ArticleNature communications2023
Label-free identification of protein aggregates using deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Probing biomolecular condensates with a minimally perturbative experimental readout framework.The Biochemical journal · 2026Review
- GFP-free live neuron quantitative imaging reveals compartmentalization and growth dynamics of polyQ aggregates.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.Nature communications · 2025Article
- Genetically-Encoded Phase Separation Sensors Enable High-Fidelity Live-Cell Probing of Biomolecular Condensates.ACS sensors · 2025Article
- Genetically-encoded phase separation sensors for intracellular probing of biomolecular condensates.bioRxiv : the preprint server for biology · 2024Article
- AI-driven detection and analysis of label-free protein aggregates.Nature reviews. Molecular cell biology · 2024Article
- Label-free identification of protein aggregates using deep learning.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington's disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescent labels commonly used to visualize and monitor the dynamics of protein expression have been shown to alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. Our models are robust across imaging conditions and on aggregates formed by different constructs of Httex1. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process, offering high speed, specificity, and simplicity to analyze protein aggregation dynamics and obtain high-fidelity information.
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What Socratic holds
Registered trials
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 Socratic graph.