Dual-force aggregation of magnetic particles enhances label-free quantification of DNA at the sub-single cell level

2014 ◽  
Vol 819 ◽  
pp. 34-41 ◽  
Author(s):  
Daniel A. Nelson ◽  
Briony C. Strachan ◽  
Hillary S. Sloane ◽  
Jingyi Li ◽  
James P. Landers
Keyword(s):  
Single Cell ◽  
Magnetic Particles ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Label Free Quantification ◽  
Free Quantification

Label-Free Cancer Stem-like Cell Assay Conducted at a Single Cell Level Using Microfluidic Mechanotyping Devices

2021 ◽  
Author(s):  
Miyu Terada ◽  
Sachiko Ide ◽  
Toyohiro Naito ◽  
Niko Kimura ◽  
Michiya Matsusaki ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Cell Assay

scholarly journals High-resolution label-free 3D mapping of extracellular pH of single living cells

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Yanjun Zhang ◽  
Yasufumi Takahashi ◽  
Sung Pil Hong ◽  
Fengjie Liu ◽  
Joanna Bednarska ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
Extracellular Ph ◽  
Single Cell Level ◽  
Ph Sensing ◽  
Label Free ◽  
Cell Level ◽  
Spatiotemporal Resolution ◽  
Ion Conductance ◽  
Scanning Ion Conductance Microscopy

AbstractDynamic mapping of extracellular pH (pHe) at the single-cell level is critical for understanding the role of H+ in cellular and subcellular processes, with particular importance in cancer. While several pHe sensing techniques have been developed, accessing this information at the single-cell level requires improvement in sensitivity, spatial and temporal resolution. We report on a zwitterionic label-free pH nanoprobe that addresses these long-standing challenges. The probe has a sensitivity > 0.01 units, 2 ms response time, and 50 nm spatial resolution. The platform was integrated into a double-barrel nanoprobe combining pH sensing with feedback-controlled distance dependance via Scanning Ion Conductance Microscopy. This allows for the simultaneous 3D topographical imaging and pHe monitoring of living cancer cells. These classes of nanoprobes were used for real-time high spatiotemporal resolution pHe mapping at the subcellular level and revealed tumour heterogeneity of the peri-cellular environments of melanoma and breast cancer cells.

Alterations of Raman profiles of nasopharyngeal carcinoma cells after treated with chemodrugs detected by laser tweezer Raman spectroscopy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15513-e15513
Author(s):  
Miaomiao Li ◽  
Xiaochuan Chen ◽  
Ting Lin ◽  
Zongwei Huang ◽  
Shihong Wu ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Nasopharyngeal Carcinoma ◽  
Single Cell ◽  
Control Group ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Analytical Strategy ◽  
Linear Discriminant ◽  
Laser Tweezer

e15513 Background: To explore the metabolic alterations of nasopharyngeal carcinoma (NPC) cells after treated with chemodrugs, the Raman profiles were characterized with laser tweezer Raman spectroscopy. Methods: Two NPC cell lines (CNE2 and C666-1) were treated with gemcitabine, cisplatin, and paclitaxel, respectively. The high-quality Raman spectra of cells without or with treatments were recorded at the single-cell level with label-free laser tweezers Raman spectroscopy (LTRS) and analyzed for the differences of alterations of Raman profiles. Results: Tentative assignments of Raman peaks indicated that the cellular specific biomolecular changes associated with drug treatment, including changes in protein structure (e.g. 1655 cm−1), changes in DNA content and structure (e.g. 830 cm−1), destruction of DNA base pairs (e.g. 785 cm−1), and reduction in lipids (e.g. 970 cm−1). Besides, both principal components analysis (PCA) combined with linear discriminant analysis (LDA) and the classification and regression trees (CRT) algorithms were employed to further analyze and classify the spectral data between control group and treated group, with the best discriminant accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. Conclusions: This exploratory work demonstrated that LTRS technology combined with multivariate statistical analysis has promising potential to be a novel analytical strategy at the single-cell level for the evaluation of NPC-related chemotherapeutic drugs.

Marker-free detection of progenitor cell differentiation by analysis of Brownian motion in micro-wells

2015 ◽  
Vol 7 (2) ◽  
pp. 178-183 ◽  
Author(s):  
Farzad Sekhavati ◽  
Max Endele ◽  
Susanne Rappl ◽  
Anna-Kristina Marel ◽  
Timm Schroeder ◽  
...  
Keyword(s):  
Brownian Motion ◽  
Cell Differentiation ◽  
Single Cell ◽  
High Throughput ◽  
Progenitor Cell ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Marker Free

The analysis of Brownian motion is a sensitive and robust tool for a label-free high-throughput investigation of cell differentiation at the single-cell level.

Label-free and ultrasensitive detection of polynucleotide kinase activity at the single-cell level

2018 ◽  
Vol 54 (13) ◽  
pp. 1583-1586 ◽  
Author(s):  
Meng Liu ◽  
Fei Ma ◽  
Qianyi Zhang ◽  
Chun-yang Zhang
Keyword(s):  
Single Cell ◽  
Kinase Activity ◽  
Fluorescence Method ◽  
Kinase Assay ◽  
Ultrasensitive Detection ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Polynucleotide Kinase

We develop a label-free fluorescence method for the polynucleotide kinase assay at the single-cell level based on phosphorylation-triggered isothermal exponential amplification.

DNA-fueled molecular machine for label-free and non-enzymatic ultrasensitive detection of telomerase activity

The Analyst ◽  
2016 ◽  
Vol 141 (16) ◽  
pp. 4855-4858 ◽  
Author(s):  
Panpan Sun ◽  
Xiang Ran ◽  
Chaoqun Liu ◽  
Chaoying Liu ◽  
Fang Pu ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Cell ◽  
Telomerase Activity ◽  
Molecular Machine ◽  
Ultrasensitive Detection ◽  
Single Cell Level ◽  
Label Free ◽  
Enzymatic Method ◽  
Cell Level ◽  
Cell Extracts

A label-free and non-enzymatic method based on DNA-fueled molecular machine has been introduced for ultrasensitive detection of telomerase activity in cancer cell extracts even at the single-cell level.

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