A map of tumor–host interactions in glioma at single-cell resolution

ABSTRACT Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

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G2S3: A gene graph-based imputation method for single-cell RNA sequencing data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009029 ◽

2021 ◽

Vol 17 (5) ◽

pp. e1009029

Author(s):

Weimiao Wu ◽

Yunqing Liu ◽

Qile Dai ◽

Xiting Yan ◽

Zuoheng Wang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

High Data ◽

Study Gene Expression ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

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G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

10.1101/2020.04.01.020586 ◽

2020 ◽

Author(s):

Weimiao Wu ◽

Qile Dai ◽

Yunqing Liu ◽

Xiting Yan ◽

Zuoheng Wang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

High Data ◽

Study Gene Expression ◽

Single Cell Rna Sequencing ◽

Novel Method

AbstractSingle-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

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A MAP of tumor-host interactions in glioma at single cell resolution

10.1101/827758 ◽

2019 ◽

Author(s):

Francesca Pia Caruso ◽

Luciano Garofano ◽

Fulvio D’Angelo ◽

Kai Yu ◽

Fuchou Tang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Malignant Cells ◽

Human Glioma ◽

Sequencing Data ◽

Host Interactions ◽

Host Interaction ◽

Receptor Interactions ◽

Single Cell Rna Sequencing ◽

Novel Method

ABSTRACTSingle-cell RNA sequencing is the reference technique to characterize the heterogeneity of tumor microenvironment and can be efficiently used to discover cross-talk mechanisms between immune cells and cancer cells. We present a novel method, single cell Tumor-Host Interaction tool (scTHI), to identify significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand-receptor interactions in glioma using six publicly available human glioma datasets encompassing 71 patients. We provide a comprehensive map of the signalling mechanisms between malignant cells and non-malignant cells in glioma uncovering potential novel therapeutic targets.

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Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽

10.1126/science.aba5257 ◽

2020 ◽

Vol 371 (6531) ◽

pp. eaba5257 ◽

Cited By ~ 2

Author(s):

Anna Kuchina ◽

Leandra M. Brettner ◽

Luana Paleologu ◽

Charles M. Roco ◽

Alexander B. Rosenberg ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Single Cell Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Growth Stages ◽

High Throughput Analysis ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

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SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

10.1101/344242 ◽

2018 ◽

Cited By ~ 2

Author(s):

Xianwen Ren ◽

Liangtao Zheng ◽

Zemin Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Random Projection ◽

Rna Seq ◽

Sequencing Data ◽

Computational Framework ◽

Human Blood Cells ◽

Single Cell Rna Sequencing ◽

Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

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A bivariate zero-inflated negative binomial model for identifying underlying dependence with application to single cell RNA sequencing data

10.1101/2020.03.06.977728 ◽

2020 ◽

Author(s):

Hunyong Cho ◽

Chuwen Liu ◽

John S. Preisser ◽

Di Wu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Latent Variable ◽

Large Scale ◽

Negative Binomial ◽

Model Fitting ◽

Sequencing Data ◽

Excess Zeros ◽

Binomial Distributions ◽

Single Cell Rna Sequencing

SummaryMeasuring gene-gene dependence in single cell RNA sequencing (scRNA-seq) count data is often of interest and remains challenging, because an unidentified portion of the zero counts represent non-detected RNA due to technical reasons. Conventional statistical methods that fail to account for technical zeros incorrectly measure the dependence among genes. To address this problem, we propose a bivariate zero-inflated negative binomial (BZINB) model constructed using a bivariate Poisson-gamma mixture with dropout indicators for the technical (excess) zeros. Parameters are estimated based on the EM algorithm and are used to measure the underlying dependence by decomposing the two sources of zeros. Compared to existing models, the proposed BZINB model is specifically designed for estimating dependence and is more flexible, while preserving the marginal zero-inflated negative binomial distributions. Additionally, it has a simple latent variable framework, allowing parameters to have clear and intuitive interpretations, and its computation is feasible with large scale data. Using a recent scRNA-seq dataset, we illustrate model fitting and how the model-based measures can be different from naive measures. The inferential ability of the proposed model is evaluated in a simulation study. An R package ‘bzinb’ is available on CRAN.

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Reclassification of classic T cell subsets and identification of novel cell types by single-cell RNA sequencing

10.21203/rs.3.rs-79282/v1 ◽

2020 ◽

Author(s):

Xiangru Shen ◽

Xuefei Wang ◽

Shan Chen ◽

Hongyi Liu ◽

Ni Hong ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Rna Sequencing ◽

T Cell Activation ◽

Cell Activation ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

T Cell Subsets ◽

Single Cell Rna Sequencing

Abstract Single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets is unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naïve, CD8 Naïve and CD4 memory were mainly clustered into their scCPop counterparts, while the other T cell subsets were clustered into multiple scCPops including unexpected mucosal-associated invariant T cell and natural killer T cell. Most interestingly, we discovered a new T cell type that highly expressed Interferon Signaling Associated Genes (ISAGs), namely IFNhi T. We further enriched IFNhi T for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE after removing ISAGs, and IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ cells and BST2- cells showing different efficiencies of T cell activation indicates high ISAGs may contribute to quick immune responses.

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Single-Cell RNA Sequencing Elucidates the Structure and Organization of Microbial Communities

Frontiers in Microbiology ◽

10.3389/fmicb.2021.713128 ◽

2021 ◽

Vol 12 ◽

Author(s):

Melanie A. Brennan ◽

Adam Z. Rosenthal

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Expression Profiles ◽

Human Life ◽

Bacterial Species ◽

Population Heterogeneity ◽

Specific Gene ◽

Valuable Insight ◽

Single Cell Rna Sequencing

Clonal bacterial populations exhibit various forms of heterogeneity, including co-occurrence of cells with different morphological traits, biochemical properties, and gene expression profiles. This heterogeneity is prevalent in a variety of environments. For example, the productivity of large-scale industrial fermentations and virulence of infectious diseases are shaped by cell population heterogeneity and have a direct impact on human life. Due to the need and importance to better understand this heterogeneity, multiple methods of examining single-cell heterogeneity have been developed. Traditionally, fluorescent reporters or probes are used to examine a specific gene of interest, providing a useful but inherently biased approach. In contrast, single-cell RNA sequencing (scRNA-seq) is an agnostic approach to examine heterogeneity and has been successfully applied to eukaryotic cells. Unfortunately, current extensively utilized methods of eukaryotic scRNA-seq present difficulties when applied to bacteria. Specifically, bacteria have a cell wall which makes eukaryotic lysis methods incompatible, bacterial mRNA has a shorter half-life and lower copy numbers, and isolating an individual bacterial species from a mixed community is difficult. Recent work has demonstrated that these technical hurdles can be overcome, providing valuable insight into factors influencing microbial heterogeneity. This perspective describes the emerging microbial scRNA-seq toolkit. We outline the benefit of these new tools in elucidating numerous scientific questions in microbiological studies and offer insight about the possible rules that govern the segregation of traits in individual microbial cells.

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Comparison of high-throughput single-cell RNA sequencing data processing pipelines

Briefings in Bioinformatics ◽

10.1093/bib/bbaa116 ◽

2020 ◽

Author(s):

Mingxuan Gao ◽

Mingyi Ling ◽

Xinwei Tang ◽

Shun Wang ◽

Xu Xiao ◽

...

Keyword(s):

Data Processing ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Large Scale ◽

Evaluation Framework ◽

Integrated Analysis ◽

Sequencing Data ◽

Single Experiment ◽

Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

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SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

Briefings in Bioinformatics ◽

10.1093/bib/bbz166 ◽

2020 ◽

Cited By ~ 13

Author(s):

Meichen Dong ◽

Aatish Thennavan ◽

Eugene Urrutia ◽

Yun Li ◽

Charles M Perou ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Mixed Cell ◽

Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

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