An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data

Murakami, Ken; Iida, Keita; Okada, Mariko

Genes to Cells, 2025, e70000

Number of Access:301（2025-07-18 17:06 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/100542

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Title
An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data
Creator
CreatorName
Murakami, Ken
NRID
1000010709653 1000010709653
ORCID
0000-0002-1076-830X 0000-0002-1076-830X
researchmap permalink
7000005567 7000005567
Scopus Author ID
35409372400 35409372400
CreatorName
Iida, Keita
NRID
1000010342833 1000010342833
ORCID
0000-0002-6210-8223 0000-0002-6210-8223
researchmap permalink
mariko_okada mariko_okada
Scopus Author ID
57194562293 57194562293
CreatorName
Okada, Mariko
Subject
attention-based neural network
cis-regulatory elements
deep learning
enhancers
explainable artificial intelligence
gene regulation
intra-tumor heterogeneity
single-cell analysis
single-cell ATAC-seq
single-cell multiome
Description
Murakami K., Iida K., Okada M. An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data. Genes to Cells 30, e70000 (2025); https://doi.org/10.1111/gtc.70000.
Abstract
Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activity at the single-cell level is essential. However, current analytical methods can only capture the average behavior of cREs in cell populations, thereby obscuring cell-specific variations. To address this limitation, we proposed an attention-based deep neural network framework that integrates DNA sequences, genomic distances, and single-cell multi-omics data to detect cREs and their activities in individual cells. Our model shows higher accuracy in identifying cREs within single-cell multi-omics data from healthy human peripheral blood mononuclear cells than other existing methods. Furthermore, it clusters cells more precisely based on predicted cRE activities, enabling a finer differentiation of cell states. When applied to publicly available single-cell data from patients with glioma, the model successfully identified tumor-specific SOX2 activity. Additionally, it revealed the heterogeneous activation of the ZEB1 transcription factor, a regulator of epithelial-to-mesenchymal transition-related genes, which conventional methods struggle to detect. Overall, our model is a powerful tool for detecting cRE regulation at the single-cell level, which may contribute to revealing drug resistance mechanisms in cell sub-populations.
Publisher
John Wiley and Sons Inc
Source Title
Genes to Cells
Page
e70000
Date of Issued
2025-03-01
Language
English
Handle URL
https://hdl.handle.net/11094/100542
PISSN
13569597
eISSN
13652443
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1111/gtc.70000
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article