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

Smith, Eric A.; Hodges, H. Courtney

doi:10.1016/j.trecan.2019.05.009

Cited by 49 publications

(36 citation statements)

References 114 publications

(154 reference statements)

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“…Meanwhile, the introduction of scRNA-seq, ATAC-seq, chromatin immunocleavage sequencing, or CUT&;RUN has expanded studies into single-cell scale over the past decade. 249 Among them, scRNA-seq is viewed as one of the most helpful tools to analyze the cell subpopulation state. However, scRNA-seq alone cannot accurately reflect the interaction of the whole tumor with its microenvironment, especially in terms of spatial structure.…”

Section: Discussion and Outlookmentioning

confidence: 99%

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The updated landscape of tumor microenvironment and drug repurposing

Jin

2020

Sig Transduct Target Ther

775

483

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Accumulating evidence shows that cellular and acellular components in tumor microenvironment (TME) can reprogram tumor initiation, growth, invasion, metastasis, and response to therapies. Cancer research and treatment have switched from a cancer-centric model to a TME-centric one, considering the increasing significance of TME in cancer biology. Nonetheless, the clinical efficacy of therapeutic strategies targeting TME, especially the specific cells or pathways of TME, remains unsatisfactory. Classifying the chemopathological characteristics of TME and crosstalk among one another can greatly benefit further studies exploring effective treating methods. Herein, we present an updated image of TME with emphasis on hypoxic niche, immune microenvironment, metabolism microenvironment, acidic niche, innervated niche, and mechanical microenvironment. We then summarize conventional drugs including aspirin, celecoxib, β-adrenergic antagonist, metformin, and statin in new antitumor application. These drugs are considered as viable candidates for combination therapy due to their antitumor activity and extensive use in clinical practice. We also provide our outlook on directions and potential applications of TME theory. This review depicts a comprehensive and vivid landscape of TME from biology to treatment.

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Section: Discussion and Outlookmentioning

confidence: 99%

“… 250 ST was invented to obtain complete transcript data (spatial barcodes). 249 , 251 – 253 The integration of scRNA-seq and multi-omics (transcriptomics, proteomics, metabonomics, and microbiome) may enhance our understanding of TME in a single-cell scale.…”

Section: Discussion and Outlookmentioning

confidence: 99%

The updated landscape of tumor microenvironment and drug repurposing

Jin

2020

Sig Transduct Target Ther

775

483

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“…SWI/SNF and related complexes are altered in ~20% of all cancers 7 , and frequently act as tissue-specific tumor suppressors 14,24 . Unlike 2D cell culture, most tumors are spatially heterogenous 3D tissues that exhibit a high degree of phenotypic zoning 25,26 . Such spatially variable features include regions of differential proliferation, hypoxia, and/or nutrient utilization 27-29 . Unfortunately, the interplay between SWI/SNF activity and variable 3D conditions has remained uncertain.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the interplay between SWI/SNF activity and variable 3D conditions has remained uncertain. Improved experimental models that mimic 3D-specific metabolic conditions 25,26 while limiting cell-type heterogeneity would provide a new opportunity to investigate the activities of SWI/SNF in an experimentally controlled 3D tissue.…”

Section: Introductionmentioning

confidence: 99%

SMARCA4 regulates spatially restricted metabolic plasticity in 3D multicellular tissue

Čermáková

Ea²,

Chan³

et al. 2021

Preprint

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SWI/SNF and related chromatin remodeling complexes act as tissue-specific tumor suppressors and are frequently inactivated in different cancers. Although many regulatory activities of SWI/SNF have been identified using 2D cell culture, the effects of SWI/SNF alterations in more complex 3D tissues have remained poorly understood. Here we employed 3D cell culture conditions that yield transcriptomic states mirroring primary lung adenocarcinoma (LUAD) specimens better than 2D culture. By analyzing spatial patterns of gene expression and DNA accessibility in 3D spheroids using single-cell RNA-seq and ATAC-seq, we find that the SWI/SNF ATPase SMARCA4 (BRG1) induces state-specific changes to DNA accessibility that influence spatially heterogeneous expression patterns and metabolism. In 3D conditions, SMARCA4 promotes accessibility for AP-1 transcription factors, including ATF3, a regulator of metabolism and repressor of NRF2 antioxidant signaling. These changes reduce expression of SLC7A11 in a distinct portion of cells, which sensitizes A549 spheroids to cell death via ferroptosis under oxidizing conditions. Consistent with these results, we find that SMARCA4 alterations are associated with derepression of NRF2 targets in human tumors independently of NRF2/KEAP1 status. Our work reveals new 3D-specific features and unanticipated spatial complexity associated with chromatin remodeling in multicellular tissues.

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“…Based on in-situ capturing technologies, more recent spatial transcriptomics RNA sequencing (sptRNA-seq) [12][13][14][15] combines positional barcoded arrays and RNA sequencing with single-cell imaging to spatially resolve RNA expressions in each measured spot in the array [12,[16][17][18]. These new technologies have transformed the transcriptome analysis into a new paradigm for connecting single-cell molecular profiling to tissue micro-environment and the dynamics of a tissue region [19][20][21].…”

mentioning

confidence: 99%

Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion

Song

Yong

et al. 2020

Preprint

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High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. One major limitation of in-situ capturing is the high dropout rate of mRNAs that fail the capture or the amplification, which leads to incomplete profiling of the gene expressions. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed several best performing single-cell RNAseq data imputation methods. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney. The source code and data are available at https://github.com/kuanglab/FIST.Author summaryBiological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Similar to single-cell RNA sequencing data, spatial transcriptomics data also suffers from a high dropout rate of mRNAs in in-situ capture. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

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

Cited by 49 publications

References 114 publications

The updated landscape of tumor microenvironment and drug repurposing

The updated landscape of tumor microenvironment and drug repurposing

SMARCA4 regulates spatially restricted metabolic plasticity in 3D multicellular tissue

Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion

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