CUT&amp;Tag for efficient epigenomic profiling of small samples and single cells

Kaya-Okur, Hatice S; Wu, Steven J.; Codomo, Christine A.; Pledger, Erica S.; Bryson, Terri D.; Henikoff, Steven; Ahmad, Kami; Henikoff, Steven

doi:10.1101/568915

Cited by 220 publications

(455 citation statements)

References 36 publications

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“…Emerging novel technologies including CUT&Tag, low cell input protocols for ChIPseq and ATACseq, and using isolated nuclei from frozen tissues as containers of chemistry could overcome some of the technical obstacles and allow future examination on larger cohorts of specimens. 84,85 Our data document the molecular profiles at two ends of the physiological spectrum of myometrial tissues. Aside from the different functional and structural states, the NP specimens were at a low serum progesterone environment while the TP samples were exposed to high levels of serum progesterone.…”

Section: Limitation and Future Directionmentioning

confidence: 62%

Dynamic transcriptome, accessible genome, and PGR cistrome profiles in the human myometrium

Anderson

Wang

et al. 2019

FASEB j.

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The myometrium undergoes structural and functional remodeling during pregnancy.We hypothesize that myometrial genomic elements alter correspondingly in preparation for parturition. Human myometrial tissues from nonpregnant (NP) and term pregnant (TP) human subjects were examined by RNAseq, ATACseq, and PGR ChIPseq assays to profile transcriptome, assessible genome, and PGR occupancy.NP and TP specimens exhibit 2890 differentially expressed genes, reflecting an increase of metabolic, inflammatory, and PDGF signaling, among others, in adaptation to pregnancy. At the epigenome level, patterns of accessible genome change between NP and TP myometrium, leading to the altered enrichment of binding motifs for hormone and muscle regulators such as the progesterone receptor (PGR), Krüppellike factors, and MEF2A transcription factors. PGR genome occupancy exhibits a significant difference between the two stages of the myometrium, concomitant with distinct transcriptomic profiles including genes such as ENO1, LHDA, and PLCL1 in the glycolytic and calcium signaling pathways. Over-representation of SRF, MYOD, and STAT binding motifs in PGR occupying sites further suggests interactions between PGR and major muscle regulators for myometrial gene expression. In conclusion, changes in accessible genome and PGR occupancy are part of the myometrial remodeling process and may serve as mechanisms to formulate the state-specific transcriptome profiles.

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Section: Limitation and Future Directionmentioning

confidence: 62%

Dynamic transcriptome, accessible genome, and PGR cistrome profiles in the human myometrium

Anderson

Wang

et al. 2019

FASEB j.

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“…Remarkable studies in recent years have enabled detailed profiling of liver transcripts and metabolites at unprecedented temporal and spatial resolution, providing a much‐needed granularity to the known gene expression programs that characterize different liver cell populations and different disease states. It is expected that future studies involving high‐throughput single‐cell epigenomics, such as scATAC‐seq (Lareau et al, ; Preissl et al, ) to identify accessible chromatin regions, scChIP (Grosselin et al, ) and CUT&Tag (Kaya‐Okur et al, ) to profile chromatin proteins, and ChIA‐Drop (M. Zheng et al, ) to capture chromatin topology information, will expand our understanding of the processes that determine the fine line between liver metabolic homeostasis and NAFLD. Hopefully such studies will further address how NAFLD and other pathophysiological states affect nonparenchymal cell lineages, which are generally masked in bulk liver analyses due to their lower representation.…”

Section: Resultsmentioning

confidence: 99%

Liver gene regulatory networks: Contributing factors to nonalcoholic fatty liver disease

Cebola

2020

WIREs Mechanisms of Disease

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Metabolic diseases such as nonalcoholic fatty liver disease (NAFLD) result from complex interactions between intrinsic and extrinsic factors, including genetics and exposure to obesogenic environments. These risk factors converge in aberrant gene expression patterns in the liver, which are underlined by altered cis‐regulatory networks. In homeostasis and in disease states, liver cis‐regulatory networks are established by coordinated action of liver‐enriched transcription factors (TFs), which define enhancer landscapes, activating broad gene programs with spatiotemporal resolution. Recent advances in DNA sequencing have dramatically expanded our ability to map active transcripts, enhancers and TF cistromes, and to define the 3D chromatin topology that contains these elements. Deployment of these technologies has allowed investigation of the molecular processes that regulate liver development and metabolic homeostasis. Moreover, genomic studies of NAFLD patients and NAFLD models have demonstrated that the liver undergoes pervasive regulatory rewiring in NAFLD, which is reflected by aberrant gene expression profiles. We have therefore achieved an unprecedented level of detail in the understanding of liver cis‐regulatory networks, particularly in physiological conditions. Future studies should aim to map active regulatory elements with added levels of resolution, addressing how the chromatin landscapes of different cell lineages contribute to and are altered in NAFLD and NAFLD‐associated metabolic states. Such efforts would provide additional clues into the molecular factors that trigger this disease. This article is categorized under: Biological Mechanisms > Metabolism Biological Mechanisms > Regulatory Biology Laboratory Methods and Technologies > Genetic/Genomic Methods

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“…The data used in this work originate from a population of cells. With the recent advancements of single cell or small cell population ChIP-seq techniques [68,69], methods to identify enhancer and their chromatin landscape within single cells could benefit from probabilistic approaches, as the single cell data is very noisy.…”

Section: Discussionmentioning

confidence: 99%

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

Osmala

Lähdesmäki

2019

Preprint

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Background: The human genome is far from completely annotated. Specifically, the locations ofgenedistal regulatory enhancers are difficult to locate. Enhancers are binding sites of transcription factors and occupied by nucleosomes with modified histones. The binding sites of transcription factors (TFs) and the localization of histone modifications can be quantified by the chromatin immunoprecipitation assay coupled with next generation sequencing (ChIP-seq). The resulting data has been successfully adopted for genomewide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type, and they do not utilize the pattern of the ChIP-seq coverage profiles efficiently. It is also difficult to estimate the accuracy and specificity of the genome-wide enhancer predictions. Results:We developed PREPRINT, a PRobabilistic Enhancer PRedictIoN Tool. We considered the pattern of, for example, the ChIP-seq coverage profile around the enhancers. The data at the positive and negative examples of enhancers was utilized to probabilistically model the enhancer coverage pattern and to train a kernel-based classifier. We demonstrated the performance of the method using ENCODE data from two cell lines. The predicted enhancers were computationally validated based on the TFs and co-regulatory factor binding sites. We compared our enhancer predictions to the ones obtained by other methods. The effects of different parameter choices during training, testing and validation were studied, and finally, the approach to validate the genome-wide predictions was investigated. Conclusion: PREPRINT performed comparably to the state-of-the-art methods and provided probabilistic interpretation (i.e. uncertainty) for the predictions. PREPRINT generalized to data from cell type not utilized for training, and often the performance of PREPRINT was superior to RFECS. We observed that the choice of training data and the choice of parameter values at different steps of the enhancer prediction and validation influenced on the final set of predictions. PREPRINT identifed biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.

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CUT&Tag for efficient epigenomic profiling of small samples and single cells

Cited by 220 publications

References 36 publications

Dynamic transcriptome, accessible genome, and PGR cistrome profiles in the human myometrium

Dynamic transcriptome, accessible genome, and PGR cistrome profiles in the human myometrium

Liver gene regulatory networks: Contributing factors to nonalcoholic fatty liver disease

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

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