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EmbryoTempoFormer: clip-based developmental tempo inference from zebrafish brightfield time-lapse microscopy

Deng, L.; Lin, P.; Xie, L.

2026-03-11 developmental biology
10.64898/2026.03.09.710433 bioRxiv
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Nominal hours post fertilization (hpf) are widely used to index zebrafish embryogenesis, yet under condition shifts--such as temperature change, genetic perturbation, or environmental stress--nominal time can decouple from true developmental progression. In such settings, biologically meaningful variation is better described as a systematic change in developmental tempo rather than a simple temporal offset. Here we introduce an embryo-resolved framework that treats developmental tempo as the primary quantity of interest in brightfield time-lapse imaging. We present EmbryoTempoFormer (ETF), a clip-based CNN-Transformer that predicts developmental progression from short time-lapse clips and is trained with a within-embryo temporal-difference consistency regularizer to promote temporally coherent trajectories. Crucially, we couple model predictions with an embryo-level inference and statistical workflow: temporally correlated clip-level outputs are aggregated into interpretable embryo-level tempo and stability readouts, and cross-condition effects are quantified using embryo-bootstrap confidence intervals with embryos--rather than frames or clips--as independent units, avoiding pseudo-replication. Using temperature perturbation as a representative domain shift, we robustly quantify condition-induced changes in global developmental dynamics and show that developmental delay predominantly manifests as reduced developmental tempo. This framework enables statistically principled, high-throughput phenotyping for perturbation screens, drug assays, and environmental stress studies. HIGHLIGHTSO_LIClip-based CNN-Transformer predicts developmental time from brightfield time-lapse microscopy. C_LIO_LIWithin-embryo temporal-difference consistency improves trajectory self-consistency. C_LIO_LIEmbryo-level anchored tempo slopes enable interpretable cross-condition comparisons. C_LIO_LIReproducible pipeline via code, scripts, and a Zenodo bundle with embryo-level inference C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/710433v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@19e4028org.highwire.dtl.DTLVardef@8622b9org.highwire.dtl.DTLVardef@51b78borg.highwire.dtl.DTLVardef@e93c2e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in Artificial Intelligence in the Life Sciences (predicted rank #4) · training set

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