Evidence map›Paper›PMID 38195868›Full record

ReviewNature reviews. Genetics2024

Real-time single-molecule imaging of transcriptional regulatory networks in living cells.

Dong-Woo Hwang, Anna Maekiniemi, Robert H Singer, Hanae Sato

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
22.5field-weighted citation impact, top 1% of its field
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

15 citing papers in PubMed, 34 citations in OpenAlex.

  1. Review
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  13. Aberrant pre-mRNA processing in cancer.The Journal of experimental medicine · 2024
    Review
  14. Single-molecule live-cell RNA imaging with CRISPR-Csm.bioRxiv : the preprint server for biology · 2024
    Article
  15. Review
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

4 authors at 2 institutions in 2 countries.

Dong-Woo HwangDepartment of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0001-5806-067X
Anna MaekiniemiDepartment of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0002-3036-2606
Robert H SingerDepartment of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0002-6725-0093
Hanae SatoDepartment of Cell Biology, Albert Einstein College of Medicine, New York, NY, USA. hanae-sato@staff.kanazawa-u.ac.jp.ORCID http://orcid.org/0000-0002-7953-5643
Albert Einstein College of Medicine · USKanazawa University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene regulatory networks drive the specific transcriptional programmes responsible for the diversification of cell types during the development of multicellular organisms. Although our knowledge of the genes involved in these dynamic networks has expanded rapidly, our understanding of how transcription is spatiotemporally regulated at the molecular level over a wide range of timescales in the small volume of the nucleus remains limited. Over the past few decades, advances in the field of single-molecule fluorescence imaging have enabled real-time behaviours of individual transcriptional components to be measured in living cells and organisms. These efforts are now shedding light on the dynamic mechanisms of transcription, revealing not only the temporal rules but also the spatial coordination of underlying molecular interactions during various biological events.

Indexed as

Gene Regulatory NetworksSingle Molecule ImagingTranscription, Genetic

Identifiers

PMID38195868
OpenAlexW4390707053

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.