Automatic identification of informative regions with epigenomic changes associated to hematopoiesis

AbstractHematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify forty-two human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet

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Epigenetic mechanisms mediating cell state transitions in chondrocytes

10.1101/2020.10.26.319822 ◽

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.

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Varied interactions between proviruses and adjacent host chromatin

Molecular and Cellular Biology ◽

10.1128/mcb.6.11.3999-4007.1986 ◽

1986 ◽

Vol 6 (11) ◽

pp. 3999-4007

Author(s):

K F Conklin ◽

M Groudine

Keyword(s):

Cell Types ◽

Chromatin Conformation ◽

Cell Type ◽

Chromatin Domains ◽

Site Structure ◽

The Relationship ◽

Structure And Activity ◽

Different Levels ◽

Host Chromatin ◽

Nuclease Sensitivity

Retroviruses integrated at unique locations in the host genome can be expressed at different levels. We have analyzed the preintegration sites of three transcriptionally competent avian endogenous proviruses (evs) to determine whether the various levels of provirus expression correlate with their location in active or inactive regions of chromatin. Our results show that in three of four cell types, the chromatin conformation (as defined by relative nuclease sensitivity) of virus preintegration sites correlates with the level of expression of the resident provirus in ev+ cells: two inactive proviruses (ev-1 and ev-2) reside in nuclease-resistant chromatin domains and one active provirus (ev-3) resides in a nuclease-sensitive domain. Nuclear runoff transcription assays reveal that the preintegration sites of the active and inactive viruses are not transcribed. However, in erythrocytes of 15-day-old chicken embryos (15d RBCs), the structure and activity of the ev-3 provirus is independent of the conformation of its preintegration site. In this cell type, the ev-3 preintegration site is organized in a nuclease-resistant conformation, while the ev-3 provirus is in a nuclease-sensitive conformation and is transcribed. In addition, the nuclease sensitivity of host sequences adjacent to ev-3 is altered in ev-3+ 15d RBCs relative to that found in 15d RBCs that lack ev-3. These data suggest that the relationship between preintegration site structure and retrovirus expression is more complex than previously described.

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Accurate imputation of histone modifications using transcription

10.1101/2020.04.08.032730 ◽

2020 ◽

Cited By ~ 3

Author(s):

Zhong Wang ◽

Alexandra G. Chivu ◽

Lauren A. Choate ◽

Edward J. Rice ◽

Donald C. Miller ◽

...

Keyword(s):

Transcription Initiation ◽

Cell Types ◽

Chromatin Accessibility ◽

Cell Type ◽

Active Chromatin ◽

Histone Marks ◽

Repressive Mark ◽

Cell Type Specific ◽

The Relationship ◽

Machine Learning Tool

AbstractWe trained a sensitive machine learning tool to infer the distribution of histone marks using maps of nascent transcription. Transcription captured the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. The relationship between active histone marks and transcription was conserved in all cell types examined, allowing individual labs to annotate active functional elements in mammals with similar richness as major consortia. Using imputation as an interpretative tool uncovered cell-type specific differences in how the PRC2-dependent repressive mark, H3K27me3, corresponds to transcription, and revealed that transcription initiation requires both chromatin accessibility and an active chromatin environment demonstrating that initiation is less promiscuous than previously thought.

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Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes

10.1101/2021.11.29.470374 ◽

2021 ◽

Author(s):

Yunhee Jeong ◽

Reka Toth ◽

Marlene Ganslmeier ◽

Kersten Breuer ◽

Christoph Plass ◽

...

Keyword(s):

Cell Types ◽

Systematic Evaluation ◽

Cell Type ◽

Factors Affecting ◽

Genome Wide ◽

Cell Type Composition ◽

Type Composition ◽

Level Information ◽

Genomic Regions ◽

The Impact

DNA methylation sequencing is becoming increasingly popular, yielding genome-wide methylome data at single-base pair resolution through the novel cost- and labor-optimized protocols. It has tremendous potential for cell-type heterogeneity analysis, particularly in tumors, due to intrinsic read-level information. Although diverse deconvolution methods were developed to infer cell-type composition based on bulk sequencing-based methylomes, their systematic evaluation has not been performed so far. Here, we thoroughly review and evaluate five previously published deconvolution methods: Bayesian epiallele detection (BED), PRISM, csmFinder + coMethy, ClubCpG and MethylPurify, together with two array-based methods, MeDeCom and Houseman as a comparison group. Sequencing-based deconvolution methods consist of two main steps, informative region selection and cell-type composition estimation. Accordingly, we individually assessed the performance of each step and demonstrated the impact of the former step upon the performance of the following one. In conclusion, we demonstrate the best method showing the highest accuracy in different samples, and infer factors affecting cell-type deconvolution performance according to the number of cell types in the mixture. We found that cell-type deconvolution performance is influenced by different factors according to the number of components in the mixture. Whereas selecting similar genomic regions to DMRs generally contributed to increasing the performance in bi-component mixtures, the uniformity of cell-type distribution showed a high correlation with the performance in five cell-type bulk analyses.

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Universal principles of lineage architecture and stem cell identity in renewing tissues

10.1101/2020.03.10.984898 ◽

2020 ◽

Author(s):

Philip Greulich ◽

Ben D. MacArthur ◽

Cristina Parigini ◽

Rubén J. Sánchez-García

Keyword(s):

Stem Cell ◽

Cell Lineage ◽

Adult Stem Cell ◽

Feedback Regulation ◽

Cell Types ◽

Universal Principles ◽

Formal Framework ◽

Cell Niche ◽

Multicellular Organisms ◽

The Relationship

Adult tissues in multicellular organisms typically contain a variety of stem, progenitor and differentiated cell types arranged in a lineage hierarchy that regulates healthy tissue turnover and repair. Lineage hierarchies in disparate tissues often exhibit common features, yet the general principles regulating their architecture are not known. Here, we provide a formal framework for understanding the relationship between cell molecular ‘states’ (patterns of gene, protein expression etc. in the cell) and cell ‘types’ that uses notions from network science to decompose the structure of cell state trajectories into functional units. Using this framework we show that many widely experimentally observed features of cell lineage architectures – including the fact that a single adult stem cell type always resides at the apex of a lineage hierarchy – arise as a natural consequence of homeostasis, and indeed are the only possible way that lineage architectures can be constructed to support homeostasis in renewing tissues. Furthermore, under suitable feedback regulation, for example from the stem cell niche, we show that the property of ‘stemness’ is entirely determined by the cell environment. Thus, we argue that stem cell identities are contextual and not determined by hard-wired, cell-intrinsic, characteristics.

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Differential DNA methylation and changing cell-type proportions as fibrotic stage progresses in NAFLD

Clinical Epigenetics ◽

10.1186/s13148-021-01129-y ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Nicholas D. Johnson ◽

Xiumei Wu ◽

Christopher D. Still ◽

Xin Chu ◽

Anthony T. Petrick ◽

...

Keyword(s):

Dna Methylation ◽

Cell Types ◽

Disease Stage ◽

Rna Seq ◽

Cell Type ◽

Alcoholic Fatty Liver ◽

Cell Composition ◽

Cpg Sites ◽

Plausible Mechanism ◽

The Relationship

Abstract Background Non-alcoholic fatty liver disease (NAFLD) is characterized by changes in cell composition that occur throughout disease pathogenesis, which includes the development of fibrosis in a subset of patients. DNA methylation (DNAm) is a plausible mechanism underlying these shifts, considering that DNAm profiles differ across tissues and cell types, and DNAm may play a role in cell-type differentiation. Previous work investigating the relationship between DNAm and fibrosis in NAFLD has been limited by sample size and the number of CpG sites interrogated. Results Here, we performed an epigenome-wide analysis using Infinium MethylationEPIC array data from 325 individuals with NAFLD, including 119 with severe fibrosis and 206 with no histological evidence of fibrosis. After adjustment for latent confounders, we identified 7 CpG sites whose DNAm associated with fibrosis (p < 5.96 × 10–8). Analysis of RNA-seq data collected from a subset of individuals (N = 56) revealed that gene expression at 288 genes associated with DNAm at one or more of the 7 fibrosis-related CpGs. DNAm-based estimates of cell-type proportions showed that estimated proportions of natural killer cells increased, while epithelial cell proportions decreased with disease stage. Finally, we used an elastic net regression model to assess DNAm as a biomarker of fibrotic stage and found that our model predicted fibrosis with a sensitivity of 0.93 and provided information beyond a model based solely on cell-type proportions. Conclusion These findings are consistent with DNAm as a mechanism underpinning or marking fibrosis-related shifts in cell composition and demonstrate the potential of DNAm as a possible biomarker of NAFLD fibrosis.

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A single-cell analysis of the molecular lineage of chordate embryogenesis

Science Advances ◽

10.1126/sciadv.abc4773 ◽

2020 ◽

Vol 6 (45) ◽

pp. eabc4773

Author(s):

Tengjiao Zhang ◽

Yichi Xu ◽

Kaoru Imai ◽

Teng Fei ◽

Guilin Wang ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Cell Lineage ◽

Cell Types ◽

Gene Signature ◽

Mouse Cell ◽

Embryonic Cell ◽

Sequencing Analysis ◽

Lineage Differentiation ◽

In The Wild

Progressive unfolding of gene expression cascades underlies diverse embryonic lineage development. Here, we report a single-cell RNA sequencing analysis of the complete and invariant embryonic cell lineage of the tunicate Ciona savignyi from fertilization to the onset of gastrulation. We reconstructed a developmental landscape of 47 cell types over eight cell cycles in the wild-type embryo and identified eight fate transformations upon fibroblast growth factor (FGF) inhibition. For most FGF-dependent asymmetric cell divisions, the bipotent mother cell displays the gene signature of the default daughter fate. In convergent differentiation of the two notochord lineages, we identified additional gene pathways parallel to the master regulator T/Brachyury. Last, we showed that the defined Ciona cell types can be matched to E6.5-E8.5 stage mouse cell types and display conserved expression of limited number of transcription factors. This study provides a high-resolution single-cell dataset to understand chordate early embryogenesis and cell lineage differentiation.

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Saturating Single-Cell atlas Datasets

10.1101/218370 ◽

2017 ◽

Cited By ~ 2

Author(s):

Aparna Bhaduri ◽

Tomasz J. Nowakowski ◽

Alex A. Pollen ◽

Arnold R. Kriegstein

Keyword(s):

Population Structure ◽

Single Cell ◽

Mouse Brain ◽

Large Scale ◽

Single Cells ◽

Cost Effective ◽

Cell Types ◽

Cell Number ◽

Cell Type ◽

The Relationship

AbstractHigh throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. Efficient generation of such an atlas will depend on sufficient sampling of the diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. To examine the relationship between cell number and transcriptional heterogeneity in the context of unbiased cell type classification, we explicitly explored the population structure of a publically available 1.3 million cell dataset from the E18.5 mouse brain. We propose a computational framework for inferring the saturation point of cluster discovery in a single cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a “complexity index”, which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells (20,000). Together, these findings suggest that most of the biologically interpretable insights from the 1.3 million cells can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high “cellular coverage”, the much anticipated cell atlasing studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage.Recent efforts seek to create a comprehensive cell atlas of the human body1,2 Current technology, however, makes it precipitously expensive to perform analysis of every cell. Therefore, designing effective sampling strategies be critical to generate a working atlas in an efficient, cost-effective, and streamlined manner. The advent of single cell and single nucleus mRNA sequencing (RNAseq) in droplet format3,4 now enables large scale sampling of cells from any tissue, and a recently released publicly available dataset of 1.3 million single cells from the E18.5 mouse brain generated with the 10X Chromium5 provides an opportunity to explore the relationship between population structure and the number of sampled cells necessary to reveal the underlying diversity of cell types. Here, we present a framework for how researchers can evaluate whether a dataset has reached saturation, and we estimate how many cells would be required to generate an atlas of the sample analyzed here. This framework can be applied to any organ or cell type specific atlas for any organism.

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Single cell lineage dynamics of the endosymbiotic cell type in a soft coral Xenia species

10.1101/2019.12.12.874602 ◽

2019 ◽

Author(s):

Minjie Hu ◽

Xiaobin Zheng ◽

Chen-Ming Fan ◽

Yixian Zheng

Keyword(s):

Single Cell ◽

Immune Modulation ◽

Soft Coral ◽

Marine Ecosystem ◽

Cell Lineage ◽

Cell Types ◽

Host Cells ◽

Coral Host ◽

Cell Type ◽

Host Coral

AbstractMany hard and soft corals harbor algae for photosynthesis. The algae live inside coral cells in a specialized membrane compartment called symbiosome, which shares the photosynthetically fixed carbon with coral host cells, while host cells provide inorganic carbon for photosynthesis1. This endosymbiotic relationship is critical for corals, but increased environmental stresses are causing corals to expel their endosymbiotic algae, i.e. coral bleaching, leading to coral death and degradation of marine ecosystem2. To date, the molecular pathways that orchestrate algal recognition, uptake, and maintenance in coral cells remain poorly understood. We report chromosome-level genome assembly of a fast-growing soft coral, Xenia species (sp.)3, and its use as a model to decipher the coral-algae endosymbiosis. Single cell RNA-sequencing (scRNA-seq) identified 13 cell types, including gastrodermis and cnidocytes, in Xenia sp. Importantly, we identified the endosymbiotic cell type that expresses a unique set of genes implicated in the recognition, phagocytosis/endocytosis, maintenance of algae, and host coral cell immune modulation. By applying scRNA-seq to investigate algal uptake in our new Xenia sp.. regeneration model, we uncovered a dynamic lineage progression from endosymbiotic progenitor state to mature endosymbiotic and post-endosymbiotic cell states. The evolutionarily conserved genes associated with the endosymbiotic process reported herein open the door to decipher common principles by which different corals uptake and expel their endosymbionts. Our study demonstrates the potential of single cell analyses to examine the similarities and differences of the endosymbiotic lifestyle among different coral species.

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