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Data from: Real-time, cross-modal genotype mapping of free-moving Drosophila larvae via simultaneous mechano-electrophysiological recording

Dong, Kairu1; Song, Hao1; Zhao, Qianhui1; Song, Zhiying1; Lv, Fu1; Wan, Siouwen1; Zheng, Tianyu1; Fan, Yunlong1; Liu, Wenzhe1; Zhang, Shaomin1; Wu, Yongjun1; Huang, Yuhui1; Song, Jizhou1; Gong, Zhefeng1; Zheng, Nenggan1; Nan, Kewang1

Published Sep 03, 2026 on Dryad. https://doi.org/10.5061/dryad.s1rn8pkpr

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Sep 03, 2026 version files 39.07 MB

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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.