scholarly journals scONE-seq: A one-tube single-cell multi-omics method enables simultaneous dissection of molecular phenotype and genotype heterogeneity from frozen tumors

2021 ◽  
Author(s):  
Angela Wu ◽  
Lei Yu ◽  
Xinlei Wang ◽  
Quanhua Mu ◽  
Sindy Tam ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Cell Cancer ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Molecular Phenotype ◽  
Physical Separation ◽  
Dna And Rna ◽  
Normal Cellular Function ◽  
Tumor Clone

Abstract Genomic and transcriptomic heterogeneity both play important roles in normal cellular function as well as in disease development. To be able to characterize these different forms of cellular heterogeneity in diverse sample types, we developed scONE-seq, which enables simultaneous transcriptome and genome profiling in a one-tube reaction. Previous single-cell-whole-genome-RNA-sequencing (scWGS-RNA-seq) methods require physical separation of DNA and RNA, often by physical separation of the nucleus from the cytoplasm. These methods are labor-intensive and technically demanding, time-consuming, or require special devices, and they are not applicable to frozen samples that cannot generate intact single-cell suspensions. scONE-seq is a one-tube reaction, thus is highly scalable and is the first scWGS-RNA-seq method compatible with frozen biobanked tissue. We benchmarked scONE-seq against existing methods using cell lines and lymphocytes from a healthy donor, and we applied it to a 2-year-frozen astrocytoma sample profiling over 1,200 nuclei, subsequently identifying a unique transcriptionally normal-like tumor clone. scONE-seq makes it possible to perform large-scale single-cell multi-omics interrogation with ease on the vast quantities of biobanked tissue, which could transform the scale of future multi-omics single-cell cancer profiling studies.

scholarly journals scONE-seq: A one-tube single-cell multi-omics method enables simultaneous dissection of molecular phenotype and genotype heterogeneity from frozen tumors

2021 ◽  
Author(s):  
Angela Wu ◽  
Lei Yu ◽  
Xinlei Wang ◽  
Quanhua Mu ◽  
Sindy Tam ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Cell Cancer ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Molecular Phenotype ◽  
Physical Separation ◽  
Dna And Rna ◽  
Normal Cellular Function ◽  
Tumor Clone

Abstract Genomic and transcriptomic heterogeneity both play important roles in normal cellular function as well as in disease development. To be able to characterize these different forms of cellular heterogeneity in diverse sample types, we developed scONE-seq, which enables simultaneous transcriptome and genome profiling in a one-tube reaction. Previous single-cell-whole-genome-RNA-sequencing (scWGS-RNA-seq) methods require physical separation of DNA and RNA, often by physical separation of the nucleus from the cytoplasm. Most of these methods are labor-intensive and technically demanding, time-consuming, or require special devices, and they are not applicable to frozen samples that cannot generate intact single-cell suspensions. scONE-seq is a one-tube reaction which eliminates loss due to transfer steps, and thus is highly scalable and compatible with frozen biobanked tissue, generating data that is superior in quality compared to other applicable methods. We benchmarked scONE-seq against existing methods using cell lines and lymphocytes from a healthy donor, and we applied it to a 2-year-frozen astrocytoma sample profiling over 1,200 nuclei, subsequently identifying a unique transcriptionally normal-like tumor clone. scONE-seq makes it possible to perform large-scale single-cell multi-omics interrogation with ease on the vast quantities of biobanked tissue, which could transform the scale of future multi-omics single-cell cancer profiling studies.

scholarly journals Exploring Additional Valuable Information From Single-Cell RNA-Seq Data

2020 ◽  
Vol 8 ◽  
Author(s):  
Yunjin Li ◽  
Qiyue Xu ◽  
Duojiao Wu ◽  
Geng Chen
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Large Scale ◽  
Cell Communication ◽  
Cell Types ◽  
Copy Number Variations ◽  
Developmental Trajectory ◽  
Cellular Heterogeneity ◽  
Circular Rnas ◽  
Rna Seq

Single-cell RNA-seq (scRNA-seq) technologies are broadly applied to dissect the cellular heterogeneity and expression dynamics, providing unprecedented insights into single-cell biology. Most of the scRNA-seq studies mainly focused on the dissection of cell types/states, developmental trajectory, gene regulatory network, and alternative splicing. However, besides these routine analyses, many other valuable scRNA-seq investigations can be conducted. Here, we first review cell-to-cell communication exploration, RNA velocity inference, identification of large-scale copy number variations and single nucleotide changes, and chromatin accessibility prediction based on single-cell transcriptomics data. Next, we discuss the identification of novel genes/transcripts through transcriptome reconstruction approaches, as well as the profiling of long non-coding RNAs and circular RNAs. Additionally, we survey the integration of single-cell and bulk RNA-seq datasets for deconvoluting the cell composition of large-scale bulk samples and linking single-cell signatures to patient outcomes. These additional analyses could largely facilitate corresponding basic science and clinical applications.

scholarly journals Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
High Accuracy ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals EpiScanpy: integrated single-cell epigenomic analysis

2019 ◽  
Author(s):  
Anna Danese ◽  
Maria L. Richter ◽  
David S. Fischer ◽  
Fabian J. Theis ◽  
Maria Colomé-Tatché
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Large Scale ◽  
Feature Space ◽  
Rna Seq ◽  
Computational Framework ◽  
Learning Techniques ◽  
Multiple Feature ◽  
The Many ◽  
Cell Data

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

SCOTCluster: Deep Clustering with Optimal Transport for Large-scale Single-cell RNA-seq Data

2021 ◽  
Author(s):  
Faning Long ◽  
Xiaojun Ding ◽  
Xiaoqing Peng ◽  
Jianxin Wang ◽  
Xiaoshu Zhu
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Optimal Transport ◽  
Rna Seq

scholarly journals scAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Kaikun Xie ◽  
Yu Huang ◽  
Feng Zeng ◽  
Zehua Liu ◽  
Ting Chen
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Random Projection ◽  
Good Representation ◽  
Rna Seq ◽  
Unsupervised Deep Learning ◽  
High Level ◽  
Computational Resources

Abstract Recent advancements in both single-cell RNA-sequencing technology and computational resources facilitate the study of cell types on global populations. Up to millions of cells can now be sequenced in one experiment; thus, accurate and efficient computational methods are needed to provide clustering and post-analysis of assigning putative and rare cell types. Here, we present a novel unsupervised deep learning clustering framework that is robust and highly scalable. To overcome the high level of noise, scAIDE first incorporates an autoencoder-imputation network with a distance-preserved embedding network (AIDE) to learn a good representation of data, and then applies a random projection hashing based k-means algorithm to accommodate the detection of rare cell types. We analyzed a 1.3 million neural cell dataset within 30 min, obtaining 64 clusters which were mapped to 19 putative cell types. In particular, we further identified three different neural stem cell developmental trajectories in these clusters. We also classified two subpopulations of malignant cells in a small glioblastoma dataset using scAIDE. We anticipate that scAIDE would provide a more in-depth understanding of cell development and diseases.

scholarly journals scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

2019 ◽  
Author(s):  
Congting Ye ◽  
Qian Zhou ◽  
Xiaohui Wu ◽  
Chen Yu ◽  
Guoli Ji ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Rna Seq ◽  
Computational Tool ◽  
Cell Level ◽  
Wilcoxon Rank Sum Test ◽  
Transcriptional Regulatory ◽  
Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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