ArticleScientific reports2017
Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 107 citations in OpenAlex.
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- Label-Free Longitudinal Imaging of Single Cell Drug Response with a 3D-Printed Cell Culture Platform.Analytical chemistry · 2025Article
- High-Speed Hyperspectral Imaging for Near Infrared Fluorescence and Environmental Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- AI in cellular engineering and reprogramming.Biophysical journal · 2024Review
- Evaluation of a Deep Learning Based Approach to Computational Label Free Cell Viability Quantification.bioRxiv : the preprint server for biology · 2024Article
- Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.Lab on a chip · 2024Article
- Semi-automated, high-content imaging of drug transporter knockout sea urchin (Lytechinus pictus) embryos.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2024Article
- Enhancing single-cell biology through advanced AI-powered microfluidics.Biomicrofluidics · 2023Article
- Bio-inspired microfluidics: A review.Biomicrofluidics · 2023Review
- Evaluating the utility of brightfield image data for mechanism of action prediction.PLoS computational biology · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator.Patterns (New York, N.Y.) · 2023Article
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
- Deep learning with microfluidics for on-chip droplet generation, control, and analysis.Frontiers in bioengineering and biotechnology · 2023Review
- Label-Free Imaging Analysis of Patient-Derived Cholangiocarcinoma Organoids after Sorafenib Treatment.Cells · 2022Article
- Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.International journal of molecular sciences · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.
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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.