ReviewThe Journal of cell biology2010
Automated microscopy for high-content RNAi screening.
Review in The Journal of cell biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
65 citing papers in PubMed, 169 citations in OpenAlex.
- Reconstructing EBV reactivation and DNA damage response kinetics in morphologic pseudotime.bioRxiv : the preprint server for biology · 2025Article
- DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.Genomics, proteomics & bioinformatics · 2022Article
- Different cell imaging methods did not significantly improve immune cell image classification performance.PloS one · 2022Article
- Unleashing high content screening in hit detection - Benchmarking AI workflows including novelty detection.Computational and structural biotechnology journal · 2022Article
- In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2021Article
- Discovering antiviral restriction factors and pathways using genetic screens.The Journal of general virology · 2021Review
- High-Throughput Methods in the Discovery and Study of Biomaterials and Materiobiology.Chemical reviews · 2021Review
- Machine learning-enabled multiplexed microfluidic sensors.Biomicrofluidics · 2020Review
- High-Content Image-Based Single-Cell Phenotypic Analysis for the Testicular Toxicity Prediction Induced by Bisphenol A and Its Analogs Bisphenol S, Bisphenol AF, and Tetrabromobisphenol A in a Three-Dimensional Testicular Cell Co-culture Model.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Article
- From observing to predicting single-cell structure and function with high-throughput/high-content microscopy.Essays in biochemistry · 2019Review
- Cellular Imaging of Intracellular Bacterial Pathogens.Microbiology spectrum · 2019Article
- Gal8 Visualization of Endosome Disruption Predicts Carrier-Mediated Biologic Drug Intracellular Bioavailability.ACS nano · 2019Article
- ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data.PloS one · 2019Article
- A computational framework to study sub-cellular RNA localization.Nature communications · 2018Article
- Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.Methods in molecular biology (Clifton, N.J.) · 2018Article
- A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay.GigaScience · 2017Article
- Digging deep into Golgi phenotypic diversity with unsupervised machine learning.Molecular biology of the cell · 2017Article
- A deep learning and novelty detection framework for rapid phenotyping in high-content screening.Molecular biology of the cell · 2017Article
- High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease.Trends in genetics : TIG · 2017Review
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs.
Indexed as
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What OpenQuestion holds
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.