Cited by 473Single-cell and spatial transcriptomicsCancer Genomics and DiagnosticsAdvanced Proteomics Techniques and Applications

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Single-cell analysis tools for drug discovery and development

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Heath, James R., Ribas, Antoni, Mischel, Paul S.·2016·Nature Reviews Drug Discovery

DOI 10.1038/nrd.2015.16PMC PMC4883669

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Abstract

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed.

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The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
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Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
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Nowell, P. C. (1976) · Science · doi:10.1126/science.959840 · Cited by 6,471
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McDonald, M. P., Gemeinhardt, A., König, K., Piliarik, M., Schaffer, S., Völkl, S., Aigner, M., Mackensen, A., & Sandoghdar, V. (2017) · Nano Letters · doi:10.1021/acs.nanolett.7b04494 · Cited by 65
Bridging the gap: microfluidic devices for short and long distance cell–cell communication
Vu, T. Q., de Castro, R. M. B., & Qin, L. (2017) · Lab on a Chip · doi:10.1039/c6lc01367h · Cited by 63
Leveraging premalignant biology for immune-based cancer prevention
Spira, A. E., Disis, M. L., Schiller, J. T., Vilar, E., Rebbeck, T. R., Bejar, R. L., Ideker, T., Arts, J., Yurgelun, M. B. B., Mesirov, J. P., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608077113 · Cited by 64
A roadmap towards personalized immunology
Delhalle, S., Bode, S. F. N., Balling, R., Ollert, M. W., & HeFeng, F. Q. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0045-9 · Cited by 70
Recent advances in cancer metabolism: a technological perspective
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Integrated dual-tomography for refractive index analysis of free-floating single living cell with isotropic superresolution
Balasubramani, V., Lai, X.-J., Lin, Y., Tu, H.-Y., & Cheng, C.-J. (2018) · Scientific Reports · doi:10.1038/s41598-018-24408-w · Cited by 62
Profiling Single Cancer Cells with Volatolomics Approach
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Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance
Cheng, X., Peng, T., Chu, T., Yang, Y., Liu, J., Gao, Q., Cao, C., & Wei, J. (2025) · Journal of Hematology & Oncology · doi:10.1186/s13045-025-01722-1 · Cited by 66
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Lee, S., Sun, Y., Cao, Y., & Kang, S. H. (2019) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2019.05.006 · Cited by 61
Microchip-based single-cell functional proteomics for biomedical applications
Lu, Y., Liu, Y., Wei, W., & Shi, Q. (2017) · Lab on a Chip · doi:10.1039/c7lc00037e · Cited by 61
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Chen, Z., Chen, J. J. F., & Fan, R. (2019) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-061318-115055 · Cited by 62
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He, B., Xiao, Y., Liang, H., Huang, Q., Du, Y., Li, Y., Garmire, D., Sun, D., & Garmire, L. X. (2023) · Nature Communications · doi:10.1038/s41467-023-36637-3 · Cited by 57
Genetics in Atopic Dermatitis: Historical Perspective and Future Prospects
Brown, S., Elias, M. S., & Bradley, M. (2020) · Acta Dermato Venereologica · doi:10.2340/00015555-3513 · Cited by 64
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Andjelković, U., & Josić, D. (2018) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2018.04.008 · Cited by 61
High-Speed Imaging Meets Single-Cell Analysis
Mikami, H., Lei, C., Nitta, N., Sugimura, T., Ito, T., Ozeki, Y., & Goda, K. (2018) · Chem · doi:10.1016/j.chempr.2018.06.011 · Cited by 56
Redox signaling by glutathione peroxidase 2 links vascular modulation to metabolic plasticity of breast cancer
Ren, Z., Liang, H., Galbo, P. M., Dharmaratne, M., Kulkarni, A. S., Fard, A. T., Aoun, M. L., Martínez-López, N., Suyama, K., Benard, O., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2107266119 · Cited by 57
Intracellular Hydrogen Peroxide Detection with Functionalised Nanoelectrodes
Marquitan, M., Clausmeyer, J., Actis, P., Córdoba, A. L., Korchev, Y. E., Mark, M. D., Herlitze, S., & Schuhmann, W. W. (2016) · ChemElectroChem · doi:10.1002/celc.201600390 · Cited by 55
Application of single-cell technology in cancer research
Liang, S.-B., & Fu, L. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2017.04.001 · Cited by 56
Single cell imaging with near‐field terahertz scanning microscopy
Li, Z., Yan, S., Zang, Z., Geng, G., Yang, Z., Li, J., Wang, L., Yao, C., Cui, H., Chang, C., et al. (2020) · Cell Proliferation · doi:10.1111/cpr.12788 · Cited by 55
Towards quantitative and multiplexed in vivo functional cancer genomics
Winters, I. P., Murray, C. W., & Winslow, M. M. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0053-7 · Cited by 54

Authors: Heath, J. R., Ribas, A., & Mischel, P. S.
Affiliations: California Institute of Technology (US)
University of California, Los Angeles (US)
University of California San Diego (US)
Corresponding author: Heath, James R.
ORCID: Heath, James R.
Ribas, Antoni
Mischel, Paul S.
Venue: Nature Reviews Drug Discovery
Publisher: Springer Science and Business Media LLC
Published: 2015-12-16
Volume: 15
Issue: 3
Pages: 204-216
ISSN: 1474-1776 (ISSN-L)
1474-1784
DOI: 10.1038/nrd.2015.16
PMC: PMC4883669
OpenAlex: W2248179576
Cited by: 473
References: 173
Citations by year: 2026: 13 · 2025: 28 · 2024: 30 · 2023: 51 · 2022: 39 · 2021: 43 · 2020: 56 · 2019: 61 · 2018: 75 · 2017: 56 · 2016: 20
FWCI: 4.88
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Reviews Drug Discovery (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
CaltechAUTHORS (California Institute of Technology) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Institutes of Health (5u54ca119347-03)
National Cancer Institute (5U54 CA119347, U54-CA119347, U54CA199090, R01CA170689)
National Institute of Neurological Disorders and Stroke (R01NS073831)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Keywords: single-cell analysis, drug discovery, drug development, cellular heterogeneity, single-cell genomics, single-cell transcriptomics, single-cell proteomics, multiplex proteomics, biomarkers, disease models
MeSH terms: Animals, Humans, Genomics, Proteomics, Drug Discovery, Single-Cell Analysis

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Single-cell analysis tools for drug discovery and development

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