scholarly journals Single-cell multi-omic profiling of chromatin conformation and DNA methylome

2018 ◽  
Author(s):  
Dong-Sung Lee ◽  
Chongyuan Luo ◽  
Jingtian Zhou ◽  
Sahaana Chandran ◽  
Angeline Rivkin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Cell Types ◽  
Mouse Cell ◽  
Regulatory Sequences ◽  
Chromatin Conformation ◽  
Cell Type ◽  
Dynamic Regulation ◽  
Single Nucleus ◽  
Cell Type Specific

AbstractRecent advances in the development of single cell epigenomic assays have facilitated the analysis of gene regulatory landscapes in complex biological systems. Methods for detection of single-cell epigenomic variation such as DNA methylation sequencing and ATAC-seq hold tremendous promise for delineating distinct cell types and identifying their critical cis-regulatory sequences. Emerging evidence has shown that in addition to cis-regulatory sequences, dynamic regulation of 3D chromatin conformation is a critical mechanism for the modulation of gene expression during development and disease. It remains unclear whether single-cell Chromatin Conformation Capture (3C) or Hi-C profiles are suitable for cell type identification and allow the reconstruction of cell-type specific chromatin conformation maps. To address these challenges, we have developed a multi-omic method single-nucleus methyl-3C sequencing (sn-m3C-seq) to profile chromatin conformation and DNA methylation from the same cell. We have shown that bulk m3C-seq and sn-m3C-seq accurately capture chromatin organization information and robustly separate mouse cell types. We have developed a fluorescent-activated nuclei sorting strategy based on DNA content that eliminates nuclei multiplets caused by crosslinking. The sn-m3C-seq method allows high-resolution cell-type classification using two orthogonal types of epigenomic information and the reconstruction of cell-type specific chromatin conformation maps.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Author(s):  
Andreas Fønss Møller ◽  
Kedar Nath Natarajan
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Types ◽  
Mouse Cell ◽  
Cell Type ◽  
Integrated Network ◽  
Mouse Tissues ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

scholarly journals Virtual methylome dissection facilitated by single-cell analyses

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Liduo Yin ◽  
Yanting Luo ◽  
Xiguang Xu ◽  
Shiyu Wen ◽  
Xiaowei Wu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Nonnegative Matrix ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Mixed Cell ◽  
Numerous Cell ◽  
Cell Type Specific ◽  
Methylation Patterns

Abstract Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

scholarly journals The impact of chromatin remodeling on gene expression at the single cell level in Arabidopsis thaliana

2020 ◽  
Author(s):  
Andrew Farmer ◽  
Sandra Thibivilliers ◽  
Kook Hui Ryu ◽  
John Schiefelbein ◽  
Marc Libault
Keyword(s):  
Gene Expression ◽  
Arabidopsis Thaliana ◽  
Single Cell ◽  
Chromatin Remodeling ◽  
Cell Types ◽  
Regulatory Mechanisms ◽  
Cell Type ◽  
Single Nucleus ◽  
Cell Type Specific ◽  
The Impact

AbstractSimilar to other complex organisms, plants consist of diverse and highly specialized cell types. The gain of unique biological functions of these different cell types is the consequence of the establishment of cell-type-specific transcriptional programs and their associated regulatory mechanisms. Recently, single cell transcriptomic approaches have been applied on Arabidopsis thaliana root protoplasts allowing the accurate characterization of the transcriptional profiles of the cell-types composing seedling roots. As a first step in gaining a deeper understanding of the regulatory mechanisms controlling Arabidopsis gene expression, we report the use of single nucleus RNA sequencing (sNucRNA-seq) and single nucleus Assay for Transposase Accessible Chromatin sequencing (sNucATAC-seq) technologies on Arabidopsis roots. The comparison of our single nuclei transcriptomes to previously published protoplast transcriptomes validated the use of nuclei as biological entities to establish cell-type specific transcriptomes from multicellular organs. Furthermore, our sNucRNA-seq results uncovered the transcriptome of additional cell subtypes not identified by scRNA-seq. Similar to our transcriptomic approach, the sNucATAC-seq approach led to the distribution of the Arabidopsis nuclei into distinct clusters suggesting the differential remodeling of the chromatin between groups of cells according to their identity. To reveal the impact of chromatin remodeling on gene transcription, we integrated sNucRNA-seq and sNucATAC-seq data and demonstrated that cell-type-specific marker genes also display cell-type-specific pattern of chromatin accessibility. Our data suggest that the differential remodeling of the chromatin is a critical mechanism to regulate gene activity at the cell-type level.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals Functional annotation of human long noncoding RNAs using chromatin conformation data

2021 ◽  
Author(s):  
Saumya Agrawal ◽  
Tanvir Alam ◽  
Masaru Koido ◽  
Ivan V. Kulakovskiy ◽  
Jessica Severin ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Rna Binding ◽  
Functional Characterization ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Spatial Proximity ◽  
Chromatin Conformation ◽  
Cell Type ◽  
Cell Type Specific ◽  
Rna Domains

AbstractTranscription of the human genome yields mostly long non-coding RNAs (lncRNAs). Systematic functional annotation of lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. Currently, 95% of human lncRNAs have no functional characterization. Using chromatin conformation and Cap Analysis of Gene Expression (CAGE) data in 18 human cell types, we systematically located genomic regions in spatial proximity to lncRNA genes and identified functional clusters of interacting protein-coding genes, lncRNAs and enhancers. Using these clusters we provide a cell type-specific functional annotation for 7,651 out of 14,198 (53.88%) lncRNAs. LncRNAs tend to have specialized roles in the cell type in which it is first expressed, and to incorporate more general functions as its expression is acquired by multiple cell types during evolution. By analyzing RNA-binding protein and RNA-chromatin interaction data in the context of the spatial genomic interaction map, we explored mechanisms by which these lncRNAs can act.

scholarly journals CellO: Comprehensive and hierarchical cell type classification of human cells with the Cell Ontology

2019 ◽  
Author(s):  
Matthew N. Bernstein ◽  
Zhongjie Ma ◽  
Michael Gleicher ◽  
Colin N. Dewey
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Training Set ◽  
Sequence Read Archive ◽  
Cell Ontology ◽  
Cell Type Specific ◽  
Type Classification

SummaryCell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification by considering the rich hierarchical structure of known cell types, a source of prior knowledge that is not utilized by existing methods. Furthemore, CellO comes pre-trained on a novel, comprehensive dataset of human, healthy, untreated primary samples in the Sequence Read Archive, which to the best of our knowledge, is the most diverse curated collection of primary cell data to date. CellO’s comprehensive training set enables it to run out-of-the-box on diverse cell types and achieves superior or competitive performance when compared to existing state-of-the-art methods. Lastly, CellO’s linear models are easily interpreted, thereby enabling exploration of cell type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO’s models across the ontology.HighlightWe present CellO, a tool for hierarchically classifying cell type from single-cell RNA-seq data against the graph-structured Cell OntologyCellO is pre-trained on a comprehensive dataset comprising nearly all bulk RNA-seq primary cell samples in the Sequence Read ArchiveCellO achieves superior or comparable performance with existing methods while featuring a more comprehensive pre-packaged training setCellO is built with easily interpretable models which we expose through a novel web application, the CellO Viewer, for exploring cell type-specific signatures across the Cell OntologyGraphical Abstract

scholarly journals ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data

2018 ◽  
Author(s):  
Wennan Chang ◽  
Changlin Wan ◽  
Xiaoyu Lu ◽  
Szu-wei Tu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Training Data ◽  
Marker Genes ◽  
Cell Detection ◽  
Omics Data ◽  
Deconvolution Method ◽  
Cell Type ◽  
Data Set ◽  
Cell Type Specific

AbstractWe developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

scholarly journals CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

2021 ◽  
Author(s):  
Zhengyu Ouyang ◽  
Nathanael Bourgeois ◽  
Eugenia Lyashenko ◽  
Paige Cundiff ◽  
Patrick F Cullen ◽  
...  
Keyword(s):  
Single Cell ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Model Systems ◽  
Rna Seq ◽  
Cell Type ◽  
Fine Grained ◽  
Single Nucleus ◽  
Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

scholarly journals Differences in DNA methylation of white blood cell types at birth and in adulthood reflect postnatal immune maturation and influence accuracy of cell type prediction

2018 ◽  
Author(s):  
Meaghan J Jones ◽  
Louie Dinh ◽  
Hamid Reza Razzaghian ◽  
Olivia de Goede ◽  
Julia L MacIsaac ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Immune System ◽  
Blood Cell ◽  
Cord Blood ◽  
White Blood Cell ◽  
Blood Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Specific ◽  
The Impact

AbstractBackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.ResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naïve status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.ConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.

scholarly journals Single-cell RNA sequencing reveals cell type- and artery type-specific vascular remodelling in male spontaneously hypertensive rats

2020 ◽  
Author(s):  
Jun Cheng ◽  
Wenduo Gu ◽  
Ting Lan ◽  
Jiacheng Deng ◽  
Zhichao Ni ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spontaneously Hypertensive Rats ◽  
Cell Types ◽  
Vascular Remodelling ◽  
Cell Type ◽  
Hypertensive Rats ◽  
Spontaneously Hypertensive ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

Abstract Aims Hypertension is a major risk factor for cardiovascular diseases. However, vascular remodelling, a hallmark of hypertension, has not been systematically characterized yet. We described systematic vascular remodelling, especially the artery type- and cell type-specific changes, in hypertension using spontaneously hypertensive rats (SHRs). Methods and results Single-cell RNA sequencing was used to depict the cell atlas of mesenteric artery (MA) and aortic artery (AA) from SHRs. More than 20 000 cells were included in the analysis. The number of immune cells more than doubled in aortic aorta in SHRs compared to Wistar Kyoto controls, whereas an expansion of MA mesenchymal stromal cells (MSCs) was observed in SHRs. Comparison of corresponding artery types and cell types identified in integrated datasets unravels dysregulated genes specific for artery types and cell types. Intersection of dysregulated genes with curated gene sets including cytokines, growth factors, extracellular matrix (ECM), receptors, etc. revealed vascular remodelling events involving cell–cell interaction and ECM re-organization. Particularly, AA remodelling encompasses upregulated cytokine genes in smooth muscle cells, endothelial cells, and especially MSCs, whereas in MA, change of genes involving the contractile machinery and downregulation of ECM-related genes were more prominent. Macrophages and T cells within the aorta demonstrated significant dysregulation of cellular interaction with vascular cells. Conclusion Our findings provide the first cell landscape of resistant and conductive arteries in hypertensive animal models. Moreover, it also offers a systematic characterization of the dysregulated gene profiles with unbiased, artery type-specific and cell type-specific manners during hypertensive vascular remodelling.

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