Authors
Kairu Dong, Hao Song, Qianhui Zhao, Zhiying Song, Fu Lv, Siouwen Wan, Tianyu Zheng, Yunlong Fan, Wen-Che Liu, Shaomin Zhang, Yongjun Wu, Yuhui Huang, Jizhou Song, Zhefeng Gong, Nenggan Zheng, Kewang Nan
Published in
Science advances. Volume 12. Issue 38. Pages eaef7492. Sep 18, 2026. Epub Sep 18, 2026.
Abstract
Drosophila larvae provide a powerful model for interrogating genes associated with human muscle and neurological disorders; however, existing genotyping and phenotyping approaches remain low-throughput and often rely on destructive, invasive, or toxic procedures. Here, we present a scalable bioelectronic platform that enables real-time, simultaneous mechano-electrophysiological recording from freely moving Drosophila larvae in an open three-dimensional (3D) space, allowing high-throughput cross-modal genotype mapping (CMGM). The system integrates conductive and piezoelectric microneedle electrodes into a flexible sensory array that achieves stable, long-term signal acquisition during unrestricted and complex 3D locomotion. By coupling dual-modal signal acquisition with machine-learning-assisted classification, we directly identify muscle defects in unlabeled RNAi-knockdown larvae within 30 minutes, without invasive manipulation or time-consuming sample preparation. Incorporation of both electrophysiological and mechanical waveform features improves overall classification accuracy to 96%, outperforming single-modality approaches. This non-destructive, high-throughput CMGM strategy establishes a generalizable framework for bridging genotype and phenotype in intact, freely behaving organisms, with broad implications for functional genetics and disease modeling.
PMID:
42758840
Bibliographic data and abstract were imported from PubMed on 19 Sep 2026.
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