ArticleScience advances2021
Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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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
38 citing papers in PubMed, 80 citations in OpenAlex.
- Single-cell mass spectrometry imaging: platform advances for multimodal spatial omics.Analytical and bioanalytical chemistry · 2026Review
- Hyper-dimensional computing for enhanced label-free particle analysis in a flow-based optical detection system.Scientific reports · 2026Article
- Regularized Single-Cell Imaging Enables Generalizable AI Models for Stain-Free Cell Viability Screening.Small methods · 2026Article
- Temporal variance mapping with machine learning for label-free 3D chromatin imaging using optical interferometric microscopy.Biomedical optics express · 2026Article
- Cell context-dependent in silico organelle localization in label-free microscopy images.Nature methods · 2026Article
- Advances in machine learning-enhanced microfluidic cell sorting.Science advances · 2025Review
- Multiplexed Integrin Detection and Cancer Cell Classification Using Multicolor Gap-Enhanced Gold Nanorods and Machine Learning Algorithm.Nanomaterials (Basel, Switzerland) · 2025Article
- On the utility of virtual staining for downstream applications as it relates to task network capacity.Biomedical optics express · 2025Article
- AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.Diagnostic pathology · 2025Article
- Article
- Imaging 3D cell cultures with optical microscopy.Nature methods · 2025Review
- Fast segmentation and multiplexing imaging of organelles in live cells.Nature communications · 2025Article
- Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.Science advances · 2025Article
- Deep learning-enabled filter-free fluorescence microscope.Science advances · 2025Article
- Fluorescent marker prediction for non-invasive optical imaging in bovine satellite cells using deep learning.Frontiers in artificial intelligence · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens.Light, science & applications · 2024Article
- Single-cell laser emitting cytometry for label-free nucleolus fingerprinting.Nature communications · 2024Article
- Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell-level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.
Identifiers
What OpenQuestion 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 OpenQuestion graph.