Analysis of single-cell differences by use of an on-chip microculture system and optical trapping

2001 ◽  
Vol 371 (2) ◽  
pp. 276-281 ◽  
Author(s):  
Yuichi Wakamoto ◽  
Ippei Inoue ◽  
Hiroyuki Moriguchi ◽  
Kenji Yasuda
Keyword(s):  
Single Cell ◽  
Optical Trapping ◽  
On Chip

On-Chip Cell Sorter for Single Cell Expression Analysis

2001 ◽  
pp. 271-273 ◽  
Author(s):  
T. Ichiki ◽  
T. Ujiie ◽  
T. Hara ◽  
Y. Horiike ◽  
K. Yasuda
Keyword(s):  
Single Cell ◽  
Expression Analysis ◽  
Cell Sorter ◽  
On Chip ◽  
Cell Expression

On-Chip Single-Cell Lysis for Extracting Intracellular Material

2007 ◽  
Vol 46 (9B) ◽  
pp. 6410-6414 ◽  
Author(s):  
Norifumi Ikeda ◽  
Nobuaki Tanaka ◽  
Yasuko Yanagida ◽  
Takeshi Hatsuzawa
Keyword(s):  
Single Cell ◽  
Cell Lysis ◽  
On Chip

scholarly journals An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Mathias Girault ◽  
Hyonchol Kim ◽  
Hisayuki Arakawa ◽  
Kenji Matsuura ◽  
Masao Odaka ◽  
...  
Keyword(s):  
Single Cell ◽  
Real Time ◽  
Shape Recognition ◽  
Target Cells ◽  
Recognition Method ◽  
System A ◽  
Time Shape ◽  
Droplet Sorting ◽  
On Chip ◽  
Sorting System

scholarly journals Massively parallel quantification of phenotypic heterogeneity in single cell drug responses

2020 ◽  
Author(s):  
Benjamin B. Yellen ◽  
Jon S. Zawistowski ◽  
Eric A. Czech ◽  
Caleb I. Sanford ◽  
Elliott D. SoRelle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Single Cell Analysis ◽  
Phenotypic Heterogeneity ◽  
Massively Parallel ◽  
Cell Analysis ◽  
On Chip ◽  
Acute Myeloid

AbstractSingle cell analysis tools have made significant advances in characterizing genomic heterogeneity, however tools for measuring phenotypic heterogeneity have lagged due to the increased difficulty of handling live biology. Here, we report a single cell phenotyping tool capable of measuring image-based clonal properties at scales approaching 100,000 clones per experiment. These advances are achieved by exploiting a novel flow regime in ladder microfluidic networks that, under appropriate conditions, yield a mathematically perfect cell trap. Machine learning and computer vision tools are used to control the imaging hardware and analyze the cellular phenotypic parameters within these images. Using this platform, we quantified the responses of tens of thousands of single cell-derived acute myeloid leukemia (AML) clones to targeted therapy, identifying rare resistance and morphological phenotypes at frequencies down to 0.05%. This approach can be extended to higher-level cellular architectures such as cell pairs and organoids and on-chip live-cell fluorescence assays.

A valve-based microfluidic device for on-chip single cell treatments

2018 ◽  
Vol 40 (6) ◽  
pp. 961-968 ◽  
Author(s):  
Yue Sun ◽  
Bo Cai ◽  
Xiaoyun Wei ◽  
Zixiang Wang ◽  
Lang Rao ◽  
...  
Keyword(s):  
Single Cell ◽  
Microfluidic Device ◽  
On Chip

(Invited) On-Chip Characterization of Microcapsules Using a Capacitive Sensor for Microencapsulation and Single-Cell Analysis Applications

2021 ◽  
Vol MA2021-01 (60) ◽  
pp. 1603-1603
Author(s):  
Sajjad Janfaza ◽  
Seyedehhamideh Razavi ◽  
Arash Dalili ◽  
Mina Hoorfar
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Capacitive Sensor ◽  
Cell Analysis ◽  
On Chip
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