The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Multidimensional Drug Profiling By Automated Microscopy
Abstract
We present a method for high-throughput cytological profiling by microscopy. Our system provides quantitative multidimensional measures of individual cell states over wide ranges of perturbations. We profile dose-dependent phenotypic effects of drugs in human cell culture with a titration-invariant similarity score (TISS). This method successfully categorized blinded drugs and suggested targets for drugs of uncertain mechanism. Multivariate single-cell analysis is a starting point for identifying relationships among drug effects at a systems level and a step toward phenotypic profiling at the single-cell level. Our methods will be useful for discovering the mechanism and predicting the toxicity of new drugs.
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“Most published HCS studies do not measure how cellular responses change as a function of compound dosage, nor do they treat cell count as an indicator of stress response14.”
From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [14]
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Metadata
- Authors
- Perlman, Z. E., Slack, M. D., Feng, Y., Mitchison, T. J., Wu, L. F., & Altschuler, S. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Perlman, Zachary E.
- Slack, Michael D.
- Feng, Yan
- Wu, Lani F.
- Altschuler, Steven J.
- Venue
- Science
- Published
- 2004-11-12
- Volume
- 306
- Issue
- 5699
- Pages
- 1194-1198
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1993669821
- Cited by
- 660
- References
- 29
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 26 · 2023: 13 · 2022: 29 · 2021: 23 · 2020: 25 · 2019: 22 · 2018: 15 · 2017: 22 · 2016: 47 · 2015: 26 · 2014: 27 · 2013: 37 · 2012: 33
- FWCI
- 29.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (P01CA078048)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- drug profiling, automated microscopy, phenotypic profiling, drug target identification, mechanism of action, drug toxicity prediction, single-cell analysis, human cell culture
- MeSH terms
- Automation, Cell Cycle, DNA, Dose-Response Relationship, Drug, Drug Evaluation, Preclinical, Fluorescent Dyes, HeLa Cells, Humans, Image Processing, Computer-Assisted, Microscopy, Fluorescence, Pharmacology, Phenotype, Statistics as Topic, Cluster Analysis, Toxicity Tests
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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