Title :

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

Author(s) :

Ahmadian, Bahram [Auteur]
JUNIA [JUNIA]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Mbujamba, Deborah [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
Gerbedoen, Jean Claude [Auteur]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
Kumemura, Momoko [Auteur]
Kyushu Institute of Technology [Kyutech]
Laboratory for Integrated Micro Mechatronics Systems [LIMMS]
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 (Admin), 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]
JUNIA [JUNIA]
Bio-Micro-Electro-Mechanical Systems - IEMN [BIOMEMS - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]

Conference title :

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

City :

Tokyo

Country :

Japon

Start date of the conference :

2022-01-09

Journal title :

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

Publication date :

2022

English keyword(s) :

Single-cell characterization
supervised learning
MEMS tweezers
cancer cell classification

HAL domain(s) :

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

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Cancer Heterogeneity, Plasticity and Resistance to Therapies (CANTHER) - UMR 9020 - UMR 1277

Source :

Harvested from HAL