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 ◽  
...  
Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1178 ◽  
Author(s):  
Jorge Prada ◽  
Christina Cordes ◽  
Carsten Harms ◽  
Walter Lang

This contribution outlines the design and manufacturing of a microfluidic device implemented as a biosensor for retrieval and detection of bacteria RNA. The device is fully made of Cyclo-Olefin Copolymer (COC), which features low auto-fluorescence, biocompatibility and manufacturability by hot-embossing. The RNA retrieval was carried on after bacteria heat-lysis by an on-chip micro-heater, whose function was characterized at different working parameters. Carbon resistive temperature sensors were tested, characterized and printed on the biochip sealing film to monitor the heating process. Off-chip and on-chip processed RNA were hybridized with capture probes on the reaction chamber surface and identification was achieved by detection of fluorescence tags. The application of the mentioned techniques and materials proved to allow the development of low-cost, disposable albeit multi-functional microfluidic system, performing heating, temperature sensing and chemical reaction processes in the same device. By proving its effectiveness, this device contributes a reference to show the integration potential of fully thermoplastic devices in biosensor systems.


Lab on a Chip ◽  
2021 ◽  
Author(s):  
Huichao Chai ◽  
Yongxiang Feng ◽  
Fei Liang ◽  
Wenhui Wang

Successful single-cell isolation is a pivotal technique for subsequent biological and chemical analysis of single cells. Although significant advances have been made in single-cell isolation and analysis techniques, most passive...


Author(s):  
T. Ichiki ◽  
T. Ujiie ◽  
T. Hara ◽  
Y. Horiike ◽  
K. Yasuda

Lab on a Chip ◽  
2011 ◽  
Vol 11 (1) ◽  
pp. 104-114 ◽  
Author(s):  
Min Jung Kim ◽  
Su Chul Lee ◽  
Sukdeb Pal ◽  
Eunyoung Han ◽  
Joon Myong Song

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

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Mathias Girault ◽  
Hyonchol Kim ◽  
Hisayuki Arakawa ◽  
Kenji Matsuura ◽  
Masao Odaka ◽  
...  

2016 ◽  
Vol 757 ◽  
pp. 012010
Author(s):  
Emre Altinagac ◽  
Selen Taskin ◽  
Huseyin Kizil

Author(s):  
Benjamin B. Yellen ◽  
Jon S. Zawistowski ◽  
Eric A. Czech ◽  
Caleb I. Sanford ◽  
Elliott D. SoRelle ◽  
...  

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.


Sign in / Sign up

Export Citation Format

Share Document