Challenges and future directions of AIRR-seq-based diagnostics
Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a promising diagnostic method across various clinical conditions, yet its widespread implementation faces several challenges. This perspective examines the current landscape of AIRR-seq diagnostics and outlines key obstacles and opportunit...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | ImmunoInformatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667119025000096 |
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| author | Ulrik Stervbo Paraskevas Filippidis Felix Breden Lindsay G. Cowell Frederic Davi Victor Greiff Anton W. Langerak Eline T. Luning Prak Alexandra F. Sharland Enkelejda Miho Pieter Meysman |
| author_facet | Ulrik Stervbo Paraskevas Filippidis Felix Breden Lindsay G. Cowell Frederic Davi Victor Greiff Anton W. Langerak Eline T. Luning Prak Alexandra F. Sharland Enkelejda Miho Pieter Meysman |
| author_sort | Ulrik Stervbo |
| collection | DOAJ |
| description | Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a promising diagnostic method across various clinical conditions, yet its widespread implementation faces several challenges. This perspective examines the current landscape of AIRR-seq diagnostics and outlines key obstacles and opportunities for advancement. Critical challenges include the need for standardized quality controls, privacy protection under General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) frameworks, and the development of clinically compatible bioinformatics pipelines. Machine learning approaches offer potential solutions for interpreting complex repertoire signatures, though these models must balance accuracy with interpretability for clinical adoption. Future applications may include early disease detection, prognosis, and monitoring of treatment and vaccine responses. However, successful clinical integration will require sustained collaboration among funding bodies, regulatory agencies, researchers, diagnosticians, and clinicians to establish clear guidelines and expand existing repositories with well-characterized patient samples. The collaborative efforts of the AIRR Diagnostics Working Group and the AIRR Community's initiatives are working towards unlocking the potential of AIRR-seq in precision medicine and enhancing diagnostic capabilities. |
| format | Article |
| id | doaj-art-d5ebc63ef4414bcb8edc426f65650fb1 |
| institution | Kabale University |
| issn | 2667-1190 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ImmunoInformatics |
| spelling | doaj-art-d5ebc63ef4414bcb8edc426f65650fb12025-08-20T03:56:17ZengElsevierImmunoInformatics2667-11902025-09-011910005610.1016/j.immuno.2025.100056Challenges and future directions of AIRR-seq-based diagnosticsUlrik Stervbo0Paraskevas Filippidis1Felix Breden2Lindsay G. Cowell3Frederic Davi4Victor Greiff5Anton W. Langerak6Eline T. Luning Prak7Alexandra F. Sharland8Enkelejda Miho9Pieter Meysman10Center for Translational Medicine and Immune Diagnostics Laboratory, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany; Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin-Brandenburg Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany; Corresponding author.Department of Pathology, Yale School of Medicine, New Haven, 06511, CT, USADepartment of Biological Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaDepartment of Health Data Science and Biostatistics, O’Donnell School of Public Health and Department of Immunology, School of Biomedical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USALaboratory of Molecular Hematology, Department of Hematology, Hôpital Pitié-Salpêtrière, APHP, Sorbonne Université, Paris, FranceDepartment of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway; Imprint Labs, LLC, NY, NY, USALaboratory Medical Immunology, Department Immunology, Erasmus MC, University Medical Center, Rotterdam, the NetherlandsDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA 19104, USASydney Medical School, Faculty of Medicine and Health, University of Sydney, NSW 2006, AustraliaInstitute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied University of Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland; aiNET GmbH, Basel, Switzerland; Swiss Bioinformatics Institute, Lausanne, SwitzerlandAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, BelgiumAdaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a promising diagnostic method across various clinical conditions, yet its widespread implementation faces several challenges. This perspective examines the current landscape of AIRR-seq diagnostics and outlines key obstacles and opportunities for advancement. Critical challenges include the need for standardized quality controls, privacy protection under General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) frameworks, and the development of clinically compatible bioinformatics pipelines. Machine learning approaches offer potential solutions for interpreting complex repertoire signatures, though these models must balance accuracy with interpretability for clinical adoption. Future applications may include early disease detection, prognosis, and monitoring of treatment and vaccine responses. However, successful clinical integration will require sustained collaboration among funding bodies, regulatory agencies, researchers, diagnosticians, and clinicians to establish clear guidelines and expand existing repositories with well-characterized patient samples. The collaborative efforts of the AIRR Diagnostics Working Group and the AIRR Community's initiatives are working towards unlocking the potential of AIRR-seq in precision medicine and enhancing diagnostic capabilities.http://www.sciencedirect.com/science/article/pii/S2667119025000096AIRR-seqDiagnosticsStandardizationClinical translationMachine learningInterpretability |
| spellingShingle | Ulrik Stervbo Paraskevas Filippidis Felix Breden Lindsay G. Cowell Frederic Davi Victor Greiff Anton W. Langerak Eline T. Luning Prak Alexandra F. Sharland Enkelejda Miho Pieter Meysman Challenges and future directions of AIRR-seq-based diagnostics ImmunoInformatics AIRR-seq Diagnostics Standardization Clinical translation Machine learning Interpretability |
| title | Challenges and future directions of AIRR-seq-based diagnostics |
| title_full | Challenges and future directions of AIRR-seq-based diagnostics |
| title_fullStr | Challenges and future directions of AIRR-seq-based diagnostics |
| title_full_unstemmed | Challenges and future directions of AIRR-seq-based diagnostics |
| title_short | Challenges and future directions of AIRR-seq-based diagnostics |
| title_sort | challenges and future directions of airr seq based diagnostics |
| topic | AIRR-seq Diagnostics Standardization Clinical translation Machine learning Interpretability |
| url | http://www.sciencedirect.com/science/article/pii/S2667119025000096 |
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