scholarly journals Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics

2017 ◽  
Vol 114 (37) ◽  
pp. E7786-E7795 ◽  
Author(s):  
Jason C. H. Tsang ◽  
Joaquim S. L. Vong ◽  
Lu Ji ◽  
Liona C. Y. Poon ◽  
Peiyong Jiang ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Single Cell ◽  
Human Placenta ◽  
Single Cells ◽  
Specific Gene ◽  
Hypertensive Disorder ◽  
Cellular Dynamics ◽  
Maternal Cell ◽  
Plasma Rna ◽  
Free Plasma

The human placenta is a dynamic and heterogeneous organ critical in the establishment of the fetomaternal interface and the maintenance of gestational well-being. It is also the major source of cell-free fetal nucleic acids in the maternal circulation. Placental dysfunction contributes to significant complications, such as preeclampsia, a potentially lethal hypertensive disorder during pregnancy. Previous studies have identified significant changes in the expression profiles of preeclamptic placentas using whole-tissue analysis. Moreover, studies have shown increased levels of targeted RNA transcripts, overall and placental contributions in maternal cell-free nucleic acids during pregnancy progression and gestational complications, but it remains infeasible to noninvasively delineate placental cellular dynamics and dysfunction at the cellular level using maternal cell-free nucleic acid analysis. In this study, we addressed this issue by first dissecting the cellular heterogeneity of the human placenta and defined individual cell-type–specific gene signatures by analyzing more than 24,000 nonmarker selected cells from full-term and early preeclamptic placentas using large-scale microfluidic single-cell transcriptomic technology. Our dataset identified diverse cellular subtypes in the human placenta and enabled reconstruction of the trophoblast differentiation trajectory. Through integrative analysis with maternal plasma cell-free RNA, we resolved the longitudinal cellular dynamics of hematopoietic and placental cells in pregnancy progression. Furthermore, we were able to noninvasively uncover the cellular dysfunction of extravillous trophoblasts in early preeclamptic placentas. Our work showed the potential of integrating transcriptomic information derived from single cells into the interpretation of cell-free plasma RNA, enabling the noninvasive elucidation of cellular dynamics in complex pathological conditions.

scholarly journals Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Elliott Swanson ◽  
Cara Lord ◽  
Julian Reading ◽  
Alexander T Heubeck ◽  
Palak C Genge ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Human Peripheral Blood ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Test Case ◽  
Cell Assays ◽  
Paired Measurement

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

scholarly journals TEA-seq: a trimodal assay for integrated single cell measurement of transcription, epitopes, and chromatin accessibility

2020 ◽  
Author(s):  
Elliott Swanson ◽  
Cara Lord ◽  
Julian Reading ◽  
Alexander T. Heubeck ◽  
Adam K. Savage ◽  
...  
Keyword(s):  
Cell Surface ◽  
Single Cell ◽  
Human Peripheral Blood ◽  
Signal To Noise Ratio ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Test Case ◽  
Cell Assays

AbstractSingle-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to extracellular signals, and human disease states. scATAC-seq has been particularly challenging due to the large size of the human genome and processing artefacts resulting from DNA damage that are an inherent source of background signal. Downstream analysis and integration of scATAC-seq with other single-cell assays is complicated by the lack of clear phenotypic information linking chromatin state and cell type. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases the signal-to-noise ratio and allows simultaneous measurement of cell surface markers: Integrated Cellular Indexing of Chromatin Landscape and Epitopes (ICICLE-seq). We extended this approach using a droplet-based multiomics platform to develop a trimodal assay to simultaneously measure Transcriptomic state (scRNA-seq), cell surface Epitopes, and chromatin Accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2019 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

scholarly journals Exploring and analysing single cell multi-omics data with VDJView

2020 ◽  
Author(s):  
Jerome Samir ◽  
Simone Rizzetto ◽  
Money Gupta ◽  
Fabio Luciani
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cells ◽  
Hypothesis Testing ◽  
Single Cell ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
T And B Cells ◽  
Gene Signatures

Abstract Background Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information.Results We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8+ T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians.Conclusions VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview .

scholarly journals SNV identification from single-cell RNA sequencing data

2019 ◽  
Vol 28 (21) ◽  
pp. 3569-3583 ◽  
Author(s):  
Patricia M Schnepp ◽  
Mengjie Chen ◽  
Evan T Keller ◽  
Xiang Zhou
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Specific Gene ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Cell Rna Sequencing ◽  
Sequencing Studies ◽  
Genomic Regions

Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

scholarly journals Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zi-Hang Wen ◽  
Jeremy L. Langsam ◽  
Lu Zhang ◽  
Wenjun Shen ◽  
Xin Zhou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Group ◽  
Specific Gene ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Expression Matrix ◽  
Study Gene Expression ◽  
Expression Matrix

AbstractSingle-cell RNA-seq (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. We introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than other six publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene or cell related information that users provide to increase the performance. Availability: Bfimpute is implemented in R and is freely available at https://github.com/maiziezhoulab/Bfimpute.

scholarly journals SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data

2019 ◽  
Author(s):  
Yiliang Zhang ◽  
Kexuan Liang ◽  
Molei Liu ◽  
Yue Li ◽  
Hao Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Dropout Rate ◽  
Biological Variation ◽  
Specific Gene ◽  
Batch Effects ◽  
Cell Recovery

AbstractSingle-cell RNA sequencing technologies are widely used in recent years as a powerful tool allowing the observation of gene expression at the resolution of single cells. Two of the major challenges in scRNA-seq data analysis are dropout events and batch effects. The inflation of zero(dropout rate) varies substantially across single cells. Evidence has shown that technical noise, including batch effects, explains a notable proportion of this cell-to-cell variation. To capture biological variation, it is necessary to quantify and remove technical variation. Here, we introduce SCRIBE (Single-Cell Recovery Imputation with Batch Effects), a principled framework that imputes dropout events and corrects batch effects simultaneously. We demonstrate, through real examples, that SCRIBE outperforms existing scRNA-seq data analysis tools in recovering cell-specific gene expression patterns, removing batch effects and retaining biological variation across cells. Our software is freely available online at https://github.com/YiliangTracyZhang/SCRIBE.

scholarly journals Functional single cell proteomic profiling of cells with abnormal DNA damage response dynamics

2021 ◽  
Author(s):  
Pin-Rui Su ◽  
Li You ◽  
Cecile Beerens ◽  
Karel Bezstarosti ◽  
Jeroen Demmers ◽  
...  
Keyword(s):  
Dna Damage ◽  
Single Cell ◽  
Dna Damage Response ◽  
Cell Tracking ◽  
Single Cells ◽  
Proteomic Profiling ◽  
Response Dynamics ◽  
Isolated Cells ◽  
Cellular Dynamics ◽  
Damage Response

Tumor heterogeneity is an important source of cancer therapy resistance. Single cell proteomics has the potential to decipher protein content leading to heterogeneous cellular phenotypes. Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) is a recently developed, promising, unbiased proteomic profiling techniques, which allows profiling several tens of single cells for >1000 proteins per cell. However, a method to link single cell proteomes with cellular behaviors is needed to advance this type of profiling technique. Here, we developed a microscopy-based functional single cell proteomic profiling technology, called FUNpro, to link the proteome of individual cells with phenotypes of interest, even if the phenotypes are dynamic or the cells of interest are sparse. FUNpro enables one i) to screen thousands of cells with subcellular resolution and monitor (intra)cellular dynamics using a custom-built microscope, ii) to real-time analyze (intra)cellular dynamics of individual cells using an integrated cell tracking algorithm, iii) to promptly isolate the cells displaying phenotypes of interest, and iv) to single cell proteomically profile the isolated cells. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified PDS5A and PGAM5 proteins contributing to the abnormal DDR dynamics and helping the cells survive after IR.

scholarly journals Single-cell transcriptome profiling of the Ciona larval brain

2018 ◽  
Author(s):  
Sarthak Sharma ◽  
Wei Wang ◽  
Alberto Stolfi
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Transcriptome Profiling ◽  
Specific Gene ◽  
Larval Brain ◽  
Rnaseq Data ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractThe tadpole-type larva of Ciona has emerged as an intriguing model system for the study of neurodevelopment. The Ciona intestinalis connectome has been recently mapped, revealing the smallest central nervous system (CNS) known in any chordate, with only 177 neurons. This minimal CNS is highly reminiscent of larger CNS of vertebrates, sharing many conserved developmental processes, anatomical compartments, neuron subtypes, and even specific neural circuits. Thus, the Ciona tadpole offers a unique opportunity to understand the development and wiring of a chordate CNS at single-cell resolution. Here we report the use of single-cell RNAseq to profile the transcriptomes of single cells isolated by fluorescence-activated cell sorting (FACS) from the whole brain of Ciona robusta (formerly intestinalis Type A) larvae. We have also compared these profiles to bulk RNAseq data from specific subsets of brain cells isolated by FACS using cell type-specific reporter plasmid expression. Taken together, these datasets have begun to reveal the compartment- and cell-specific gene expression patterns that define the organization of the Ciona larval brain.

scholarly journals Single-Cell Analysis of the 3D Topologies of Genomic Loci Using Genome Architecture Mapping

2020 ◽  
Author(s):  
Lonnie R. Welch ◽  
Catherine Baugher ◽  
Yingnan Zhang ◽  
Trenton Davis ◽  
William F. Marzluff ◽  
...  
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Geometric Model ◽  
Embryonic Stem ◽  
Specific Gene ◽  
Genome Architecture ◽  
Cell Analysis ◽  
Thin Sections

AbstractAlthough each cell within an organism contains a nearly identical genome sequence, the three-dimensional (3D) packing of the genome varies among individual cells, influencing cell-type-specific gene expression. Genome Architecture Mapping (GAM) is the first genome-wide experimental method for capturing 3D proximities between any number of genomic loci without ligation. GAM overcomes several limitations of 3C-based methods by sequencing DNA from a large collection of thin sections sliced from individual nuclei. The GAM technique measures locus co-segregation, extracts radial positions, infers chromatin compaction, requires small numbers of cells, does not depend on ligation, and provides rich single-cell information. However, previous analyses of GAM data focused exclusively on population averages, neglecting the variation in 3D topology among individual cells.We present the first single-cell analysis of GAM data, demonstrating that the slices from individual cells reveal intercellular heterogeneity in chromosome conformation. By simultaneously clustering both slices and genomic loci, we identify topological variation among single cells, including differential compaction of cell cycle genes. We also develop a geometric model of the nucleus, allowing prediction of the 3D positions of each slice. Using GAM data from mouse embryonic stem cells, we make new discoveries about the structure of the major mammalian histone gene locus, which is incorporated into the Histone Locus Body (HLB), including structural fluctuations and putative causal molecular mechanisms. Our methods are packaged as SluiceBox, a toolkit for mining GAM data. Our approach represents a new method of investigating variation in 3D genome topology among individual cells across space and time.

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