Evidence map›Paper›PMID 38641660›Full record

ArticleNature cell biology2024

Pooled multicolour tagging for visualizing subcellular protein dynamics.

Andreas Reicher, Jiří Reiniš, Maria Ciobanu, Pavel Růžička, Monika Malik, Marton Siklos, Victoria Kartysh, Tatjana Tomek, Anna Koren, André F Rendeiro and 1 more

Abstract read
In one paragraph

Article in Nature cell biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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  17. Monitoring subcellular protein localization at scale in live cells.Nature reviews. Molecular cell biology · 2024
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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

11 authors.

Andreas Reicher *CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Jiří Reiniš *CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Maria CiobanuCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Pavel RůžičkaCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Monika MalikCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Marton SiklosCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Victoria KartyshCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Tatjana TomekCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Anna KorenCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
André F RendeiroCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
Stefan KubicekCeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria. skubicek@cemm.oeaw.ac.at.ORCID 0000-0003-0855-8343

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) ERC-CoG-772437Vienna Science and Technology Fund (Wiener Wissenschafts-, Forschungs- und Technologiefonds) LS21-01
6 · The paper itself

Abstract

Imaging-based methods are widely used for studying the subcellular localization of proteins in living cells. While routine for individual proteins, global monitoring of protein dynamics following perturbation typically relies on arrayed panels of fluorescently tagged cell lines, limiting throughput and scalability. Here, we describe a strategy that combines high-throughput microscopy, computer vision and machine learning to detect perturbation-induced changes in multicolour tagged visual proteomics cell (vpCell) pools. We use genome-wide and cancer-focused intron-targeting sgRNA libraries to generate vpCell pools and a large, arrayed collection of clones each expressing two different endogenously tagged fluorescent proteins. Individual clones can be identified in vpCell pools by image analysis using the localization patterns and expression level of the tagged proteins as visual barcodes, enabling simultaneous live-cell monitoring of large sets of proteins. To demonstrate broad applicability and scale, we test the effects of antiproliferative compounds on a pool with cancer-related proteins, on which we identify widespread protein localization changes and new inhibitors of the nuclear import/export machinery. The time-resolved characterization of changes in subcellular localization and abundance of proteins upon perturbation in a pooled format highlights the power of the vpCell approach for drug discovery and mechanism-of-action studies.

Indexed as

ProteomicsCell Line, TumorHumansMachine LearningMicroscopy, Fluorescence

Identifiers

PMID38641660
PMCPMC11098740

What OpenQuestion holds

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Registered trials

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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.