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Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell populations in a tissue, but characterizing their spatial organization remains challenging. We combine 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 sample. To annotate the precise cellular composition of distinct tissue regions, we introduce a method for multimodal intersection analysis. Applying multimodal intersection analysis to primary pancreatic tumors, we find that subpopulations of ductal cells, macrophages, dendritic cells and cancer cells have spatially restricted enrichments, as well as distinct coenrichments with other cell types. Furthermore, we identify colocalization of inflammatory fibroblasts and cancer cells expressing a stress-response gene module. Our approach for mapping the architecture of scRNA-seq-defined subpopulations can be applied to reveal the interactions inherent to complex tissues. Combining single-cell RNA-seq data and microarray-based spatial transcriptomics maps the location of different cell types and cell states in pancreatic tumors.

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions—including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization. This review describes the state of spatial transcriptomics technologies and analysis tools that are being used to generate biological insights in diverse areas of biology.

Advancements in precision oncology over the past decades have led to new therapeutic interventions, but the efficacy of such treatments is generally limited by an adaptive process that fosters drug resistance 1 . In addition to genetic mutations 2 , recent research has identified a role for non-genetic plasticity in transient drug tolerance 3 and the acquisition of stable resistance 4 , 5 . However, the dynamics of cell-state transitions that occur in the adaptation to cancer therapies remain unknown and require a systems-level longitudinal framework. Here we demonstrate that resistance develops through trajectories of cell-state transitions accompanied by a progressive increase in cell fitness, which we denote as the ‘resistance continuum’. This cellular adaptation involves a stepwise assembly of gene expression programmes and epigenetically reinforced cell states underpinned by phenotypic plasticity, adaptation to stress and metabolic reprogramming. Our results support the notion that epithelial-to-mesenchymal transition or stemness programmes—often considered a proxy for phenotypic plasticity—enable adaptation, rather than a full resistance mechanism. Through systematic genetic perturbations, we identify the acquisition of metabolic dependencies, exposing vulnerabilities that can potentially be exploited therapeutically. The concept of the resistance continuum highlights the dynamic nature of cellular adaptation and calls for complementary therapies directed at the mechanisms underlying adaptive cell-state transitions. Tumour cells adapt to anticancer drug treatments by a series of cellular state transitions, each inducing distinct gene expression programmes and leading to increased drug resistance.

A Correction to this paper has been published: https://doi.org/10.1038/s41587-019-0392-8.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis. Pan-cancer single-cell and spatial transcriptomic profiling identifies recurrent gene modules that underlie a continuum of cancer cell states. Tumor microenvironment influences the occurrence of these states.

While genetic tumor heterogeneity has long been recognized, recent work has revealed significant variation among cancer cells at the epigenetic and transcriptional levels. Profiling tumors at the single-cell level in individual cancer types has shown that transcriptional heterogeneity is organized into cancer cell states, implying that diverse cell states may represent stable and functional units with complementary roles in tumor maintenance and progression. However, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Furthermore, the role of cancer cell states in tumor progression and their specific interactions with cells of the tumor microenvironment remain to be elucidated. Here, we perform a pan-cancer single-cell RNA-Seq analysis across 15 cancer types and identify a catalog of 16 gene modules whose expression defines recurrent cancer cell states, including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Using mouse models, we find that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Moreover, spatial transcriptomic analysis further links the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance, and metastasis.

Single-cell RNA-Seq measurements are commonly affected by high levels of technical noise, posing challenges for data analysis and visualization. A diverse array of methods has been proposed to computationally remove noise by sharing information across similar cells or genes, however their respective accuracies have been difficult to establish. Here, we propose a simple denoising strategy based on principal component analysis (PCA). We show that while PCA performed on raw data is biased towards highly expressed genes, this bias can be mitigated with a cell aggregation step, allowing the recovery of denoised expression values for both highly and lowly expressed genes. We benchmark our resulting ENHANCE algorithm and three previously described methods on simulated data that closely mimic real datasets, showing that ENHANCE provides the best overall denoising accuracy, recovering modules of co-expressed genes and cell subpopulations. Implementations of our algorithm are available at https://github.com/yanailab/enhance.

Desmosomes are transmembrane protein complexes that contribute to cell-cell adhesion in epithelia and other tissues. Here, we report the discovery of frequent genetic alterations in the desmosome in human cancers, with the strongest signal seen in cutaneous melanoma where desmosomes are mutated in >70% of cases. In primary but not metastatic melanoma biopsies, the burden of coding mutations in desmosome genes associates with a strong reduction in desmosome gene expression. Analysis by spatial transcriptomics and protein immunofluorescence suggests that these expression decreases occur in keratinocytes in the microenvironment rather than in primary melanoma cells. In further support of a microenvironmental origin, we find that desmosome gene knockdown in keratinocytes yields markedly increased proliferation of adjacent melanoma cells in keratinocyte/melanoma co-cultures. Similar increases in melanoma proliferation are observed in media preconditioned by desmosome-deficient keratinocytes. Thus, gradual accumulation of desmosome mutations in neighboring cells may prime melanoma cells for neoplastic transformation.

Single-cell expression measurements are commonly affected by high levels of technical noise, posing challenges for data analysis and interpretation. Here, we propose ENHANCE, an algorithm that denoises single-cell RNA-Seq data by first performing nearest-neighbor aggregation and then inferring expression levels from principal components. We benchmark ENHANCE and three previously described methods on simulated data that closely mimic real datasets, and show that ENHANCE provides the best overall denoising accuracy.

While genetic tumor heterogeneity has long been recognized, recent work has revealed significant variation among cancer cells at the epigenetic and transcriptional levels. Profiling tumors at the single-cell level in individual cancer types has shown that transcriptional heterogeneity is organized into cancer cell states, implying that diverse cell states may represent stable and functional units with complementary roles in tumor maintenance and progression. However, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Furthermore, the role of cancer cell states in tumor progression and their specific interactions with cells of the tumor microenvironment remain to be elucidated. Here, we perform a pan-cancer single-cell RNA-Seq analysis across 15 cancer types and identify a catalog of 16 gene modules whose expression defines recurrent cancer cell states, including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Using mouse models, we find that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Moreover, spatial transcriptomic analysis further links the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance, and metastasis.