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

Abstract

ABSTRACT 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. HIGHLIGHTS Clip-based CNN–Transformer predicts developmental time from brightfield time-lapse microscopy. Within-embryo temporal-difference consistency improves trajectory self-consistency. Embryo-level anchored tempo slopes enable interpretable cross-condition comparisons. Reproducible pipeline via code, scripts, and a Zenodo bundle with embryo-level inference Graphical abstract

Plant phenotyping relevance

ゼブラフィッシュ胚の発生進行・テンポをタイムラプス画像から推定するCNN–Transformerと、胚単位の統計的推定ワークフローを開発しており、表現型取得・抽出手法が中心である。ただし植物ではなく動物対象のため、この植物フェノタイピング索引では除外すべき内容。

Code and data availability

The paper analyzes public zebrafish brightfield time-lapse data (BioImage Archive S-BIAD531) and provides a public GitHub code repository plus a Zenodo reproducibility bundle containing processed arrays, model checkpoints, dataset splits, and checksums. All three are paper-specific, public, and actionable.