A Novel Label-free modality for DNA Quantification and Cell Counting towards Clinical Analysis in Microfluidic Systems

2012 ◽  
Author(s):  
Jingyi Li
Keyword(s):  
Clinical Analysis ◽  
Dna Quantification ◽  
Cell Counting ◽  
Label Free ◽  
Microfluidic Systems

CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning

Lab on a Chip ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 3888-3898 ◽  
Author(s):  
Domenico Rossi ◽  
David Dannhauser ◽  
Mariarosaria Telesco ◽  
Paolo A. Netti ◽  
Filippo Causa
Keyword(s):  
Machine Learning ◽  
Microfluidic Chip ◽  
Cell Counting ◽  
Learning Approach ◽  
Label Free ◽  
Biophysical Properties ◽  
Cd8 Cells ◽  
Cd8 T Lymphocyte ◽  
Machine Learning Approach ◽  
Compact Dimension

Human CD4+ and CD8+ cells are label-free investigated in a compact-dimension microfluidic chip for detailing biophysical properties. A machine learning approach on obtained results allows an accuracy of cell counting and classification up to 88%.

The potential of autofluorescence for the detection of single living cells for label-free cell sorting in microfluidic systems

2004 ◽  
Vol 25 (21-22) ◽  
pp. 3740-3745 ◽  
Author(s):  
Jurjen Emmelkamp ◽  
Floor Wolbers ◽  
Helene Andersson ◽  
Ralph S. DaCosta ◽  
Brian C. Wilson ◽  
...  
Keyword(s):  
Cell Sorting ◽  
Free Cell ◽  
Living Cells ◽  
Label Free ◽  
Microfluidic Systems ◽  
Single Living Cells ◽  
Single Living

Label-free high-throughput detection and content sensing of individual droplets in microfluidic systems

Lab on a Chip ◽  
2015 ◽  
Vol 15 (20) ◽  
pp. 4008-4019 ◽  
Author(s):  
Gurkan Yesiloz ◽  
Muhammed Said Boybay ◽  
Carolyn L. Ren
Keyword(s):  
High Throughput ◽  
Integrated Approach ◽  
Label Free ◽  
Microfluidic Systems ◽  
High Throughput Detection ◽  
Individual Droplet

We report a microwave-microfluidics integrated approach capable of detecting droplet at high-throughput and label-free sensing of individual droplet content without physical intrusion.

A label-free DC impedance-based microcytometer for circulating rare cancer cell counting

Lab on a Chip ◽  
2013 ◽  
Vol 13 (5) ◽  
pp. 970 ◽  
Author(s):  
Hyoungseon Choi ◽  
Kwang Bok Kim ◽  
Chang Su Jeon ◽  
Inseong Hwang ◽  
Saram Lee ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Cell Counting ◽  
Rare Cancer ◽  
Label Free

CD4+ T Cell Counting by Impedance Measurement on a Chip with Fluidic Electrodes

2012 ◽  
Vol 13 (5) ◽  
Author(s):  
Shanshan Wang ◽  
Qiong Wei ◽  
Tao Zhu ◽  
Jianyong Huang ◽  
Min Yu ◽  
...  
Keyword(s):  
T Cells ◽  
Microfluidic Chip ◽  
Cell Concentration ◽  
Impedance Measurement ◽  
Solid Metal ◽  
Cd4 T Cell ◽  
Cell Counting ◽  
Label Free ◽  
Hydrodynamic Focusing ◽  
Metal Electrodes

AbstractA practical label-free method for counting CD4+ T cells is proposed on the basis of a microfluidic chip with fluidic electrodes. With the help of hydrodynamic focusing, two sheath flows of KCl solution, serving as electric conductors to replace solid metal electrodes, are used to squeeze the cell suspension. By measuring the electrical impedances between the fluidic electrodes, a linear relationship is found between the logarithmic value of cell concentration and the impedance value (R

scholarly journals A Microchip Approach for Practical Label-Free CD4+T-Cell Counting of HIV-Infected Subjects in Resource-Poor Settings

2007 ◽  
Vol PAP ◽  
Author(s):  
Xuanhong Cheng ◽  
Daniel Irimia ◽  
Meredith Dixon ◽  
Joshua C Ziperstein ◽  
Utkan Demirci ◽  
...  
Keyword(s):  
T Cell ◽  
Cd4 T Cell ◽  
Cell Counting ◽  
Label Free ◽  
Resource Poor

scholarly journals Non-invasive, label-free cell counting and quantitative analysis of adherent cells using digital holography

2008 ◽  
Vol 232 (2) ◽  
pp. 240-247 ◽  
Author(s):  
A. MÖLDER ◽  
M. SEBESTA ◽  
M. GUSTAFSSON ◽  
L. GISSELSON ◽  
A. GJÖRLOFF WINGREN ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Digital Holography ◽  
Free Cell ◽  
Cell Counting ◽  
Adherent Cells ◽  
Label Free ◽  
Non Invasive

scholarly journals Potential Molecular Mechanisms of AURKB in the Oncogenesis and Progression of Osteosarcoma Cells: A Label-Free Quantitative Proteomics Analysis

2019 ◽  
Vol 18 ◽  
pp. 153303381985326 ◽  
Author(s):  
Wen-Sen Pi ◽  
Zhi-Yuan Cao ◽  
Jia-Ming Liu ◽  
Ai-Fen Peng ◽  
Wen-Zhao Chen ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Functional Enrichment ◽  
Cell Counting ◽  
Migration And Invasion ◽  
Nuclear Factor Kappab ◽  
Aurora B ◽  
Nuclear Factor ◽  
Label Free ◽  
Osteosarcoma Cells ◽  
Protein Protein Interaction

Our previous study indicated that knockdown of Aurora-B inhibit the proliferation of osteosarcoma cells. But the function and molecular mechanisms of Aurora-B in osteosarcoma cells growth and metastasis remains unclear. The aim of this study was to investigate the molecular mechanisms of Aurora-B in the progression of osteosarcoma. Osteosarcoma cells (U2-OS and 143B) were treated with specific Lentivirus-Vectors (up or downregulation Aurora-B). The ability of cells proliferation, migration, and invasion was measured using Cell-Counting Kit-8, wound healing and transwell invasion assays. Furthermore, based on label-free quantitative proteomic analysis of potential molecular mechanisms of Aurora-B in human 143B cells. A total of 25 downregulated and 76 upregulated differentially expressed proteins were screened in terms of the change in their expression abundance. We performed functional annotation and functional enrichment analyses. Gene ontology enrichment, KEGG analysis, and protein–protein interaction networks were constructed and analyzed. We found that the PTK2 may play an important role in the progression of osteosarcoma cells. Finally, Western blot revealed that expression of PTK2, AKT, PI3K, and nuclear factor-kappaB increased after over expression of Aurora-B. Overall, these data highlight that Aurora-B may promote the malignant phenotype of osteosarcoma cells by activating the PTK2/PI3K/AKt/nuclear factor-KappaB pathway.

scholarly journals Comparison of a Label-Free Quantitative Proteomic Method Based on Peptide Ion Current Area to the Isotope Coded Affinity Tag Method

2008 ◽  
Vol 6 ◽  
pp. CIN.S385 ◽  
Author(s):  
Soyoung Ryu ◽  
Byron Gallis ◽  
Young Ah Goo ◽  
Scott A. Shaffer ◽  
Dragan Radulovic ◽  
...  
Keyword(s):  
Direct Measurement ◽  
Expression Profiling ◽  
Mean Squared Error ◽  
Clinical Analysis ◽  
Ion Current ◽  
Label Free ◽  
Affinity Tag ◽  
Stable Isotope Dilution ◽  
Large Numbers ◽  
Current Area

Recently, several research groups have published methods for the determination of proteomic expression profiling by mass spectrometry without the use of exogenously added stable isotopes or stable isotope dilution theory. These so-called label-free, methods have the advantage of allowing data on each sample to be acquired independently from all other samples to which they can later be compared in silico for the purpose of measuring changes in protein expression between various biological states. We developed label free software based on direct measurement of peptide ion current area (PICA) and compared it to two other methods, a simpler label free method known as spectral counting and the isotope coded affinity tag (ICAT) method. Data analysis by these methods of a standard mixture containing proteins of known, but varying, concentrations showed that they performed similarly with a mean squared error of 0.09. Additionally, complex bacterial protein mixtures spiked with known concentrations of standard proteins were analyzed using the PICA label-free method. These results indicated that the PICA method detected all levels of standard spiked proteins at the 90% confidence level in this complex biological sample. This finding confirms that label-free methods, based on direct measurement of the area under a single ion current trace, performed as well as the standard ICAT method. Given the fact that the label-free methods provide ease in experimental design well beyond pair-wise comparison, label-free methods such as our PICA method are well suited for proteomic expression profiling of large numbers of samples as is needed in clinical analysis.

Automated Label-Free Cell Counting

2017 ◽  
Vol 37 (14) ◽  
pp. 12-13 ◽  
Author(s):  
Joe Clayton
Keyword(s):  
Free Cell ◽  
Cell Counting ◽  
Label Free
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