scholarly journals SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs

2018 ◽  
Author(s):  
Luyi Tian ◽  
Jaring Schreuder ◽  
Daniela Zalcenstein ◽  
Jessica Tran ◽  
Nikolce Kocovski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Cell Subset ◽  
Dendritic Cell Subset ◽  
Rna Seq ◽  
Time Machine ◽  
Clonal Heterogeneity ◽  
And Function

AbstractConventional single cell RNA-seq methods are destructive, such that a given cell cannot also then be tested for fate and function, without a time machine. Here, we develop a clonal method SIS-seq, whereby single cells are allowed to divide, and progeny cells are assayed separately in SISter conditions; some for fate, others by RNA-seq. By cross-correlating progenitor gene expression with mature cell fate within a clone, and doing this for many clones, we can identify the earliest gene expression signatures of dendritic cell subset development. SIS-seq could be used to study other populations harboring clonal heterogeneity, including stem, reprogrammed and cancer cells to reveal the transcriptional origins of fate decisions.

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 Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

scholarly journals SHERRY2: A method for rapid and sensitive single cell RNA-seq

2021 ◽  
Author(s):  
Lin Di ◽  
Bo Liu ◽  
Yuzhu Lyu ◽  
Shihui Zhao ◽  
Yuhong Pang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Dynamic Range ◽  
Single Cells ◽  
Rna Seq ◽  
Wide Dynamic Range ◽  
Uniform Coverage ◽  
Optimized Protocol ◽  
Tn5 Transposase ◽  
Higher Sensitivity

Many single cell RNA-seq applications aim to probe a wide dynamic range of gene expression, but most of them are still challenging to accurately quantify low-aboundance transcripts. Based on our previous finding that Tn5 transposase can directly cut-and-tag DNA/RNA hetero-duplexes, we present SHERRY2, an optimized protocol for sequencing transcriptomes of single cells or single nuclei. SHERRY2 is robust and scalable, and it has higher sensitivity and more uniform coverage in comparison with prevalent scRNA-seq methods. With throughput of a few thousand cells per batch, SHERRY2 can reveal the subtle transcriptomic differences between cells and facilitate important biological discoveries.

scholarly journals Chromatin potential identified by shared single cell profiling of RNA and chromatin

2020 ◽  
Author(s):  
Sai Ma ◽  
Bing Zhang ◽  
Lindsay LaFave ◽  
Zachary Chiang ◽  
Yan Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Mouse Skin ◽  
Chromatin Accessibility ◽  
Lineage Commitment ◽  
Single Cell Profiling ◽  
Lineage Priming ◽  
And Function ◽  
Computational Strategy

SummaryCell differentiation and function are regulated across multiple layers of gene regulation, including the modulation of gene expression by changes in chromatin accessibility. However, differentiation is an asynchronous process precluding a temporal understanding of the regulatory events leading to cell fate commitment. Here, we developed SHARE-seq, a highly scalable approach for measurement of chromatin accessibility and gene expression within the same single cell. Using 34,774 joint profiles from mouse skin, we develop a computational strategy to identify cis-regulatory interactions and define Domains of Regulatory Chromatin (DORCs), which significantly overlap with super-enhancers. We show that during lineage commitment, chromatin accessibility at DORCs precedes gene expression, suggesting changes in chromatin accessibility may prime cells for lineage commitment. We therefore develop a computational strategy (chromatin potential) to quantify chromatin lineage-priming and predict cell fate outcomes. Together, SHARE-seq provides an extensible platform to study regulatory circuitry across diverse cells within tissues.

scholarly journals Bayesian inference of the gene expression states of single cells from scRNA-seq data

2019 ◽  
Author(s):  
Jérémie Breda ◽  
Mihaela Zavolan ◽  
Erik van Nimwegen
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Downstream Processing ◽  
Noise Removal ◽  
Rna Seq ◽  
Expression Of Genes ◽  
Normalization Methods ◽  
Quantify Gene Expression ◽  
Selection Of

AbstractIn spite of a large investment in the development of methodologies for analysis of single-cell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters.Comparison of Sanity with other recent normalization methods on a selection of scRNA-seq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

scholarly journals Single-cell RNA-seq data reveals TNBC tumor heterogeneity through characterizing subclone compositions and proportions

2019 ◽  
Author(s):  
Weida Wang ◽  
Jinyuan Xu ◽  
Shuyuan Wang ◽  
Peng Xia ◽  
Li Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
Single Cells ◽  
Rna Seq ◽  
Biological Functions ◽  
Gene Markers ◽  
Gene Expression Matrix ◽  
Deconvolution Algorithm ◽  
Expression Matrix

AbstractUnderstanding subclonal architecture and their biological functions poses one of the key challenges to deeply portray and investigative the cause of triple-negative breast cancer (TNBC). Here we combine single-cell and bulk sequencing data to analyze tumor heterogeneity through characterizing subclone compositions and proportions. Based on sing-cell RNA-seq data (GSE118389) we identified five distinct cell subpopulations and characterized their biological functions based on their gene markers. According to the results of functional annotation, we found that C1 and C2 are related to immune functions, while C5 is related to programmed cell death. Then based on subclonal basis gene expression matrix, we applied deconvolution algorithm on TCGA tissue RNA-seq data and observed that microenvironment is diverse among TNBC subclones, especially C1 is closely related to T cells. What’s more, we also found that high C5 proportions would led to poor survival outcome, log-rank test p-value and HR [95%CI] for five years overall survival in GSE96058 dataset were 0.0158 and 2.557 [1.160-5.636]. Collectively, our analysis reveals both intra-tumor and inter-tumor heterogeneity and their association with subclonal microenvironment in TNBC (subclone compositions and proportions), and uncovers the organic combination of subclones dictating poor outcomes in this disease.HighlightsWe applied deconvolution algorithm on subclonal basis gene expression matrix to link single cells and bulk tissue together.

scholarly journals Using Single Nucleotide Variations in Single-Cell RNA-Seq to Identify Subpopulations and Genotype-phenotype Linkage

2016 ◽  
Author(s):  
Olivier Poirion ◽  
Xun Zhu ◽  
Travers Ching ◽  
Lana X. Garmire
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Transcript Abundance ◽  
Rna Seq ◽  
Linear Modeling ◽  
Modeling Framework ◽  
Single Nucleotide ◽  
Single Nucleotide Variations

AbstractDespite its popularity, characterization of subpopulations with transcript abundance is subject to a significant amount of noise. We propose to use effective and expressed nucleotide variations (eeSNVs) from scRNA-seq as alternative features for tumor subpopulation identification. We developed a linear modeling framework, SSrGE, to link eeSNVs associated with gene expression. In all the datasets tested, eeSNVs achieve better accuracies than gene expression for identifying subpopulations. Previously validated cancer-relevant genes are also highly ranked, confirming the significance of the method. Moreover, SSrGE is capable of analyzing coupled DNA-seq and RNA-seq data from the same single cells, demonstrating its value in integrating multi-omics single cell techniques. In summary, SNV features from scRNA-seq data have merits for both subpopulation identification and linkage of genotype-phenotype relationship. The method SSrGE is available at https://github.com/lanagarmire/SSrGE.

scholarly journals Solo: doublet identification via semi-supervised deep learning

2019 ◽  
Author(s):  
Nicholas Bernstein ◽  
Nicole Fong ◽  
Irene Lam ◽  
Margaret Roy ◽  
David G. Hendrickson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Single Cells ◽  
Detection Methods ◽  
Learning Approach ◽  
Rna Seq ◽  
Previous Approach ◽  
Cell Technology

AbstractSingle cell RNA-seq (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these “doublets” violate the fundamental premise of single cell technology and can lead to incorrect inferences. Here, we describe Solo, a semi-supervised deep learning approach that identifies doublets with greater accuracy than existing methods. Solo can be applied in combination with experimental doublet detection methods to further purify scRNA-seq data to true single cells beyond any previous approach.

Comparative analysis of sequencing technologies platforms for single-cell transcriptomics

2018 ◽  
Author(s):  
Kedar Nath Natarajan ◽  
Zhichao Miao ◽  
Miaomiao Jiang ◽  
Xiaoyun Huang ◽  
Hongpo Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
K562 Cells ◽  
Library Preparation ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Technical Variability ◽  
Sequencing Technologies ◽  
Sequencing Platforms

AbstractAll single-cell RNA-seq protocols and technologies require library preparation prior to sequencing on a platform such as Illumina. Here, we present the first report to utilize the BGISEQ-500 platform for scRNA-seq, and compare the sensitivity and accuracy to Illumina sequencing. We generate a scRNA-seq resource of 468 unique single-cells and 1,297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on mESCs and K562 cells with RNA spike-ins. We sequence these libraries on both BGISEQ-500 and Illumina HiSeq platforms using single- and paired-end reads. The two platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardised scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms.

Sign in / Sign up

Export Citation Format

Share Document