Cited by 662Cell Image Analysis TechniquesSingle-cell and spatial transcriptomicsComputational Drug Discovery Methods

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Multidimensional Drug Profiling By Automated Microscopy

Perlman, Zachary E., Slack, Michael D., Feng, Yan, Mitchison, Timothy J., Wu, Lani F., Altschuler, Steven J.·2004·Science

DOI 10.1126/science.1100709

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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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Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Functional organization of the yeast proteome by systematic analysis of protein complexes
Gavin, A., Böesche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J. M., Michon, A., Cruciat, C., et al. (2002) · Nature · doi:10.1038/415141a · Cited by 4,729
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
Global analysis of protein localization in budding yeast
Huh, W.-K., Falvo, J. V., Gerke, L. C., Carroll, A. S., Howson, R. W., Weissman, J. S., & O'Shea, E. K. (2003) · Nature · doi:10.1038/nature02026 · Cited by 4,248
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry
Ho, Y., Gruhler, A., Heilbut, A. M., Bader, G. D., Moore, L., Adams, S., Millar, A., Taylor, P. P., Bennett, K. L., Boutilier, K., et al. (2002) · Nature · doi:10.1038/415180a · Cited by 3,573
A gene expression database for the molecular pharmacology of cancer
Scherf, U., Ross, D. T., Waltham, M. C., Smith, L. H., Lee, J. K., Tanabe, L. K., Kohn, K. W., Reinhold, W. C., Myers, T. G., Andrews, D. T., et al. (2000) · Nature Genetics · doi:10.1038/73439 · Cited by 1,450
Protein Kinase Inhibitors: Insights into Drug Design from Structure
Noble, M. E. M., Endicott, J., & Johnson, L. N. (2004) · Science · doi:10.1126/science.1095920 · Cited by 1,216
An Information-Intensive Approach to the Molecular Pharmacology of Cancer
Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace, A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L., Anderson, N. L., et al. (1997) · Science · doi:10.1126/science.275.5298.343 · Cited by 1,183
Dynamics of the p53-Mdm2 feedback loop in individual cells
Lahav, G., Rosenfeld, N., Sigal, A., Geva‐Zatorsky, N., Levine, A. J., Elowitz, M. B., & Alon, U. (2004) · Nature Genetics · doi:10.1038/ng1293 · Cited by 1,026
Drug target validation and identification of secondary drug target effects using DNA microarrays
Marton, M. J., DeRisi, J. L., Bennett, H. A., Iyer, V. R., Meyer, M. R., Roberts, C. J., Stoughton, R. B., Burchard, J., Slade, D., Dai, H. A., et al. (1998) · Nature Medicine · doi:10.1038/3282 · Cited by 643

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Research works printed but not linked: 3

J. H. Price et al., J. Cell. Biochem. Suppl.39, 194 (2002).
J. G. Hardman, L. E. Limbird, A. G. Gilman, Eds., The Pharmacological Basis of Therapeutics (McGraw-Hill, New York, ed. 10, 2001), pp. 39–42.
K. D. Paull, C. M. Lin, L. Malspeis, E. Hamel, Cancer Res.52, 3892 (1992).

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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.
ORCID: Mitchison, Timothy J.
Wu, Lani F.
Altschuler, Steven J.
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2004-11-12
Volume: 306
Issue: 5699
Pages: 1194-1198
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.1100709
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

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Multidimensional Drug Profiling By Automated Microscopy

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