Nextflow Pipeline for Visium and H&E Data from Patient-Derived Xenograft Samples.

Highlights: We have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework. Summary: We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.