ENACT: End-to-End Analysis of Visium High Definition (HD) Data

Kamel, M.; Song, Y.; Solbas, A.; Villordo, S.; Sarangi, A.; Senin, P.; Mathew, S.; Ayestas, L. C.; Wang, S.; Classe, M.; Bar-Joseph, Z.; Planas, A. P.

doi:10.1101/2024.10.17.618905

bioRxiv · 10.1101/2024.10.17.618905

ENACT: End-to-End Analysis of Visium High Definition (HD) Data

Kamel, M.·Song, Y.·Solbas, A.·Villordo, S.·Sarangi, A.·Senin, P.·Mathew, S.·Ayestas, L. C.·Wang, S.·Classe, M.·Bar-Joseph, Z.·Planas, A. P.

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Abstract

MotivationSpatial transcriptomics (ST) enables the study of gene expression within its spatial context in histopathology samples. To date, a limiting factor has been the resolution of sequencing based ST products. The introduction of the Visium High Definition (HD) technology opens the door to cell resolution ST studies. However, challenges remain in the ability to accurately map transcripts to cells and in assigning cell types based on the transcript data. ResultsWe developed ENACT, the first tissue-agnostic pipeline that integrates advanced cell segmentation with Visium HD transcriptomics data to infer cell types across whole tissue sections. Our pipeline incorporates novel bin-to-cell assignment methods, enhancing the accuracy of single-cell transcript estimates. Validated on diverse synthetic and real datasets, our approach is both scalable and effective offering a robust solution for spatially resolved transcriptomics analysis. Availability and implementationENACT source code is available at https://github.com/Sanofi-Public/enact-pipeline. Experimental data is available at https://zenodo.org/records/13887921. Supplementary information: Supplementary data are available at BiorXiv online.

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Kamel, M., Song, Y., Solbas, A., Villordo, S., Sarangi, A., Senin, P., Mathew, S., Ayestas, L. C., Wang, S., Classe, M., Bar-Joseph, Z., Planas, A. P.. 2024-10-20. ENACT: End-to-End Analysis of Visium High Definition (HD) Data. https://doi.org/10.1101/2024.10.17.618905

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