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dc.contributor.authorElbez, Remy
dc.contributor.authorFolz, Jeff
dc.contributor.authorMcLean, Alan
dc.contributor.authorRoca, Hernan
dc.contributor.authorLabuz, Joseph M
dc.contributor.authorPienta, Kenneth J
dc.contributor.authorTakayama, Shuichi
dc.contributor.authorKopelman, Raoul
dc.date.accessioned2022-01-07T00:31:55Z
dc.date.available2022-01-07T00:31:55Z
dc.date.issued2021
dc.identifier.issn1932-6203
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332264
dc.description.abstractWe define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell magnetorotation, with the aid of cell embedded magnetic nanoparticles, our method allows each cell to move freely in 3 dimensions, with a rapid following of cell deformations in all 3-dimensions, so as to identify and classify a cell by its dynamic morphology. Using object recognition and machine learning algorithms, we continuously measure the real-time shape dynamics of each cell, where from we successfully resolve the inherent broad heterogeneity of the morphological phenotypes found in a given cancer cell population. In three illustrative experiments we have achieved clustering, differentiation, and identification of cells from (A) two distinct cell lines, (B) cells having gone through the epithelial-to-mesenchymal transition, and (C) cells differing only by their motility. This microfluidic method may enable a fast screening and identification of invasive cells, e.g., metastatic cancer cells, even in the absence of biomarkers, thus providing a rapid diagnostics and assessment protocol for effective personalized cancer therapy.
dc.format.mediumElectronic-eCollection
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCluster Analysis
dc.subjectHumans
dc.subjectImmunologic Tests
dc.subjectMachine Learning
dc.subjectNeoplasms, Second Primary
dc.titleCell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning.
dc.typeArticle
dc.publisher.departmentDepartment of Chemistry
dc.date.updated2022-01-06T11:44:43Z
prism.issueIdentifier11
prism.publicationDate2021
prism.publicationNamePLoS One
prism.startingPagee0259462
prism.volume16
dc.identifier.doi10.17863/CAM.79709
dcterms.dateAccepted2021-10-19
rioxxterms.versionofrecord10.1371/journal.pone.0259462
rioxxterms.versionVoR
dc.contributor.orcidFolz, Jeff [0000-0002-4823-3343]
dc.identifier.eissn1932-6203
rioxxterms.typeJournal Article/Review
cam.issuedOnline2021-11-17
cam.depositDate2022-01-06
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International