scholarly journals Massive single-cell RNA-seq analysis and imputation via deep learning

2018 ◽  
Author(s):  
Yue Deng ◽  
Feng Bao ◽  
Qionghai Dai ◽  
Lani F. Wu ◽  
Steven J. Altschuler
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Fine Grained ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Gene Expression

Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within heterogeneous tissues. We present scScope, a scalable deep-learning based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.

scholarly journals A novel computational complete deconvolution method using RNA-seq data

2018 ◽  
Author(s):  
Kai Kang ◽  
Qian Meng ◽  
Igor Shats ◽  
David M. Umbach ◽  
Melissa Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific

AbstractThe cell type composition of many biological tissues varies widely across samples. Such sample heterogeneity hampers efforts to probe the role of each cell type in the tissue microenvironment. Current approaches that address this issue have drawbacks. Cell sorting or single-cell based experimental techniques disrupt in situ interactions and alter physiological status of cells in tissues. Computational methods are flexible and promising; but they often estimate either sample-specific proportions of each cell type or cell-type-specific gene expression profiles, not both, by requiring the other as input. We introduce a computational Complete Deconvolution method that can estimate both sample-specific proportions of each cell type and cell-type-specific gene expression profiles simultaneously using bulk RNA-Seq data only (CDSeq). We assessed our method’s performance using several synthetic and experimental mixtures of varied but known cell type composition and compared its performance to the performance of two state-of-the art deconvolution methods on the same mixtures. The results showed CDSeq can estimate both sample-specific proportions of each component cell type and cell-typespecificgene expression profiles with high accuracy. CDSeq holds promise for computationally deciphering complex mixtures of cell types, each with differing expression profiles, using RNA-seq data measured in bulk tissue (MATLAB code is available at https://github.com/kkang7/CDSeq_011).

scholarly journals Chamber-enriched gene expression profiles in failing human hearts with reduced ejection fraction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Luo ◽  
Jun Yin ◽  
Denise Dwyer ◽  
Tracy Yamawaki ◽  
Hong Zhou ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Human Genetics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Left Heart ◽  
Reduced Ejection Fraction ◽  
Cell Type Composition ◽  
Type Composition ◽  
Underlying Mechanisms

AbstractHeart failure with reduced ejection fraction (HFrEF) constitutes 50% of HF hospitalizations and is characterized by high rates of mortality. To explore the underlying mechanisms of HFrEF etiology and progression, we studied the molecular and cellular differences in four chambers of non-failing (NF, n = 10) and HFrEF (n = 12) human hearts. We identified 333 genes enriched within NF heart subregions and often associated with cardiovascular disease GWAS variants. Expression analysis of HFrEF tissues revealed extensive disease-associated transcriptional and signaling alterations in left atrium (LA) and left ventricle (LV). Common left heart HFrEF pathologies included mitochondrial dysfunction, cardiac hypertrophy and fibrosis. Oxidative stress and cardiac necrosis pathways were prominent within LV, whereas TGF-beta signaling was evident within LA. Cell type composition was estimated by deconvolution and revealed that HFrEF samples had smaller percentage of cardiomyocytes within the left heart, higher representation of fibroblasts within LA and perivascular cells within the left heart relative to NF samples. We identified essential modules associated with HFrEF pathology and linked transcriptome discoveries with human genetics findings. This study contributes to a growing body of knowledge describing chamber-specific transcriptomics and revealed genes and pathways that are associated with heart failure pathophysiology, which may aid in therapeutic target discovery.

scholarly journals The single-cell transcriptional landscape of lung carcinoid tumors

2021 ◽  
Author(s):  
Philip Bischoff ◽  
Alexandra Trinks ◽  
Jennifer Wiederspahn ◽  
Benedikt Obermayer ◽  
Jan Patrick Pett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Carcinoid Tumor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Carcinoid Tumors ◽  
Cellular Composition ◽  
Cell Gene Expression ◽  
Cell Gene ◽  
Lung Carcinoid

AbstractLung carcinoid tumors, also referred to as pulmonary neuroendocrine tumors or lung carcinoids, are rare neoplasms of the lung with a more favorable prognosis than other subtypes of lung cancer. Still, some patients suffer from relapsed disease and metastatic spread while no consensus treatment exists for metastasized carcinoids. Several recent single-cell studies have provided detailed insights into the cellular heterogeneity of more common lung cancers, such as adeno- and squamous cell carcinoma. However, the characteristics of lung carcinoids on the single-cell level are yet completely unknown.To study the cellular composition and single-cell gene expression profiles in lung carcinoids, we applied single-cell RNA sequencing to three lung carcinoid tumor samples and normal lung tissue. The single-cell transcriptomes of carcinoid tumor cells reflected intertumoral heterogeneity associated with clinicopathological features, such as tumor necrosis and proliferation index. The immune microenvironment was specifically enriched in noninflammatory monocyte-derived myeloid cells. Tumor-associated endothelial cells were characterized by distinct gene expression profiles. A spectrum of vascular smooth muscle cells and pericytes predominated the stromal microenvironment. We found a small proportion of myofibroblasts exhibiting features reminiscent of cancer-associated fibroblasts. Stromal and immune cells exhibited potential paracrine interactions which may shape the microenvironment via NOTCH, VEGF, TGFβ and JAK/STAT signaling. Moreover, single-cell gene signatures of pericytes and myofibroblasts demonstrated prognostic value in bulk gene expression data.Here, we provide first comprehensive insights into the cellular composition and single-cell gene expression profiles in lung carcinoids, demonstrating the non-inflammatory and vessel-rich nature of their tumor microenvironment, and outlining relevant intercellular interactions which could serve as future therapeutic targets.

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

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Francesca Pia Caruso ◽  
Luciano Garofano ◽  
Fulvio D'Angelo ◽  
Kai Yu ◽  
Fuchou Tang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cross Talk ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
Host Interaction ◽  
Receptor Interactions ◽  
Single Cell Rna Sequencing

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.

scholarly journals LRcell: detecting the source of differential expression at the sub-cell type level from bulk RNA-seq data

2021 ◽  
Author(s):  
Wenjing Ma ◽  
Sumeet Sharma ◽  
Peng Jin ◽  
Shannon L Gourley ◽  
Zhaohui Qin
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Cell Type ◽  
Reference Dataset ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Gene

The rapid proliferation of single-cell RNA-sequencing (scRNA-seq) datasets have revealed cell heterogeneity at unprecedented scales. Several deconvolution methods have been developed to decompose bulk experiments to reveal cell type contributions. However, these methods lack power in identifying the accurate cell type composition when having a considerable amount of sub-cell types in the reference dataset. Here, we present LRcell, a R Bioconductor package (http://bioconductor.org/packages/release/bioc/html/LRcell.html) aiming to identify specific sub-cell type(s) that drives the changes observed in a bulk RNA-seq differential gene expression experiment. In addition, LRcell provides pre-embedded marker genes computed from putative single-cell RNA-seq experiments as options to execute the analyses.

scholarly journals Single-nucleus RNA-seq identifies Huntington disease astrocyte states

2019 ◽  
Author(s):  
Osama Al-Dalahmah ◽  
Alexander A Sosunov ◽  
A Shaik ◽  
Kenneth Ofori ◽  
Yang Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Huntington Disease ◽  
Expression Profiles ◽  
Lipid Synthesis ◽  
Gene Expression Profiles ◽  
Cag Repeats ◽  
Cell Gene Expression ◽  
Single Nucleus ◽  
Cell Gene

AbstractHuntington Disease (HD) is an inherited movement disorder caused by expanded CAG repeats in the Huntingtin gene. We have used single nucleus RNASeq (snRNASeq) to uncover cellular phenotypes that change in the disease, investigating single cell gene expression in cingulate cortex of patients with HD and comparing the gene expression to that of patients with no neurological disease. In this study, we focused on astrocytes, although we found significant gene expression differences in neurons, oligodendrocytes, and microglia as well. In particular, the gene expression profiles of astrocytes in HD showed multiple signatures, varying in phenotype from cells that had markedly upregulated metallothionein and heat shock genes, but had not completely lost the expression of genes associated with normal protoplasmic astrocytes, to astrocytes that had substantially upregulated GFAP and had lost expression of many normal protoplasmic astrocyte genes as well as metallothionein genes. When compared to astrocytes in control samples, astrocyte signatures in HD also showed downregulated expression of a number of genes, including several associated with protoplasmic astrocyte function and lipid synthesis. Thus, HD astrocytes appeared in variable transcriptional phenotypes, and could be divided into several different “states”, defined by patterns of gene expression. Ultimately, this study begins to fill the knowledge gap of single cell gene expression in HD and provide a more detailed understanding of the variation in changes in gene expression during astrocyte “reactions” to the disease.

scholarly journals Single-cell characterization of CRISPR-modified transcript isoforms with nanopore sequencing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Heon Seok Kim ◽  
Susan M. Grimes ◽  
Anna C. Hooker ◽  
Billy T. Lau ◽  
Hanlee P. Ji
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Splicing Factors ◽  
Mrna Isoforms ◽  
Structure Detection ◽  
Long Read ◽  
Cell Gene Expression ◽  
Transcript Structure

AbstractWe developed a single-cell approach to detect CRISPR-modified mRNA transcript structures. This method assesses how genetic variants at splicing sites and splicing factors contribute to alternative mRNA isoforms. We determine how alternative splicing is regulated by editing target exon-intron segments or splicing factors by CRISPR-Cas9 and their consequences on transcriptome profile. Our method combines long-read sequencing to characterize the transcript structure and short-read sequencing to match the single-cell gene expression profiles and gRNA sequence and therefore provides targeted genomic edits and transcript isoform structure detection at single-cell resolution.

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

2020 ◽  
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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