Continual integration of single-cell multimodal data with MIRACLE.

Authors

Jiahao Zhou, Jing Wang, Shuofeng Hu, Tongtong Kan, Chao Feng, Xiaohan Qiang, Guohua Dong, Jinhui Shi, Runyan Liu, Xiaochen Bo, Le Ou-Yang, Xiaomin Ying, Zhen He

Published in

Nature computational science. Jul 31, 2026. Epub Jul 31, 2026.

Abstract

Single-cell sequencing has transformed our understanding of cellular heterogeneity, enabling the construction of multi-omics atlases through data integration. However, conventional atlas updates require full reintegration of all datasets, creating scalability challenges that limit the timeliness and adaptability of biomedical research. Here we present multimodal integration with continual learning (MIRACLE), an online learning framework for scalable multimodal integration. Using dynamic architecture adaptation and data rehearsal, MIRACLE continually integrates diverse datasets while preserving biological fidelity. Across evaluations, MIRACLE achieves accurate online integration with substantially improved efficiency, refining and expanding atlases with new cross-modal, cross-tissue and cross-disease data. Applied to respiratory infections, it reveals both shared and pathogen-specific immune mechanisms in coronavirus disease 2019, influenza A and tuberculosis. Overall, MIRACLE provides an efficient and collaborative solution for the continual integration, sharing and exploration of biological knowledge.

PMID:
42538449
Bibliographic data and abstract were imported from PubMed on 01 Aug 2026.

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