Single-cell transcriptome analysis of lineage diversity in high-grade glioma

Yuan, Jinzhou; Levitin, Hanna Mendes; Frattini, Véronique; Bush, Erin C.; Boyett, Deborah; Samanamud, Jorge; Ceccarelli, Michele; Dovas, Athanassios; Zanazzi, George; Canoll, Peter; Bruce, Jeffrey N.; Lasorella, Anna; Iavarone, Antonio; Sims, Peter A.

doi:10.1186/s13073-018-0567-9

Cited by 175 publications

(290 citation statements)

References 50 publications

(87 reference statements)

Supporting

Mentioning

264

Contrasting

Order By: Relevance

Section: Bayesian Inference Of Cell Type Composition and Tumor Expresmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Chu

Danko

2020

Preprint

View full text Add to dashboard Cite

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...

show abstract

Section: Bayesian Inference Of Cell Type Composition and Tumor Expresmentioning

confidence: 99%

mentioning

confidence: 99%

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

Chu

Danko

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This predominant grouping of cancer cells by their tumor of origin has been reported for a number of cerebral and non-cerebral tumors [11,14,33,37,50,54,61]. For identifying traits common to all tumors, data can be analyzed tumor per tumor [62,63,69], or merged and analyzed as a whole after standardization (i.e. subtracting from each expression value the gene expression mean and dividing by its standard deviation across cells within a given tumor) [48].…”

Section: Unsupervised Clustering Analysis Highlights First Gbm Cells'mentioning

confidence: 69%

Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells

Saurty

Bellenger

El-Habr

et al. 2019

Preprint

View full text Add to dashboard Cite

Glioblastoma cell ability to adapt their functioning to microenvironment changes is a source of the extensive intra-tumor heterogeneity characteristic of this devastating malignant brain tumor. A systemic view of the metabolic pathways underlying glioblastoma cell functioning states is lacking.We analyzed public single cell RNA-sequencing data from glioblastoma surgical resections, which offer the closest available view of tumor cell heterogeneity as encountered at the time of patients' diagnosis. Unsupervised analyses revealed that information dispersed throughout the cell transcript repertoires encoded the identity of each tumor and masked information related to cell functioning states. Data reduction based on an experimentally-defined signature of transcription factors overcame this hurdle. It allowed cell grouping according to their tumorigenic potential, regardless of their tumor of origin. The approach relevance was validated using an independent dataset of glioblastoma tissue transcriptomes, patient-derived cell lines and orthotopic xenografts.Overexpression of genes coding for amino acid and lipid metabolism enzymes involved in antioxidative, energetic and cell membrane processes characterized cells with high tumorigenic potential. Modeling of their expression network highlighted the very long chain polyunsaturated fatty acid synthesis pathway at the core of the network. Expression of its most downstream enzymatic component, ELOVL2, was associated with worsened patient survival, and required for cell tumorigenic properties in vivo. Our results demonstrate the power of signature-driven analyses of single cell transcriptomes to obtain an integrated view of metabolic pathways at play within the heterogeneous cell landscape of patient tumors.

show abstract

“…Samples of three patients were chosen for analysis: MP29, MP34 and MP59 and, within these samples, only tumor cells that presented low (below the population median) CD45 expression were considered. The GBM scRNA-seq dataset was downloaded from GEO (GEO accession GSE103224) and consisted of GBM patient-derived samples obtained from different donors (11). The MEL-MC dataset was already normalized and gated (7), and we further selected the option provided in SCOUTS to exclude poorly stained cells, where for each cell the mean expression of all markers is calculated and cells with a mean value lower than 0.1 are considered as poorly stained and excluded from the analysis.…”

Section: Methodsmentioning

confidence: 99%

“…2B, lower right). SCOUT was next applied to single-cell RNA-seq data using a dataset of glioblastoma (GBM) patient-derived samples downloaded from GEO (11). Even though dealing with much smaller numbers of cells per sample (around 1,000 cells, 40x less than the mass cytometry dataset), SCOUT was able to detect outliers and reveal distinct patterns of gene expression in OutS of different samples.…”

Section: R a F Tmentioning

confidence: 99%

SCOUT: Single-cell outlier analysis in cancer

Onzi

Faccioni

Alvarado

et al. 2020

Preprint

View full text Add to dashboard Cite

a These authors contributed equally to this work.Outliers are often ignored or even removed from data analysis. In cancer, however, single outlier cells can be of major importance, since they have uncommon characteristics that may confer capacity to invade, metastasize, or resist to therapy. Here we present the Single-Cell OUTlier analysis (SCOUT), a resource for single-cell data analysis focusing on outlier cells, and the SCOUT Selector (SCOUTS), an application to systematically apply SCOUT on a dataset over a wide range of biological markers. Using publicly available datasets of cancer samples obtained from mass cytometry and single-cell RNA-seq platforms, outlier cells for the expression of proteins or RNAs were identified and compared to their non-outlier counterparts among different samples. Our results show that analyzing single-cell data using SCOUT can uncover key information not easily observed in the analysis of the whole population. Cancer | Single-cell | Outlier | RNA-seq | Mass CytometryCorrespondence: HIK: HKornblum@mednet.ucla.edu; GL: lenz@ufrgs.br.

show abstract

Single-cell transcriptome analysis of lineage diversity in high-grade glioma

Cited by 175 publications

References 50 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

Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells

SCOUT: Single-cell outlier analysis in cancer

Contact Info

Product

Resources

About