Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges

Suvà, Mario L.; Tirosh, Itay

doi:10.1016/j.molcel.2019.05.003

Cited by 325 publications

(269 citation statements)

References 31 publications

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“…A large body of literature now provides numerous examples of stromal influence on malignant cell function 25,26 , confirming more than a century of speculation about the critical role of the stroma 1 . However, our knowledge remains largely anecdotal and based mostly on work in animal models rather than human subjects.…”

Section: Discussionmentioning

confidence: 95%

“…Two layers of information are critical for understanding the tumor stroma and its interactions with malignant cells 26 : (1) the quantity of different cell types, and (2) each cell type can have systematic differences in gene expression pathways, also called cell "state". Measurements of both cell type and state can be made using single cell RNA sequencing (scRNAseq) [27][28][29][30][31][32] .…”

Section: Introductionmentioning

confidence: 99%

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Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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Understanding the dynamic interactions between malignant cells and the tumor stroma is a major goal of cancer research. Here we developed a Bayesian model that jointly infers both cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 85 single-cell and 1,412 bulk RNA-seq datasets in primary human glioblastoma, head and neck squamous cell carcinoma, and melanoma. We identified cell types correlated with clinical outcomes and explored regional heterogeneity in tumor state and stromal composition. We redefined common molecular subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T-cells, fibroblasts, and endothelial cells across multiple tumor types. Our work provides a new lens that we used to measure cellular composition and expression in a statistically powered cohort of three primary human malignancies. Results Bayesian inference of cell type composition and tumor expressionTED uses a scRNA-seq reference dataset to infer two parameters of interest from bulk RNA-seq data: (i) the proportion of cell types in the bulk population and (ii) the average expression profiles of each cell type. TED describes the proportion and expression profiles of each cell type as latent variables that it infers from the data ( Fig. 1a, Supplementary Fig. 1, Supplementary Note 1). TED makes the key simplifying assumption that each non-malignant cell type shares a common gene expression profile across patients, as observed in the cases analyzed to date 28,30,31 .Critically, each bulk RNA-seq sample is then assumed to have a unique tumor expression profile that we infer from the data.Expression in the reference and bulk RNA-seq data often differ substantially due to batch effects or tumor heterogeneity. To account for uncertainty in the reference cell type expression matrix, TED implements a fully Bayesian inference of tumor composition. First, TED uses Gibbs sampling to estimate the posterior joint distribution of cell type composition, θ0, and gene expression profiles, Z, i.e. P(θ0, Z | φ, X; α) ( Fig. 1a, red, top). Second, to account for tumor cells, or other cell types which cannot be observed in the reference dataset, TED infers a maximum likelihood estimate (MLE) for tumor expression profiles, ψtum (Fig. 1a, red, mid). During this step TED also infers the maximum a posterior estimate (MAP) of the expression profile of non-malignant stromal cells, ψstr, to correct for batch effects between bulk and single cell RNAseq platforms. Last, TED uses the updated expression profile for each patient and cell type to resample the posterior distribution of cell type composition, θ, i.e. P(θ | ψtum , ψstr ,X; α) ( Fig. 1a, red, bottom). Optionally, TED has an additional mode which can be used to learn common patterns of expression heterogeneit...

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Chu

Danko

2020

Preprint

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“…Recent technological advances have enabled unprecedented, high-throughput single-cell molecular level insight of intratumor heterogeneity (33,34). The ability to precisely quantify intratumor heterogeneity (1), and illuminate key subpopulations involved in response to treatment (11), has the potential to improve both prognostic and therapeutics for cancer treatment.…”

Section: Discussionmentioning

confidence: 99%

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

Johnson

Howard

Morgan

et al. 2020

Preprint

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A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the 1 2 J K L C O D − C O : , O R: DS J T UVWXU OY6

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“…The categories, origins, and organization of tumor cell-to-cell heterogeneity are open questions of fundamental importance to cancer biology (1). Within normal tissues, single cells differ by lineage type and regulatory state (2).…”

Section: Introductionmentioning

confidence: 99%

Fragmentation of Small-cell Lung Cancer Regulatory States in Heterotypic Microenvironments

Schaff

Singh

Kim

et al. 2020

Preprint

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Small-cell lung cancers derive from pulmonary neuroendocrine cells, which have stemlike properties to reprogram into other cell types upon lung injury. It is difficult to uncouple the plasticity of these transformed cells from heritable changes that evolve in primary tumors or select in metastases to distant organs. Approaches to single-cell profiling are also problematic if the required sample dissociation activates injury-like signaling and reprogramming. Here, we defined cell-state heterogeneities in situ through laser capture microdissection-based 10-cell transcriptomics coupled with stochastic-profiling fluctuation analysis. Using labeled cells from a small-cell lung cancer mouse model initiated by neuroendocrine deletion of p53 and Rb, we profiled cell-to-cell transcriptional-regulatory heterogeneity in spheroid cultures and liver colonies seeded intravenously. Fluctuating transcripts in vitro were partly shared with other epithelial-spheroid models, and candidate heterogeneities increased considerably when cells were delivered to the liver. Colonization of immunocompromised animals drove the fractional appearance of alveolar type II-like markers and poised cells for paracrine stimulation from immune cells and hepatocytes. Immunocompetency further exaggerated the fragmentation of tumor states in the liver, yielding mixed stromal signatures evident in bulk sequencing from autochthonous tumors and metastases. We identified dozens of transcript heterogeneities that recur irrespective of biological context; their mapped orthologs brought together observations of murine and human small-cell lung cancer. Candidate heterogeneities recurrent in the liver also stratified primary human tumors into discrete groups not readily explained by molecular subtype.We conclude that heterotypic interactions in the liver and lung are an accelerant for intratumor heterogeneity in small-cell lung cancer. Statement of significanceThe single-cell regulatory heterogeneity of small-cell lung cancer becomes increasingly elaborate in the liver, a common metastatic site for the disease.

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Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges

Cited by 325 publications

References 31 publications

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

Fragmentation of Small-cell Lung Cancer Regulatory States in Heterotypic Microenvironments

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