scholarly journals Cell-specific alterations in Pitx1 regulatory landscape activation caused by the loss of a single enhancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Raquel Rouco ◽  
Olimpia Bompadre ◽  
Antonella Rauseo ◽  
Olivier Fazio ◽  
Rodrigue Peraldi ◽  
...  
Keyword(s):  
Connective Tissue ◽  
Single Cell ◽  
Regulatory Elements ◽  
Embryo Cell ◽  
Developmental Genes ◽  
Cell Tracing ◽  
Cellular Identity ◽  
Regulatory Landscape ◽  
Regulatory Landscapes

AbstractDevelopmental genes are frequently controlled by multiple enhancers sharing similar specificities. As a result, deletions of such regulatory elements have often failed to reveal their full function. Here, we use the Pitx1 testbed locus to characterize in detail the regulatory and cellular identity alterations following the deletion of one of its enhancers (Pen). By combining single cell transcriptomics and an in-embryo cell tracing approach, we observe an increased fraction of Pitx1 non/low-expressing cells and a decreased fraction of Pitx1 high-expressing cells. We find that the over-representation of Pitx1 non/low-expressing cells originates from a failure of the Pitx1 locus to coordinate enhancer activities and 3D chromatin changes. This locus mis-activation induces a localized heterochrony and a concurrent loss of irregular connective tissue, eventually leading to a clubfoot phenotype. This data suggests that, in some cases, redundant enhancers may be used to locally enforce a robust activation of their host regulatory landscapes.

scholarly journals Cell-specific alterations inPitx1regulatory landscape activation caused by the loss of a single enhancer

2021 ◽  
Author(s):  
Raquel Rouco ◽  
Olimpia Bompadre ◽  
Antonella Rauseo ◽  
Olivier Fazio ◽  
Fabrizio Thorel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Connective Tissue ◽  
Partial Loss ◽  
Transcriptional Enhancers ◽  
Cell Tracing ◽  
Accurate Expression ◽  
Cellular Identity ◽  
Underlying Mechanisms ◽  
Regulatory Landscapes

AbstractMost developmental genes rely on multiple transcriptional enhancers for their accurate expression during embryogenesis. Because enhancers may have partially redundant activities, the loss of one of them often leads to a partial loss of gene expression and concurrent moderate phenotypic outcome, if any. While such a phenomenon has been observed in many instances, the nature of the underlying mechanisms remains elusive. We used thePitx1testbed locus to characterize in detail the regulatory and cellular identity alterations following the deletionin vivoof one of its enhancers (Pen), which normally accounts for 30 percent ofPitx1expression in hindlimb buds. By combining single cell transcriptomics and a novelin embryocell tracing approach, we observed that this global decrease inPitx1expression results from both an increase in the number of non- or low-expressing cells, and a decrease in the number of high-expressing cells. We found that the over-representation ofPitx1non/low-expressing cells originates from a failure of thePitx1locus to coordinate enhancer activities and 3D chromatin changes. The resulting increase inPitx1non/low-expressing cells eventually affects the proximal limb more severely than the distal limb, leading to a clubfoot phenotype likely produced through a localized heterochrony and concurrent loss of irregular connective tissue. This data suggests that, in some cases, redundant enhancers may be used to locally enforce a robust activation of their host regulatory landscapes.

scholarly journals Single-cell ATAC-seq Signal Extraction and Enhancement with SCATE

2019 ◽  
Author(s):  
Zhicheng Ji ◽  
Weiqiang Zhou ◽  
Hongkai Ji
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Single Cells ◽  
Regulatory Elements ◽  
Single Cell Sequencing ◽  
Statistical Framework ◽  
Genome Wide ◽  
Cell Subpopulations ◽  
Accessible Chromatin ◽  
Regulatory Landscape

AbstractSingle-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscape in single cells. Single-cell ATAC-seq data are sparse and noisy. Analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We show that using SCATE, one can better reconstruct the regulatory landscape of a heterogeneous sample.

scholarly journals Complexity and conservation of regulatory landscapes underlie evolutionary resilience of mammalian gene expression

2017 ◽  
Author(s):  
Camille Berthelot ◽  
Diego Villar ◽  
Julie E. Horvath ◽  
Duncan T. Odom ◽  
Paul Flicek
Keyword(s):  
Gene Expression ◽  
Regulatory Elements ◽  
List Type ◽  
Enhancer Activity ◽  
Expression Levels ◽  
Mammalian Gene ◽  
Highly Expressed Genes ◽  
Regulatory Landscape ◽  
Regulatory Landscapes ◽  
Mammalian Gene Expression

AbstractTo gain insight into how mammalian gene expression is controlled by rapidly evolving regulatory elements, we jointly analysed promoter and enhancer activity with downstream transcription levels in liver samples from twenty species. Genes associated with complex regulatory landscapes generally exhibit high expression levels that remain evolutionarily stable. While the number of regulatory elements is the key driver of transcriptional output and resilience, regulatory conservation matters: elements active across mammals most effectively stabilise gene expression. In contrast, recently-evolved enhancers typically contribute weakly, consistent with their high evolutionary plasticity. These effects are observed across the entire mammalian clade and robust to potential confounders, such as gene expression level. Overall, our results illuminate how the evolutionary stability of gene expression is profoundly entwined with both the number and conservation of surrounding promoters and enhancers.HighlightsGene expression levels and stability are linked to the number of elements in the regulatory landscape.Conserved regulatory elements associate with tightly controlled, highly expressed genes.Recently evolved enhancers weakly influence gene expression, but promoters are similarly active regardless of conservation.The interplay between complexity of the regulatory landscape and conservation of individual promoters and enhancers shapes gene expression in mammals.

scholarly journals PeakVI: A Deep Generative Model for Single Cell Chromatin Accessibility Analysis

2021 ◽  
Author(s):  
Tal Ashuach ◽  
Daniel A Reidenbach ◽  
Adam Gayoso ◽  
Nir Yosef
Keyword(s):  
Single Cell ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Quality Data ◽  
Library Size ◽  
Single Region ◽  
Latent Space ◽  
Biological Heterogeneity ◽  
Public Datasets ◽  
Regulatory Landscape

Single-cell ATAC sequencing (scATAC-seq) is a powerful and increasingly popular technique to explore the regulatory landscape of heterogeneous cellular populations. However, the high noise levels, degree of sparsity, and scale of the generated data make its analysis challenging. Here we present PeakVI, a probabilistic framework that leverages deep neural networks to analyze scATAC-seq data. PeakVI fits an informative latent space that preserves biological heterogeneity while correcting batch effects and accounting for technical effects such as library size and region-specific biases. Additionally, PeakVI provides a technique for identifying differential accessibility at a single region resolution, which can be used for cell-type annotation as well as identification of key cis-regulatory elements. We use public datasets to demonstrate that PeakVI is scalable, stable, robust to low-quality data, and outperforms current analysis methods on a range of critical analysis tasks. PeakVI is publicly available and implemented in the scvi-tools framework: https://docs.scvi-tools.org

scholarly journals Transcriptional Regulation of RUNX1: An Informatics Analysis

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1175
Author(s):  
Amarni L. Thomas ◽  
Judith Marsman ◽  
Jisha Antony ◽  
William Schierding ◽  
Justin M. O’Sullivan ◽  
...  
Keyword(s):  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Runx1 Gene ◽  
Gene Regulatory Elements ◽  
Important Regulator ◽  
Aml1 Gene ◽  
Regulatory Landscape

The RUNX1/AML1 gene encodes a developmental transcription factor that is an important regulator of haematopoiesis in vertebrates. Genetic disruptions to the RUNX1 gene are frequently associated with acute myeloid leukaemia. Gene regulatory elements (REs), such as enhancers located in non-coding DNA, are likely to be important for Runx1 transcription. Non-coding elements that modulate Runx1 expression have been investigated over several decades, but how and when these REs function remains poorly understood. Here we used bioinformatic methods and functional data to characterise the regulatory landscape of vertebrate Runx1. We identified REs that are conserved between human and mouse, many of which produce enhancer RNAs in diverse tissues. Genome-wide association studies detected single nucleotide polymorphisms in REs, some of which correlate with gene expression quantitative trait loci in tissues in which the RE is active. Our analyses also suggest that REs can be variant in haematological malignancies. In summary, our analysis identifies features of the RUNX1 regulatory landscape that are likely to be important for the regulation of this gene in normal and malignant haematopoiesis.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

scholarly journals A generalization of t-SNE and UMAP to single-cell multimodal omics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Van Hoan Do ◽  
Stefan Canzar
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Types ◽  
High Dimensional ◽  
Omics Data ◽  
Relative Contribution ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Concise Representation ◽  
Cellular Identity

AbstractEmerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes.

scholarly journals Tailoring Cardiac Synthetic Transcriptional Modulation Towards Precision Medicine

2022 ◽  
Vol 8 ◽  
Author(s):  
Eric Schoger ◽  
Sara Lelek ◽  
Daniela Panáková ◽  
Laura Cecilia Zelarayán
Keyword(s):  
Gene Expression ◽  
Precision Medicine ◽  
Single Cell ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Regulatory Elements ◽  
Comprehensive Overview ◽  
Endogenous Gene ◽  
Cell Technologies ◽  
Organ Systems

Molecular and genetic differences between individual cells within tissues underlie cellular heterogeneities defining organ physiology and function in homeostasis as well as in disease states. Transcriptional control of endogenous gene expression has been intensively studied for decades. Thanks to a fast-developing field of single cell genomics, we are facing an unprecedented leap in information available pertaining organ biology offering a comprehensive overview. The single-cell technologies that arose aided in resolving the precise cellular composition of many organ systems in the past years. Importantly, when applied to diseased tissues, the novel approaches have been immensely improving our understanding of the underlying pathophysiology of common human diseases. With this information, precise prediction of regulatory elements controlling gene expression upon perturbations in a given cell type or a specific context will be realistic. Simultaneously, the technological advances in CRISPR-mediated regulation of gene transcription as well as their application in the context of epigenome modulation, have opened up novel avenues for targeted therapy and personalized medicine. Here, we discuss the fast-paced advancements during the recent years and the applications thereof in the context of cardiac biology and common cardiac disease. The combination of single cell technologies and the deep knowledge of fundamental biology of the diseased heart together with the CRISPR-mediated modulation of gene regulatory networks will be instrumental in tailoring the right strategies for personalized and precision medicine in the near future. In this review, we provide a brief overview of how single cell transcriptomics has advanced our knowledge and paved the way for emerging CRISPR/Cas9-technologies in clinical applications in cardiac biomedicine.

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

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.

scholarly journals Genomic characterization of the adolescent idiopathic scoliosis associated transcriptome and regulome

2020 ◽  
Author(s):  
Nadja Makki ◽  
Jingjing Zhao ◽  
Zhaoyang Liu ◽  
Walter L. Eckalbar ◽  
Aki Ushiki ◽  
...  
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Connective Tissue ◽  
Idiopathic Scoliosis ◽  
Association Studies ◽  
Regulatory Elements ◽  
Intervertebral Discs ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Enhancer Activity ◽  
Genome Wide

AbstractAdolescent idiopathic scoliosis (AIS), a sideways curvature of the spine, is the most common pediatric musculoskeletal disorder, affecting ∼3% of the population worldwide. However, its genetic bases and tissues of origin remain largely unknown. Several genome-wide association studies (GWAS) have implicated nucleotide variants in noncoding sequences that control genes with important roles in cartilage, muscle, bone, connective tissue and intervertebral discs (IVDs) as drivers of AIS susceptibility. Here, we set out to define the expression of AIS-associated genes and active regulatory elements by performing RNA-seq and ChIP-seq against H3K27ac in these tissues in mouse and human. Our study highlights genetic pathways involving AIS-associated loci that regulate chondrogenesis, IVD development and connective tissue maintenance and homeostasis. In addition, we identify thousands of putative AIS-associated regulatory elements which may orchestrate tissue-specific expression in musculoskeletal tissues of the spine. Quantification of enhancer activity of several candidate regulatory elements from our study identifies three functional enhancers carrying AIS-associated GWAS SNPs at the ADGRG6 and BNC2 loci. Our findings provide a novel genome-wide catalog of AIS-relevant genes and regulatory elements and aid in the identification of novel targets for AIS causality and treatment.

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