Dissecting human disease with single-cell omics: application in model systems and in the clinic.
dc.contributor.author | Strzelecka, Paulina M | |
dc.contributor.author | Ranzoni, Anna M | |
dc.contributor.author | Cvejic, Ana | |
dc.date.accessioned | 2018-12-04T00:30:50Z | |
dc.date.available | 2018-12-04T00:30:50Z | |
dc.date.issued | 2018-11-05 | |
dc.identifier.issn | 1754-8403 | |
dc.identifier.uri | https://www.repository.cam.ac.uk/handle/1810/286254 | |
dc.description.abstract | Probing cellular population diversity at single-cell resolution became possible only in recent years. The popularity of single-cell 'omic' approaches, which allow researchers to dissect sample heterogeneity and cell-to-cell variation, continues to grow. With continuous technological improvements, single-cell omics are becoming increasingly prevalent and contribute to the discovery of new and rare cell types, and to the deciphering of disease pathogenesis and outcome. Animal models of human diseases have significantly facilitated our understanding of the mechanisms driving pathologies and resulted in the development of more efficient therapies. The application of single-cell omics to animal models improves the precision of the obtained insights, and brings single-cell technology closer to the clinical field. This Review focuses on the use of single-cell omics in cellular and animal models of diseases, as well as in samples from human patients. It also highlights the potential of these approaches to further improve the diagnosis and treatment of various pathologies, and includes a discussion of the advantages and remaining challenges in implementing these technologies into clinical practice. | |
dc.description.sponsorship | The study was supported by Cancer Research UK grant number C45041/A14953 and European Research Council project 677501 – ZF_Blood. The authors would like to thank Jana Elias for generating Figure 1. | |
dc.format.medium | Electronic | |
dc.language | eng | |
dc.publisher | The Company of Biologists | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Humans | |
dc.subject | Disease | |
dc.subject | Proteomics | |
dc.subject | Genomics | |
dc.subject | Models, Biological | |
dc.subject | Metabolomics | |
dc.subject | Single-Cell Analysis | |
dc.title | Dissecting human disease with single-cell omics: application in model systems and in the clinic. | |
dc.type | Article | |
prism.issueIdentifier | 11 | |
prism.publicationDate | 2018 | |
prism.publicationName | Dis Model Mech | |
prism.volume | 11 | |
dc.identifier.doi | 10.17863/CAM.33566 | |
dcterms.dateAccepted | 2018-09-19 | |
rioxxterms.versionofrecord | 10.1242/dmm.036525 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2018-11-05 | |
dc.contributor.orcid | Cvejic, Ana [0000-0003-3204-9311] | |
dc.identifier.eissn | 1754-8411 | |
rioxxterms.type | Journal Article/Review | |
pubs.funder-project-id | Cancer Research Uk (None) | |
pubs.funder-project-id | Medical Research Council (MC_PC_12009) | |
pubs.funder-project-id | European Research Council (677501) | |
cam.issuedOnline | 2018-11-05 |
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