ArticleeLife2021
In silico-labeled ghost cytometry.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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
21 citing papers in PubMed, 33 citations in OpenAlex.
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry.Scientific reports · 2025Article
- BCR::ABL1-induced mitochondrial morphological alterations as a potential clinical biomarker in chronic myeloid leukemia.Cancer science · 2025Article
- High throughput analysis of rare nanoparticles with deep-enhanced sensitivity via unsupervised denoising.Nature communications · 2025Article
- Environmental community transcriptomics: strategies and struggles.Briefings in functional genomics · 2025Review
- Review
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Label-free ghost cytometry for manufacturing of cell therapy products.Scientific reports · 2024Article
- Cell sorting based on pulse shapes from angle resolved detection of scattered light.Communications biology · 2024Article
- Pooled CRISPR screening of high-content cellular phenotypes using ghost cytometry.Cell reports methods · 2024Article
- Imaging Flow Cytometry: Development, Present Applications, and Future Challenges.Methods and protocols · 2024Review
- Label-free enrichment of human pluripotent stem cell-derived early retinal progenitor cells for cell-based regenerative therapies.Stem cell reports · 2024Article
- Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry.Biomicrofluidics · 2024Article
- COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning.Communications biology · 2023Article
- Machine learning implementation strategy in imaging and impedance flow cytometry.Biomicrofluidics · 2023Review
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity.Frontiers in microbiology · 2022Article
- Imaging flow cytometry: a primer.Nature reviews. Methods primers · 2022Article
Corrections and comments
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Authors and funding
24 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.
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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.