nature Biotechnol. 2023; 41 (6): 788–93. DOI: https://doi.org/10.1038%2Fs41587-022-01536-3

30. Boev A.S., Rakitko A.S., Usmanov S.R., Kobzeva A.N., Popov I.V., Ilinsky V.V., Kiktenko E.O., Fedorov A.K. Genome assembly using quantum and quantum-inspired annealing. Scientific Reports. 2021; 11 (1): 13183. DOI: https://doi.org/10.1038/s41598-021-88321-5

31. Kösoglu-Kind B., Loredo R., Grossi M., Bernecker C., Burks J.M., Buchkremer R. A biological sequence comparison algorithm using quantum computers. Scientific Rep. 2023; 13 (1): 14552. DOI: https://doi.org/10.1038/s41598-023-41086-5

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