Evidence map›Paper›PMID 33203764›Full record

ArticleGenome research2020

Pooled protein tagging, cellular imaging, and in situ sequencing for monitoring drug action in real time.

Andreas Reicher, Anna Koren, Stefan Kubicek

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
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  6. Review
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  9. Article
  10. Monitoring subcellular protein localization at scale in live cells.Nature reviews. Molecular cell biology · 2024
    Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Frontiers in aging · 2021
    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

3 authors at 1 institution in 1 country.

Andreas ReicherCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.
Anna KorenCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.
Stefan KubicekCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.ORCID 0000-0003-0855-8343
Austrian Academy of Sciences · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The levels and subcellular localizations of proteins regulate critical aspects of many cellular processes and can become targets of therapeutic intervention. However, high-throughput methods for the discovery of proteins that change localization either by shuttling between compartments, by binding larger complexes, or by localizing to distinct membraneless organelles are not available. Here we describe a scalable strategy to characterize effects on protein localizations and levels in response to different perturbations. We use CRISPR-Cas9-based intron tagging to generate cell pools expressing hundreds of GFP-fusion proteins from their endogenous promoters and monitor localization changes by time-lapse microscopy followed by clone identification using in situ sequencing. We show that this strategy can characterize cellular responses to drug treatment and thus identify nonclassical effects such as modulation of protein-protein interactions, condensate formation, and chemical degradation.

Indexed as

Clone CellsCRISPR-Cas SystemsGene EditingHEK293 CellsHigh-Throughput Nucleotide SequencingHumansMolecular ImagingPharmaceutical PreparationsProteinsProtein TransportSequence Analysis, DNATime-Lapse ImagingPharmaceutical PreparationsProteins

Identifiers

PMID33203764
PMCPMC7706735
OpenAlexW3105177201

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.