Evidence map›Paper›PMID 29577183›Full record

ReviewCell biology and toxicology2018

Developing novel methods to image and visualize 3D genomes.

Tszshan Ma, Long Chen, Maoxiang Shi, Jing Niu, Xu Zhang, Xusan Yang, Karl Zhanghao, Miaoyan Wang, Peng Xi, Dayong Jin and 2 more

Abstract readReview
In one paragraph

Review in Cell biology and toxicology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
  2. Infection Dynamics of ATG8 inMolecules (Basel, Switzerland) · 2022
    Article
  3. Review
  4. Article
  5. Association of mutation and low expression of theSaudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2020
    Article
  6. Review
  7. Review
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Tszshan MaSchool of Medicine, Tsinghua University, Beijing, 100084, China.
Long ChenMOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
Maoxiang ShiDepartment of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China.
Jing NiuSchool of Life Sciences, Tsinghua University, Beijing, 100084, China.
Xu ZhangDepartment of Automation, Tsinghua University, Beijing, 100084, China.
Xusan YangDepartment of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Karl ZhanghaoDepartment of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Miaoyan WangDepartment of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Peng XiDepartment of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
Dayong JinInstitute for Biomedical Materials and Devices (IBMD), Faculty of Science, University of Technology Sydney, Sydney, NSW, 2007, Australia.
Michael ZhangMOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
Juntao GaoMOE Key Laboratory of Bioinformatics; Bioinformatics Division, Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China. jtgao@biomed.tsinghua.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To investigate three-dimensional (3D) genome organization in prokaryotic and eukaryotic cells, three main strategies are employed, namely nuclear proximity ligation-based methods, imaging tools (such as fluorescence in situ hybridization (FISH) and its derivatives), and computational/visualization methods. Proximity ligation-based methods are based on digestion and re-ligation of physically proximal cross-linked chromatin fragments accompanied by massively parallel DNA sequencing to measure the relative spatial proximity between genomic loci. Imaging tools enable direct visualization and quantification of spatial distances between genomic loci, and advanced implementation of (super-resolution) microscopy helps to significantly improve the resolution of images. Computational methods are used to map global 3D genome structures at various scales driven by experimental data, and visualization methods are used to visualize genome 3D structures in virtual 3D space-based on algorithms. In this review, we focus on the introduction of novel imaging and visualization methods to study 3D genomes. First, we introduce the progress made recently in 3D genome imaging in both fixed cell and live cells based on long-probe labeling, short-probe labeling, RNA FISH, and the CRISPR system. As the fluorescence-capturing capability of a particular microscope is very important for the sensitivity of bioimaging experiments, we also introduce two novel super-resolution microscopy methods, SDOM and low-power super-resolution STED, which have potential for time-lapse super-resolution live-cell imaging of chromatin. Finally, we review some software tools developed recently to visualize proximity ligation-based data. The imaging and visualization methods are complementary to each other, and all three strategies are not mutually exclusive. These methods provide powerful tools to explore the mechanisms of gene regulation and transcription in cell nuclei.

Indexed as

Cell NucleusChromatinChromosomesChromosome StructuresComputational BiologyDNAGenomeHumansImaging, Three-DimensionalIn Situ Hybridization, FluorescenceSequence Analysis, DNAChromatinDNA3D genomesChromatinsFISH method

Identifiers

PMID29577183
PMCPMC6133007

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.