scholarly journals Minimalistic 3D Chromatin Models: Sparse Interactions in Single Cells Drive the Chromatin Fold and Form Many-Body Units

2021 ◽  
Author(s):  
Jie Liang ◽  
Alan Perez-Rathke
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Genome Structure ◽  
Structural Heterogeneity ◽  
Specific Interactions ◽  
Many Body ◽  
Polymer Model ◽  
High Coverage ◽  
Confined Polymers ◽  
Chromatin Folding

Computational modeling of 3D chromatin plays an important role in understanding the principles of genome organization. We discuss methods for modeling 3D chromatin structures, with focus on a minimalistic polymer model which inverts population Hi-C into high-resolution, high-coverage single-cell chromatin conformations. Utilizing only basic physical properties such as nuclear volume and no adjustable parameters, this model uncovers a few specific Hi-C interactions (15-35 for enhancer-rich loci in human cells) that can fold chromatin into individual conformations consistent with single-cell imaging, Dip-C, and FISH-measured genomic distance distributions. Aggregating an ensemble of conformations also reproduces population Hi-C interaction frequencies. Furthermore, this single-cell modeling approach allows quantification of structural heterogeneity and discovery of specific many-body units of chromatin interactions. This minimalistic 3D chromatin polymer model has revealed a number of insights: 1) chromatin scaling rules are a result of volume-confined polymers; 2) TADs form as a byproduct of 3D chromatin folding driven by specific interactions; 3) chromatin folding at many loci is driven by a small number of specific interactions; 4) cell subpopulations equipped with different chromatin structural scaffolds are developmental stage-dependent; and 5) characterization of the functional landscape and epigenetic marks of many-body units which are simultaneously spatially co-interacting within enhancer-rich, euchromatic regions. The implications of these findings in understanding the genome structure-function relationship are also discussed.

The 3D Genome Structure of Single Cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tianming Zhou ◽  
Ruochi Zhang ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Spatial Organization ◽  
Method Development ◽  
Single Cells ◽  
Genome Structure ◽  
Computational Method ◽  
Publication Date ◽  
3D Genome ◽  
Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Vivekananda Sarangi ◽  
Alexandre Jourdon ◽  
Taejeong Bae ◽  
Arijit Panda ◽  
Flora Vaccarino ◽  
...  
Keyword(s):  
Single Cell ◽  
Multiple Displacement Amplification ◽  
Single Cells ◽  
Sequencing Data ◽  
High Coverage ◽  
Amplification Bias ◽  
Single Cell Profiling ◽  
High Concordance ◽  
Human Neuronal Cells

Abstract Background The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. Results We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Conclusion Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

scholarly journals HiCluster: A Robust Single-Cell Hi-C Clustering Method Based on Convolution and Random Walk

2018 ◽  
Author(s):  
Jingtian Zhou ◽  
Jianzhu Ma ◽  
Yusi Chen ◽  
Chuankai Cheng ◽  
Bokan Bao ◽  
...  
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Genome Structure ◽  
Real Data ◽  
Cell Types ◽  
3D Genome ◽  
Cell Clustering

3D genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe HiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real data as benchmarks, HiCluster significantly improves clustering accuracy when applied to low coverage Hi-C datasets compared to existing methods. After imputation by HiCluster, structures similar to topologically associating domains (TADs) could be identified within single cells, and their consensus boundaries among cells were enriched at the TAD boundaries observed in bulk samples. In summary, HiCluster facilitates visualization and comparison of single-cell 3D genomes.

scholarly journals BIRD: identifying cell doublets via biallelic expression from single cells

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i251-i257
Author(s):  
Kerem Wainer-Katsir ◽  
Michal Linial
Keyword(s):  
Single Cell ◽  
Genetic Background ◽  
Human Peripheral Blood ◽  
Single Cells ◽  
Genomic Diversity ◽  
Experimental Methodology ◽  
Supplementary Information ◽  
Sequence Coverage ◽  
High Coverage ◽  
Biallelic Expression

ABSTRACT Summary Current technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods raises the risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell experimental data focus on doublets of different species, cell types or individuals. In this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power toward doublets’ identification. We present a pipeline called BIallelic Ratio for Doublets (BIRD) that relies on heterologous genetic variations, from single-cell RNA sequencing. For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-seq data from 163 primary fibroblast single cells. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets were verified based on a biallelic expression signal amongst X-chromosome of female fibroblasts. Data from 10X Genomics microfluidics of human peripheral blood cells achieved in average 83% (±3.7%) accuracy, and an area under the curve of 0.88 (±0.04) for a collection of ∼13 300 single cells. BIRD addresses instances of doublets, which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved for high-coverage data from Smart-seq. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage and depth. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single cell quantification of ribosome occupancy in early mouse development

2021 ◽  
Author(s):  
Tori Tonn ◽  
Hakan Ozadam ◽  
Crystal Han ◽  
Alia Segura ◽  
Duc Tran ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Germinal Vesicle ◽  
Ribosome Profiling ◽  
Protein Abundance ◽  
Translation Efficiency ◽  
Proteomics Data ◽  
Mouse Development ◽  
High Coverage ◽  
Early Mouse

Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N6-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.

scholarly journals Single-cell molecular profiling provides a high-resolution map of basophil and mast cell differentiation

2019 ◽  
Author(s):  
Fiona K. Hamey ◽  
Winnie W.Y. Lau ◽  
Iwo Kucinski ◽  
Xiaonan Wang ◽  
Evangelia Diamanti ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Mast Cell ◽  
Mast Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Molecular Profiling ◽  
Multicolor Flow Cytometry ◽  
High Coverage ◽  
Hematopoietic Stem

AbstractDifferentiation of hematopoietic stem and progenitor cells ensure a continuous supply of mature blood cells. Recent models of differentiation are represented as a landscape, in which individual progenitors traverse a continuum of multipotent cell states before reaching an entry point that marks lineage commitment. Basophils and mast cells have received little attention in these models and their differentiation trajectories are yet to be explored. Here, we have performed multicolor flow cytometry and high-coverage single-cell RNA sequencing analyses to chart the differentiation of hematopoietic progenitors into basophils and mast cells in mouse. Analysis of flow cytometry data reconstructed a detailed map of the differentiation, including a bifurcation of progenitors into two specific trajectories. Molecular profiling and pseudotime ordering of the single cells revealed gene expression changes during differentiation, with temporally separated regulation of mast cell protease genes. We validate that basophil and mast cell signature genes increased along the trajectories into their respective lineage, and we demonstrate how genes critical for each respective lineage are upregulated during the formation of the mature cells. Cell fate assays showed that multicolor flow cytometry and transcriptional profiling successfully predict the bipotent phenotype of a previously uncharacterized population of basophil-mast cell progenitor-like cells in mouse peritoneum. Taken together, we provide a detailed roadmap of basophil and mast cell development through a combination of molecular and functional profiling.

scholarly journals BIRD: Identifying Cell Doublets via Biallelic Expression from Single cells

2019 ◽  
Author(s):  
Kerem Wainer-Katsir ◽  
Michal Linial
Keyword(s):  
Single Cell ◽  
Genetic Background ◽  
Single Cells ◽  
Area Under The Curve ◽  
Genomic Diversity ◽  
Experimental Methodology ◽  
Sequence Coverage ◽  
High Coverage ◽  
Single Experiment ◽  
Biallelic Expression

ABSTRACTMotivationCurrent technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods led to a higher risk of cell doublets’ contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell expression data focus on cell doublets from different species, cell types or individuals.ResultsIn this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power towards doublets’ identification. We present a pipeline called BIRD (BIallelic Ratio for Doublets) that relies on heterologous genetic variations extracted from single-cell RNA-seq (scRNA-seq). For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-Seq data from 163 primary fibroblasts. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets from female-origin fibroblasts were verified by the unexpected biallelic expression from X-chromosome. Data from 10X Genomics microfluidics of peripheral blood cells analyzed by BIRD achieved in average 83% (± 3.7%) accuracy with an area under the curve of 0.88 (± 0.04) for a collection of ∼13,300 single cells.ConclusionsBIRD addresses instances of doublets which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved with high coverage data. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage, and depth.

scholarly journals Robust single-cell Hi-C clustering by convolution- and random-walk–based imputation

2019 ◽  
Vol 116 (28) ◽  
pp. 14011-14018 ◽  
Author(s):  
Jingtian Zhou ◽  
Jianzhu Ma ◽  
Yusi Chen ◽  
Chuankai Cheng ◽  
Bokan Bao ◽  
...  
Keyword(s):  
Random Walk ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Genome Structure ◽  
Three Dimensional ◽  
Cell Types ◽  
Cell Clustering ◽  
Low Coverage

Three-dimensional genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe scHiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real single-cell Hi-C data as benchmarks, scHiCluster significantly improves clustering accuracy when applied to low coverage datasets compared with existing methods. After imputation by scHiCluster, topologically associating domain (TAD)-like structures (TLSs) can be identified within single cells, and their consensus boundaries were enriched at the TAD boundaries observed in bulk cell Hi-C samples. In summary, scHiCluster facilitates visualization and comparison of single-cell 3D genomes.

scholarly journals Resource: Scalable whole genome sequencing of 40,000 single cells identifies stochastic aneuploidies, genome replication states and clonal repertoires

2018 ◽  
Author(s):  
Emma Laks ◽  
Hans Zahn ◽  
Daniel Lai ◽  
Andrew McPherson ◽  
Adi Steif ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Sequencing ◽  
Large Scale ◽  
Single Cells ◽  
Genome Structure ◽  
Cancer Tissue ◽  
Genome Replication ◽  
Wide Range ◽  
Cell Genome ◽  
Single Cell Genome

SummaryEssential features of cancer tissue cellular heterogeneity such as negatively selected genome topologies, sub-clonal mutation patterns and genome replication states can only effectively be studied by sequencing single-cell genomes at scale and high fidelity. Using an amplification-free single-cell genome sequencing approach implemented on commodity hardware (DLP+) coupled with a cloud-based computational platform, we define a resource of 40,000 single-cell genomes characterized by their genome states, across a wide range of tissue types and conditions. We show that shallow sequencing across thousands of genomes permits reconstruction of clonal genomes to single nucleotide resolution through aggregation analysis of cells sharing higher order genome structure. From large-scale population analysis over thousands of cells, we identify rare cells exhibiting mitotic mis-segregation of whole chromosomes. We observe that tissue derived scWGS libraries exhibit lower rates of whole chromosome anueploidy than cell lines, and loss of p53 results in a shift in event type, but not overall prevalence in breast epithelium. Finally, we demonstrate that the replication states of genomes can be identified, allowing the number and proportion of replicating cells, as well as the chromosomal pattern of replication to be unambiguously identified in single-cell genome sequencing experiments. The combined annotated resource and approach provide a re-implementable large scale platform for studying lineages and tissue heterogeneity.

scholarly journals A single-cell method to map higher-order 3D genome organization in thousands of individual cells reveals structural heterogeneity in mouse ES cells

2020 ◽  
Author(s):  
Mary V. Arrastia ◽  
Joanna W. Jachowicz ◽  
Noah Ollikainen ◽  
Matthew S. Curtis ◽  
Charlotte Lai ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Genome Structure ◽  
Higher Order ◽  
Nuclear Bodies ◽  
Nuclear Function ◽  
Genome Wide ◽  
A Genome ◽  
Order Structures

ABSTRACTIn eukaryotes, the nucleus is organized into a three dimensional structure consisting of both local interactions such as those between enhancers and promoters, and long-range higher-order structures such as nuclear bodies. This organization is central to many aspects of nuclear function, including DNA replication, transcription, and cell cycle progression. Nuclear structure intrinsically occurs within single cells; however, measuring such a broad spectrum of 3D DNA interactions on a genome-wide scale and at the single cell level has been a great challenge. To address this, we developed single-cell split-pool recognition of interactions by tag extension (scSPRITE), a new method that enables measurements of genome-wide maps of 3D DNA structure in thousands of individual nuclei. scSPRITE maximizes the number of DNA contacts detected per cell enabling high-resolution genome structure maps within each cells and is easy-to-use and cost-effective. scSPRITE accurately detects chromosome territories, active and inactive compartments, topologically associating domains (TADs), and higher-order structures within single cells. In addition, scSPRITE measures cell-to-cell heterogeneity in genome structure at different levels of resolution and shows that TADs are dynamic units of genome organization that can vary between different cells within a population. scSPRITE will improve our understanding of nuclear architecture and its relationship to nuclear function within an individual nucleus from complex cell types and tissues containing a diverse population of cells.

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