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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="review-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">RUDN Journal of Medicine</journal-id><journal-title-group><journal-title xml:lang="en">RUDN Journal of Medicine</journal-title><trans-title-group xml:lang="ru"><trans-title>Вестник Российского университета дружбы народов. Серия: Медицина</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2313-0245</issn><issn publication-format="electronic">2313-0261</issn><publisher><publisher-name xml:lang="en">Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">52067</article-id><article-id pub-id-type="doi">10.22363/2313-0245-2025-30-3-328-338</article-id><article-id pub-id-type="edn">KMPBFW</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>CELL BIOLOGY</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>КЛЕТОЧНАЯ БИОЛОГИЯ</subject></subj-group><subj-group subj-group-type="article-type"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Typing of adaptive immunity cells: markers of populations and their functional significance</article-title><trans-title-group xml:lang="ru"><trans-title>Типирование клеток адаптивного иммунитета: маркеры популяций и их функциональное значение</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-5121-2535</contrib-id><contrib-id contrib-id-type="spin">1315-8100</contrib-id><name-alternatives><name xml:lang="en"><surname>Fedorov</surname><given-names>Anton A.</given-names></name><name xml:lang="ru"><surname>Федоров</surname><given-names>А. А.</given-names></name></name-alternatives><email>anton.fedorov.2014@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3297-1680</contrib-id><contrib-id contrib-id-type="spin">8092-0070</contrib-id><name-alternatives><name xml:lang="en"><surname>Fedorenko</surname><given-names>Anastasia A.</given-names></name><name xml:lang="ru"><surname>Федоренко</surname><given-names>А. А.</given-names></name></name-alternatives><email>anton.fedorov.2014@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2865-7576</contrib-id><contrib-id contrib-id-type="spin">5714-4611</contrib-id><name-alternatives><name xml:lang="en"><surname>Patysheva</surname><given-names>Marina R.</given-names></name><name xml:lang="ru"><surname>Патышева</surname><given-names>М. Р.</given-names></name></name-alternatives><email>anton.fedorov.2014@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7283-0092</contrib-id><contrib-id contrib-id-type="spin">7900-9700</contrib-id><name-alternatives><name xml:lang="en"><surname>Gerashchenko</surname><given-names>Tatiana S.</given-names></name><name xml:lang="ru"><surname>Геращенко</surname><given-names>Т. С.</given-names></name></name-alternatives><email>anton.fedorov.2014@mail.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Cancer Research Institute, Tomsk National Research Medical Center</institution></aff><aff><institution xml:lang="ru">Научно-исследовательский институт онкологии, Томский национальный исследовательский медицинский центр</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">RUDN University</institution></aff><aff><institution xml:lang="ru">Российский университет дружбы народов</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2026-08-30" publication-format="electronic"><day>30</day><month>08</month><year>2026</year></pub-date><volume>30</volume><issue>3</issue><issue-title xml:lang="en">CELL BIOLOGY</issue-title><issue-title xml:lang="ru">КЛЕТОЧНАЯ БИОЛОГИЯ</issue-title><fpage>328</fpage><lpage>338</lpage><history><date date-type="received" iso-8601-date="2026-08-31"><day>31</day><month>08</month><year>2026</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2026, Fedorov A.A., Fedorenko A.A., Patysheva M.R., Gerashchenko T.S.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2026, Федоров А.А., Федоренко А.А., Патышева М.Р., Геращенко Т.С.</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="en">Fedorov A.A., Fedorenko A.A., Patysheva M.R., Gerashchenko T.S.</copyright-holder><copyright-holder xml:lang="ru">Федоров А.А., Федоренко А.А., Патышева М.Р., Геращенко Т.С.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/medicine/article/view/52067">https://journals.rudn.ru/medicine/article/view/52067</self-uri><abstract xml:lang="en"><p>Relevance. Single-cell sequencing technologies (scRNA-seq) provide unique opportunities for studying the heterogeneity of immune populations, enabling the analysis of expression profiles, ligand-receptor interactions, and differentiation trajectories at the single-cell level. However, the quality of cell typing significantly depends on the accuracy of data annotation. Marker ambiguity, variability in their expression depending on physiological context, limitations of automated tools such as CellTypist, Azimuth, and SingleR, and the incompleteness of databases like PanglaoDB and CellMarker, including insufficient information on the functional roles of markers, substantially hinder the identification of cell populations and require additional efforts to achieve meaningful results. Aim. To systematize markers of T- and B-cells of the adaptive immune response to facilitate scRNA-seq data annotation. Materials and Methods. An analysis of 30 scientific publications from PubMed, Scopus, and Web of Science (2008-2024) and marker data from PanglaoDB and CellMarker was conducted. Results and discussion. Key and additional T-cell (CD3D, CD3E, CD3G, CD4, CD8A, CD8B, FOXP3, GZMA, TBX21) and B-cell (CD19, MS4A1, CD27, IGHM, IGHD) markers, their roles in immune processes, including activation, cytotoxicity, and regulation, were examined. Subpopulations such as Th1 cells (T-bet, IFN-γ), follicular helper cells (CXCR5, BCL-6), and regulatory B-cells (CD24, IL-10) and their functions in health and disease were described. Conclusion. The systematization of T- and B-cell markers was developed to improve the quality of single-cell sequencing data annotation and is applicable for enhanced typing of cell functional states in health and pathological conditions, including infectious, autoimmune, and oncological diseases. This opens opportunities for a deeper understanding of immune processes and the development of immunotherapy approaches, such as CAR-T therapy. However, the limited specificity of markers and the lack of standardized annotation algorithms highlight the need for further research to refine typing methods.</p></abstract><trans-abstract xml:lang="ru"><p>Актуальность. Технологии секвенирования единичных клеток (scRNA-seq) открывают уникальные возможности для изучения гетерогенности иммунных популяций, позволяя анализировать экспрессионные профили, лиганд-рецепторные взаимодействия и траектории дифференцировки на уровне отдельных клеток. Однако качество типирования клеток существенно зависит от точности аннотации данных. Неоднозначность маркеров, вариабельность их экспрессии в зависимости от физиологического контекста, ограничения автоматических инструментов, таких как CellTypist, Azimuth и SingleR, а также неполнота баз данных PanglaoDB и CellMarker, включая недостаток сведений о функциональной роли маркеров, значительно затрудняют идентификацию клеточных популяций и требуют дополнительных усилий для достижения значимых результатов. Цель. Систематизировать маркеры Т- и В-клеток адаптивного иммунного ответа для упрощения аннотации данных scRNA-seq. Материалы и методы. Проведен анализ более 30 научных публикаций из баз PubMed, Scopus и Web of Science за 2008-2024 годы, а также данных маркеров из баз PanglaoDB и CellMarker. Результаты и обсуждение. Рассмотрены ключевые и дополнительные маркеры Т-клеток (CD3D, CD3E, CD3G, CD4, CD8A, CD8B, FOXP3, GZMA, TBX21) и В-клеток (CD19, MS4A1, CD27, IGHM, IGHD), их роль в иммунных процессах, включая активацию, цитотоксичность и регуляцию. Описаны субпопуляции, такие как Th1-клетки (T-bet, IFN-γ), фолликулярные хелперы (CXCR5, BCL-6), регуляторные В-клетки (CD24, IL-10), и их функции в норме и патологии. Выводы. Систематизация маркеров Т- и В-клеток разработана для улучшения качества аннотации данных секвенирования единичных клеток и применима для расширенного типирования функциональных состояний клеток в норме и при патологии (инфекционные, аутоиммунные и онкологические заболевания). Это открывает возможности для углубленного понимания иммунных процессов и разработки подходов к иммунотерапии, включая CAR-T-терапию. Однако ограниченная специфичность маркеров и отсутствие стандартизированных алгоритмов аннотации подчеркивают необходимость дальнейших исследований для совершенствования методов типирования.</p></trans-abstract><kwd-group xml:lang="en"><kwd>T-cells</kwd><kwd>B-cells</kwd><kwd>adaptive immunity</kwd><kwd>single-cell sequencing</kwd><kwd>immune markers</kwd><kwd>typing</kwd><kwd>immunotherapy</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>Т-клетки</kwd><kwd>В-клетки</kwd><kwd>адаптивный иммунитет</kwd><kwd>секвенирование единичных клеток</kwd><kwd>иммунные маркеры</kwd><kwd>типирование</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution xml:lang="ru">Данный проект был поддержан Российским Научным Фондом [грант № 22-75-10128].</institution></institution-wrap><institution-wrap><institution xml:lang="en">This project was supported by the Russian Science Foundation [grant no. 22–75–10128].</institution></institution-wrap></funding-source></award-group></funding-group></article-meta><fn-group/></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Gerashchenko T, Frolova A, Patysheva M, Fedorov A, Stakheyeva M, Denisov E, Cherdyntseva N. Breast cancer immune landscape: interplay between systemic and local immunity. 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