Next Article in Journal
Caveolin-3 and Caveolin-1 Interaction Decreases Channel Dysfunction Due to Caveolin-3 Mutations
Next Article in Special Issue
Immunoassays: Analytical and Clinical Performance, Challenges, and Perspectives of SERS Detection in Comparison with Fluorescent Spectroscopic Detection
Previous Article in Journal
Clerodendrum chinense Stem Extract and Nanoparticles: Effects on Proliferation, Colony Formation, Apoptosis Induction, Cell Cycle Arrest, and Mitochondrial Membrane Potential in Human Breast Adenocarcinoma Breast Cancer Cells
Previous Article in Special Issue
Revealing the SARS-CoV-2 Spike Protein and Specific Antibody Immune Complex Formation Mechanism for Precise Evaluation of Antibody Affinity
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Special Issue “Immunoanalytical and Bioinformatics Methods in Immunology Research”

by
Anton Popov
1,2 and
Almira Ramanaviciene
1,2,*
1
NanoTechnas—Center of Nanotechnology and Materials Science, Institute of Chemistry, Faculty of Chemistry and Geosciences, Vilnius University, Naugarduko St. 24, LT-03225 Vilnius, Lithuania
2
Department of Immunology and Bioelectrochemistry, State Research Institute Centre for Innovative Medicine, LT-08406 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(2), 979; https://doi.org/10.3390/ijms25020979
Submission received: 24 December 2023 / Accepted: 4 January 2024 / Published: 12 January 2024
(This article belongs to the Special Issue Immunoanalytical and Bioinformatics Methods in Immunology Research)
To effectively control and prevent diseases on a global scale, it is essential to employ precise, sensitive, selective, and rapid immunoanalytical methods. Additionally, for monitoring biomolecules within the organism and predicting their interactions, bioinformatics methods show great promise. Immunosensors and other immunoanalytical techniques play a crucial role in this context, detecting specific interactions between antigens and antibodies occurring on the transducer surface. These approaches hold significant potential in both biomedical and bioanalytical fields.
The objectives of disease control include reducing the incidence and prevalence of disease, minimizing its spread, and improving health outcomes. Immunology, as a scientific discipline that includes the study of the immune system, goes hand in hand with immunoanalytical and bioinformatics techniques in addressing disease control issues. Immunoanalytical methods are based on the formation of an immune complex between immobilized antibodies and antigens of interest present in the sample. Various immunoanalytical methods such as enzyme-linked immunosorbent assay (ELISA), fluorescence immunoassay (FIA), immunohistochemistry (IHC), and many others have already become routine methods for use in clinical laboratories, and some diagnostics tests are even designed to be used by patients at home, providing convenient ways to monitor their health status. These methods are particularly important in analysing and quantifying immune system components such as antibodies, antigens, cytokines, cells, etc. Moreover, these techniques have already found applications not just in medicine but also in veterinary, food control, and environmental monitoring [1,2]. The particular importance of immunoanalytical and bioinformatics methods in the field of public health control became extremely clear during the COVID-19 pandemic. Their availability and abundance make it possible to rapidly study this viral disease, suggest possible methods of control, and bring vaccination of the population closer. Additionally, scientists have devoted much effort to understanding the differences in the interaction of specific antibodies with SARS-CoV-2 spike protein of wild-type and the variants of concern [3] and to select the best antigens and antibodies to develop immunosensors, rapid lateral flow tests, and other immunoanalytical techniques for COVID-19 confirmation and monitoring [4]. To get more precise quantitative information about antibody flexibility and conformational changes during interaction with immobilized antigens, the advantages of combined spectroscopic ellipsometry and quartz crystal microbalance with dissipation were revealed [5].
Despite the abundance of techniques already on the market, there is a constant search for opportunities to improve the analytical parameters of existing techniques. For instance, the incorporation of nanomaterials into immunosensors and other immunoanalytical techniques is driving progress by enabling more sensitive, rapid, and even multiplexed detection of analytes [6,7]. The modification of the immunosensor surface by nanostructures and their application as a label are possible strategies for fabricating an immunoanalytical system [8]. One of the key factors in the development of nanomaterial-based systems is the proper binding of antibodies to nanomaterials [9,10]. The physical adsorption and covalent binding of antibodies are commonly used immobilization methods; however, site-directed immobilization of native antibodies, for instance, using proteins A and G, provides increased sensitivity [11]. Moreover, the use of reduced antibody fragments may be a possible alternative for the site-directed orientation of antigen-binding sites of antibodies developing sensitive immunosensors and other immunoanalytical methods [12]. The affinity of used antibodies or their fragments towards antigens is highly important for improving the performance of the immunoanalytical system.
Despite the enormous potential of immunoanalytical methods, the beginning of the bioinformatics era has shown a new direction for research in immunology. With Dayhoff and Ledley’s pioneering work on bioinformatics, it became clear that these methods could be applied in many areas [13]. The active development of bioinformatics is evidenced by the increase in the number of articles in this field from 556 in 2000 to more than 17,000 in 2022 (Web of Science Core Collection). For instance, the convergence of bioinformatics and computational intelligence techniques in the field of computational oncology, particularly in the diagnosis, prognosis, and optimization of cancer therapy, has witnessed significant progress. These synergistic approaches have made remarkable progress in unravelling the intricacies of cancer development and progression, as well as in developing effective therapeutic strategies. This integration of computational methods plays a key role in various aspects of cancer research and treatment [14]. Moreover, machine learning and artificial intelligence are opening a new chapter of bioinformatics in immunology research [15,16]. Bioinformatics is now an integral part of the search for new biomarkers, research into new possible drugs, and vaccine development. It also plays an important role in immune response profiling, immunodiagnostics, and antibody engineering [17,18].
This Special Issue of the International Journal of Molecular Science entitled “Immunoanalytical and Bioinformatics Methods in Immunology Research” is devoted to the application of optical analytical methods and a variety of bioinformatics techniques to find indicative solutions for improving the performance of immunoanalytical systems and disease control. Surface plasmon resonance spectroscopy was proposed to evaluate the conjugation of antibodies with quantum dots. An increase in conjugation efficiency should favorably affect the sensitivity of such bioconjugate-based immunoanalytical systems. A theoretical study showed a new approach suitable for the sensitive detection of dye-labelled antibodies using total internal reflection ellipsometry (TIRE) [19]. Moreover, the combination of TIRE with quartz crystal microbalance with dissipation was applied for the investigation of SARS-CoV-2 spike protein with specific antibodies and the modeling of this interaction. Such a system provides insight into the interaction mechanism, allows more precise calculation of kinetic parameters, and its use can be extended to other antigen–antibody pairs. Integrated bioinformatics analysis was applied to identify possible biomarkers and risk factors of psoriasis [20].

Author Contributions

Conceptualization, A.P. and A.R.; writing—original draft preparation, A.P. and A.R.; writing—review and editing, A.P. and A.R.; project administration, A.R.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a grant (No. S-MIP-22-46) from the Research Council of Lithuania.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zherdev, A.V.; Dzantiev, B.B. Detection limits of immunoanalytical systems: Limiting factors and methods of reduction. J. Anal. Chem. 2022, 77, 391–401. [Google Scholar] [CrossRef]
  2. Pulendran, B.; Davis, M.M. The science and medicine of human immunology. Science 2020, 369, 1582. [Google Scholar] [CrossRef] [PubMed]
  3. Plikusiene, I.; Maciulis, V.; Juciute, S.; Ramanavicius, A.; Ramanaviciene, A. Study of SARS-CoV-2 spike protein wild-type and the variants of concern real-time interactions with monoclonal antibodies and convalescent human serum. Biosensors 2023, 13, 784. [Google Scholar] [CrossRef] [PubMed]
  4. Owusu-Boaitey, N.; Russell, T.W.; Meyerowitz-Katz, G.; Levin, A.T.; Herrera-Esposito, D. Dynamics of SARS-CoV-2 seroassay sensitivity: A systematic review and modelling study. Eurosurveillance 2023, 28, 2200809. [Google Scholar] [CrossRef] [PubMed]
  5. Plikusiene, I.; Maciulis, V.; Juciute, S.; Ramanavicius, A.; Balevicius, Z.; Slibinskas, R.; Kucinskaite-Kodze, I.; Simanavicius, M.; Balevicius, S.; Ramanaviciene, A. Investigation of SARS-CoV-2 nucleocapsid protein interaction with a specific antibody by combined spectroscopic ellipsometry and quartz crystal microbalance with dissipation. J. Colloid Interface Sci. 2022, 626, 113–122. [Google Scholar] [CrossRef] [PubMed]
  6. Hou, F.; Sun, S.; Abdullah, S.W.; Tang, Y.; Li, X.; Guo, H. The application of nanoparticles in point-of-care testing (POCT) immunoassays. Anal. Methods 2023, 15, 2154–2180. [Google Scholar] [CrossRef] [PubMed]
  7. Farka, Z.; Brandmeier, J.C.; Mickert, M.J.; Pastucha, M.; Lacina, K.; Skládal, P.; Soukka, T.; Gorris, H.H. Nanoparticle-based bioaffinity assays: From the research laboratory to the market. Adv. Mater. 2023, 2307653. [Google Scholar] [CrossRef]
  8. Li, Z.; Zhang, J.; Huang, Y.; Zhai, J.; Liao, G.; Wang, Z.; Ning, C. Development of electroactive materials-based immunosensor towards early-stage cancer detection. Coord. Chem. Rev. 2022, 471, 214723. [Google Scholar] [CrossRef]
  9. Popov, A.; Brasiunas, B.; Kausaite-Minkstimiene, A.; Ramanaviciene, A. Metal nanoparticle and quantum dot tags for signal amplification in electrochemical immunosensors for biomarker detection. Chemosensors 2021, 9, 85. [Google Scholar] [CrossRef]
  10. Ruiz, G.; Tripathi, K.; Okyem, S.; Driskell, J.D. PH impacts the orientation of antibody adsorbed onto gold nanoparticles. Bioconjug. Chem. 2019, 30, 1182–1191. [Google Scholar] [CrossRef]
  11. Gao, S.; Guisán, J.M.; Rocha-Martin, J. Oriented immobilization of antibodies onto sensing platforms—A critical review. Anal. Chim. Acta 2022, 1189, 338907. [Google Scholar] [CrossRef] [PubMed]
  12. Rani, A.Q.; Zhu, B.; Ueda, H.; Kitaguchi, T. Recent progress in homogeneous immunosensors based on fluorescence or bioluminescence using antibody engineering. Analyst 2023, 148, 1422–1429. [Google Scholar] [CrossRef] [PubMed]
  13. Gauthier, J.; Vincent, A.T.; Charette, S.J.; Derome, N. A brief history of bioinformatics. Brief. Bioinform. 2019, 20, 1981–1996. [Google Scholar] [CrossRef] [PubMed]
  14. Charoentong, P.; Angelova, M.; Efremova, M.; Gallasch, R.; Hackl, H.; Galon, J.; Trajanoski, Z. Bioinformatics for cancer immunology and immunotherapy. Cancer Immunol. Immunother. 2012, 61, 1885–1903. [Google Scholar] [CrossRef] [PubMed]
  15. Xu, Y.; Su, G.-H.; Ma, D.; Xiao, Y.; Shao, Z.-M.; Jiang, Y.-Z. Technological advances in cancer immunity: From immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct. Target. Ther. 2021, 6, 312. [Google Scholar] [CrossRef] [PubMed]
  16. Shimizu, F.M.; de Barros, A.; Braunger, M.L.; Gaal, G.; Riul, A., Jr. Information visualization and machine learning driven methods for impedimetric biosensing. TrAC Trends Anal. Chem. 2023, 165, 117115. [Google Scholar] [CrossRef]
  17. Greiff, V.; Miho, E.; Menzel, U.; Reddy, S.T. Bioinformatic and statistical analysis of adaptive immune repertoires. Trends Immunol. 2015, 36, 738–749. [Google Scholar] [CrossRef] [PubMed]
  18. Oli, A.N.; Obialor, W.O.; Ifeanyichukwu, M.O.; Odimegwu, D.C.; Okoyeh, J.N.; Emechebe, G.O.; Adejumo, S.A.; Ibeanu, G.C. Immunoinformatics and vaccine development: An overview. ImmunoTargets Ther. 2020, 9, 13–30. [Google Scholar] [CrossRef] [PubMed]
  19. Jurkšaitis, P.; Bužavaitė-Vertelienė, E.; Balevičius, Z. Strong coupling between surface plasmon resonance and exciton of labeled protein-dye complex for immunosensing applications. Int. J. Mol. Sci. 2023, 24, 2029. [Google Scholar] [CrossRef] [PubMed]
  20. Yue, Q.; Li, Z.; Zhang, Q.; Jin, Q.; Zhang, X.; Jin, G. Identification of novel hub genes associated with psoriasis using integrated bioinformatics analysis. Int. J. Mol. Sci. 2022, 23, 15286. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Popov, A.; Ramanaviciene, A. Special Issue “Immunoanalytical and Bioinformatics Methods in Immunology Research”. Int. J. Mol. Sci. 2024, 25, 979. https://doi.org/10.3390/ijms25020979

AMA Style

Popov A, Ramanaviciene A. Special Issue “Immunoanalytical and Bioinformatics Methods in Immunology Research”. International Journal of Molecular Sciences. 2024; 25(2):979. https://doi.org/10.3390/ijms25020979

Chicago/Turabian Style

Popov, Anton, and Almira Ramanaviciene. 2024. "Special Issue “Immunoanalytical and Bioinformatics Methods in Immunology Research”" International Journal of Molecular Sciences 25, no. 2: 979. https://doi.org/10.3390/ijms25020979

APA Style

Popov, A., & Ramanaviciene, A. (2024). Special Issue “Immunoanalytical and Bioinformatics Methods in Immunology Research”. International Journal of Molecular Sciences, 25(2), 979. https://doi.org/10.3390/ijms25020979

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop