Network-based method for regions with statistically frequent interchromosomal interactions at single-cell resolution

Abstract Background Chromosome conformation capture-based methods, especially Hi-C, enable scientists to detect genome-wide chromatin interactions and study the spatial organization of chromatin, which plays important roles in gene expression regulation, DNA replication and repair etc. Thus, developing computational methods to unravel patterns behind the data becomes critical. Existing computational methods focus on intrachromosomal interactions and ignore interchromosomal interactions partly because there is no prior knowledge for interchromosomal interactions and the frequency of interchromosomal interactions is much lower while the search space is much larger. With the development of single-cell technologies, the advent of single-cell Hi-C makes interrogating the spatial structure of chromatin at single-cell resolution possible. It also brings a new type of frequency information, the number of single cells with chromatin interactions between two disjoint chromosome regions. Results Considering the lack of computational methods on interchromosomal interactions and the unsurprisingly frequent intrachromosomal interactions along the diagonal of a chromatin contact map, we propose a computational method dedicated to analyzing interchromosomal interactions of single-cell Hi-C with this new frequency information. To the best of our knowledge, our proposed tool is the first to identify regions with statistically frequent interchromosomal interactions at single-cell resolution. We demonstrate that the tool utilizing networks and binomial statistical tests can identify interesting structural regions through visualization, comparison and enrichment analysis and it also supports different configurations to provide users with flexibility. Conclusions It will be a useful tool for analyzing single-cell Hi-C interchromosomal interactions.

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The 3D Genome Structure of Single Cells

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-020121-084709 ◽

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.

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Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells

Science ◽

10.1126/science.aau1783 ◽

2018 ◽

Vol 362 (6413) ◽

pp. eaau1783 ◽

Cited By ~ 269

Author(s):

Bogdan Bintu ◽

Leslie J. Mateo ◽

Jun-Han Su ◽

Nicholas A. Sinnott-Armstrong ◽

Mirae Parker ◽

...

Keyword(s):

Spatial Organization ◽

Single Cells ◽

Domain Boundary ◽

Super Resolution ◽

Chromatin Domain ◽

Imaging Method ◽

Imaging Data ◽

Chromatin Interactions ◽

Population Average ◽

Binding Factor

The spatial organization of chromatin is pivotal for regulating genome functions. We report an imaging method for tracing chromatin organization with kilobase- and nanometer-scale resolution, unveiling chromatin conformation across topologically associating domains (TADs) in thousands of individual cells. Our imaging data revealed TAD-like structures with globular conformation and sharp domain boundaries in single cells. The boundaries varied from cell to cell, occurring with nonzero probabilities at all genomic positions but preferentially at CCCTC-binding factor (CTCF)- and cohesin-binding sites. Notably, cohesin depletion, which abolished TADs at the population-average level, did not diminish TAD-like structures in single cells but eliminated preferential domain boundary positions. Moreover, we observed widespread, cooperative, multiway chromatin interactions, which remained after cohesin depletion. These results provide critical insight into the mechanisms underlying chromatin domain and hub formation.

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Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation

10.1101/2021.11.11.468039 ◽

2021 ◽

Author(s):

Hyobin Jeong ◽

Karen Grimes ◽

Peter-Martin Bruch ◽

Tobias Rausch ◽

Patrick Hasenfeld ◽

...

Keyword(s):

Single Cell ◽

Nucleosome Occupancy ◽

Single Cells ◽

Chromosomal Rearrangements ◽

Regulatory Elements ◽

Computational Method ◽

Tumour Heterogeneity ◽

Cancer Genomes ◽

Functional Consequences ◽

Oncogenic Transcription Factor

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.

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A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

10.1101/247114 ◽

2018 ◽

Cited By ~ 1

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

R. Krishna Murthy Karuturi ◽

Joshy George

Keyword(s):

Experimental Design ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Number ◽

Fold Change ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

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HBP: an integrative and flexible pipeline for the interaction analysis of Hi-C dataset

10.1101/083576 ◽

2016 ◽

Author(s):

Chao He ◽

Ping Li ◽

Minglei Shi ◽

Yan Zhang ◽

Bingyu Ye ◽

...

Keyword(s):

Spatial Organization ◽

Molecular Mechanisms ◽

Gene Expression Regulation ◽

Interaction Analysis ◽

Rapid Development ◽

3D Structure ◽

Biological Significance ◽

Sequencing Data ◽

Chromatin Interactions ◽

Wide Range

AbstractBackgroundThe spatial organization of interphase chromatin in the nucleus play an important role in gene expression regulation and function. With the rapid development of revolutionized chromosome conformation capture technology and its genome-wide derivatives such as Hi-C, investigation of the genome folding becomes more efficient and convenient. How to robustly deal with these massive datasets and infer accurate 3D model and within-nucleus compartmentalization of chromosomes becomes a new challenge.ResultThe implemented pipeline HBP (Hi-C BED file analysis Pipeline) integrates existing pipelines focusing on individual steps of Hi-C data processing into an all-in-one package with adjustable parameters to infer the consensus 3D structure of genome from raw Hi-C sequencing data. What’s more, HBP could assign statistical confidence estimation for chromatin interactions, and clustering interaction loci according to enrichment tracks or topological structure automatically.ConclusionThe freely available HBP is an optimized and flexible pipeline for analyzing the folding of whole chromosome and interactions between some specific sites from the Hi-C raw sequencing reads to the partially processed datasets. The other complex genetic and epigenetic datasets from public sources such as GWAS, ENCODE consortiums etc. will also easily be integrated into HBP, hence the final output results of HBP could provide a comprehensive in-depth understanding for the specific chromatin interactions, potential molecular mechanisms and biological significance. We believe that HBP is a reliable tool for the rapidly analysis of Hi-C data and will be very useful for a wide range of researchers, particularly those who lack of background in computational biology. HBP is freely accessible at https://github.com/hechao0407/HBP/blob/master/HBP_1.0.tar.gz.

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Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anticancer drug response

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab054 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Xin Wang ◽

Jane Frederick ◽

Hongbin Wang ◽

Sheng Hui ◽

Vadim Backman ◽

...

Keyword(s):

Single Cell ◽

Cancer Cells ◽

Transcriptional Repression ◽

Drug Response ◽

Transcriptional Activity ◽

Gene Expression Regulation ◽

Single Cells ◽

Rna Seq ◽

Study Gene Expression

Abstract The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anticancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here, we used single-cell RNA-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that cellular mRNA abundance shows dynamic regulation after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.

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Spatial charting of single cell transcriptomes in tissues

10.1101/2021.11.24.469915 ◽

2021 ◽

Author(s):

Nicholas Navin ◽

Runmin Wei ◽

Siyuan He ◽

Shanshan Bai ◽

Emi Sei ◽

...

Keyword(s):

Single Cell ◽

Spatial Organization ◽

Spatial Information ◽

Ductal Carcinoma ◽

Single Cells ◽

Cell Types ◽

Spatial Structures ◽

Tissue Sections ◽

Normal Mouse Brain

Single cell RNA sequencing (scRNA-seq) methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics (ST) assays can profile spatial regions in tissue sections, but do not have single cell genomic resolution. Here, we developed a computational approach called SChart, that combines these two datasets to achieve single cell spatial mapping of cell types, cell states and continuous phenotypes. We applied SChart to reconstruct cellular spatial structures in existing datasets from normal mouse brain and kidney tissues to validate our approach. We also performed scRNA-seq and ST experiments on two ductal carcinoma in situ (DCIS) tissues and applied SChart to identify subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data shows that SChart can accurately map single cells in diverse tissue types to resolve their spatial organization into cellular neighborhoods and tissue structures.

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Unsupervised manifold alignment for single-cell multi-omics data

10.1101/2020.06.13.149195 ◽

2020 ◽

Cited By ~ 2

Author(s):

Ritambhara Singh ◽

Pinar Demetci ◽

Giancarlo Bonora ◽

Vijay Ramani ◽

Choli Lee ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Practical Interest ◽

Search Space ◽

Chromatin Accessibility ◽

Small Scale ◽

Latent Space ◽

Cell Experiment ◽

Manifold Alignment ◽

Unsupervised Algorithms

AbstractIntegrating single-cell measurements that capture different properties of the genome is vital to extending our understanding of genome biology. This task is challenging due to the lack of a shared axis across datasets obtained from different types of single-cell experiments. For most such datasets, we lack corresponding information among the cells (samples) and the measurements (features). In this scenario, unsupervised algorithms that are capable of aligning single-cell experiments are critical to learning an in silico co-assay that can help draw correspondences among the cells. Maximum mean discrepancy-based manifold alignment (MMD-MA) is such an unsupervised algorithm. Without requiring correspondence information, it can align single-cell datasets from different modalities in a common shared latent space, showing promising results on simulations and a small-scale single-cell experiment with 61 cells. However, it is essential to explore the applicability of this method to larger single-cell experiments with thousands of cells so that it can be of practical interest to the community. In this paper, we apply MMD-MA to two recent datasets that measure transcriptome and chromatin accessibility in ~2000 single cells. To scale the runtime of MMD-MA to a more substantial number of cells, we extend the original implementation to run on GPUs. We also introduce a method to automatically select one of the user-defined parameters, thus reducing the hyperparameter search space. We demonstrate that the proposed extensions allow MMD-MA to accurately align state-of-the-art single-cell experiments.

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BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

Genome Biology ◽

10.1186/s13059-021-02461-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yuanhua Huang ◽

Guido Sanguinetti

Keyword(s):

Alternative Splicing ◽

Single Cell ◽

Rna Splicing ◽

Single Cells ◽

Computational Method ◽

Cell Level ◽

Transcriptomic Data ◽

Alternative Transcripts ◽

Computational Identification ◽

Selection Of

AbstractRNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

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Cellular taxonomy and spatial organization of the ventral posterior hypothalamus reveals neuroanatomical parcellation of the mammillary bodies

10.1101/2020.05.14.096818 ◽

2020 ◽

Author(s):

Laura E. Mickelsen ◽

William F. Flynn ◽

Kristen Springer ◽

Lydia Wilson ◽

Eric J. Beltrami ◽

...

Keyword(s):

Single Cell ◽

Spatial Organization ◽

Single Cells ◽

Neuronal Cell ◽

Cell Types ◽

Posterior Hypothalamus ◽

Neuronal Populations ◽

Mammillary Bodies ◽

Distinct Cell ◽

And Behavior

ABSTRACTThe ventral posterior hypothalamus (VPH) is an anatomically complex brain region implicated in arousal, reproduction, energy balance and memory processing. However, neuronal cell type diversity within the VPH is poorly understood, an impediment to deconstructing the roles of distinct VPH circuits in physiology and behavior. To address this question, we employed a droplet-based single cell RNA sequencing (scRNA-seq) approach to systematically classify molecularly distinct cell types in the mouse VPH. Analysis of >16,000 single cells revealed 20 neuronal and 18 non-neuronal cell populations, defined by suites of discriminatory markers. We validated differentially expressed genes in a selection of neuronal populations through fluorescence in situ hybridization (FISH). Focusing on the mammillary bodies (MB), we discovered transcriptionally-distinct clusters that exhibit a surprising degree of segregation within neuroanatomical subdivisions of the MB, while genetically-defined MB cell types project topographically to the anterior thalamus. This single cell transcriptomic atlas of cell types in the VPH provides a detailed resource for interrogating the circuit-level mechanisms underlying the diverse functions of VPH circuits in health and disease.

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