scholarly journals Modeling and Visualizing Cell Type Switching

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ahmadreza Ghaffarizadeh ◽  
Gregory J. Podgorski ◽  
Nicholas S. Flann
Keyword(s):  
Expression Profiles ◽  
Control Cell ◽  
Three Dimensional ◽  
Myeloid Cell ◽  
Cell Types ◽  
State Transitions ◽  
Cell Type ◽  
New Approach ◽  
Gene Expression Noise ◽  
Lineage Trees

Understanding cellular differentiation is critical in explaining development and for taming diseases such as cancer. Differentiation is conventionally represented using bifurcating lineage trees. However, these lineage trees cannot readily capture or quantify all the types of transitions now known to occur between cell types, including transdifferentiation or differentiation off standard paths. This work introduces a new analysis and visualization technique that is capable of representing all possible transitions between cell states compactly, quantitatively, and intuitively. This method considers the regulatory network of transcription factors that control cell type determination and then performs an analysis of network dynamics to identify stable expression profiles and the potential cell types that they represent. A visualization tool calledCellDiff3Dcreates an intuitive three-dimensional graph that shows the overall direction and probability of transitions between all pairs of cell types within a lineage. In this study, the influence of gene expression noise and mutational changes during myeloid cell differentiation are presented as a demonstration of theCellDiff3Dtechnique, a new approach to quantify and envision all possible cell state transitions in any lineage network.

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

scholarly journals Epigenetic mechanisms mediating cell state transitions in chondrocytes

2020 ◽  
Author(s):  
Manuela Wuelling ◽  
Christoph Neu ◽  
Andrea M. Thiesen ◽  
Simo Kitanovski ◽  
Yingying Cao ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Cell Lineage ◽  
Cell Types ◽  
State Transitions ◽  
Epigenetic Mechanisms ◽  
Cell Type ◽  
Transition Analysis ◽  
Lineage Differentiation ◽  
A Cell ◽  
Cell Type Specific

AbstractEpigenetic modifications play critical roles in regulating cell lineage differentiation, but the epigenetic mechanisms guiding specific differentiation steps within a cell lineage have rarely been investigated. To decipher such mechanisms, we used the defined transition from proliferating (PC) into hypertrophic chondrocytes (HC) during endochondral ossification as a model. We established a map of activating and repressive histone modifications for each cell type. ChromHMM state transition analysis and Pareto-based integration of differential levels of mRNA and epigenetic marks revealed that differentiation associated gene repression is initiated by the addition of H3K27me3 to promoters still carrying substantial levels of activating marks. Moreover, the integrative analysis identified genes specifically expressed in cells undergoing the transition into hypertrophy.Investigation of enhancer profiles detected surprising differences in enhancer number, location, and transcription factor binding sites between the two closely related cell types. Furthermore, cell type-specific upregulation of gene expression was associated with a shift from low to high H3K27ac decoration. Pathway analysis identified PC-specific enhancers associated with chondrogenic genes, while HC-specific enhancers mainly control metabolic pathways linking epigenetic signature to biological functions.

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽  
2018 ◽  
Vol 38 (13) ◽  
pp. 1976-1983 ◽  
Author(s):  
William Renthal
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Cell Types ◽  
Susceptibility Genes ◽  
Brain Cell ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

scholarly journals Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Julien Racle ◽  
Kaat de Jonge ◽  
Petra Baumgaertner ◽  
Daniel E Speiser ◽  
David Gfeller
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Response To Therapy ◽  
Expression Data ◽  
Cell Type ◽  
Tumor Gene Expression ◽  
Tumor Gene

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

scholarly journals Multimodal cell type correspondence by intersectional mFISH in intact tissues

2019 ◽  
Author(s):  
Philip R. Nicovich ◽  
Michael J. Taormina ◽  
Christopher A. Baker ◽  
Thuc Nghi Nguyen ◽  
Elliot R. Thomsen ◽  
...  
Keyword(s):  
Single Cells ◽  
Morphological Characteristics ◽  
Cell Types ◽  
Synaptic Connectivity ◽  
Cell Type ◽  
New Approach ◽  
Two Photon ◽  
Mouse Cortex ◽  
Complete Set

AbstractDefining a complete set of cell types within the cortex requires reconciling disparate results achieved through diverging methodologies. To address this correspondence problem, multiple methodologies must be applied to the same cells across multiple single-cell experiments. Here we present a new approach applying spatial transcriptomics using multiplexed fluorescencein situhybridization, (mFISH) to brain tissue previously interrogated through two photon optogenetic mapping of synaptic connectivity. This approach can resolve the anatomical, transcriptomic, connectomic, electrophysiological, and morphological characteristics of single cells within the mouse cortex.

scholarly journals Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bárbara Andrade Barbosa ◽  
Saskia D. van Asten ◽  
Ji Won Oh ◽  
Arantza Farina-Sarasqueta ◽  
Joanne Verheij ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Expression Variability ◽  
Variable Nature ◽  
Log Normal

AbstractDeconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.

scholarly journals MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps

2018 ◽  
Author(s):  
Yusen Ye ◽  
Lin Gao ◽  
Shihua Zhang
Keyword(s):  
Efficient Method ◽  
Three Dimensional ◽  
Cell Types ◽  
Cell Type ◽  
Range Interaction ◽  
Distinct Cell Type ◽  
Chromosome Conformation ◽  
Topological Domains ◽  
Multi Scale ◽  
Cell Type Specific

AbstractThe chromosome conformation capture (3C) technique and its variants have been employed to reveal the existence of a hierarchy of structures in three-dimensional (3D) chromosomal architecture, including compartments, topologically associating domains (TADs), sub-TADs and chromatin loops. However, existing methods for domain detection were only designed based on symmetric Hi-C maps, ignoring long-range interaction structures between domains. To this end, we proposed a generic and efficient method to identify multi-scale topological domains (MSTD), including cis- and trans-interacting regions, from a variety of 3D genomic datasets. We first applied MSTD to detect promoter-anchored interaction domains (PADs) from promoter capture Hi-C datasets across 17 primary blood cell types. The boundaries of PADs are significantly enriched with one or the combination of multiple epigenetic factors. Moreover, PADs between functionally similar cell types are significantly conserved in terms of domain regions and expression states. Cell type-specific PADs involve in distinct cell type-specific activities and regulatory events by dynamic interactions within them. We also employed MSTD to define multi-scale domains from typical symmetric Hi-C datasets and illustrated its distinct superiority to the-state-of-art methods in terms of accuracy, flexibility and efficiency.

scholarly journals Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2018 ◽  
Author(s):  
R. Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Tree Structures ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

AbstractAdvances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. Consequently, it becomes possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data and further impute temporal gene expression profiles along the reconstructed tree structures. We demonstrate MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

scholarly journals Probing transcription factor combinatorics in different promoter classes and in enhancers

2017 ◽  
Author(s):  
Jimmy Vandel ◽  
Océane Cassan ◽  
Sophie Lèbre ◽  
Charles-Henri Lecellier ◽  
Laurent Bréhélin
Keyword(s):  
Binding Sites ◽  
Target Genes ◽  
Cell Types ◽  
Model Parameters ◽  
Binding Motif ◽  
Cell Type ◽  
Regulatory Regions ◽  
New Approach ◽  
Cell Type Specific ◽  
Common Target

In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combina-tions among different gene classes, regulatory regions and cell types. We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a tar-get TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combina-tions involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for pre-dicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in pro-moters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and en-hancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the dif-ferent targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately dis-tinguish promoters associated with specific biological processes.

scholarly journals BLADE: Bayesian Log-normAl DEconvolution for enhanced in silico microdissection of bulk gene expression data

2020 ◽  
Author(s):  
Bárbara Andrade Barbosa ◽  
Saskia van Asten ◽  
Ji-won Oh ◽  
Arantza Fariña-Sarasqueta ◽  
Joanne Verheij ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Expression Variability ◽  
Variable Nature ◽  
Log Normal

Abstract High-resolution deconvolution of bulk gene expression profiles is pivotal to characterize the complex cellular make-up of tissues, such as tumor microenvironment. Single-cell RNA-seq provides reliable prior knowledge for deconvolution, however, a comprehensive statistical model is required for efficient utilization due to the inherently variable nature of gene expression. We introduce BLADE (Bayesian Log-normAl Deconvolution), a comprehensive probabilistic framework to estimate both cellular make-up and gene expression profiles of each cell type in each sample. Unlike previous comprehensive statistical approaches, BLADE can handle >20 cell types thanks to the efficient variational inference. Throughout an intensive evaluation using >700 datasets, BLADE showed enhanced robustness against gene expression variability and better completeness than conventional methods, in particular to reconstruct gene expression profiles of each cell type. All-in-all, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems based on standard bulk gene expression data.

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