ArticleProceedings of the National Academy of Sciences of the United States of America2023
Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.Nature communications · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Live-cell omics with Raman spectroscopy.Microscopy (Oxford, England) · 2025Review
- KLF2 expression in IgG plasma cells at their induction site regulates the migration program.The Journal of experimental medicine · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Finding differentially expressed genes between cell fates predicted by image-based deep learning.Biophysics and physicobiology · 2025Article
- Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?International journal of molecular sciences · 2024Review
- Pigmentation level of human iPSC-derived RPE does not indicate a specific gene expression profile.eLife · 2024Article
- Integration of single-cell manipulation, whole transcriptome analysis, and image-based deep learning for studying "Singularity Biology".Biophysics and physicobiology · 2024Article
- Strength in numbers: Unleashing the potential of trans-scale scope AMATERAS for massive cell quantification.Biophysics and physicobiology · 2024Review
- Visualizing Singularity Phenomenon.Biophysics and physicobiology · 2024Article
- Single-cell trapping and retrieval in open microfluidics.iScience · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.
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