Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies

González-Silva, Laura; Quevedo, Laura; Varela, Ignacio

doi:10.1016/j.trecan.2019.11.010

Cited by 150 publications

(111 citation statements)

References 57 publications

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“…As future works, we will further explore our approach in its ability to model massive scale singlecell sequencing data including cross-species, multi-omics (e.g., simultaneously modeling singlecell RNA-seq, single-cell ATAC-seq, and single-cell methylation while accounting for platformdependent batch effects), multi-tissues, and multi-subjects. We will harness recently available human/mouse atlas data [41,42] and disease-focused data such as scRNA-seq in patients with Alzheimer's disease [6] and cancer patients tumors [45]. From methodological stand-point, like all of the neural network-based method, scGAN requires specification of the network architecture before training.…”

Section: Discussionmentioning

confidence: 99%

Deep feature extraction of single-cell transcriptomes by generative adversarial network

Bahrami

Maitra

Nagy

et al. 2020

Preprint

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Motivation: Single-cell RNA-sequencing (scRNA-seq) has opened the opportunities to dissect the heterogeneous cellular composition and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition design. Results: We present single-cell Generative Adversarial Network (scGAN). Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network, which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects. We demonstrate scGAN on three public scRNAseq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder. Availability: The code is available at https://github.com/li-lab-mcgill/singlecell-deepfeature Contact: yueli@cs.mcgill.ca IntroductionSingle-cell RNA sequencing (scRNA-seq) technologies profile the transcriptomes of individual cells rather than bulk samples [1,2]. The wide adoption of scRNA-seq technologies enables the investigations of the molecular footprints at the unprecedentedly high-resolution for a wide spectrum of human diseases including cancer [3], autoimmune diseases [4, 5], Alzheimer's disease [6], and major depressive disorder (MDD) [7]. However, single-cell data analysis still remains challenging due to confounding and nuisance factors, that manifest as individual variations or experimental biases such as different scRNA-seq technologies rather than biological variation. These confounding factors are often known as batch effects. Batch effects are the subsets of measurements that have different distributions because of being affected by laboratory conditions, reagent lots and personnel differences. [8]. The massive parallel sequencing [1] enable measurements with more than tens of thousands single-cell samples cross tens of human subjects in a single study (e.g., [3,6]) further underscores the importance of addressing subject-level demographic confounders such as age and sex. Currently, there is a lack of highly scalable and robust model that enables systematic analysis of large-scale datasets while accounting for various confounding batch effects. A number of methods have been developed for normalization, batch-effect correction, embedding, visualization and clustering of scRNA-seq gene expression profiles.[9] used mutual nearest neighbors (MNN) matching to account for batch effects. MNN operates on either the original space of the raw gene expression counts or the projected linear embedding space from the principal components analysis (PCA). However, MNN may be inadequate to model the non-linear effects known to exist in the scRNA-seq data [8]. Seurat [10] is another useful approach,...

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Section: Discussionmentioning

confidence: 99%

Deep feature extraction of single-cell transcriptomes by generative adversarial network

Bahrami

Maitra

Nagy

et al. 2020

Preprint

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“…Tumors contain different cell populations in endless evolution. This diversity is commonly referred to as tumor heterogeneity, and is considered the main driver of resistance to therapy and metastasis (106). The full comprehension of this heterogeneity would be extremely important to optimize existing therapeutic intervention and find new strategies to break down relapses and mortality.…”

Section: Immune Cells In the Tumor Microenvironmentmentioning

confidence: 99%

“…The recent development of technologies based on sequencing individual cells has been crucial to address tumor heterogeneity and to elucidate how cells are organized into multicellular systems. Single cell profiles not only revealed that human tumors comprise subpopulations of genetically different diverse malignant cells, but also that a profusion of different cell types from the surrounding tissues and the immune system, each with a precise role in pathogenicity, is present within the TME (106,107). The immune components of the tumor microenvironment in different kind of malignancies, including non-small cell lung cancer (NSCLC), clear cell RCC (ccRCC), breast cancer (BC), HCC, glioblastoma multiforme (GMB), microsatellite instability-stable CRC have been recently annotated and finely characterized (88,(108)(109)(110)(111).…”

Section: Immune Cells In the Tumor Microenvironmentmentioning

confidence: 99%

Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors

et al. 2020

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Novel treatments based upon the use of immune checkpoint inhibitors have an impressive efficacy in different types of cancer. Unfortunately, most patients do not derive benefit or lasting responses, and the reasons for the lack of therapeutic success are not known. Over the past two decades, a pressing need to deeply profile either the tumor microenvironment or cells responsible for the immune response has led investigators to integrate data obtained from traditional approaches with those obtained with new, more sophisticated, single-cell technologies, including high parameter flow cytometry, single-cell sequencing and high resolution imaging. The introduction and use of these technologies had, and still have a prominent impact in the field of cancer immunotherapy, allowing delving deeper into the molecular and cellular crosstalk between cancer and immune system, and fostering the identification of predictive biomarkers of response. In this review, besides the molecular and cellular cancer-immune system interactions, we are discussing how cutting-edge single-cell approaches are helping to point out the heterogeneity of immune cells in the tumor microenvironment and in blood.

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“…Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study development, tissue, and disease biology [1][2][3][4][5][6][7][8][9] . scRNA-seq analyses currently rely on the availability of high quality genome annotations to define cell features and to perform cell clustering, dimensionality reduction, differential gene expression, and other analyses [10][11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis

Wang

Mantri

Chou

et al. 2020

Preprint

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Single-cell RNA sequencing (scRNA-seq) enables the study of cell biology with high resolution. scRNA-seq expression analyses rely on the availability of a high quality annotation of genes in the genome. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, gene annotations often fail to cover the full transcriptome of every cell type at every stage of development, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant information beyond the scope of the best available gene annotation. This is achieved by performing single-cell expression analysis on any region in the genome for which transcriptional products are detected. Our routine identifies transcriptionally active regions (TARs) using a hidden Markov model, generates a matrix of expression levels for all TARs across all cells in a dataset, performs single-cell TAR expression analysis to identify TARs that are biologically significant, and then annotates biologically significant TARs using gene homology analysis. This procedure leverages single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts in complex tissues and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

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Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies

Cited by 150 publications

References 57 publications

Deep feature extraction of single-cell transcriptomes by generative adversarial network

Deep feature extraction of single-cell transcriptomes by generative adversarial network

Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors

Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis

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