Chromatin integration labeling for mapping DNA-binding proteins and modifications with low input

Handa, Tetsuya; Harada, Akihito; Maehara, Kazuhira; Sato, Shoko; Nakao, Masaru; Gotō, Naoki; Kurumizaka, Hitoshi; Ohkawa, Yasuyuki; Kimurâ, Hiroshi

doi:10.1038/s41596-020-0375-8

Cited by 16 publications

(19 citation statements)

References 27 publications

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“…To validate the feasibility of ChIL for sensitive and accurate epigenomic analysis, we performed ChIL-seq using a single thin section containing 1,000-10,000 cells (Table 2), which was generally assumed as a low number of cells in culture (Harada et al, 2019;Handa et al, 2020). The number of cells used was less than that of conventional epigenomic methods used especially for tissue A Schematic diagram of the ChIL protocol for tissue.…”

Section: Resultsmentioning

confidence: 99%

Modeling population size independent tissue epigenomes by ChIL‐seq with single thin sections

Maehara

Tomimatsu

Harada

et al. 2021

Molecular Systems Biology

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Recent advances in genome-wide technologies have enabled analyses using small cell numbers of even single cells. However, obtaining tissue epigenomes with cell-type resolution from large organs and tissues still remains challenging, especially when the available material is limited. Here, we present a ChIL-based approach for analyzing the diverse cellular dynamics at the tissue level using high-depth epigenomic data. "ChIL for tissues" allows the analysis of a single tissue section and can reproducibly generate epigenomic profiles from several tissue types, based on the distribution of target epigenomic states, tissue morphology, and number of cells. The proposed method enabled the independent evaluation of changes in cell populations and gene activation in cells from regenerating skeletal muscle tissues, using a statistical model of RNA polymerase II distribution on gene loci. Thus, the integrative analyses performed using ChIL can elucidate in vivo cell-type dynamics of tissues.

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Section: Resultsmentioning

confidence: 99%

Modeling population size independent tissue epigenomes by ChIL‐seq with single thin sections

Maehara

Tomimatsu

Harada

et al. 2021

Molecular Systems Biology

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“…MulTI-Tag establishes a rigorous baseline for unambiguously profiling multiple epigenome proteins with direct sequence tags, maintaining both exemplary assay efficiency and target-assignment fidelity relative to other similar approaches 39,40 . Single-readout Multifactorial profiling holds a distinct advantage over Multimodal profiling, which often requires highly complicated integration of semi-compatible protocols and analysis methods.…”

Section: Main Textmentioning

confidence: 99%

Multifactorial chromatin regulatory landscapes at single cell resolution

Llagas

Janssens

Codomo

et al. 2021

Preprint

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Chromatin profiling at locus resolution uncovers gene regulatory features that define cell types and developmental trajectories, but it remains challenging to map and compare distinct chromatin-associated proteins within the same sample. Here we describe a scalable antibody barcoding approach for profiling multiple chromatin features simultaneously in the same individual cells, Multiple Target Identification by Tagmentation (MulTI-Tag). MulTI-Tag is optimized to retain high sensitivity and specificity of enrichment for multiple chromatin targets in the same assay. We use MulTI-Tag to resolve distinct cell types using multiple chromatin features on a commercial single-cell platform, and to distinguish unique, coordinated patterns of active and repressive element regulatory usage in the same individual cells. Multifactorial profiling allows us to detect novel associations between histone marks in single cells and holds promise for comprehensively characterizing cell-specific gene regulatory landscapes in development and disease.

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“…To validate the feasibility of tsChIL for sensitive and accurate epigenomic analysis, we performed tsChIL-seq using a single thin section containing 1,000-10,000 cells ( Table 1), which was generally assumed as a low number of cells in culture 7,8 . The number of cells used was less than that of conventional epigenomic methods used especially for tissue analysis (Table S1).…”

Section: Thin-section Chil-seq Enabled Spatial Epigenomics With Singlmentioning

confidence: 99%

“…CC-BY 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 18, 2020. ; https://doi.org/10.1101/2020.12.18.423434 doi: bioRxiv preprint tsChIL-seq was performed according to ChIL 7,8 with some modifications: longer incubation time was employed for some steps (1 h extended Tn5 binding and 2 h fill-in step), Thermo T7 RNA polymerase (100 U/well; Toyobo), and cycles of polymerase chain reaction (PCR) amplification. Column purification (Zymo #D4013) and ×0.5 volume of AMpure beads (Beckman Coulter) selection were performed to obtain 200 to 500 bp average of the library.…”

Section: Tschil-seqmentioning

confidence: 99%

“…It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 18, 2020. ; https://doi.org/10.1101/2020.12.18.423434 doi: bioRxiv preprint defining the target cell types and markers, the sectioning of narrower area, dissection, or cell-sorting is utilized. Recently, new methods for analyzing a small number of cells with higher genome coverage at the single-cell level, including our ChIL and others, have been developed [7][8][9][10][11][12][13][14] . In addition, isolating cells causes potential effects to cells owing to the physical separation of the tissues.…”

Section: Introductionmentioning

confidence: 99%

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Modeling population size independent tissue epigenomes by ChIL-seq with single-thin sections

Maehara

Tomimatsu

Harada

et al. 2020

Preprint

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Recent advances in omics studies have enabled analysis at the single-cell level; however, methods for analyzing the whole cell of large organs and tissues remain challenging. Here, we developed a method named tsChIL to understand the diverse cellular dynamics at the tissue level using high-depth epigenomic data. tsChIL allowed the analysis of a single tissue section and could reproducibly acquire epigenomic profiles from several types of tissues, based on the distribution of target epigenomic states, tissue morphology, and number of cells. The proposed method enabled the independent evaluation of changes in cell populations and gene activation of cells in regenerating skeletal muscle tissues, using a statistical model of RNA polymerase II distribution on gene loci. Thus, the integrative analysis by tsChIL can elucidate in vivo cell-type dynamics of tissues.

show abstract

Chromatin integration labeling for mapping DNA-binding proteins and modifications with low input

Cited by 16 publications

References 27 publications

Modeling population size independent tissue epigenomes by ChIL‐seq with single thin sections

Modeling population size independent tissue epigenomes by ChIL‐seq with single thin sections

Multifactorial chromatin regulatory landscapes at single cell resolution

Modeling population size independent tissue epigenomes by ChIL-seq with single-thin sections

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