B-S.B.59: Integrated analysis of Visium HD spatial transcriptomics and imaging data with the nfdata-omics/spatialomics pipeline
Author
Keywords
spatial transcriptomics, Visium HD, Nextflow, spatialomics, image segmentation
[sponser-meet-now-chat][/sponser-meet-now-chat]
Spatial transcriptomics (ST) is an emerging technology that combines sequencing-based molecular profiling with high-resolution histological imaging to enable spatially resolved gene expression analysis within tissues.
The convergence of these two modalities is central to the interpretation of high-resolution datasets such as 10x Genomics Visium HD, where gene expression data are overlaid onto microscopy images of tissue sections to integrate molecular and spatial information.
To address the fragmentation and integration challenges of current spatial transcriptomics analysis workflows, we developed nfdata-omics/spatialomics, a reproducible and scalable pipeline for ST data analysis that unifies molecular and imaging workflows.
The pipeline is implemented in Nextflow [1] following nf-core [2] standards, ensuring modularity, portability across computational infrastructures, and traceability.
Starting from raw sequencing data and slide-associated microscopy images, the pipeline performs quality control, spatial alignment, quantification via Space Ranger, and generation of spatial objects with automated quality assessment.
A central feature of the pipeline is the integration of image segmentation, implemented using Cellpose [3], enabling identification of cellular and tissue structures from histological images. Segmentation outputs are integrated with transcriptomic data by assigning bins to segmented cells, allowing a shift from bin-level to cell-level resolution.
The pipeline also provides an integrated reporting layer that aggregates quality metrics, execution metadata, and analytical summaries across samples, supporting interpretation, reproducibility and scalability.
Here we show the application of this workflow to a Visium HD dataset of metastatic renal cell carcinoma, comparing patient cohorts stratified by their blood serum cholesterol levels, demonstrating its applicability to clinically relevant, heterogeneous biological conditions.
Co-authors: Matteo Bonfanti, Eugenia Cammarota, Roberta Bosotti, Monica Pluchino, Letizia Gnetti, Elisa Araldi, Sebastiano Buti, Alberto Riva
Contact Attendee
Warning: Attempt to read property "user_email" on string in /home/1276969.cloudwaysapps.com/ydbgzhdjeq/public_html/wp-content/plugins/my-conference-now/functions.php on line 2826