ArticleNature methods2023
Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed.
- Heterogeneous Reactivity of Palladium Nanoparticles Revealed by Wavelength-Resolved Interferometric Scattering.Nano letters · 2026Article
- Molecule by Molecule Characterization of a Polymer Molecular Mass Distribution via Mass Photometry.Angewandte Chemie (International ed. in English) · 2026Article
- Label-Free Optical Differentiation of Single Diffusing Amino Acids at Picomolar Concentrations.Research square · 2026Article
- Rapid trapping and label-free optical characterization of single nanoscale extracellular vesicles and nanoparticles in solution.Light, science & applications · 2026Article
- Visualizing Dynamic Processes in Energy Materials by Interferometric Scattering Microscopy.ACS energy letters · 2026Review
- Label-free mass and size characterization of few-kDa biomolecules by hierarchical vision transformer augmented nanofluidic scattering microscopy.Nature communications · 2026Article
- From biomolecular condensates to functional nanomaterials: LLPS-inspired frameworks for nanoscale hydrogels and adaptive materials.Journal of nanobiotechnology · 2026Review
- Deep Learning-Based Event Classification of Mass Photometry Data for Optimal Mass Measurement at the Single-Molecule Level.ACS nano · 2026Article
- Meta-amplified dark-field interferometric scattering microscopy.Nature communications · 2026Article
- Associations of neonatal birth outcomes with amino acids and acylcarnitines: an observational study of over 3.3 million newborns in China.BMC medicine · 2025Observational
- Article
- Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Leveraging Partial Coherence to Enhance Nanoparticle Detection Sensitivity and Throughput in Interferometric Scattering Microscopy.ACS photonics · 2025Article
- Multiparametric Characterization of Individual Suspended Nanoparticles Using Confocal Fluorescence and Interferometric Scattering Microscopy with Microfluidic Confinement.Nano letters · 2025Article
- Label-Free Single-Molecule Immunoassay.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Enhanced Interferometric Imaging by Rotating Coherent Scattering Microscopy.ACS photonics · 2025Article
- Review
- Real-time label-free imaging of living crystallization-driven self-assembly.Nature communications · 2025Article
- The cyanobacterial protein VIPP1 forms ESCRT-III-like structures on lipid bilayers.Nature structural & molecular biology · 2025Article
- Multispectral Interferometric Scattering Microscopy Imaging of Gold and Silver Nanoparticles.ACS applied nano materials · 2025Article
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5 authors.
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Abstract
Interferometric scattering (iSCAT) microscopy is a label-free optical method capable of detecting single proteins, localizing their binding positions with nanometer precision, and measuring their mass. In the ideal case, iSCAT is limited by shot noise such that collection of more photons should extend its detection sensitivity to biomolecules of arbitrarily low mass. However, a number of technical noise sources combined with speckle-like background fluctuations have restricted the detection limit in iSCAT. Here, we show that an unsupervised machine learning isolation forest algorithm for anomaly detection pushes the mass sensitivity limit by a factor of 4 to below 10 kDa. We implement this scheme both with a user-defined feature matrix and a self-supervised FastDVDNet and validate our results with correlative fluorescence images recorded in total internal reflection mode. Our work opens the door to optical investigations of small traces of biomolecules and disease markers such as α-synuclein, chemokines and cytokines.
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