SCALPEL: A pipeline for processing large-scale spatial transcriptomics data

Kunst, M.; Ching, L.; Quon, J.; Mathieu, R.; Hewitt, M.; Seeman, S.; Ayala, A.; Gelfand, E.; Long, B.; Martin, N.; Nagra, J.; Olsen, P.; Oyama, A.; Valera, N.; Pagen, C.; Sunkin, S.; Ariza, J.; Smith, K.; McMillen, D.; Zeng, H.; Waters, J.

doi:10.64898/2026.01.09.698732

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bioRxiv · 10.64898/2026.01.09.698732

SCALPEL: A pipeline for processing large-scale spatial transcriptomics data

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Abstract

Spatial transcriptomics enables the precise mapping of gene expression patterns within tissue architecture, offering unprecedented insights into cellular interactions, tissue heterogeneity, and disease pathology that are unattainable with traditional transcriptomic approaches. We present a tool for processing spatial transcriptomics data, SCALPEL (Spatial Cell Analysis, Labeling, Processing, and Expression Linking). SCALPEL is specifically designed to support the analysis of large, atlas-level datasets. Our new workflow features advanced 3D segmentation optimized for dense and heterogeneous tissues, refined filtering criteria, and transcriptome-based doublet detection to remove low-quality or artifactual cells. Cell type label transfer from existing taxonomies is further improved through updated filtering thresholds. Spatial domain detection is incorporated to capture local transcriptomic organization, and tissue sections are registered to the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv3) for precise anatomical alignment. Genome-wide expression imputation from single-cell RNA-sequencing (scRNAseq) further enriches the dataset. Crucially, we benchmark the performance of this updated pipeline against a previously published version of our whole-mouse-brain (WMB) dataset (Yao et al., 2023b), demonstrating substantial improvements in cell number, expression profile clarity, and spatial registration. These advances provide a robust foundation for downstream spatial analyses and set a new standard for large-scale spatial transcriptomics studies.

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Kunst, M., Ching, L., Quon, J., Mathieu, R., Hewitt, M., Seeman, S., Ayala, A., Gelfand, E., Long, B., Martin, N., Nagra, J., Olsen, P., Oyama, A., Valera, N., Pagen, C., Sunkin, S., Ariza, J., Smith, K., McMillen, D., Zeng, H., Waters, J.. 2026-01-12. SCALPEL: A pipeline for processing large-scale spatial transcriptomics data. https://doi.org/10.64898/2026.01.09.698732

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