vavilovВавиловский журнал генетики и селекцииVavilov Journal of Genetics and Breeding2500-3259Institute of Cytology and Genetics of Siberian Branch of the RAS10.18699/vjgb-24-104vavilov-4418Research ArticleСИСТЕМНАЯ КОМПЬЮТЕРНАЯ БИОЛОГИЯSYSTEMS COMPUTATIONAL BIOLOGYМетоды реконструкции генных регуляторных сетей на основе транскриптомных данных отдельных клетокReconstruction of gene regulatory networks from single cell transcriptomic dataРыбаковМ. А.RybakovM. A.

Новосибирск

Novosibirsk

https://orcid.org/0009-0004-0738-5625ОмельянчукН. А.OmelyanchukN. A.

Новосибирск

Novosibirsk

https://orcid.org/0009-0005-7316-7690ЗемлянскаяЕ. В.ZemlyanskayaE. V.

Новосибирск

Novosibirsk

ezemlyanskaya@bionet.nsc.ruФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук; Новосибирский национальный исследовательский государственный университетРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences; Novosibirsk State UniversityRussian FederationФедеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наукРоссияInstitute of Cytology and Genetics of the Siberian Branch of the Russian Academy of SciencesRussian Federation202426012025288974981Copyright © Рыбаков М.А., Омельянчук Н.А., Землянская Е.В., 20252025Рыбаков М.А., Омельянчук Н.А., Землянская Е.В.Rybakov M.A., Omelyanchuk N.A., Zemlyanskaya E.V.This work is licensed under a Creative Commons Attribution 4.0 License.https://vavilov.elpub.ru/jour/article/view/4418

Генные регуляторные сети – интерпретируемые графовые модели регуляции экспрессии генов – являются важным инструментом для понимания и исследования механизмов, которые клетки реализуют в процессе развития и при ответе на различные внутренние и внешние стимулы. Исторически первый подход для реконструкции генных регуляторных сетей основывался на анализе литературных сведений, в том числе обобщенных в базах данных. В настоящее время основной способ системной реконструкции генных регуляторных сетей – анализ омиксных (в первую очередь транскриптомных) данных; разработан ряд математических подходов для решения этой задачи. Развитие технологий получения омиксных данных для отдельных клеток сделало возможным проведение широкомасштабных молекулярно-генетических исследований с беспрецедентно высоким уровнем разрешения. В частности, появилась возможность реконструировать генные регуляторные сети для отдельных клеточных типов и для различных стадий развития клеток. Однако технические и биологические особенности омиксных данных отдельных клеток требуют специальных программ для решения этой задачи. В обзоре описаны подходы и программы, которые разработаны и используются для построения генных регуляторных сетей по транскриптомным данным отдельных клеток (scRNA-seq). Разбираются преимущества применения транскриптомных данных для отдельных клеток по сравнению с транскриптомами многоклеточных образцов, а также их недостатки в рамках решения задачи реконструкции регуляторных генных сетей. Существенное внимание уделяется повышению точности генных регуляторных сетей, построенных по транскриптомным данным отдельных клеток с помощью привлечения других омиксных данных, в первую очередь данных по сайтам связывания транскрипционных факторов и профилирования районов открытого хроматина (scATAC-seq). Рассматриваются вопросы применимости получаемых сетей в молекулярно-генетических исследованиях, приводятся примеры успешного использования генных регуляторных сетей, реконструированных различными методами с применением омиксных данных отдельных клеток для решения конкретных биологических задач. Обсуждаются перспективные направления развития этой области.

Gene regulatory networks (GRNs) – interpretable graph models of gene expression regulation – are a pivotal tool for understanding and investigating the mechanisms utilized by cells during development and in response to various internal and external stimuli. Historically, the first approach for the GRN reconstruction was based on the analysis of published data (including those summarized in databases). Currently, the primary GRN inference approach is the analysis of omics (mainly transcriptomic) data; a number of mathematical methods have been adapted for that. Obtaining omics data for individual cells has made it possible to conduct large-scale molecular genetic studies with an extremely high resolution. In particular, it has become possible to reconstruct GRNs for individual cell types and for various cell states. However, technical and biological features of single-cell omics data require specific approaches for GRN inference. This review describes the approaches and programs that are used to reconstruct GRNs from single-cell RNA sequencing (scRNA-seq) data. We consider the advantages of using scRNA-seq data compared to bulk RNA-seq, as well as challenges in GRN inference. We pay specific attention to state-of-the-art methods for GRN reconstruction from single-cell transcriptomes recruiting other omics data, primarily transcription factor binding sites and open chromatin profiles (scATAC-seq), in order to increase inference accuracy. The review also considers the applicability of GRNs reconstructed from single-cell omics data to recover and characterize various biological processes. Future perspectives in this area are discussed.

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The authors declare that there are no conflicts of interest present.