Titre :

Single cell classification using statistical learning on mechanical properties measured by mems tweezers

Auteur(s) :

Ahmadian, Bahram [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
JUNIA [JUNIA]
Mbujamba, Deborah [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
Gerbedoen, Jean Claude [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
Kumemura, Momoko [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
Kyushu Institute of Technology [Kyutech]
Fujita, Hiroyuki [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
Collard, Dominique [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Lagadec, Chann [Auteur]
Hétérogénéité, Plasticité et Résistance aux Thérapies des Cancers = Cancer Heterogeneity, Plasticity and Resistance to Therapies - UMR 9020 - U 1277 [CANTHER]
Tarhan, Mehmet-Cagatay [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
JUNIA [JUNIA]

Titre de la manifestation scientifique :

IEEE 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS 2022)

Ville :

Tokyo

Pays :

Japon

Date de début de la manifestation scientifique :

2022-01-09

Titre de la revue :

Proceedings of the 35th International Conference on Micro Electro Mechanical Systems Conference (MEMS 2022)

Date de publication :

2022

Mot(s)-clé(s) en anglais :

Single-cell characterization
supervised learning
MEMS tweezers
cancer cell classification

Discipline(s) HAL :

Sciences de l'ingénieur [physics]
Statistiques [stat]

Résumé en anglais : [en]

Cell population is heterogenous and so presents a wide range of properties as metastatic potential. But using rare cells for clinical applications requires precise classification of individual cells. Here, we propose a ...
Lire la suite >Cell population is heterogenous and so presents a wide range of properties as metastatic potential. But using rare cells for clinical applications requires precise classification of individual cells. Here, we propose a multi-parameter analysis of single cells to classify them using statistical learning techniques and to predict the sub-population of each cell, although they may have close characteristics. We used MEMS tweezers to analyze mechanical properties (stiffness, viscosity, and size) of single cells from two different breast cancer cell lines in a controlled environment and run supervised learning methods to predict the population they belong to. This label-free method is a significant step forward to distinguish rare cell sub-populations for clinical applications.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre d'Infection et d'Immunité de Lille (CIIL) - U1019 - UMR 9017

Source :

Harvested from HAL

Date de dépôt :

2024-02-17T04:01:30Z