Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma

Abstract

Cutaneous malignant melanoma (melanoma) is characterized by a high mutational load, extensive intertumoral and intratumoral genetic heterogeneity, and complex tumor microenvironment (TME) interactions. Further insights into the mechanisms underlying melanoma are crucial for understanding tumor progression and responses to treatment. Here we adapted the technology of spatial transcriptomics (ST) to melanoma lymph node biopsies and successfully sequenced the transcriptomes of over 2,200 tissue domains. Deconvolution combined with traditional approaches for dimensional reduction of transcriptome-wide data enabled us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Our unsupervised analysis revealed a complex spatial intratumoral composition of melanoma metastases that was not evident through morphologic annotation. Each biopsy showed distinct gene expression profiles and included examples of the coexistence of multiple melanoma signatures within a single tumor region as well as shared profiles for lymphoid tissue characterized according to their spatial location and gene expression profiles. The lymphoid area in close proximity to the tumor region displayed a specific expression pattern, which may reflect the TME, a key component to fully understanding tumor progression. In conclusion, using the ST technology to generate gene expression profiles reveals a detailed landscape of melanoma metastases. This should inspire researchers to integrate spatial information into analyses aiming to identify the factors underlying tumor progression and therapy outcome. Applying ST technology to gene expression profiling in melanoma lymph node metastases reveals a complex transcriptional landscape in a spatial context, which is essential for understanding the multiple components of tumor progression and therapy outcome. .

Spatially resolved analyses link genomic and immune diversity and reveal unfavorable neutrophil activation in melanoma

“…At the extreme of cellular resolution, studies employing single cell techniques, whilst informative of the multidimensional cellular heterogeneity within bulk tumor cell populations, necessarily destroy spatial information during sample processing and arguably do not comprehensively survey the transcriptome within any individual cell 11 . Thrane and colleagues performed a proof-of-principle high resolution spatial transcriptomics analysis of four lymph node metastases obtained from patients with stage III melanoma, finding evidence of variably distinct gene expression profiles between regions of tumor, lymphoid tissue, and an apparent transition zone that may have represented functional interaction between tumor, stroma and lymphoid cells 37 . Relative intratumoral transcriptomic homogeneity in one sample was associated with long-term overall survival, however other domains of heterogeneity were not evaluable with this technique.…”

A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification

“…Advancements in spatial transcriptomics (ST) have enabled scientists to relate cells with their location within a tissue. Specifically, it has been shown how combining ST with gene expression profiling in cancer data helps understand multiple components of tumor progression and therapy outcomes (Thrane et al, 2018). 10x Genomics Visium is another interesting assay that provides higher resolution and throughput for spatial gene expression analysis.…”

Discovery of molecular features underlying morphological landscape by integrating spatial transcriptomic data with deep features of tissue image

“…In particular, recent progress in spatial gene expression technologies which applies nextgeneration sequencing with spatial barcode, fluorescence in situ hybridization (FISH), or in situ sequencing (ISS) have innovated experimental approaches to decipher the spatial heterogeneity of biological process [2][3][4][5] . A spatial context at single-cell level resolution has allowed the analysis of the location of heterogeneous cells and their spatial interactions in tumor tissues as well as brain, the human heart, and inflammatory tissues 4,[6][7][8][9][10][11] .…”

“…Even though spatial gene expression analyses have been actively developed and applied to various tissues and diseases, analytic methods that integrate transcriptome and imaging data are lacking. In spite of the feasibility of analysis that combines gene expression, spatial interaction between different spots of spatial barcodes and image patterns, most methods have regarded gene expression from spots as independent samples and interpreted like singlecell RNA-sequencing (scRNA-seq) data 4,[6][7][8][9] . Especially, one of the advantages of spatial gene expression data is additional information of co-registered images which contains morphological as well as functional patterns.…”