Bridging single-cell and spatial ‘omics for a multi-layered characterization of the tumor microenviroment
Reuben Moncada, MPhil; Itai Yanai, PhD
Institute for Computational Medicine, NYU Langone Health
Many genomics tools have been massively scaled down to capture information at the resolution of individual cells, rather than at the level of bulk tissue (single-cell RNA-seq, single-cell ATAC-seq, single-cell Hi-C). With single-cell RNA-seq (scRNA-seq) in particular, massive advancements in the technology have enabled the study of millions of individual cells in healthy and diseased tissue environments. With these tools we can now characterize rare cell populations and unique gene expression phenotypes that may underlie disease progression. However, the contribution of the surrounding tissue environment to these complex phenotypes is still underappreciated; this important factor is missing in most single-cell genomics experiments because of the way in which the samples are prepared. Previously, I combined a microarray-based spatial transcriptomics method that reveals spatial patterns of gene expression using an array of spots, each capturing the transcriptomes of multiple adjacent cells, with scRNA-Seq generated from the same human pancreatic cancer samples. The combination of such methods allows for the simultaneous identification of cell types and underlying cell states with scRNA-seq, and mapping of these populations onto tissue space with spatial transcriptomics. These approaches enabled me to identify a possible spatial relationship between pancreatic cancer cells expressing a stress-response program and inflammatory fibroblasts. Currently, I am developing methods for uniting single-cell lineage tracing methods – which capture both phylogenetic and transcriptomic information from single-cells – with spatial transcriptomics in a zebrafish model of melanoma. By mapping cancer cell lineages across tissue space, I hope to characterize spatial/microenvironmental constraints to tumor evolution in vivo.
Keywords: Single-cell/spatial transcriptomics, tumor biology