Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning

We 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 lookin...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Remy Elbez, Jeff Folz, Alan McLean, Hernan Roca, Joseph M. Labuz, Kenneth J. Pienta, Shuichi Takayama, Raoul Kopelman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fdb6ebe73a384d29930d6889ca7e3b22
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fdb6ebe73a384d29930d6889ca7e3b22
record_format	dspace
spelling	oai:doaj.org-article:fdb6ebe73a384d29930d6889ca7e3b222021-11-25T06:13:54ZCell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning1932-6203https://doaj.org/article/fdb6ebe73a384d29930d6889ca7e3b222021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8598033/?tool=EBIhttps://doaj.org/toc/1932-6203We 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.Remy ElbezJeff FolzAlan McLeanHernan RocaJoseph M. LabuzKenneth J. PientaShuichi TakayamaRaoul KopelmanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Remy Elbez Jeff Folz Alan McLean Hernan Roca Joseph M. Labuz Kenneth J. Pienta Shuichi Takayama Raoul Kopelman Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
description	We 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.
format	article
author	Remy Elbez Jeff Folz Alan McLean Hernan Roca Joseph M. Labuz Kenneth J. Pienta Shuichi Takayama Raoul Kopelman
author_facet	Remy Elbez Jeff Folz Alan McLean Hernan Roca Joseph M. Labuz Kenneth J. Pienta Shuichi Takayama Raoul Kopelman
author_sort	Remy Elbez
title	Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
title_short	Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
title_full	Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
title_fullStr	Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
title_full_unstemmed	Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
title_sort	cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/fdb6ebe73a384d29930d6889ca7e3b22
work_keys_str_mv	AT remyelbez cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning AT jefffolz cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning AT alanmclean cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning AT hernanroca cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning AT josephmlabuz cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning AT kennethjpienta cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning AT shuichitakayama cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning AT raoulkopelman cellmorphodynamicphenotypeclassificationwithapplicationtocancermetastasisusingcellmagnetorotationandmachinelearning
_version_	1718414021494833152

Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning

Ejemplares similares