ReviewEssays in biochemistry2019
From observing to predicting single-cell structure and function with high-throughput/high-content microscopy.
Review in Essays in biochemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
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
18 citing papers in PubMed.
- Comprehensive Mechanistic Characterisation of the Antidiabetic Profile ofLife (Basel, Switzerland) · 2026Article
- A vertically integrated system for tracking and assessing cell-cycle-aware phenotypes under confinement.APL bioengineering · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Advancing antibiotic discovery with bacterial cytological profiling: a high-throughput solution to antimicrobial resistance.Frontiers in microbiology · 2025Review
- From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.Molecular biology of the cell · 2024Article
- ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells.Scientific data · 2023Article
- Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology.International journal of molecular sciences · 2023Review
- DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.Cell reports methods · 2023Article
- Article
- Photo Phenosizer, a rapid machine learning-based method to measure cell dimensions in fission yeast.microPublication biology · 2022Article
- Does Organelle Shape Matter?: Exploring Patterns in Cell Shape and Structure with High-Throughput (HT) Imaging.CourseSource · 2022Article
- From imaging a single cell to implementing precision medicine: an exciting new era.Emerging topics in life sciences · 2021Article
- Lessons in Organic Fluorescent Probe Discovery.Chembiochem : a European journal of chemical biology · 2021Review
- Image-based profiling for drug discovery: due for a machine-learning upgrade?Nature reviews. Drug discovery · 2021Review
- Article
- Sensing Senses: Optical Biosensors to Study Gustation.Sensors (Basel, Switzerland) · 2020Review
- Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.Molecular systems biology · 2020Article
- OpSeF: Open Source Python Framework for Collaborative Instance Segmentation of Bioimages.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.
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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.