A chromatin integration labelling method enables epigenomic profiling with lower input

Harada, Akihito; Maehara, Kazuhira; Handa, Tetsuya; Arimura, Yasuhiro; Nogami, Jumpei; Hayashi‐Takanaka, Yoko; Shirahige, Katsuhiko; Kurumizaka, Hitoshi; Kimurâ, Hiroshi; Ohkawa, Yasuyuki

doi:10.1038/s41556-018-0248-3

Cited by 139 publications

(133 citation statements)

References 39 publications

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“…While ChIP-based methods have been extensively used in model cell line systems, the vagaries of crosslinking and fragmenting chromatin have limited chromatin profiling by ChIP-seq to an artisan technique where each experiment requires optimization. Likewise, a recently described alternative cross-linked chromatin profiling method, ChIL-seq 27 , requires many more steps than CUT&Tag and requires 3-4 days to perform all of the steps. In contrast, the CUT&Tag procedure, like CUT&RUN, is an unfixed in situ method, and is easily implemented in a standardized approach.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, CUT&Tag should be suitable for the 10X Genomics encapsulation system 13 by adaptation of their recently announced ATAC-seq single-cell protocol 28 . Adaptability to high-throughput single-cell platforms is possible for CUT&Tag because adapters are added in bulk, whereas previous single-cell adaptations of antibody-based profiling methods, including ChIP-seq 29 , ChIL-seq 27 and CUT&RUN 24 require reactions to be performed after cells are separated.…”

Section: Discussionmentioning

confidence: 99%

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

Kaya-Okur

Codomo

et al. 2019

Preprint

221

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Many chromatin features play critical roles in regulating gene expression. A complete understanding of gene regulation will require the mapping of specific chromatin features in small samples of cells at high resolution. Here we describe Cleavage Under Targets and Tagmentation (CUT&Tag), an enzyme-tethering strategy that provides efficient high-resolution sequencing libraries for profiling diverse chromatin components. In CUT&Tag, a chromatin protein is bound in situ by a specific antibody, which then tethers a protein A-Tn5 transposase fusion protein. Activation of the transposase efficiently generates fragment libraries with high resolution and exceptionally low background. All steps from live cells to sequencing-ready libraries can be performed in a single tube on the benchtop or a microwell in a high-throughput pipeline, and the entire procedure can be performed in one day. We demonstrate the utility of CUT&Tag by profiling histone modifications, RNA Polymerase II and transcription factors on low cell numbers and single cells.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Kaya-Okur

Codomo

et al. 2019

Preprint

221

439

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“…These regulatory systems have been studied at the genome-wide level to investigate open chromatin, proteingenome binding, and genome methylation, but it remains challenging to perform such analyses on cells located in a limited area. Given that some technologies, such as ATAC-seq, ChIL-seq and PBAT (post-bisulfite adapter tagging), commonly use ODNs as a starting material (34)(35)(36)(37), caged ODNs provide the advantage of being able to amplify sequence libraries only from photo-irradiated ROIs. Thus, PIC will be able to determine epigenetic landscapes as well as expression profiles in an ROI-specific manner.…”

Section: Discussionmentioning

confidence: 99%

High resolution spatial transcriptome analysis by photo-isolation chemistry

Honda

Oki

Harada

et al. 2020

Preprint

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In multicellular organisms, individual cells are characterized by their gene expression profiles and the spatial interactions among cells enable the elaboration of complex functions. Expression profiling in spatially defined regions is crucial to elucidate cell interactions and functions. Here, we established a transcriptome profiling method coupled with photo-isolation chemistry (PIC) that allows the determination of expression profiles specifically from photo-irradiated regions of whole tissues. PIC uses photo-caged oligodeoxynucleotides for in situ reverse transcription. After photoirradiation of limited areas, gene expression was detected from at least 10 cells in the tissue sections. PIC transcriptome analysis detected genes specifically expressed in small distinct areas of the mouse embryo. Thus, PIC enables transcriptome profiles to be determined from limited regions at a spatial resolution up to the diffraction limit.

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“…Although the methodology uses the similarity of measured data points, this does not deduce causal directions among cells. Designing edges using emerging technologies of statistical causal inference [24,25,26,27] combined with single-cell omics data other than transcriptomes, e.g., epigenomes [28] could pave the way for the reconstruction of causal flows including cyclic relations.…”

Section: Discussionmentioning

confidence: 99%

Modeling latent flows on single-cell data using the Hodge decomposition

Maehara

Ohkawa

2019

Preprint

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Single-cell analysis is a powerful technique used to identify a specific cell population of interest during differentiation, aging, or oncogenesis. Individual cells occupy a particular transient state in the cell cycle, circadian rhythm, or during cell death. An appealing concept of pseudo-time trajectory analysis of single-cell RNA sequencing data was proposed in the software Monocle, and several methods of trajectory analysis have since been published to date. These aim to infer the ordering of cells and enable the tracing of gene expression profile trajectories in cell differentiation and reprogramming. However, the methods are restricted in terms of time structure because of the pre-specified structure of trajectories (linear, branched, tree or cyclic) which contrasts with the mixed state of single cells.Here, we propose a technique to extract underlying flows in single-cell data based on the Hodge decomposition (HD). HD is a theorem of vector fields on a manifold which guarantees that any given flow can decompose into three types of orthogonal component: gradient-flow (acyclic), curl-, and harmonic-flow (cyclic). HD is generalized on a simplicial complex (graph) and the discretized HD has only a weak assumption that the graph is directed. Therefore, in principle, HD can extract flows from any mixture of tree and cyclic time flows of observed cells. The decomposed flows provide intuitive interpretations about complex flow because of their linearity and orthogonality. Thus, each extracted flow can be focused on separately with no need to consider crosstalk.We developed ddhodge software, which aims to model the underlying flow structure that implies unobserved time or causal relations in the hodge-podge collection of data points. We demonstrated that the mathematical framework of HD is suitable to reconstruct a sparse graph representation of diffusion process as a candidate model of differentiation while preserving the divergence of the original fully-connected graph. The preserved divergence can be used as an indicator of the source and sink cells in the observed population. A sparse graph representation of the diffusion process transforms data analysis of the non-linear structure embedded in the high-dimensional space of single-cell data into inspection of the visible flow using graph algorithms. Hence, ddhodge is a suitable toolkit to visualize, inspect, and subsequently interpret large data sets including, but not limited to, high-throughput measurements of biological data.The beta version of ddhodge R package is available at:https://github.com/kazumits/ddhodge

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A chromatin integration labelling method enables epigenomic profiling with lower input

Cited by 139 publications

References 39 publications

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

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

High resolution spatial transcriptome analysis by photo-isolation chemistry

Modeling latent flows on single-cell data using the Hodge decomposition

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