An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

Xiang, Guanjue; Keller, Cheryl A.; Heuston, Elisabeth F.; Giardine, Belinda; An, Lin; Wixom, Alexander Q.; Miller, Amber; Cockburn, April; Sauria, Michael E.G.; Weaver, Kathryn J.; Lichtenberg, Jens; Göttgens, Berthold; Li, Qunhua; Bodine, David M.; Mahony, Shaun; Taylor, James; Blobel, Gerd A.; Weiss, Mitchell J.; Cheng, Yong; Yue, Feng; Hughes, Jim R.; Higgs, Douglas R.; Zhang, Yu; Hardison, R

doi:10.1101/gr.255760.119

Cited by 43 publications

(75 citation statements)

References 82 publications

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“…(2) The length of a gene had to be over 1 kb to ensure that enough reads were obtained over the gene body (+500 bp from TSS to TES) and discernible from the reads at the TSS. (3) We further filtered out the genes with the lowest (10%) H3K27ac signal or ATAC signal at the promoter regions (−250 to 250 bp of TSS, data from asynchronous G1E-ER4 cells) 48 , 49 . A PolII ChIP-seq peak at the TSS does not necessarily mean that the corresponding gene is active.…”

Section: Methodsmentioning

confidence: 99%

CTCF and transcription influence chromatin structure re-configuration after mitosis

et al. 2021

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During mitosis, transcription is globally attenuated and chromatin architecture is dramatically reconfigured. We exploited the M- to G1-phase progression to interrogate the contributions of the architectural factor CTCF and the process of transcription to genome re-sculpting in newborn nuclei. Depletion of CTCF during the M- to G1-phase transition alters short-range compartmentalization after mitosis. Chromatin domain boundary re-formation is impaired upon CTCF loss, but a subset of boundaries, characterized by transitions in chromatin states, is established normally. Without CTCF, structural loops fail to form, leading to illegitimate contacts between cis-regulatory elements (CREs). Transient CRE contacts that are normally resolved after telophase persist deeply into G1-phase in CTCF-depleted cells. CTCF loss-associated gains in transcription are often linked to increased, normally illegitimate enhancer-promoter contacts. In contrast, at genes whose expression declines upon CTCF loss, CTCF seems to function as a conventional transcription activator, independent of its architectural role. CTCF-anchored structural loops facilitate formation of CRE loops nested within them, especially those involving weak CREs. Transcription inhibition does not significantly affect global architecture or transcription start site-associated boundaries. However, ongoing transcription contributes considerably to the formation of gene domains, regions of enriched contacts along gene bodies. Notably, gene domains emerge in ana/telophase prior to completion of the first round of transcription, suggesting that epigenetic features in gene bodies contribute to genome reconfiguration prior to transcription. The focus on the de novo formation of nuclear architecture during G1 entry yields insights into the contributions of CTCF and transcription to chromatin architecture dynamics during the mitosis to G1-phase progression.

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Section: Methodsmentioning

confidence: 99%

CTCF and transcription influence chromatin structure re-configuration after mitosis

et al. 2021

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“…We then tested the hypothesized impact of chromatin size on the success rate of ChIP-seq in several ways. First, we categorized the success or failure of each ChIP-seq experiment by inspection of the signal tracks in loci for which the CTCF and TAL1 patterns have been studied extensively by ChIP-seq and genetic experiments ( Wilson et al 2010 , 2016 ; Dogan et al 2015 ; Xiang et al 2020 ) . As illustrated for the Gfi1b locus ( Figure 1, A–D ), datasets with good signal-to-noise ratios and concordance with prior knowledge were classified as “Pass,” those with some peaks present but missing others were classified as “Low pass,” and those with almost no peaks were classified as “Fail” ( Figure 1, A–D ).…”

Section: Resultsmentioning

confidence: 99%

“…ChIP-seq has been used extensively across a broad spectrum of species, tissues, and cell types to interrogate the locations of TFs occupying specific sites in chromatin or mapping the profile of histone modifications in chromatin ( Wold and Myers 2008 ; Rivera and Ren 2013 ). This powerful technique has moved studies of gene regulation to a global (genome-wide) scale, and the data produced by this method form the foundation for many efforts to develop coherent, integrated models for gene regulation ( Ching et al 2018 ; Zhou et al 2019 ; Xiang et al 2020 ). However, the technique is not uniformly successful for all samples, and even when apparently successful, the resulting ChIP-seq datasets vary widely in quality ( Marinov et al 2014 ; Devailly et al 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Effects of sheared chromatin length on ChIP-seq quality and sensitivity

Keller

Wixom

Heuston

et al. 2021

G3 Genes|Genomes|Genetics

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Chromatin immunoprecipitation followed by massively parallel, high throughput sequencing (ChIP-seq) is the method of choice for genome-wide identification of DNA segments bound by specific transcription factors or in chromatin with particular histone modifications. However, the quality of ChIP-seq datasets varies widely, with a substantial fraction being of intermediate to poor quality. Thus, it is important to discern and control the factors that contribute to variation in ChIP-seq. In this study, we focused on sonication, a user-controlled variable, to produce sheared chromatin. We systematically varied the amount of shearing of fixed chromatin from a mouse erythroid cell line, carefully measuring the distribution of resultant fragment lengths prior to ChIP-seq. This systematic study was complemented with a retrospective analysis of additional experiments. We found that the level of sonication had a pronounced impact on the quality of ChIP-seq signals. Over-sonication consistently reduced quality, while the impact of under-sonication differed among transcription factors, with no impact on sites bound by CTCF but frequently leading to the loss of sites occupied by TAL1 or bound by POL2. The bound sites not observed in low quality datasets were inferred to be a mix of both direct and indirect binding. We leveraged these findings to produce a set of CTCF ChIP-seq datasets in rare, primary hematopoietic progenitor cells. Our observation that the amount of chromatin sonication is a key variable in success of ChIP-seq experiments indicates that monitoring the level of sonication can improve ChIP-seq quality and reproducibility and facilitate ChIP-seq in rare cell types.

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“…The tens of thousands of epigenomic datasets now available are potentially great resources to better understand the associations of epigenetic modifications with mechanisms of transcriptional regulation ( Bernstein et al., 2010 ; ENCODE Project Consortium, 2012 ; Martens and Stunnenberg, 2013 ; Moore et al , 2020 ; Stunnenberg et al , 2016 ; Xiang, et al., 2020a , b ; Yue et al , 2014 ). However, integrating these resources for global inferences about regulation is challenging for many reasons.…”

Section: Introductionmentioning

confidence: 99%

“…In this application note, we focus on two issues. First, technical differences in procedures and biological samples analyzed in different laboratories introduce noise and biases that can obscure true biological differences ( Meyer and Liu, 2014 ; Shao et al., 2012 ; Xiang et al., 2020a,b ). Second, certain combinations of epigenetic modifications often appear together, but those combinations of modifications (epigenetic states) need to be learned from integrative modeling across epigenomic datasets simultaneously across multiple cell types ( Ernst and Kellis, 2012 ; Hoffman et al., 2012 ; Zhang et al., 2016 ).…”

Section: Introductionmentioning

confidence: 99%

S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types

et al. 2021

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Summary Epigenetic modifications reflect key aspects of transcriptional regulation, and many epigenomic data sets have been generated under different biological contexts to provide insights into regulatory processes. However, the technical noise in epigenomic data sets and the many dimensions (features) examined make it challenging to effectively extract biologically meaningful inferences from these data sets. We developed a package that reduces noise while normalizing the epigenomic data by a novel normalization method, followed by integrative dimensional reduction by learning and assigning epigenetic states. This package, called S3V2-IDEAS, can be used to identify epigenetic states for multiple features, or identify discretized signal intensity levels and a master peak list across different cell types for a single feature. We illustrate the outputs and performance of S3V2-IDEAS using 137 epigenomics data sets from the VISION project that provides ValIdated Systematic IntegratiON of epigenomic data in hematopoiesis. Availability and implementation S3V2-IDEAS pipeline is freely available as open source software released under an MIT license at: https://github.com/guanjue/S3V2_IDEAS_ESMP Supplementary information S3V2_IDEAS_supplementary_materials.docx

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An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

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Cited by 43 publications

References 82 publications

CTCF and transcription influence chromatin structure re-configuration after mitosis

CTCF and transcription influence chromatin structure re-configuration after mitosis

Effects of sheared chromatin length on ChIP-seq quality and sensitivity

S3V2-IDEAS: a package for normalizing, denoising and integrating epigenomic datasets across different cell types

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