ArticleScientific reports2016
A high-content image-based method for quantitatively studying context-dependent cell population dynamics.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Mapping spatial organization of in vitro neuronal networks using high-content imaging.Scientific reports · 2025Article
- Live-cell painting: Image-based profiling in live cells using acridine orange.Molecular biology of the cell · 2025Article
- A self-supervised learning approach for high throughput and high content cell segmentation.Communications biology · 2025Article
- Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer.NPJ systems biology and applications · 2025Article
- More than just life and death: advances in imaging and analysis for 3D-bioprinted tissues.Frontiers in bioengineering and biotechnology · 2025Article
- Article
- Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.bioRxiv : the preprint server for biology · 2024Article
- High-Throughput Empirical and Virtual Screening to Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.bioRxiv : the preprint server for biology · 2024Article
- Targeting stress induction of GRP78 by cardiac glycoside oleandrin dually suppresses cancer and COVID-19.Cell & bioscience · 2024Article
- Examination of the role of mutualism in immune evasion.Frontiers in oncology · 2024Article
- Single-cell morphological and transcriptome analysis unveil inhibitors of polyploid giant breast cancer cells in vitro.Communications biology · 2023Article
- An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- High-content imaging as a tool to quantify and characterize malaria parasites.Cell reports methods · 2023Article
- Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data.Cell reports methods · 2023Article
- Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data.Cell reports methods · 2023Article
- Multiplex quadruple bioluminescent assay system.Scientific reports · 2022Article
- Multiplex Hextuple Luciferase Assaying.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Optimizing CRISPR/Cas9 Editing of Repetitive Single Nucleotide Variants.Frontiers in genome editing · 2022Article
- From imaging a single cell to implementing precision medicine: an exciting new era.Emerging topics in life sciences · 2021Article
- Organoids in image-based phenotypic chemical screens.Experimental & molecular medicine · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.
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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.