Evidence map›Paper›PMID 42495784›Full record

ReviewBiochemical Society transactions2026

Genetically encoded tools for tracking metabolites in live cells.

Austin H Ablicki, Katharine L Diehl

Abstract readReview
In one paragraph

Review in Biochemical Society transactions, 2026. 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
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

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.

Austin H AblickiDepartment of Medicinal Chemistry, University of Utah, Salt Lake City, Utah, U.S.A.
Katharine L DiehlDepartment of Medicinal Chemistry, University of Utah, Salt Lake City, Utah, U.S.A.ORCID 0000-0002-9872-7501

Funding

Eavesdropping on the conversation between chromatin and metabolismR35GM143080 · NIGMS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI DIEHL, KATHARINE · 2021 to 2025
$1.9M
HHS | National Institutes of Health (NIH) R35GM143080NIGMS NIH HHS R35 GM143080
6 · The paper itself

Abstract

Biosensors enable the in situ measurement of metabolites in living systems over time and space. Fully genetically encoded metabolite biosensors (fGEMBs) use fluorescent proteins (FPs) linked to ligand binding domains (LBDs) to transduce the ligand binding event to a measurable change in the fluorescence behavior of the FP. Because these sensors are genetically encoded, they can be expressed in cells using standard protein expression approaches, and the fluorescence changes are quantified using fluorimetry, fluorescence microscopy, and/or flow cytometry. While there are general sensor design principles to follow, an fGEMB must be engineered for each metabolite based on a particular LBD. This development process can be slow, but there are strategies emerging to increase testing throughput and improve structure-guided design. While genetically-encoded FPs remain popular, there are now numerous chemigenetic and nucleic acid-based metabolite sensors (cGEMBs) that incorporate small molecule fluorophores. De novo design of LBDs is rapidly advancing as well, and the field may soon exhibit a shift away from relying on nature's catalog of LBDs. Despite the engineering challenges, the metabolite biosensor field has expanded significantly in recent years to meet the demand for new and better-performing sensors that visualize metabolites within their cellular environments.

Indexed as

Biosensing TechniquesAnimalsChemogeneticsHumansLigandsLuminescent ProteinsMicroscopy, FluorescenceProtein EngineeringLigandsLuminescent Proteinsbiosensorsfluorescencefluorescence resonance energy transfermetabolitesprotein engineering

Identifiers

PMID42495784
PMCPMC13402728

What OpenQuestion holds

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LicenceCC BY
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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.