Evidence map›Paper›PMID 39576603›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Detection and Automated Quantification of Nucleocytoplasmic RNA Fractions in Arabidopsis Using smFISH.

Alejandro Fonseca, Stefanie Rosa

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

2 authors.

Alejandro FonsecaSwedish University of Agricultural Sciences, Uppsala, Sweden. alejandro.fonseca.gardenas@slu.se.
Stefanie RosaSwedish University of Agricultural Sciences, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Subcellular RNA localization is an underexplored regulatory layer crucial for properly adapting cells to cellular or environmental conditions. Most studies describing RNA localization have been performed by cell fractionation and subsequent RNA quantification from pools of cells, thereby missing information about cell-to-cell variability. RNA single-molecule fluorescent in situ hybridization (smFISH) is an effective technique for detecting single RNA molecules and identifying subcellular accumulation patterns. Nevertheless, obtaining quantitative results from smFISH can be challenging in tissues with high autofluorescence, like in plants. Here, we describe an automated pipeline to detect and quantify nucleocytoplasmic RNA levels from Arabidopsis root smFISH images. This pipeline utilizes free image preprocessing, segmentation, and RNA detection software. The method permits users with any programming skills to analyze batches of images. Suggestions and recommendations for image acquisition, processing, and data analysis are included. This pipeline allows quantitative differences in nucleocytoplasmic distribution at the single-cell level to be studied under different cellular, environmental, and genetic contexts.

Indexed as

ArabidopsisCell NucleusImage Processing, Computer-AssistedIn Situ Hybridization, FluorescenceRNA, PlantSoftwareCytoplasmPlant RootsSingle Molecule ImagingRNA, PlantArabidopsisFluorescence microscopyFluorescent in situ hybridizationImage analysisNucleocytoplasmic localizationRNASingle-cell analysis

Identifiers

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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.