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

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 access

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

DOI 10.1038/nrd.2015.16PMC PMC4883669

Open this paper in CoChat — free account

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.

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

Crossref · 10.1038/nrd.2015.16

Cites

173 of 173 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications.
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
Short-Term Effects of Nose-Only Cigarette Smoke Exposure on Glutathione Redox Homeostasis, Cytochrome P450 1A1/2 and Respiratory Enzyme Activities in Mice Tissues
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
The Clonal Evolution of Tumor Cell Populations: Acquired genetic lability permits stepwise selection of variant sublines and underlies tumor progression.
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

Show all 173 references

Sorted by citations

Items 401–473 of 473

Three-dimensional angular scattering simulations inform analysis of scattering from single cells
Dunn, K. J., & Berger, A. J. (2023) · Journal of Biomedical Optics · doi:10.1117/1.jbo.28.8.086501 · Cited by 2
Counting Proteins in Single Cells with Addressable Droplet Microarrays
Chatzimichail, S., Supramaniam, P., Ces, O., & Salehi‐Reyhani, A. (2018) · Journal of Visualized Experiments · doi:10.3791/56110 · Cited by 2
An integrated nanoplasmonic biosensor for monitoring cytokine secretion from single cells
Li, X., Soler, M., Szydzik, C., Khoshmanesh, K., Schmidt, J., Yesilköy, F., Belushkin, A. A., Coukos, G., Mitchell, A., & Altug, H. (2019) · Conference on Ph.D. Research in Microelectronics and Electronics · doi:10.1109/prime.2019.8787840 · Cited by 2
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of DrugsPreprint
He, B., Xiao, Y., Liang, H., Huang, Q., Du, Y., Li, Y., Garmire, D., Sun, D., & Garmire, L. X. (2021) · PubMed · doi:10.48550/arxiv.2109.06377 · Cited by 2
Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profilesPreprint
Zhang, W., Maeser, D., Lee, A., Huang, Y., Gruener, R. F., Abdelbar, I. G., Jena, S., Patel, A. G., & Huang, R. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.29.564598 · Cited by 2
Massively parallel identification of single-cell immunophenotypesPreprint
Cienciala, M., Alvarez, L., Berne, L., Chena, D., Fikar, P., Holubová, M., Kasl, H., Lysák, D., Luo, M., Novackova, Z., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.04.587924 · Cited by 1
Fiber-optic devices for sensing, manipulating, and imaging cells in vitro and in vivo
Zhu, S., Zeng, S., Wang, Y., Ho, A. H. P., & Yuan, W. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823846-2.00019-5 · Cited by 1
Development of constrictional microchannels and the recurrent neural network in single-cell protein analysis
Zhang, T., Chen, X., Chen, D., Wang, J., & Chen, J. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1195940 · Cited by 1
Single-Cell Proliferation Microfluidic Device for High Throughput Investigation of Replicative Potential and Drug Resistance of Cancer Cells
Pore, A. A., Kamyabi, N., Bithi, S. S., Ahmmed, S. M., & Vanapalli, S. A. (2023) · Cellular and Molecular Bioengineering · doi:10.1007/s12195-023-00773-z · Cited by 1
Representation of high-dimensional cell morphology and morphodynamics in 2D latent space
Cunningham, C. T., & Sun, B. (2025) · Physical Biology · doi:10.1088/1478-3975/adcd37 · Cited by 1
Three-dimensional forward-scattering imaging flow cytometry system for single-cell analysis
Zhou, M., Zhao, J., Chen, X., Zhang, Z., Lai, Z., Zhou, Z., de la Zerda, A., & Lo, Y.-H. (2025) · APL Photonics · doi:10.1063/5.0301435 · Cited by 1
Label-free single cell analysis in microfluidics using digital holographic microscopy
Bansode, V., Espulgar, W., & Saito, M. (2025) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ae2dac · Cited by 1
A review of the wide applications of microfluidic devices in peri-implantitis, periodontitis, and endodontics
Rostami, M. N., Ostadkhalil, F., Shenasa, N., Jamil, N. Y., Alfarttoosi, K. H., Taher, S. G., Alwan, M., Jawad, M. J., Mushtaq, H., & Yousefbeigi, A. (2026) · Odontology · doi:10.1007/s10266-026-01311-9 · Cited by 2
Structural shifts and constraints in animal-based neuroscience
Treviño, M., & Arias-Carrión, Ó. (2026) · Academia Neuroscience and Brain Research · doi:10.20935/acadneurosci8190 · Cited by 1
Concentration Gradient‐Controlled Electrochemical Etching: A Universal Strategy for Nanoelectrodes with Tunable Geometries
Chang, Y., Zhang, D., LIU, Z., Wang, L., Guo, G., & Wang, X. (2026) · Small Methods · doi:10.1002/smtd.70723 · Cited by 1
Fundamentals of Proteomics
Xia, X. (2018) · Springer eBooks · doi:10.1007/978-3-319-90684-3_19 · Cited by 1
Biosensors for single-cell proteomic characterization
Zhao, Y., Zhao, S., Li, Y., Yang, Y., & Liu, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89841-6.00004-9 · Cited by 1
Mass Spectrometry-Based Analytical Strategy for Single-Cell Proteomics
Végvári, Á. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1936-0_6 · Cited by 1
Fundamentals and methods of atomic force microscopy for biophysics
Li, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-95360-3.00009-5 · Cited by 1
Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning
Yang, L., Dutta, P., Davuluri, R. V., & Wang, J. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c01157 · Cited by 1
Unmassked: Single-Cell Profiling of Immune Cell Populations in Tumors
Varadarajan, N. (2017) · Molecular Therapy · doi:10.1016/j.ymthe.2017.06.024 · Cited by 1
Development of Micro Inclined Well Array for Trapping Single Cells
Tsukamoto, T., Furuya, N., Shimagami, T., Sato, R., Shimokawa, F., Akimitsu, K., & Suzuki, T. (2018) · IEEJ Transactions on Sensors and Micromachines · doi:10.1541/ieejsmas.138.208 · Cited by 1
Development of a Sensitive Trace Bioanalysis System Using Microscale Flow Control and Electrophoresis
Kawai, T. (2018) · BUNSEKI KAGAKU · doi:10.2116/bunsekikagaku.67.599 · Cited by 1
Pluripotent stem cell biology and engineering
Cotovio, J. P., Fernandes, T. G., Diogo, M. M., & Cabral, J. M. S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816221-7.00001-x · Cited by 1
Endocrinology of a Single Cell: Tools and Insights
Pinças, H., Ruf-Zamojski, F. M., Turgeon, J. L., & Sealfon, S. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819801-8.00001-6 · Cited by 1
Hypothalamic Nutrient Sensing
Heeley, N. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.18622 · Cited by 1
Parameter estimation in FACS-seq enables high-throughput characterization of phenotypic heterogeneityPreprint
Feng, H., & Zhang, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03317 · Cited by 1
Multidimensional patterning and manipulating of microparticles and cells using surface acoustic waves
Nguyen, T. D. (2020) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/146006 · Cited by 1
Endocrinology of the Single Cell
Turgeon, J. L., & Waring, D. W. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-408134-5.00020-2
Developing multilayer microfluidic platforms and advancing laser induced fluorescent detection and electrochemical detection to analyze intracellular protein kinases, reactive nitrogen and oxygen species in single cells
Patabadige, D. E. W. (2017) · K-State Research Exchange (Kansas State University)
Single Cell Analysis for Drug Discovery and Development
Heath, J. R. (2017) · Clinical Lymphoma Myeloma & Leukemia · doi:10.1016/j.clml.2017.08.025
Self-citation
Microfluidic Probes for Single-Cell Proteomic Analysis
Sarkar, A., Wu, L., & Han, J. (2018) · Wiley-VCH eBooks · doi:10.1002/9783527696789.ch11
Translational Application of Single-cell Transcriptomic Analysis in Hepatocellular Carcinoma
Yoon, S. K. (2018) · Journal of Liver Cancer · doi:10.17998/jlc.18.1.1
Multiparameter cytometric analysis of complex cellular response
Šimečková, Š., Fedr, R., Remšík, J., Kahounová, Z., Slabáková, E., & Souček, K. (2017) · Cytometry Part A · doi:10.1002/cyto.a.23295
Cameraless High-throughput 3D Imaging Flow CytometryPreprint
Han, Y., Tang, R. H., Gu, Y., Zhang, A. C., Cai, W., Castor, V., Cho, S. H., Alaynick, W. A., & Lo, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.01393
Multiplexed angiogenic biomarker quantification on single cells
Chen, S. (2018) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Special Issue on Single‐Cell Multiomics for Immuno‐Oncology and Cancer Systems Biology
Wei, W., & Fan, R. (2019) · PROTEOMICS · doi:10.1002/pmic.201900235
Trajectories from Snapshots: Integrated proteomic and metabolic single-cell assays reveal multiple independent adaptive responses to drug tolerance in a BRAF-mutant melanoma cell linePreprint
Lü, Y., Li, G., Ko, M. E., Cheng, H., Zhu, R., Xue, M., Wang, J., Lee, J. W., Frankiw, L. S., Xu, A. M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/767988
Self-citation
Strategic design of flow structures for single (bio) particle analysis using droplet microfluidic platform
Nguyen, H. A. T. (2019) · UWSpace (University of Waterloo)
Examining the co-expression, transcriptome clustering and variation using fuzzy cluster network of testicular stem cells and pluripotent stem cells compared with other cell types
Guttula, P. K., & Gupta, M. K. (2020) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2020.107227
Application in Single-cell Functional Analysis
Chen, X., & Zhao, W. (2020) · Royal Society of Chemistry eBooks · doi:10.1039/9781839162855-00175
NOMA: a high-throughput microchip for robust, sequential measurements of secretions from the same single-cellsPreprint
Liu, M., Ji, Y., Zhu, F., Bai, X., Li, L., Xie, H., Liu, X., Luo, Y., Liu, T., Lin, B., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.21.427049
Single Cell Study of Malaria and Antimalarial Drug Impacts on Platelets and Mast Cells
Xiong-Hang, K. (2020) · University of Minnesota Digital Conservancy (University of Minnesota)
Spatial MIST Technology for Rapid, Highly Multiplexed Detection of Protein Distribution on Brain TissuePreprint
Reddy, R., Yang, L., Liu, J. J., Liu, Z., & Wang, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.11.475923
Faculty Opinions recommendation of Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks.
Yip, K. Y.-L. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734659860.793556264
Simultaneous Multisheet Multichannel Imaging Cytometry (SMMIC) System Powered by Adaptive Vertically-Aligned Multi-Sheet Array (VAMSA) PSFPreprint
Joshi, P., Kumar, P., Aravinth, S., & Mondal, P. P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.25.497275
Single identical cell cytotoxicity assay on lithographically generated microstructures
Li, Q. (2021) · doi:10.17760/d20429146
The way forward in multi-omics data analyses
Nguyen, N. (2022) · doi:10.26481/dis.20221122tn
“Learning on a chip:” Microﬂuidics for formal and informal science education
Rackus, D. G., Riedel-Kruse, I. H., & Pamme, N. (2023) · doi:10.32920/22027739
“Learning on a chip:” Microﬂuidics for formal and informal science education
Rackus, D. G., Riedel-Kruse, I. H., & Pamme, N. (2023) · doi:10.32920/22027739.v1
scRAA: the development of a robust and automatic annotation procedure for single-cell RNA sequencing data
Yan, D., Sun, Z., Fang, J., Cao, S., Wang, W., Chang, X., Badirli, S., Fu, H., & Liu, Y. (2023) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2023.2208671
Quantitative Peripheral Live Single T-Cell Dynamic Polyfunctionality as a Predictive Biomarker in Cancer ImmunotherapyPreprint
Lim, Z.-F., Wu, X., Zhu, L., Albandar, H. J., Hafez, M., Zhao, C., Almubarak, M., Smolkin, M. B., Zheng, H., Wen, S., et al. (2024) · Preprints.org · doi:10.20944/preprints202403.0077.v1
An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells
Champagne, A., Chebra, I., Jain, P., Goulet, C. R., Lauzier, A., Guyon, A., Neveu, B., & Pouliot, F. (2024) · Biosensors · doi:10.3390/bios14040175
AI: Catalyst for Drug Discovery and Development
Nailwal, K., Durgapal, S., Dasauni, K., & Nailwal, T. K. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-1148-2_18
The Use of New Omics Techniques for Natural Products to Improve Drug Efficiency and Mechanism
Koriem, K. M. M. (2024) · Current Proteomics · doi:10.2174/0115701646320408240527081626
Customizable and Interactive Visualizations for Investigating Spatio-Temporal Single-Cell Information
Seiffarth, J., Blöbaum, L., Grünberger, A., & Nöh, K. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635297
Personalized Molecular Therapies for Advanced Non-Small Cell Lung Cancer: Overcoming Heterogeneity to Optimize Treatment Response and Clinical Outcomes
Lim, Z.-F. (2024) · The Research Repository @ WVU (West Virginia University) · doi:10.33915/etd.12594
Single Cell RNA-Seq Analysis Reveals Similarities Between Colorectal, Liver and Lung T Cells’ Populations
Saada, B., Qu, C., Siga, E., & Jing, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1907-8_2
The modern-day drug discovery
Dahman, Y., Khullar, S., Linetsky, K., Marinescu, L., Nguyen, J. M., & Sharma, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23848-2.00001-2
scDrugLink: Single-Cell Drug Repurposing for CNS Diseases via Computationally Linking Drug Targets and Perturbation Signatures
Huang, L., Lu, X., & Chen, D. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3552536
Steering a Thermocapillary Droplet Motion in Combined Couette–Poiseuille Flow
Basak, A., Lakkaraju, R., & Sekhar, G. P. R. (2025) · Journal of Engineering Mathematics · doi:10.1007/s10665-025-10432-z
Simultaneous trapping and optical spectroscopy of single samples using optoelectronic tweezers
Zaman, M. A., Wu, M., Ren, W., & Hesselink, L. (2025) · Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues · doi:10.1117/12.3043801
High resolution tissue and cell type identification via single cell transcriptomic profiling
Liu, M., Zheng, S., Li, H., Budowle, B., Wang, L., Lou, Z., & Ge, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318151 · Cited by 1
Highly Efficient Calibration‐Free Color Compensation Algorithm for Imaging Flow Cytometry
Zhou, Z., Lai, Z., Tang, R. H., Chen, X., Qu, Y., Xia, L., George, M., Munoz, A., Zhou, M., Tai, Y., et al. (2025) · Cytometry Part A · doi:10.1002/cyto.a.24931
Potential of Interactive VR and Digital Twin for Drug Development and Clinical Trials
Malviya, R., & Rajput, S. (2025) · Wiley eBooks · doi:10.1002/9781394315680.ch8
Development of infrared desolvation device for single cell element analysis using droplet sample injection inductively coupled plasma spectrometry
Fukuchi, K., Yamaji, S., Shimizu, Y., Ohta, T., Yaida, A., Maemoto, Y., Aoki, M., Umemura, T., & Okino, A. (2026) · Analytical Sciences · doi:10.1007/s44211-026-00888-z
Development of microsystems for the controlled formation of cell aggregates by dielectrophoresis
Cottet, J. (2018) · theses.fr (ABES) · doi:10.70675/2f1d4274zc3a7z478dzb2acz247fa00bacba
BLAST, FASTA, and String Algorithms
Xia, X. (2026) · Springer eBooks · doi:10.1007/978-3-032-18842-7_1
Social and Economic Aspects of Single-Cell Analysis
Nguyen, T.-Q., Bui, N. L., Nguyen, M. Q., & Chu, D.-T. (2026) · Springer eBooks · doi:10.1007/978-981-95-9789-5_18
Surface-Enhanced Raman Scattering Nanofibre Sensor for Metabolite Microextraction and Chemometrics-Aided Differentiation of Bacteria
Chisanga, M., Lemay-St-Denis, C., Gallant, S. M. V., Pelletier, J. N., & Masson, J. (2026) · ACS Applied Nano Materials · doi:10.1021/acsanm.6c00444
Dual chemohormonal silver–tamoxifen nanosystem for targeted therapy of ER+ breast cancer: Multi-omics mechanistic insights and in vivo validation
Aragoneses-Cazorla, G., García, R. Á.-F., Peñalver, G. A., Martínez‐López, A., Vallet‐Regí, M., Castillo‐Lluva, S., González, B., & Luque-Garcia, J. L. (2026) · Microporous and Mesoporous Materials · doi:10.1016/j.micromeso.2026.114315
Simultaneously mapping topography, surface electrical, and mechanical properties by scanning ion conductance microscopy
Tabares, J., Prajapati, N. J., Chandler, K. B., Shaver, M., Wang, Z., Hutcheson, J. D., & He, J. M. (2026) · Review of Scientific Instruments · doi:10.1063/5.0301582
A hybrid microfluidic single-cell platform for simultaneous drug stimulation detection and genotyping
Liu, L., Dong, X., Ma, X., Zhang, L., Mauk, M. G., Li, W., Wei, Z., Miao, G., & Qiu, X. (2026) · Microfluidics and Nanofluidics · doi:10.1007/s10404-026-02927-7

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

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

Single-cell analysis tools for drug discovery and development

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks