ArticleIntegrative biology : quantitative biosciences from nano to macro2016
Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.
Article in Integrative biology : quantitative biosciences from nano to macro, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features.iScience · 2026Article
- Cellular Morphometric Analysis (CellMorph)-a comprehensive imaging-based tool for quantifying cellular phenotype heterogeneity and dynamics across biological processes.The FEBS journal · 2026Article
- Quantitative analysis of fibroblast migration reveals migratory states characterized by force generation, cell shape and motion.bioRxiv : the preprint server for biology · 2026Article
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Inference of weak-form partial differential equations describing migration and proliferation mechanisms in wound healing experiments on cancer cells.PLoS computational biology · 2025Article
- Live-cell painting: Image-based profiling in live cells using acridine orange.Molecular biology of the cell · 2025Article
- Directing Cell Phenotype: Quantitative Single-Cell Migration Assay Leveraging Tunable Extracellular Surfaces.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- LivecellX: A Scalable Deep Learning Framework for Single-Cell Object-Oriented Analysis in Live-Cell Imaging.bioRxiv : the preprint server for biology · 2025Article
- Multi-culture label-free quantitative cell migration sensing with single-cell precision.Biomedical optics express · 2025Article
- Nanotherapeutic and Nano-Bio Interface for Regeneration and Healing.Biomedicines · 2024Review
- Exploring heterogeneous cell population dynamics in different microenvironments by novel analytical strategy based on images.NPJ systems biology and applications · 2024Article
- Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets.Cancers · 2024Article
- Quantifying Dynamic Phenotypic Heterogeneity in Resistant Escherichia coli under Translation-Inhibiting Antibiotics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity.Frontiers in systems biology · 2024Article
- Multisite assessment of reproducibility in high-content cell migration imaging data.Molecular systems biology · 2023Article
- Morphodynamical cell state description via live-cell imaging trajectory embedding.Communications biology · 2023Article
- Multichannel impedance cytometry downstream of cell separation by deterministic lateral displacement to quantify macrophage enrichment in heterogeneous samples.Advanced materials technologies · 2023Article
- Digital Holographic Microscopy to Assess Cell Behavior.Methods in molecular biology (Clifton, N.J.) · 2023Article
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Authors and funding
6 authors.
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Abstract
Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.
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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.