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Background <p>Elucidating the spatiotemporal dynamics of gene expression is essential for understanding complex physiological and pathological processes. Current spatial transcriptomics techniques are hindered by low read depths and limited gene detection.</p> Results <p>Here, we introduce Palette, a pipeline that infers detailed spatial gene expression patterns from bulk RNA-seq data, utilizing existing spatial transcriptomics data as the sole reference. This method identifies more precise expression patterns by smoothing, imputing and adjusting gene expressions. We apply Palette to reconstruct the zebrafish SpatioTemporal Expression Profiles (zSTEP) by integrating 53-slice serial bulk RNA-seq data from three developmental stages with existing spatial transcriptomics and image references. zSTEP provides a comprehensive cartographic resource for examining gene expression and investigating developmental events within zebrafish embryos. Utilizing machine learning-based screening, we identify key morphogens and transcription factors essential for anteroposterior axis development and characterized their dynamic distribution throughout embryogenesis. In addition, among these transcription factors, Hox family genes are found to be pivotal in anteroposterior axis refinement. Their expression is closely correlated with cellular anteroposterior identities, and <i>hoxb</i> genes may act as central regulators in this process.</p> Conclusions <p>This study presents Palette, a pipeline for integrating bulk RNA-seq data and spatial transcriptomics data, and zSTEP, a comprehensive cartographic resource for investigating zebrafish early embryonic development. In addition, key morphogens and transcriptional factors essential for anteroposterior axis establishment and refinement are identified.</p>

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Unravelling the progression of the zebrafish primary body axis with reconstructed spatiotemporal transcriptomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月2日

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  • Yang Dong,
  • Tao Cheng,
  • Xiang Liu,
  • Xin-Xin Fu,
  • Yang Hu,
  • Xian-Fa Yang,
  • Ling-En Yang,
  • Hao-Ran Li,
  • Zhi-Wen Bian,
  • Naihe Jing,
  • Jie Liao,
  • Xiaohui Fan,
  • Peng-Fei Xu

摘要

Background

Elucidating the spatiotemporal dynamics of gene expression is essential for understanding complex physiological and pathological processes. Current spatial transcriptomics techniques are hindered by low read depths and limited gene detection.

Results

Here, we introduce Palette, a pipeline that infers detailed spatial gene expression patterns from bulk RNA-seq data, utilizing existing spatial transcriptomics data as the sole reference. This method identifies more precise expression patterns by smoothing, imputing and adjusting gene expressions. We apply Palette to reconstruct the zebrafish SpatioTemporal Expression Profiles (zSTEP) by integrating 53-slice serial bulk RNA-seq data from three developmental stages with existing spatial transcriptomics and image references. zSTEP provides a comprehensive cartographic resource for examining gene expression and investigating developmental events within zebrafish embryos. Utilizing machine learning-based screening, we identify key morphogens and transcription factors essential for anteroposterior axis development and characterized their dynamic distribution throughout embryogenesis. In addition, among these transcription factors, Hox family genes are found to be pivotal in anteroposterior axis refinement. Their expression is closely correlated with cellular anteroposterior identities, and hoxb genes may act as central regulators in this process.

Conclusions

This study presents Palette, a pipeline for integrating bulk RNA-seq data and spatial transcriptomics data, and zSTEP, a comprehensive cartographic resource for investigating zebrafish early embryonic development. In addition, key morphogens and transcriptional factors essential for anteroposterior axis establishment and refinement are identified.