Prenet: Predictive network from ATAC-SEQ data

2020 ◽  
Vol 18 (01) ◽  
pp. 2040003 ◽  
Author(s):  
Nazmus Salehin ◽  
Patrick P. L. Tam ◽  
Pierre Osteil
Keyword(s):  
Regulatory Network ◽  
State Of The Art ◽  
Cell Types ◽  
Cell Type ◽  
Regulatory Pathways ◽  
Novel Approach ◽  
Multiple Cell ◽  
Bona Fide ◽  
Gene Regulatory ◽  
Accessible Chromatin

Assays for transposase-accessible chromatin sequencing (ATAC-seq) provides an innovative approach to study chromatin status in multiple cell types. Moreover, it is also possible to efficiently extract differentially accessible chromatin (DACs) regions by using state-of-the-art algorithms (e.g. DESeq2) to predict gene activity in specific samples. Furthermore, it has recently been shown that small dips in sequencing peaks can be attributed to the binding of transcription factors. These dips, also known as footprints, can be used to identify trans-regulating interactions leading to gene expression. Current protocols used to identify footprints (e.g. pyDNAse and HINT-ATAC) have shown limitations resulting in the discovery of many false positive footprints. We generated a novel approach to identify genuine footprints within any given ATAC-seq dataset. Herein, we developed a new pipeline embedding DACs together with bona fide footprints resulting in the generation of a Predictive gene regulatory Network (PreNet) simply from ATAC-seq data. We further demonstrated that PreNet can be used to unveil meaningful molecular regulatory pathways in a given cell type.

scholarly journals Predicting gene regulatory networks from cell atlases

2020 ◽  
Vol 3 (11) ◽  
pp. e202000658 ◽  
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 ◽  
Multiple Cell ◽  
Gene Regulatory

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three 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 wild-type 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.

scholarly journals Epigenetic reprogramming of cell identity: lessons from development for regenerative medicine

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Amitava Basu ◽  
Vijay K. Tiwari
Keyword(s):  
Regenerative Medicine ◽  
Cell Types ◽  
Direct Conversion ◽  
Cellular Reprogramming ◽  
Specific Cell ◽  
Cell Type ◽  
Pathological Conditions ◽  
Gene Regulatory ◽  
Induced Pluripotent ◽  
And Function

AbstractEpigenetic mechanisms are known to define cell-type identity and function. Hence, reprogramming of one cell type into another essentially requires a rewiring of the underlying epigenome. Cellular reprogramming can convert somatic cells to induced pluripotent stem cells (iPSCs) that can be directed to differentiate to specific cell types. Trans-differentiation or direct reprogramming, on the other hand, involves the direct conversion of one cell type into another. In this review, we highlight how gene regulatory mechanisms identified to be critical for developmental processes were successfully used for cellular reprogramming of various cell types. We also discuss how the therapeutic use of the reprogrammed cells is beginning to revolutionize the field of regenerative medicine particularly in the repair and regeneration of damaged tissue and organs arising from pathological conditions or accidents. Lastly, we highlight some key challenges hindering the application of cellular reprogramming for therapeutic purposes.

scholarly journals BRANE Cut: Biologically-Related A priori Network Enhancement with Graph cuts for Gene Regulatory Network Inference

2015 ◽  
Author(s):  
Aurélie Pirayre ◽  
Camille Couprie ◽  
Frédérique Bidard ◽  
Laurent Duval ◽  
Jean-Christophe Pesquet
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Networks ◽  
Network Inference ◽  
State Of The Art ◽  
A Priori ◽  
Graph Cuts ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Inference Methods

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions. Methods: Weighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge. Results: Our BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6% to 11%). On a real Escherichia coli compendium, an improvement of 11.8% compared to CLR and 3% compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-cut.html Conclusions: BRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the-art GRN inference methods. It is applicable as a generic network inference post-processing, due its computational efficiency.

scholarly journals Profound functional and molecular diversity of mitochondria revealed by cell type-specific profiling in vivo

2018 ◽  
Author(s):  
Caroline Fecher ◽  
Laura Trovò ◽  
Stephan A. Müller ◽  
Nicolas Snaidero ◽  
Jennifer Wettmarshausen ◽  
...  
Keyword(s):  
Molecular Diversity ◽  
Molecular Level ◽  
Cell Types ◽  
Long Chain Fatty Acids ◽  
Cell Type ◽  
Mitochondrial Proteome ◽  
Novel Approach ◽  
Cell Type Specific ◽  
And Function

AbstractMitochondria vary in morphology and function in different tissues, however little is known about their molecular diversity among cell types. To investigate mitochondrial diversity in vivo, we developed an efficient protocol to isolate cell type-specific mitochondria based on a new MitoTag mouse. We profiled the mitochondrial proteome of three major neural cell types in cerebellum and identified a substantial number of differential mitochondrial markers for these cell types in mice and humans. Based on predictions from these proteomes, we demonstrate that astrocytic mitochondria metabolize long-chain fatty acids more efficiently than neurons. Moreover, we identified Rmdn3 as a major determinant of ER-mitochondria proximity in Purkinje cells. Our novel approach enables exploring mitochondrial diversity on the functional and molecular level in many in vivo contexts.

scholarly journals Comparative cellular analysis of motor cortex in human, marmoset and mouse

Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 111-119 ◽  
Author(s):  
Trygve E. Bakken ◽  
Nikolas L. Jorstad ◽  
Qiwen Hu ◽  
Blue B. Lake ◽  
Wei Tian ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Neuronal Cell ◽  
Cell Types ◽  
Morphological Characterization ◽  
Fine Motor ◽  
Marker Genes ◽  
Cell Type ◽  
Regulatory Pathways ◽  
Cellular Analysis

AbstractThe primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

scholarly journals Complex Cell Type-Specific Roles of Autophagy in Liver Fibrosis and Cirrhosis

Pathogens ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 225
Author(s):  
Tzu-Min Hung ◽  
Chih-Chiang Hsiao ◽  
Chih-Wen Lin ◽  
Po-Huang Lee
Keyword(s):  
Liver Fibrosis ◽  
Cell Types ◽  
Degradation Pathway ◽  
Cellular Tissue ◽  
Future Research ◽  
Specific Cell ◽  
Cell Type ◽  
Stage Disease ◽  
Multiple Cell ◽  
End Stage

The lysosomal degradation pathway, or autophagy, plays a fundamental role in cellular, tissue, and organismal homeostasis. A correlation between dysregulated autophagy and liver fibrosis (including end-stage disease, cirrhosis) is well-established. However, both the up and downregulation of autophagy have been implicated in fibrogenesis. For example, the inhibition of autophagy in hepatocytes and macrophages can enhance liver fibrosis, whereas autophagic activity in hepatic stellate cells and reactive ductular cells is permissive towards fibrogenesis. In this review, the contributions of specific cell types to liver fibrosis as well as the mechanisms underlying the effects of autophagy are summarized. In view of the functional effects of multiple cell types on the complex process of hepatic fibrogenesis, integrated approaches that consider the role of autophagy in each liver cell type should be a focus of future research.

scholarly journals Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhe Cui ◽  
Ya Cui ◽  
Yan Gao ◽  
Tao Jiang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Support Vector ◽  
Cell Type ◽  
Peripheral Blood Mononuclear ◽  
Copy Numbers ◽  
Genome Wide ◽  
Accessible Chromatin

Single-cell Assay Transposase Accessible Chromatin sequencing (scATAC-seq) has been widely used in profiling genome-wide chromatin accessibility in thousands of individual cells. However, compared with single-cell RNA-seq, the peaks of scATAC-seq are much sparser due to the lower copy numbers (diploid in humans) and the inherent missing signals, which makes it more challenging to classify cell type based on specific expressed gene or other canonical markers. Here, we present svmATAC, a support vector machine (SVM)-based method for accurately identifying cell types in scATAC-seq datasets by enhancing peak signal strength and imputing signals through patterns of co-accessibility. We applied svmATAC to several scATAC-seq data from human immune cells, human hematopoietic system cells, and peripheral blood mononuclear cells. The benchmark results showed that svmATAC is free of literature-based markers and robust across datasets in different libraries and platforms. The source code of svmATAC is available at https://github.com/mrcuizhe/svmATAC under the MIT license.

scholarly journals SMNN: Batch Effect Correction for Single-cell RNA-seq data via Supervised Mutual Nearest Neighbor Detection

2019 ◽  
Author(s):  
Yuchen Yang ◽  
Gang Li ◽  
Huijun Qian ◽  
Kirk C. Wilhelmsen ◽  
Yin Shen ◽  
...  
Keyword(s):  
Single Cell ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Cell Types ◽  
Batch Effect ◽  
Batch Effects ◽  
Cell Type ◽  
Label Information ◽  
Cell Type Specific

AbstractBatch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3, and LIGER. Furthermore, SMNN retains more cell type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841%.Key PointsBatch effect correction has been recognized to be critical when integrating scRNA-seq data from multiple batches due to systematic differences in time points, generating laboratory and/or handling technician(s), experimental protocol, and/or sequencing platform.Existing batch effect correction methods that leverages information from mutual nearest neighbors across batches (for example, implemented in SC3 or Seurat) ignore cell type information and suffer from potentially mismatching single cells from different cell types across batches, which would lead to undesired correction results, especially under the scenario where variation from batch effects is non-negligible compared with biological effects.To address this critical issue, here we present SMNN, a supervised machine learning method that first takes cluster/cell-type label information from users or inferred from scRNA-seq clustering, and then searches mutual nearest neighbors within each cell type instead of global searching.Our SMNN method shows clear advantages over three state-of-the-art batch effect correction methods and can better mix cells of the same cell type across batches and more effectively recover cell-type specific features, in both simulations and real datasets.

scholarly journals MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector): a statistical framework for identifying long-range chromatin interactions from multiple cell types

2020 ◽  
Author(s):  
Weifang Liu ◽  
Armen Abnousi ◽  
Qian Zhang ◽  
Yun Li ◽  
Ming Hu ◽  
...  
Keyword(s):  
Long Range ◽  
Spatial Organization ◽  
Critical Role ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Cell Type ◽  
Statistical Framework ◽  
Chromatin Interactions ◽  
The Status ◽  
Multiple Cell

AbstractChromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple cell types. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other cell types. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting cell-type-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-cell-type analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple cell types.

scholarly journals Dynamic regulatory module networks for inference of cell type specific transcriptional networks

2020 ◽  
Author(s):  
Alireza Fotuhi Siahpirani ◽  
Deborah Chasman ◽  
Morten Seirup ◽  
Sara Knaack ◽  
Rupa Sridharan ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Dynamics ◽  
Cell Types ◽  
Small Sample ◽  
Cell Type ◽  
Regulatory Module ◽  
A Cell ◽  
Cell Type Specific ◽  
Time Point

AbstractChanges in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled transcriptomes and epigenomes at different stages of a developmental process. However, integrating these data across multiple cell types to infer cell type specific regulatory networks is a major challenge because of the small sample size for each time point. We present a novel approach, Dynamic Regulatory Module Networks (DRMNs), to model regulatory network dynamics on a cell lineage. DRMNs represent a cell type specific network by a set of expression modules and associated regulatory programs, and probabilistically model the transitions between cell types. DRMNs learn a cell type’s regulatory network from input expression and epigenomic profiles using multi-task learning to exploit cell type relatedness. We applied DRMNs to study regulatory network dynamics in two different developmental dynamic processes including cellular reprogramming and liver dedifferentiation. For both systems, DRMN predicted relevant regulators driving the major patterns of expression in each time point as well as regulators for transitioning gene sets that change their expression over time.

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