DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads

The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutiliz...

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Vydáno v:	Nature communications Ročník 16; číslo 1; s. 6202 - 26
Hlavní autoři:	Song, Kailu, Zheng, Yumin, Zhao, Bowen, Eidelman, David H., Tang, Jian, Ding, Jun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 04.07.2025 Nature Publishing Group Nature Portfolio
Témata:	631/114/1305 631/114/1314 631/114/2397 631/114/794 Alternative splicing Alternative Splicing - genetics Animals Aquatic mammals Biomarkers Clustering Datasets Deep Learning Exons - genetics Gene expression Gene Expression Profiling - methods Graph neural networks Graph representations Graphical representations Heterogeneity Humanities and Social Sciences Humans multidisciplinary Science Science (multidisciplinary) Single-Cell Analysis - methods Therapeutic targets Transcriptome - genetics Transcriptomics
ISSN:	2041-1723, 2041-1723
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Popis
Shrnutí:	The advent of single-cell sequencing has revolutionized the study of cellular dynamics, providing unprecedented resolution into the molecular states and heterogeneity of individual cells. However, the rich potential of exon-level information and junction reads within single cells remains underutilized. Conventional gene-count methods overlook critical exon and junction data, limiting the quality of cell representation and downstream analyses such as subpopulation identification and alternative splicing detection. We introduce DOLPHIN, a deep learning method that integrates exon-level and junction read data, representing genes as graph structures. These graphs are processed by a variational graph autoencoder to improve cell embeddings. DOLPHIN not only demonstrates superior performance in cell clustering, biomarker discovery, and alternative splicing detection but also provides a distinct capability to detect subtle transcriptomic differences at the exon level that are often masked in gene-level analyses. By examining cellular dynamics with enhanced resolution, DOLPHIN provides new insights into disease mechanisms and potential therapeutic targets. Single-cell RNA-seq analysis is conventionally limited to gene-level quantification, missing transcript diversity. Here, authors present DOLPHIN, a deep learning method that enables exon- and junction-level analysis to improve cell representation and detect alternative splicing.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-025-61580-w