scholarly journals Comparative DNA methylation analysis to decipher common and cell type-specific patterns among multiple cell types

2016 ◽  
pp. elw013 ◽  
Author(s):  
Xiaofei Yang ◽  
Xiaojian Shao ◽  
Lin Gao ◽  
Shihua Zhang
Keyword(s):  
Dna Methylation ◽  
Cell Types ◽  
Cell Type ◽  
Methylation Analysis ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Dna Methylation Analysis

scholarly journals Differences in DNA methylation of white blood cell types at birth and in adulthood reflect postnatal immune maturation and influence accuracy of cell type prediction

2018 ◽  
Author(s):  
Meaghan J Jones ◽  
Louie Dinh ◽  
Hamid Reza Razzaghian ◽  
Olivia de Goede ◽  
Julia L MacIsaac ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Immune System ◽  
Blood Cell ◽  
Cord Blood ◽  
White Blood Cell ◽  
Blood Cells ◽  
Cell Types ◽  
Cell Type ◽  
Cell Type Specific ◽  
The Impact

AbstractBackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.ResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naïve status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.ConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.

scholarly journals eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data

2019 ◽  
Vol 35 (22) ◽  
pp. 4767-4769 ◽  
Author(s):  
Charles E Breeze ◽  
Alex P Reynolds ◽  
Jenny van Dongen ◽  
Ian Dunham ◽  
John Lazar ◽  
...  
Keyword(s):  
Supplementary Information ◽  
Supplementary Data ◽  
Cell Type ◽  
Web Tool ◽  
Methylation Analysis ◽  
450K Array ◽  
Composition Effects ◽  
Epigenome Editing ◽  
Cell Type Specific ◽  
Dna Methylation Analysis

Abstract Summary The Illumina Infinium EPIC BeadChip is a new high-throughput array for DNA methylation analysis, extending the earlier 450k array by over 400 000 new sites. Previously, a method named eFORGE was developed to provide insights into cell type-specific and cell-composition effects for 450k data. Here, we present a significantly updated and improved version of eFORGE that can analyze both EPIC and 450k array data. New features include analysis of chromatin states, transcription factor motifs and DNase I footprints, providing tools for epigenome-wide association study interpretation and epigenome editing. Availability and implementation eFORGE v2.0 is implemented as a web tool available from https://eforge.altiusinstitute.org and https://eforge-tf.altiusinstitute.org/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Cell-type-specific alternative polyadenylation (APA) genes reveal the function of dynamic APA in complex tissues

2020 ◽  
Author(s):  
Yulong Bai ◽  
Yidi Qin ◽  
Zhenjiang Fan ◽  
Robert M. Morrison ◽  
KyongNyon Nam ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Peripheral Blood ◽  
Human Peripheral Blood ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Cell Type ◽  
Simulation Data ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Brain Data

ABSTRACTAlternative polyadenylation (APA) causes shortening or lengthening of the 3’-untranslated region (3’-UTR) of genes across multiple cell types. Bioinformatic tools have been developed to identify genes that are affected by APA (APA genes) in single-cell RNA-Seq (scRNA-Seq) data. However, they suffer from low power, and they cannot identify APA genes specific to each cell type (cell-type-specific APA) when multiple cell types are analyzed. To address these limitations, we developed scMAPA that systematically integrates two novel steps. First, scMAPA quantifies 3’-UTR long and short isoforms without requiring assumptions on the read density shape of input data. Second, scMAPA estimates the significance of the APA genes for each cell type while controlling confounders. In the analyses on our novel simulation data and human peripheral blood mono cellular data, scMAPA showed enhanced power in identifying APA genes. Further, in mouse brain data, scMAPA identifies cell-type-specific APA genes, improving interpretability for the cell-type-specific function of APA. We further showed that this improved interpretability helps to understand a novel role of APA on the interaction between neurons and blood vessels, which is critical to maintaining the operational condition of brains. With high sensitivity and interpretability, scMAPA shed novel insights into the function of dynamic APA in complex tissues.Key PointsWe developed a bioinformatic tool, scMAPA, that identifies dynamic APA across multiple cell types and a novel simulation pipeline to assess performance of such tools in APA calling.In simulation data of various scenarios from our novel simulation pipeline, scMAPA achieves sensitivity with a minimal loss of specificity.In human peripheral blood monocellular data, scMAPA identifies APA genes accurately and robustly, finding unique associations of APA with hematological processes.scMAPA identifies APA genes specific to each cell type in mouse brain data while controlling confounders that sheds novel insights into the complex molecular processes.

scholarly journals A Single Cell Transcriptomic Atlas Characterizes Aging Tissues in the Mouse

2019 ◽  
Author(s):  
◽  
Angela Oliveira Pisco ◽  
Aaron McGeever ◽  
Nicholas Schaum ◽  
Jim Karkanias ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Cellular Processes ◽  
Progressive Loss ◽  
Age Related ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Molecular Information ◽  
Insight Into

AbstractAging is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death1. Despite rapid advances over recent years, many of the molecular and cellular processes which underlie progressive loss of healthy physiology are poorly understood2. To gain a better insight into these processes we have created a single cell transcriptomic atlas across the life span of Mus musculus which includes data from 23 tissues and organs. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs. Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of aging, such as senescence3, genomic instability4 and changes in the organism’s immune system2. This Tabula Muris Senis provides a wealth of new molecular information about how the most significant hallmarks of aging are reflected in a broad range of tissues and cell types.

scholarly journals Cell-type-specific DNA methylation analysis of the frontal cortices of mutant Polg1 transgenic mice with neuronal accumulation of deleted mitochondrial DNA

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Hiroko Sugawara ◽  
Miki Bundo ◽  
Takaoki Kasahara ◽  
Yutaka Nakachi ◽  
Junko Ueda ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mitochondrial Dna ◽  
Transgenic Mice ◽  
Molecular Motor ◽  
Methylation Analysis ◽  
Behavioral Abnormalities ◽  
Gene Environment ◽  
Postmortem Brains ◽  
Cell Type Specific ◽  
Dna Methylation Analysis

AbstractBipolar disorder (BD) is a severe psychiatric disorder characterized by repeated conflicting manic and depressive states. In addition to genetic factors, complex gene–environment interactions, which alter the epigenetic status in the brain, contribute to the etiology and pathophysiology of BD. Here, we performed a promoter-wide DNA methylation analysis of neurons and nonneurons derived from the frontal cortices of mutant Polg1 transgenic (n = 6) and wild-type mice (n = 6). The mutant mice expressed a proofreading-deficient mitochondrial DNA (mtDNA) polymerase under the neuron-specific CamK2a promoter and showed BD-like behavioral abnormalities, such as activity changes and altered circadian rhythms. We identified a total of 469 differentially methylated regions (DMRs), consisting of 267 neuronal and 202 nonneuronal DMRs. Gene ontology analysis of DMR-associated genes showed that cell cycle-, cell division-, and inhibition of peptide activity-related genes were enriched in neurons, whereas synapse- and GABA-related genes were enriched in nonneurons. Among the DMR-associated genes, Trim2 and Lrpprc showed an inverse relationship between DNA methylation and gene expression status. In addition, we observed that mutant Polg1 transgenic mice shared several features of DNA methylation changes in postmortem brains of patients with BD, such as dominant hypomethylation changes in neurons, which include hypomethylation of the molecular motor gene and altered DNA methylation of synapse-related genes in nonneurons. Taken together, the DMRs identified in this study will contribute to understanding the pathophysiology of BD from an epigenetic perspective.

scholarly journals Virtual methylome dissection facilitated by single-cell analyses

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Liduo Yin ◽  
Yanting Luo ◽  
Xiguang Xu ◽  
Shiyu Wen ◽  
Xiaowei Wu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Nonnegative Matrix ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Mixed Cell ◽  
Numerous Cell ◽  
Cell Type Specific ◽  
Methylation Patterns

Abstract Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

scholarly journals ‘Chromatic’ Neuronal Jamming in a Primitive Brain

2018 ◽  
Author(s):  
Margarita Khariton ◽  
Xian Kong ◽  
Jian Qin ◽  
Bo Wang
Keyword(s):  
Cell Types ◽  
Physical Contact ◽  
Specific Interactions ◽  
Cell Type ◽  
Cell Diversity ◽  
Multiple Cell ◽  
Cell Type Specific ◽  
Inanimate Matter ◽  
The Brain ◽  
Jammed State

ABSTRACTJamming models developed in inanimate matter have been widely used to describe cell packing in tissues1–7, but predominantly neglect cell diversity, despite its prevalence in biology. Most tissues, animal brains in particular, comprise a mix of many cell types, with mounting evidence suggesting that neurons can recognize their respective types as they organize in space8–11. How cell diversity revises the current jamming paradigm is unknown. Here, in the brain of the flatworm planarian Schmidtea mediterranea, which exhibits remarkable tissue plasticity within a simple, quantifiable nervous system12–16, we identify a distinct packing state, ‘chromatic’ jamming. Combining experiments with computational modeling, we show that neurons of distinct types form independent percolating networks barring any physical contact. This jammed state emerges as cell packing configurations become constrained by cell type-specific interactions and therefore may extend to describe cell packing in similarly complex tissues composed of multiple cell types.

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