Comparison of Interassay Similarity and Cellular Deconvolution in Spatial Transcriptomics Data Using Visium CytAssist

Authors Mario Rosasco, Chi-Sing Ho, Tianyou Luo, Michelle M. Stein, Martin C. Stumpe, Jagadish Venkataraman, Sonal Khare, Ameen A. Salahudeen

Background – Next-generation sequencing (NGS) of bulk cell populations is a useful and ubiquitous tool for the molecular characterization of clinical tumor samples. Bulk NGS reveals transcript abundance within a tumor sample and can further infer cell populations via deconvolution algorithms (PMID:31570899). However, it can’t ascribe the cellular context for a given gene’s expression or elucidate the spatial organization of tumor microenvironments. These additional features are critical to our understanding of tumor biology and are key to the development of immuno-oncology therapeutics. Spatial Transcriptomics (ST) is an emerging technology that characterizes gene expression within the spatial context of tissue. ST data can be generated directly from archival formalin fixed paraffin embedded samples, enabling the study of spatial
gene expression in real-world clinical settings.

Methods – We have studied a dataset comprising 6 samples from non-small cell lung cancer (NSCLC) patients and 1 core needle biopsy from a tumor of unknown origin. We used the 10X Visium CytAssist platform to generate ST data and additionally generated paired bulk RNAseq data. To test the interassay reliability of CytAssist on archival FFPE tissue sections, we compared ST results across 3 sample preparation conditions. We further studied the state of the tumor
microenvironment by applying state-of-the-art computational approaches to deconvolve immune cell populations and produce super-resolution ST maps, validated using multiplex immunofluorescence (IF) via CODEX (PMID:30078711).

Results – We find key quality control metrics and spatial biomarkers are consistent across all 3 sample preparation conditions. When comparing deconvolution results between bulk and spatially-resolved methods we observe modest correlations for many cell types despite differences in sample preparation, supporting the idea that bulk and spatial samples contain complementary transcriptomic information. However, within samples, we find many of the correlations observed in bulk do not show a strong spatial correlation. These comparisons indicate the importance of considering spatial context when studying the tumor immune microenvironment. Finally, we find an agreement between super-resolution ST and multiplex IF across key spatial biomarkers. These results demonstrate clinical archival FFPE samples yield high interassay reliability via the CytAssist platform. Results were consistent through 3 different H&E staining protocols and findings were consistent when superresolution deconvolution was utilized which further strongly
correlated with high-resolution multiplex IF.

Conclusions – Our findings demonstrate the feasibility and translational utility of ST to discover spatial signatures and the cellular context in retrospective clinical cohorts to empower discovery and translational efforts in precision oncology and
therapeutic development.