Evidence map›Paper›PMID 40470209›Full record

ArticleResearch square2025

Multiplexed Dark FRET Biosensors: An accessible live-cell platform for target- and cell-specific monitoring of protein-protein interactions in 2D and 3D model systems.

Anthony Braun, Elly Liao, Nagamani Vunnam, Marguerite Murray, Jonathan Sachs

Abstract readPreprint
In one paragraph

Article in Research square, 2025. 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

5 · Who and what money

Authors and funding

5 authors.

Anthony BraunUniversity of Minnesota.
Elly LiaoUniversity of Minnesota.
Nagamani VunnamUniversity of Minnesota.
Marguerite MurrayUniversity of Minnesota.ORCID 0009-0008-7976-7058
Jonathan SachsUniversity of Minnesota.

Funding

How alpha-Synuclein misfolding promotes tau pathology in ADRDR01NS117968 · NINDS · UNIVERSITY OF MINNESOTA · PI SACHS, JONATHAN N · 2020 to 2024
$2.3M
Advanced multiplexing technologies with innovative Dual-Channel Dark-FRET biosensors for dynamic monitoring of alpha-synuclein pathophysiology: From cellular to in vivo modelsR21AG089930 · NIA · UNIVERSITY OF MINNESOTA · PI SACHS, JONATHAN N · 2024 to 2025
$407k
NIA NIH HHS R21 AG089930NINDS NIH HHS R01 NS117968
6 · The paper itself

Abstract

Simultaneously monitoring multiple protein-protein interactions in live cells remains a key challenge in biology and drug discovery. While multiplexed FRET enables parallel molecular readouts, existing approaches are often constrained by spectral overlap, complex instrumentation, or incompatibility with live-cell models. To overcome these limitations and increase accessibility to the broader biological community, we present Multiplexed Dark FRET (MDF), a genetically encoded platform that uses spectrally distinct donors (mNeonGreen, mScarlet-I3) paired with non-emissive acceptors (ShadowY, ShadowR). Using fluorescence lifetime detection, we demonstrate MDF's versatility through three biologically and translationally relevant examples: (1) cell-type specific biosensing in organoids, as exemplified in 3D neuro-glial spheroids; (2) target specificity for drug discovery, through discrimination of TNFR1 versus TNFR2 receptor conformations; and (3) protein misfolding, as exemplified through simultaneous monitoring of alpha-synuclein oligomerization and misfolding. MDF provides a scalable framework for real-time, live-cell biosensing across high-throughput, target-specific, and tissue-level applications in complex biological systems.

Indexed as

3D spheroidHigh-throughput screeningMultiplexed FRETTarget-Specificity

Identifiers

PMID40470209
PMCPMC12136217

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.