scholarly journals Exploring the Changing Landscape of Cell-to-Cell Variation After CTCF Knockdown via Single Cell RNA-seq

2019 ◽  
Author(s):  
Wei Wang ◽  
Gang Ren ◽  
Ni Hong ◽  
Wenfei Jin
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Zinc Finger ◽  
Ctcf Binding ◽  
Rna Seq ◽  
Expression Noise ◽  
Genome Wide ◽  
Cell Variation ◽  
Variable Genes

Abstract Background: CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation is unclear. Results: We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, Zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, indicating tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explain why knockdown of CTCF lead to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions: To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

scholarly journals Exploring the changing landscape of cell-to-cell variation after CTCF knockdown via single cell RNA-seq

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Wei Wang ◽  
Gang Ren ◽  
Ni Hong ◽  
Wenfei Jin
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Zinc Finger ◽  
Ctcf Binding ◽  
Rna Seq ◽  
Expression Noise ◽  
Genome Wide ◽  
Cell Variation ◽  
Variable Genes

Abstract Background CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation remains unclear. Results We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating that gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, implying tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explains why knockdown of CTCF leads to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found that cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

scholarly journals Exploring the Changing Landscape of Cell-to-Cell Variation After CTCF Knockdown via Single Cell RNA-seq

2019 ◽  
Author(s):  
Wei Wang ◽  
Gang Ren ◽  
Ni Hong ◽  
Wenfei Jin
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Zinc Finger ◽  
Ctcf Binding ◽  
Rna Seq ◽  
Expression Noise ◽  
Genome Wide ◽  
Cell Variation ◽  
Variable Genes

AbstractBackgroundCCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation is unclear.ResultsWe knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, Zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, indicating tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explain why knockdown of CTCF lead to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes.ConclusionsTo our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

scholarly journals Exploring the Changing Landscape of Cell-to-Cell Variation After CTCF Knockdown via Single Cell RNA-seq

2019 ◽  
Author(s):  
Wei Wang ◽  
Gang Ren ◽  
Ni Hong ◽  
Wenfei Jin
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Zinc Finger ◽  
Ctcf Binding ◽  
Rna Seq ◽  
Expression Noise ◽  
Genome Wide ◽  
Cell Variation ◽  
Variable Genes

Abstract Background: CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation is unclear. Results: We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, Zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, indicating tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explain why knockdown of CTCF lead to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions: To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

scholarly journals Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data

2018 ◽  
Vol 20 (4) ◽  
pp. 1583-1589 ◽  
Author(s):  
Shun H Yip ◽  
Pak Chung Sham ◽  
Junwen Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differentially Expressed Gene ◽  
Embryonic Stem ◽  
Rna Seq ◽  
Software Packages ◽  
Homogeneous Cell ◽  
Variable Gene ◽  
Cell Variation ◽  
Larger Sample

Abstract Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

scholarly journals CHARTS: A web application for characterizing and comparing tumor subpopulations in publicly available single-cell RNA-seq datasets

2020 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zijian Ni ◽  
Michael Collins ◽  
Mark E. Burkard ◽  
Christina Kendziorski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Individual Gene ◽  
Genome Wide ◽  
Progression Of Disease ◽  
Cell Subpopulations ◽  
Available Information

AbstractBackgroundSingle-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tissue. Perhaps nowhere is this more important than in cancer, where tumor and tumor microenvironment heterogeneity directly impact development, maintenance, and progression of disease. While publicly available scRNA-seq cancer datasets offer unprecedented opportunity to better understand the mechanisms underlying tumor progression, metastasis, drug resistance, and immune evasion, much of the available information has been underutilized, in part, due to the lack of tools available for aggregating and analysing these data.ResultsWe present CHARacterizing Tumor Subpopulations (CHARTS), a computational pipeline and web application for analyzing, characterizing, and integrating publicly available scRNA-seq cancer datasets. CHARTS enables the exploration of individual gene expression, cell type, malignancy-status, differentially expressed genes, and gene set enrichment results in subpopulations of cells across multiple tumors and datasets.ConclusionCHARTS is an easy to use, comprehensive platform for exploring single-cell subpopulations within tumors across the ever-growing collection of public scRNA-seq cancer datasets. CHARTS is freely available at charts.morgridge.org.

Single Cell Level Genome-Wide Gene Expression Analysis of Bone Marrow-Derived Oct-4+ very Small Embryonic-Like Stem Cells (VSELs) Revealed That a Polycomb Group Protein Ezh2 Regulates VSELs Pluripotency by Maintaining Bivalent Domains At Promoters of Important Homeodomain-Containing Developmental Transcription Factors

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2345-2345
Author(s):  
Magda Kucia ◽  
Rui Liu ◽  
Kasia Mierzejewska ◽  
Wan Wu ◽  
Janina Ratajczak ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Transcription Factors ◽  
Single Cell ◽  
Gene Expression Analysis ◽  
Polycomb Group ◽  
Polycomb Group Protein ◽  
Genome Wide ◽  
Group Protein ◽  
Genome Wide Gene Expression

Abstract Abstract 2345 Recently, we identified a population of very small embryonic-like (VSEL) stem cells (SCs) in adult bone marrow (BM) (Leukemia 2006:20;857). These Oct4+CXCR4+SSEA-1+Sca-1+CD45−Lin− VSELs are capable of differentiation in vitro into cells from all three germ lineages and in in vivo animal models they can be specified into mesenchymal stem cells (MSCs) (Stem Cells Dev 2010:19;1557), cardiomyocytes (Stem Cell 2008:26;1646), and long-term engrafting hematopoietic stem cells (HSCs) (Exp Hematol 2011:39;225). Be employing gene-expression and epigenetic profiling studies we reported that VSELs in BM have germ-line stem cell like epigenetic features including i) open/active chromatin structure in Oct4 promoter, ii) parent-of-origin specific reprogramming of genomic imprinting (Leukemia 2009, 23, 2042–2051), and iii) that they share several markers with epiblast-derived primordial germ cells (PGCs), in particular with migratory PGCs (Leukemia 2010, 24, 1450–1461). However, it was not clear how VSELs maintain pluripotent state. To address this issue we recently employed single cell-based genome-wide gene expression analysis and found that, Oct4+ VSELs i) express a similar, yet nonidentical, transcriptome as embryonic stem-cells (ESCs), ii) up-regulate cell-cycle checkpoint genes, and iii) down-regulate genes involved in protein turnover and mitogenic pathways. Interestingly, our single cell library studies also revelaed that Ezh2, a polycomb group protein, is highly expressed in VSELs. This protein is well known to be involved in maintaining a bivalent domains (BDs) at promoters of important homeodomain-containing developmental transcription factors. Of note a presence of BDs is characteristic for pluripotent stem cells (e.g., ESCs) and as result of Ezh2 overexpression, VSELs, like ESCs, exhibit BDs - bivalently modified nucleosomes (trimethylated H3K27 and H3K4) at promoters of important homeodomain-containing developmental transcription factors (Sox21 Nkx2.2 Dlx1 Zfpm2 Irx2 Lbx1h Hlxb9 Pax5 HoxA3). Of note, spontaneous (as seen during differentiation) or RNA interference-enforced down-regulation of Ezh2 removes BDs what, results in lose of their plurioptentiality and de-repression of several BD-regulated genes that control their tissue commitment. In conclusion, Our results show for first time that in addition to the expression of pluripotency core transcription factor Oct-4, VSELs, like other pluripotent stem-cells, maintain their pluripotent state through an Ezh2-dependent BD-mediated epigenetic mechanism. Based on this our genome-wide gene expression study not only advances our understanding of biological processes that govern VSELs pluripotency, differentiation, and quiescence but will also help to develop better protocols for ex vivo expansion of these promising cells for potential application in regenerative medicine. Disclosures: Ratajczak: Neostem Inc: Consultancy, Research Funding.

Analysis of Myelodysplastic Syndrome Stem Cells at Single Cell Resolution during DNA Methyltransferase Inhibitor Therapy

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4101-4101
Author(s):  
Stephen S. Chung ◽  
Priyanka Vijay ◽  
Diana L. Stern ◽  
Deirdre O'Sullivan ◽  
Virginia M. Klimek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Single Cell ◽  
Dna Methyltransferase ◽  
Clonal Selection ◽  
Therapeutic Resistance ◽  
Ribosomal Biogenesis ◽  
Rna Seq ◽  
Nonsense Mediated Decay ◽  
Variable Genes

Abstract The myelodysplastic syndromes (MDS) arise in and are maintained by hematopoietic stem cells (HSCs). Serial sampling of patients treated with DNA methyltransferase inhibitors (DNMTIs) and lenalidomide has demonstrated that disease HSCs (MDS HSCs) persist at significant levels even in patients achieving complete clinical and cytogenetic responses. As MDS HSCs are the functional unit of clonal selection both during therapy and subsequent disease progression, we hypothesized that the molecular heterogeneity of MDS HSCs may underlie therapeutic resistance. We therefore sought to perform single cell RNA-sequencing (RNA-seq) on MDS HSCs from patients with known responses to therapy, with the intention of identifying novel therapeutic vulnerabilities. To characterize MDS HSC heterogeneity, we FACS-purified HSCs (Lin-CD34+CD38-CD90+CD45RA-) from paired bone marrow (BM) specimens taken from four MDS patients before and after two to four 28-day cycles of the DNMTI decitabine, as well as two patients who were not treated due to stable disease, and two normal age matched controls. Specimens from both responding and non-responding patients were included. We captured and sequenced a total of 869 single cells from 14 samples, sequencing to an average depth of 4.8 million reads. In a subset of samples (n=7) we also performed bulk RNA-seq (average 1500 cells) for comparison. The sequencing data was of high quality, with an average of 80% mapped reads. We confirmed our ability to accurately quantify transcript levels using ERCC spike-in controls, observing a linear correlation between expected concentration and observed FPKM (fragments per kilobase per million). Single cell RNA-seq revealed vast intratumoral heterogeneity in MDS HSCs that was otherwise missed by bulk RNA-seq, as evidenced by the presence of transcripts variably expressed among cells from the same specimen (Fig. 1A). Despite this intratumoral heterogeneity, single cell transcriptomes were able to completely separate individual MDS patients using principal components analysis and hierarchical clustering, consistent with the known heterogeneity of MDS. MDS HSCs further clustered separately from normal age-matched HSCs, with the top 10% of genes contributing to this separation enriched for Gene Ontology (GO) categories including pathways implicated in MDS biology such as "mRNA splicing," "nonsense mediated decay," and "P53 mediated DNA Damage Response" (all P<1e-9). Unsupervised hierarchical clustering of all pre-treatment MDS HSCs revealed clustering of cells from responders separately from non-responders (Fig. 1B). Differential gene expression analysis identified a cluster of genes (FDR<0.01) enriched for GO categories including "translational termination," "SRP dependent co-translational protein targeting to membrane," and "nonsense mediated decay" (all P<1e-9). Notably, this cluster included 60 ribosomal proteins, all of which were decreased in non-responders (t-test, P<1e-16), with responders demonstrating levels of expression closer to but still lower than normal controls (t-test, P<1e-3). Thus, defective ribosomal biogenesis, a hallmark of MDS pathogenesis, may also contribute to therapeutic resistance. Finally, within each sample we measured the spread of gene expression using dispersion (log[variance/mean]) within bins based on expression levels, defining variable genes as those with a dispersion >1.75 at a mean FPKM >2. The highest number of variable genes were in normal HSCs (mean=141), with the next highest in responders prior to treatment (mean=80), and the least number of variable genes in non-responders prior to treatment (mean=9.5). We speculate that the low number of variable genes in non-responders reflects a higher degree of clonal dominance. All post-treatment MDS HSCs demonstrated a relatively low number of variable genes (mean=25), suggesting that therapy induces clonal selection. In sum, our data illustrate the robustness of single cell RNA-seq to define the intrinsic variability of individual MDS HSCs, implicating perturbed ribosomal biogenesis and transcriptional variability as novel predictors of response to therapy. As we expand our data set with additional patients, we expect to identify additional pathways that mediate therapeutic response and resistance, as well as mutations and variably expressed genes that are selected for during therapy and drive disease progression. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Time- and cost-efficient high-throughput transcriptomics enabled by Bulk RNA Barcoding and sequencing

2018 ◽  
Author(s):  
Daniel Alpern ◽  
Vincent Gardeux ◽  
Julie Russeil ◽  
Bart Deplancke
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
High Throughput ◽  
Low Cost ◽  
Cdna Libraries ◽  
List Type ◽  
Library Preparation ◽  
Rna Seq ◽  
Genome Wide ◽  
Hands On

ABSTRACTGenome-wide gene expression analyses by RNA sequencing (RNA-seq) have quickly become a standard in molecular biology because of the widespread availability of high throughput sequencing technologies. While powerful, RNA-seq still has several limitations, including the time and cost of library preparation, which makes it difficult to profile many samples simultaneously. To deal with these constraints, the single-cell transcriptomics field has implemented the early multiplexing principle, making the library preparation of hundreds of samples (cells) markedly more affordable. However, the current standard methods for bulk transcriptomics (such as TruSeq Stranded mRNA) remain expensive, and relatively little effort has been invested to develop cheaper, but equally robust methods. Here, we present a novel approach, Bulk RNA Barcoding and sequencing (BRB-seq), that combines the multiplexing-driven cost-effectiveness of a single-cell RNA-seq workflow with the performance of a bulk RNA-seq procedure. BRB-seq produces 3’ enriched cDNA libraries that exhibit similar gene expression quantification to TruSeq and that maintain this quality, also in terms of number of detected differentially expressed genes, even with low quality RNA samples. We show that BRB-seq is about 25 times less expensive than TruSeq, enabling the generation of ready to sequence libraries for up to 192 samples in a day with only 2 hours of hands-on time. We conclude that BRB-seq constitutes a powerful alternative to TruSeq as a standard bulk RNA-seq approach. Moreover, we anticipate that this novel method will eventually replace RT-qPCR-based gene expression screens given its capacity to generate genome-wide transcriptomic data at a cost that is comparable to profiling 4 genes using RT-qPCR.‘SoftwareWe developed a suite of open source tools (BRB-seqTools) to aid with processing BRB-seq data and generating count matrices that are used for further analyses. This suite can perform demultiplexing, generate count/UMI matrices and trim BRB-seq constructs and is freely available at http://github.com/DeplanckeLab/BRB-seqToolsHighlightsRapid (~2h hands on time) and low-cost approach to perform transcriptomics on hundreds of RNA samplesStrand specificity preservedPerformance: number of detected genes is equal to Illumina TruSeq Stranded mRNA at same sequencing depthHigh capacity: low cost allows increasing the number of biological replicatesProduces reliable data even with low quality RNA samples (down to RIN value = 2)Complete user-friendly sequencing data pre-processing and analysis pipeline allowing result acquisition in a day

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