The Spatial and Genomic Hierarchy of Tumor Ecosystems Revealed by Single-Cell Technologies

Abstract

Many malignancies display heterogeneous features that support cancer progression. Emerging high-resolution methods provide a view of heterogeneity that recognizes the influence of diverse cell types and cell states of the tumor microenvironment. Here we outline a hierarchical organization of tumor heterogeneity from a genomic perspective, summarize the origins of spatially patterned metabolic features, and review recent developments in single-cell and spatially resolved techniques for genome-wide study of multicellular tissues. We also discuss how integrating these approaches can yield new insights into human cancer and emerging immune therapies. Applying these technologies for the analysis of primary tumors, patient-derived xenografts, and in vitro systems holds great promise for understanding the hierarchical structure and environmental influences that underlie tumor ecosystems.

Keywords: epigenetics; genomics; hypoxia; in situ; metabolism; stroma.

Figure 1.. The hierarchy of tumor heterogeneity.

(A) Bulk tumor populations can be divided into distinct cell types from different lineages. Cells with similar cell identities are present in distinct microenvironments, which affect their epigenetic-metabolic states. Hence capturing the full functional specialization present in tumors requires finer classification of cells into distinct cell-type–specific states. (B) Gene expression patterns vary spatially within the same cell type based on tumor microenvironment conditions.

Figure 2.. Visualizing tumor heterogeneity between and within cell types.

Measuring the overall proportion of each cell type in mixed cell populations plays an important role in subtyping tumor microenvironments. Within and between cell types, the epigenetic-metabolic states of cells can be classified by examining correlation to known gene sets associated with specific states.

Figure 3.. The spatial hierarchy of tumor heterogeneity.

(A) Spatially resolved imaging- and sequence-based technologies provide insight into mechanisms that contribute to cancer biology at multiple levels of scale. (B) The establishment of hypoxia occurs over a short length scale of ~250 μm. Reaction-diffusion equations can model the spatial gradient of diffusible factors near tumor-stromal boundaries. (C) Experimental study of hypoxic gradients in vitro reveals that oxygen tension influences spatial gene expression patterns of tumor associated macrophages (TAMs).

Figure 4.. Advanced technologies enable genomic characterization of malignant processes in situ.

(A) Highly resolved RT-LAMP assay using microwells enables quantitative assessment of RNA expression changes while preserving spatial information. (B) Spatial transcriptomics using a spatially barcoded poly(dT) capture probes. (C) MERFISH enables high-dimensional investigation of transcription states using error-robust single-molecule FISH counting of transcripts. (D) FISSEQ enables in-situ Sanger-like sequencing while preserving tissue structure. (E) MALDI-IMS enables in situ mass spectrometric analysis of proteins and metabolites with better than single-cell resolution. (F) Principle of multi-round, multiplexed tissue immunofluorescence (MxIF and CycIF).