ArticleBioengineering & translational medicine2024
An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response.
Article in Bioengineering & translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Leveraging Microphysiological Systems to Facilitate Neutrophil-Based Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- From single cell analysis to 3D micro physiological systems: microfluidic tools integrating cancer cell targets for delineating natural killer cell biology.Microsystems & nanoengineering · 2026Review
- Cancer-on-a-chip for precision cancer medicine.Lab on a chip · 2025Review
- An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response.Bioengineering & translational medicine · 2024Article
- Advanced technologies for biomedical applications by emerging researchers in Asia-Pacific.Bioengineering & translational medicine · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (
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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.