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. 2024 Sep 2;40(9):btae509.
doi: 10.1093/bioinformatics/btae509.

SpatialOne: end-to-end analysis of visium data at scale

Affiliations

SpatialOne: end-to-end analysis of visium data at scale

Mena Kamel et al. Bioinformatics. .

Abstract

Motivation: Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose.

Results: Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale.

Availability and implementation: SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image.

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Conflict of interest statement

All authors are Sanofi employees and may hold shares and/or stock options in the company. The SpatialOne team is not connected in any way to 10x Genomics.

Figures

Figure 1.
Figure 1.
SpatialOne workflow and analysis examples. (a) SpatialOne uses SpaceRanger for expression processing and cell segmentation and cell deconvolution methods to determine cell types. Using the cell location estimation, it generates descriptive analytics, an HTML spatial structure report, compares regions of interest if provided, and displays results in the TissUUmaps interactive viewer. (b) H&E image of a Visium sample corresponding to a human squamous lung cancer slide. Here SpatialOne utilized CellPose for cell segmentation and Cell2Location for cell deconvolution. Single-cell data from the Lung Cancer atlas is used as reference data for deconvolution. (c) Cell type estimation shows the clear distribution of LUSC tumor cells (dark blue), plasma cells (light green), macrophages (red), alveolar cells (pink), fibroblasts (yellow), and ciliated cells (white). (d) Plasma cells (light green cross markers) are present in spots with high IGKC expression. (e) IGKC expression levels. IGKC is used as a marker for plasma cell infiltration in tumors. (f) Cell segmentation and cell type estimation results. Orange dots, in the dark areas, correspond to cells outside Visium spots that cannot be deconvolved. (g) Counts of the Top 5 identified cell types. (Table) Comparison of SpatialOne capabilities versus main existing spatial transcriptomics libraries and analysis pipelines.

References

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Publication types

Grants and funding