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SM-Omics: An automated platform for high-throughput spatial multi-omics

The spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial Transcriptomics (ST) has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of ST at scale, by presenting Spatial Multiomics (SM-Omics) as a fully automated high-throughput platform for combined and spatially resolved transcriptomics and antibody-based proteomics.

Please cite: Vickovic S & Loetstedt B et al: SM-Omics: An automated platform for high-throughput spatial multi-omics

Automation SM-Omics tech workflow

github-small Illustration kindly made by Ania Hupalowska.

Data availability

The raw and processed sequencing and image files needed to recreate all the results in this study have been made avaiable at Broad's Single Cell Portal.

For all file descriptions and metadata, please refer to: metadata.

Data pre-processing

Initial sequncing data processing was performed with ST Pipeline (v.1.7.6). For IF image pre-processing, either of Cy3 spatial gene expression footprints or traditional IFs, please check under footprints and ifs.

For using our spatial spots alignemnts and reporting tool, please go to our SpoTteR repository. For speed and accuracy tests, please check out our code here.

Spatial expression estimates using Splotch

For generating spatial gene expression estimates and spatial differential expression analysis, we advise you to follow instruction at: https://github.com/tare/Splotch and cite Äijö T, Maniatis S & Vickovic S et al: Splotch: Robust estimation of aligned spatial temporal gene expression data, doi: https://doi.org/10.1101/757096. In order to ease use, we have made the complete Splotch workflow available trough Broad's Firecloud platform.

For recreating images in the paper, we have made the following code available: genes and tags with correspoding python requirements listed in the same folders. To recreate enrichment heatmaps per spatial ROI, please go to heatmaps.

Correlating genes and protein expression

For correlating gene to IF protein expression, please refer to the this code and examples.

pyenv

Python env requirements have been listed in the yml file.

Renv

Renv requirements and sessionInfo has been listed in the sessions file file.

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