A team in Japan has developed a graphical software tool that helps biologists estimate 3D gene expression patterns across tissue samples without the need for advanced programming skills. The tool, called tomoseqr, offers a more accessible way to work with RNA tomography data. RNA tomography involves slicing a frozen biological sample along three orthogonal axes, sequencing RNA from each section, and reconstructing a 3D gene expression map. While this method can provide detailed spatial context about gene activity, the data reconstruction process has typically required significant computational expertise.
Tomoseqr changes that by offering a graphical user interface (GUI) that supports all key steps of the analysis. Users can input sequencing results, define tissue morphology, and visualize gene expression patterns in three dimensions. The software is available freely through Bioconductor, a widely used open-source repository for bioinformatics tools.
The team validated tomoseqr using zebrafish (Danio rerio), where it was able to accurately replicate previously reported spatial gene expression patterns. They then applied the tool to planarians, analyzing expression levels of around 18,000 genes. The resulting spatial maps revealed gene expression gradients across the animal’s body, including several genes showing marked spatial variability, suggesting possible roles in regeneration and tissue patterning.
The development reflects a broader interest in integrating spatial context into transcriptomics, particularly in organisms where traditional imaging or in situ hybridization approaches may be more labor-intensive. Planarians, known for their regenerative properties, have been a growing focus in regeneration research, but genome-wide spatial expression studies in this model have remained limited. The use of tomoseqr in this context opens up new possibilities for systematically studying gene function in whole organisms.
Tomoseqr joins other recent advances in spatial biology aimed at expanding access to high-resolution molecular data. While not a replacement for higher-resolution spatial transcriptomics methods, the tool provides a scalable and relatively low-cost way to study spatial dynamics at the transcriptome-wide level, especially in small organisms where complete body mapping is feasible.
Publication Details
Matsuzawa R, Kawahara D, Kashima M, Hirata H, Ozaki H (2025) tomoseqr: A Bioconductor package for spatial reconstruction and visualization of 3D gene expression patterns based on RNA tomography. PLOS ONE 20(1): e0311296. https://doi.org/10.1371/journal.pone.0311296
Tomoseqr changes that by offering a graphical user interface (GUI) that supports all key steps of the analysis. Users can input sequencing results, define tissue morphology, and visualize gene expression patterns in three dimensions. The software is available freely through Bioconductor, a widely used open-source repository for bioinformatics tools.
The team validated tomoseqr using zebrafish (Danio rerio), where it was able to accurately replicate previously reported spatial gene expression patterns. They then applied the tool to planarians, analyzing expression levels of around 18,000 genes. The resulting spatial maps revealed gene expression gradients across the animal’s body, including several genes showing marked spatial variability, suggesting possible roles in regeneration and tissue patterning.
The development reflects a broader interest in integrating spatial context into transcriptomics, particularly in organisms where traditional imaging or in situ hybridization approaches may be more labor-intensive. Planarians, known for their regenerative properties, have been a growing focus in regeneration research, but genome-wide spatial expression studies in this model have remained limited. The use of tomoseqr in this context opens up new possibilities for systematically studying gene function in whole organisms.
Tomoseqr joins other recent advances in spatial biology aimed at expanding access to high-resolution molecular data. While not a replacement for higher-resolution spatial transcriptomics methods, the tool provides a scalable and relatively low-cost way to study spatial dynamics at the transcriptome-wide level, especially in small organisms where complete body mapping is feasible.
Publication Details
Matsuzawa R, Kawahara D, Kashima M, Hirata H, Ozaki H (2025) tomoseqr: A Bioconductor package for spatial reconstruction and visualization of 3D gene expression patterns based on RNA tomography. PLOS ONE 20(1): e0311296. https://doi.org/10.1371/journal.pone.0311296
