Abstract 1205: Modeling spatially resolved cell-type-specific gene expression by weighted regression with SPACER

Abstract Transcriptomic analysis has substantially advanced our understanding of human diseases, but the complex nature of tissues is often ignored. Recent development of single-cell RNA-seq (scRNA-seq) technologies has made it possible to characterize cellular heterogeneity of solid tissues. Along with analyzing gene expression patterns on a single-cell level, there is a critical need to explore the spatial patterns exhibited by the genes across the tissue sample. Spatially resolved transcriptomics (SRT) is key to understanding cellular functions in their morphological state. However, current barcoding-based SRT technologies, such as 10x Genomics Visium, lack single-cell resolution, greatly hindering the investigation of the spatial differences in gene expression profiles as failure to account for cell-type variations can lead to an obscured understanding of the spatial patterns detected across the tissue. The overall goal of this research is to integrate spatial transcriptomics and scRNA-seq data in order to infer the gene expression profiles for each of the bulk-level spots in the spatially resolved data. This will allow us to study spatial patterns of genes with cell-type level resolution. SPACER expands the spatial transcriptomics data into the individual cell types by utilizing the spatial similarities and high-resolution histology information as a weight in a non-negative least squares regression. Having this integrated understanding allows us to gain an additional level of information about the gene activity for each cell type present in the tissue. We have performed benchmark evaluations for our method based on data generated from the 10x Visium platform and have seen promising results. The evaluations have shown high correlations for the gene expression patterns predicted by SPACER for varying cell types on the benchmark evaluations. We next analyzed spatial transcriptomics data for pancreatic cancer, breast cancer, and melanoma tissue samples to better study the complex nature of cancerous tissues. A tumor is consistently interacting with its microenvironment, which can impact tumor growth and cell proliferation. Thus, it is important to study the spatial gene expression patterns for different cell types in cancerous tissues. During our analysis, we identify the boundary of the tumor region in the bulk-spatial transcriptomics data and perform differential expression analysis on the SPACER results to compare the expression patterns of the core tumor region to the infiltrated tumor regions for each individual cell type. Identifying genes that are differentially expressed for specific cell types in varying regions of the cancerous tissue can link gene expressions to clinically important morphological features, provide critical information about how cells are interacting with neighboring cells in their macroenvironment, and be used to target different tumor cells therapeutically. Citation Format: Amelia R. Schroeder, Kyle Coleman, Jian Hu, Mingyao Li. Modeling spatially resolved cell-type-specific gene expression by weighted regression with SPACER [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1205.