scholarly journals Robustness and applicability of functional genomics tools on scRNA-seq data

2019 ◽  
Author(s):  
Christian H. Holland ◽  
Jovan Tanevski ◽  
Jan Gleixner ◽  
Manu P. Kumar ◽  
Elisabetta Mereu ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Single Cell ◽  
In Silico ◽  
Single Cells ◽  
Real Data ◽  
Drop Out ◽  
Rna Seq ◽  
Knock Out ◽  
Genomics Tools

AbstractMany tools have been developed to extract functional and mechanistic insight from bulk transcriptome profiling data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events, low library sizes and a comparatively large number of samples/cells. It is thus not clear if functional genomics tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. To address this question, we performed benchmark studies on in silico and in vitro single-cell RNA-seq data. We included the bulk-RNA tools PROGENy, GO enrichment and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compared them against the tools AUCell and metaVIPER, designed for scRNA-seq. For the in silico study we simulated single cells from TF/pathway perturbation bulk RNA-seq experiments. Our simulation strategy guarantees that the information of the original perturbation is preserved while resembling the characteristics of scRNA-seq data. We complemented the in silico data with in vitro scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on both the simulated and real data revealed comparable performance to the original bulk data. Additionally, we showed that the TF and pathway activities preserve cell-type specific variability by analysing a mixture sample sequenced with 13 scRNA-seq different protocols. Our analyses suggest that bulk functional genomics tools can be applied to scRNA-seq data, outperforming dedicated single cell tools. Furthermore we provide a benchmark for further methods development by the community.

scholarly journals Single Cell RNA-Seq Characterises Pre-Leukemic Transformation Driven By CEBPA N321D in the Hoxb8-FL Cell Line

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3887-3887
Author(s):  
Moosa Qureshi ◽  
Fernando Calero-Nieto ◽  
Iwo Kucinski ◽  
Sarah Kinston ◽  
George Giotopoulos ◽  
...  
Keyword(s):  
Dendritic Cell ◽  
Single Cell ◽  
Cell Line ◽  
Single Cells ◽  
Mutant Form ◽  
Rna Seq ◽  
Wild Type ◽  
Leukemic Transformation

Abstract The C/EBPα transcription factor plays a pivotal role in myeloid differentiation and E2F-mediated cell cycle regulation. Although CEBPA mutations are common in acute myeloid leukaemia (AML), little is known regarding pre-leukemic alterations caused by mutated CEBPA. Here, we investigated early events involved in pre-leukemic transformation driven by CEBPA N321D in the LMPP-like cell line Hoxb8-FL (Redecke et al., Nat Methods 2013), which can be maintained in vitro as a self-renewing LMPP population using Flt3L and estradiol, as well as differentiated both in vitro and in vivo into myeloid and lymphoid cell types. Hoxb8-FL cells were retrovirally transduced with Empty Vector (EV), wild-type CEBPA (CEBPA WT) or its N321D mutant form (CEBPA N321D). CEBPA WT-transduced cells showed increased expression of cd11b and SIRPα and downregulation of c-kit, suggesting that wild-type CEBPA was sufficient to promote differentiation even under LMPP growth conditions. Interestingly, we did not observe the same phenotype in CEBPA N321D-transduced cells. Upon withdrawal of estradiol, both EV and CEBPA WT-transduced cells differentiated rapidly into a conventional dendritic cell (cDC) phenotype by day 7 and died within 12 days. By contrast, CEBPA N321D-transduced cells continued to grow for in excess of 56 days, with an initial cDC phenotype but by day 30 demonstrating a plasmacytoid dendritic cell precursor phenotype. CEBPA N321D-transduced cells were morphologically distinct from EV-transduced cells. To test leukemogenic potential in vivo, we performed transplantation experiments in lethally irradiated mice. Serial monitoring of peripheral blood demonstrated that Hoxb8-FL derived cells had disappeared by 4 weeks, and did not reappear. However, at 6 months CEBPA N321D-transduced cells could still be detected in bone marrow in contrast to EV-transduced cells but without any leukemic phenotype. To identify early events involved in pre-leukemic transformation, the differentiation profiles of EV, CEBPA WT and CEBPA N321D-transduced cells were examined with single cell RNA-seq (scRNA-seq). 576 single cells were taken from 3 biological replicates at days 0 and 5 post-differentiation, and analysed using the Automated Single-Cell Analysis Pipeline (Gardeux et al., Bioinformatics 2017). Visualisation by t-SNE (Fig 1) demonstrated: (i) CEBPA WT-transduced cells formed a distinct cluster at day 0 before withdrawal of estradiol; (ii) CEBPA N321D-transduced cells separated from EV and CEBPA WT-transduced cells after 5 days of differentiation, (iii) two subpopulations could be identified within the CEBPA N321D-transduced cells at day 5, with a cluster of five CEBPA N321D-transduced single cells distributed amongst or very close to the day 0 non-differentiated cells. Differential expression analysis identified 224 genes upregulated and 633 genes downregulated specifically in the CEBPA N321D-transduced cells when compared to EV cells after 5 days of differentiation. This gene expression signature revealed that CEBPA N321D-transduced cells switched on a HSC/MEP/CMP transcriptional program and switched off a myeloid dendritic cell program. Finally, in order to further dissect the effect of the N321D mutation, the binding profile of endogenous and CEBPA N321D was compared by ChIP-seq before and after 5 days of differentiation. Integration with scRNA-seq data identified 160 genes specifically downregulated in CEBPA N321D-transduced cells which were associated with the binding of the mutant protein. This list of genes included genes previously implicated in dendritic cell differentiation (such as NOTCH2, JAK2), as well as a number of genes not previously implicated in the evolution of AML, representing potentially novel therapeutic targets. Disclosures No relevant conflicts of interest to declare.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells

2020 ◽  
Author(s):  
Ling-Ling Zheng ◽  
Jing-Hua Xiong ◽  
Wu-Jian Zheng ◽  
Jun-Hao Wang ◽  
Zi-Liang Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Tissue Specificity ◽  
Single Cells ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Expression Variability ◽  
Expression Classification

Abstract Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs’ functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

scholarly journals Single-cell RNA-seq revealed the role of Alkbh5 in reproduction

2021 ◽  
Author(s):  
Xiaozhong Shen ◽  
Gangcai Xie
Keyword(s):  
Single Cell ◽  
Messenger Rna ◽  
Single Cells ◽  
Rna Seq ◽  
Single Cell Sequencing ◽  
Differential Gene ◽  
Higher Eukaryotes

AbstractN(6)-methyladenosine (m(6)a) is the most common internal modification of messenger RNA (mRNA) in higher eukaryotes. According to previous literature reports, alkbh5, as another demethylase in mammals, can reverse the expression of m(6)a gene in vivo and in vitro. In order to reveal the effect of Alkbh5 deletion on the level of single cells in the testis during spermatogenesis in mice, the data were compared using single-cell sequencing. In this article, we discussed the transcription profile and cell type identification of mouse testis, the expression of mitochondrial and ribosomal genes in mice, the analysis of differential gene expression, and the effects of Alkbh5 deletion, and try to explain the role and influence of Alkbh5 on reproduction at the level of single-cell sequencing.

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

2020 ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Recommender System ◽  
In Silico ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

SummaryWhile understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell RNA-seq (scRNA-seq) technologies have facilitated investigations into the role of intra-tumor transcriptomic heterogeneity (ITTH) in tumor biology and evolution, but their application to in silico models of drug response has not been explored. Based on large-scale analysis of cancer omics datasets, we highlight the utility of ITTH for predicting clinical outcomes. We then show that heterogeneous gene expression signatures obtained from scRNA-seq data can be accurately analyzed (80%) in a recommender system framework (CaDRReS-Sc) for in silico drug response prediction. Patient-derived cell lines capturing transcriptomic heterogeneity from primary and metastatic tumors were used as in vitro proxies for validating monotherapy predictions (Pearson r>0.6), as well as optimal drug combinations to target different subclonal populations (>10% improvement). Applying CaDRReS-Sc to the increasing number of publicly available tumor scRNA-seq datasets can serve as an in silico screen for further in vitro and in vivo drug repurposing studies.Graphical abstractHighlightsLarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomesCalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc)Prediction of drug response in patient-derived cell lines with transcriptomic heterogeneityIn silico identification of drug combinations that work based on clonal vulnerabilities

scholarly journals ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data

2017 ◽  
Author(s):  
Davide Risso ◽  
Fanny Perraudeau ◽  
Svetlana Gribkova ◽  
Sandrine Dudoit ◽  
Jean-Philippe Vert
Keyword(s):  
Single Cell ◽  
Negative Binomial ◽  
Single Cells ◽  
Principal Component ◽  
Real Data ◽  
Estimation Procedure ◽  
Rna Seq ◽  
Genome Wide ◽  
Low Dimensional ◽  
Over Dispersion

AbstractSingle-cell RNA sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

scholarly journals Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

2017 ◽  
Author(s):  
Yohei Sasagawa ◽  
Hiroki Danno ◽  
Hitomi Takada ◽  
Masashi Ebisawa ◽  
Kaori Tanaka ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cells ◽  
Embryonic Stem ◽  
Stromal Vascular Fraction ◽  
Rna Seq ◽  
Sequencing Method ◽  
Unique Molecular Identifier

AbstractHigh-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts (at a rate of 30%–50%) and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes in total from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

scholarly journals SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

2021 ◽  
Vol 7 (8) ◽  
pp. eabe3610
Author(s):  
Conor J. Kearney ◽  
Stephin J. Vervoort ◽  
Kelly M. Ramsbottom ◽  
Izabela Todorovski ◽  
Emily J. Lelliott ◽  
...  
Keyword(s):  
Single Cell ◽  
Posttranslational Modification ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Simultaneous Detection ◽  
Surface Protein ◽  
Rna Seq ◽  
Cell Level ◽  
Simultaneous Integration ◽  
Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

scholarly journals Single cell analysis reveals the impact of age and maturation stage on the human oocyte transcriptome

2020 ◽  
Author(s):  
Silvia Llonch ◽  
Montserrat Barragán ◽  
Paula Nieto ◽  
Anna Mallol ◽  
Marc Elosua-Bayes ◽  
...  
Keyword(s):  
Single Cell ◽  
Chromosome Segregation ◽  
Maternal Age ◽  
Maturation Stage ◽  
P Value ◽  
Rna Seq ◽  
Transcript Levels ◽  
Human Oocytes ◽  
The Impact

AbstractStudy questionTo which degree does maternal age affect the transcriptome of human oocytes at the germinal vesicle (GV) stage or at metaphase II after maturation in vitro (IVM-MII)?Summary answerWhile the oocytes’ transcriptome is predominantly determined by maturation stage, transcript levels of genes related to chromosome segregation, mitochondria and RNA processing are affected by age after in vitro maturation of denuded oocytes.What is known alreadyFemale fertility is inversely correlated with maternal age due to both a depletion of the oocyte pool and a reduction in oocyte developmental competence. Few studies have addressed the effect of maternal age on the human mature oocyte (MII) transcriptome, which is established during oocyte growth and maturation, and the pathways involved remain unclear. Here, we characterize and compare the transcriptomes of a large cohort of fully grown GV and IVM-MII oocytes from women of varying reproductive age.Study design, size, durationIn this prospective molecular study, 37 women were recruited from May 2018 to June 2019. The mean age was 28.8 years (SD=7.7, range 18-43). A total of 72 oocytes were included in the study at GV stage after ovarian stimulation, and analyzed as GV (n=40) and in vitro matured oocytes (IVM-MII; n=32).Participants/materials, setting, methodsDenuded oocytes were included either as GV at the time of ovum pick-up or as IVM-MII after in vitro maturation for 30 hours in G2™ medium, and processed for transcriptomic analysis by single-cell RNA-seq using the Smart-seq2 technology. Cluster and maturation stage marker analysis were performed using the Seurat R package. Genes with an average fold change greater than 2 and a p-value < 0.01 were considered maturation stage markers. A Pearson correlation test was used to identify genes whose expression levels changed progressively with age. Those genes presenting a correlation value (R) >= |0.3| and a p-value < 0.05 were considered significant.Main results and the role of chanceFirst, by exploration of the RNA-seq data using tSNE dimensionality reduction, we identified two clusters of cells reflecting the oocyte maturation stage (GV and IVM-MII) with 4,445 and 324 putative marker genes, respectively. Next we identified genes, for which RNA levels either progressively increased or decreased with age. This analysis was performed independently for GV and IVM-MII oocytes. Our results indicate that the transcriptome is more affected by age in IVM-MII oocytes (1,219 genes) than in GV oocytes (596 genes). In particular, we found that genes involved in chromosome segregation and RNA splicing significantly increase in transcript levels with age, while genes related to mitochondrial activity present lower transcript levels with age. Gene regulatory network analysis revealed potential upstream master regulator functions for genes whose transcript levels present positive (GPBP1, RLF, SON, TTF1) or negative (BNC1, THRB) correlation with age.Limitations, reasons for cautionIVM-MII oocytes used in this study were obtained after in vitro maturation of denuded GV oocytes, therefore, their transcriptome might not be fully representative of in vivo matured MII oocytes.The Smart-seq2 methodology used in this study detects polyadenylated transcripts only and we could therefore not assess non-polyadenylated transcripts.Wider implications of the findingsOur analysis suggests that advanced maternal age does not globally affect the oocyte transcriptome at GV or IVM-MII stages. Nonetheless, hundreds of genes displayed altered transcript levels with age, particularly in IVM-MII oocytes. Especially affected by age were genes related to chromosome segregation and mitochondrial function, pathways known to be involved in oocyte ageing. Our study thereby suggests that misregulation of chromosome segregation and mitochondrial pathways also at the RNA-level might contribute to the age-related quality decline in human oocytes.Study funding/competing interest(s)This study was funded by the AXA research fund, the European commission, intramural funding of Clinica EUGIN, the Spanish Ministry of Science, Innovation and Universities, the Catalan Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) and by contributions of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and to the “Centro de Excelencia Severo Ochoa”.The authors have no conflict of interest to declare.

scholarly journals Single cell RNA sequencing of calvarial and long bone endocortical cells

2019 ◽  
Author(s):  
Ugur M. Ayturk ◽  
Joseph P. Scollan ◽  
Alexander Vesprey ◽  
Christina M. Jacobsen ◽  
Paola Divieti Pajevic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cultured Cells ◽  
Neutralizing Antibody ◽  
Long Bone ◽  
Appendicular Skeleton ◽  
Rna Seq ◽  
Isolated Cells

ABSTRACTSingle cell RNA-seq (scRNA-seq) is emerging as a powerful technology to examine transcriptomes of individual cells. We determined whether scRNA-seq could be used to detect the effect of environmental and pharmacologic perturbations on osteoblasts. We began with a commonly used in vitro system in which freshly isolated neonatal mouse calvarial cells are expanded and induced to produce a mineralized matrix. We used scRNA-seq to compare the relative cell type abundances and the transcriptomes of freshly isolated cells to those that had been cultured for 12 days in vitro. We observed that the percentage of macrophage-like cells increased from 6% in freshly isolated calvarial cells to 34% in cultured cells. We also found that Bglap transcripts were abundant in freshly isolated osteoblasts but nearly undetectable in the cultured calvarial cells. Thus, scRNA-seq revealed significant differences between heterogeneity of cells in vivo and in vitro. We next performed scRNA-seq on freshly recovered long bone endocortical cells from mice that received either vehicle or Sclerostin-neutralizing antibody for 1 week. Bone anabolism-associated transcripts were also not significantly increased in immature and mature osteoblasts recovered from Sclerostin-neutralizing antibody treated mice; this is likely a consequence of being underpowered to detect modest changes in gene expression, since only 7% of the sequenced endocortical cells were osteoblasts, and a limited portion of their transcriptomes were sampled. We conclude that scRNA-seq can detect changes in cell abundance, identity, and gene expression in skeletally derived cells. In order to detect modest changes in osteoblast gene expression at the single cell level in the appendicular skeleton, larger numbers of osteoblasts from endocortical bone are required.

Sign in / Sign up

Export Citation Format

Share Document