The DOI matches its Crossref record; 173 of the 173 references it cites were found in a registry or an index.
Single-cell analysis tools for drug discovery and development
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Recent significant advances in single-cell technologies, especially the single-cell RNA sequencing (scRNA-seq) technology, have enabled the analysis of intercellular heterogeneity at a very fine resolution8,9 and helped us to have many breakthroughs in understanding disease mechanisms10, such as breast cancer11, liver cancer12 and COVID-1913.”
From ASGARD is a single-cell guided pipeline to aid repurposing of drugs (2023) · cites this paper as [8]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Towbin, H., Staehelin, T., & Gordon, J. (1979) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.76.9.4350 · Cited by 53,218
Raza, H., John, A., & Nemmar, A. (2013) · Cellular Physiology and Biochemistry · doi:10.1159/000350087 · Cited by 47,311
Microenvironmental regulation of tumor progression and metastasis
Quail, D. F., & Joyce, J. A. (2013) · Nature Medicine · doi:10.1038/nm.3394 · Cited by 7,994
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
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
Nowell, P. C. (1976) · Science · doi:10.1126/science.959840 · Cited by 6,471
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
The Somatic Genomic Landscape of Glioblastoma
Brennan, C. W., Verhaak, R. G. W., McKenna, A., Campos, B., Noushmehr, H., Salama, S. R., Zheng, S., Chakravarty, D., Sanborn, J. Z., Berman, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.034 · Cited by 5,016
Flagged first, then by citations
Cited by
Sorted by citations
Engineering and Design of Chimeric Antigen Receptors
Guedan, S., Calderón, H., Posey, A. D., & Maus, M. V. (2018) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2018.12.009 · Cited by 471
DNA methylation: An epigenetic mark of cellular memory
Kim, M., & Costello, J. (2017) · Experimental & Molecular Medicine · doi:10.1038/emm.2017.10 · Cited by 450
Paving the way to single-molecule protein sequencing
Restrepo-Pérez, L., Joo, C., & Dekker, C. (2018) · Nature Nanotechnology · doi:10.1038/s41565-018-0236-6 · Cited by 398
Rubin, A. J., Parker, K. R., Satpathy, A. T., Qi, Y., Wu, B., Ong, A. J., Mumbach, M. R., Ji, A. L., Kim, D. S., Cho, S. W., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.022 · Cited by 314
Utilizing graph machine learning within drug discovery and development
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313
Metadata
- 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.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2015-12-16
- Volume
- 15
- Issue
- 3
- Pages
- 204-216
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- 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
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 ›
Are you an author, or do you see a problem with this page? Request a review