Evidence map›Paper›PMID 35188663›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Genetically Encoded Biosensors for the Quantitative Analysis of Auxin Dynamics in Plant Cells.

Jennifer Andres, Matias D Zurbriggen

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Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact, top 96% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 1 institution in 1 country.

Jennifer AndresInstitute of Synthetic Biology and CEPLAS, University of Düsseldorf, Düsseldorf, Germany.
Matias D ZurbriggenInstitute of Synthetic Biology and CEPLAS, University of Düsseldorf, Düsseldorf, Germany. matias.zurbriggen@uni-duesseldorf.de.
Heinrich Heine University Düsseldorf · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plants, as sessile organisms, possess complex and intertwined signaling networks to react and adapt their behavior toward different internal and external stimuli. Due to this high level of complexity, the implementation of quantitative molecular tools in planta remains challenging. Synthetic biology as an ever-growing interdisciplinary field applies basic engineering principles in life sciences. A plethora of synthetic switches, circuits, and even higher order networks has been implemented in different organisms, such as bacteria and mammalian cells, and facilitates the study of signaling and metabolic pathways. However, the application of such tools in plants lags behind, and thus only a few genetically encoded biosensors and switches have been engineered toward the quantitative investigation of plant signaling. Here, we present a protocol for the quantitative analysis of auxin signaling in Arabidopsis thaliana protoplasts. We implemented genetically encoded, ratiometric, degradation-based luminescent biosensors and applied them for studying auxin perception dynamics. For this, we utilized three different Aux/IAAs as sensor modules and analyzed their degradation behavior in response to auxin. Our experimental approach requires simple hardware and experimental reagents and can thus be implemented in every plant-related or cell culture laboratory. The system allows for the analysis of auxin perception and signaling aspects on various levels and can be easily expanded to other hormones, as for example strigolactones. In addition, the modular sensor design enables the implementation of sensor modules in a straightforward and time-saving approach.

Indexed as

ArabidopsisArabidopsis ProteinsBiosensing TechniquesGene Expression Regulation, PlantIndoleacetic AcidsPlant CellsPlants, Genetically ModifiedArabidopsis ProteinsIndoleacetic AcidsAux/IAAsAuxinProtoplastsQuantitative biosensorSynthetic biology tools

Identifiers

PMID35188663
OpenAlexW4213003952

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.