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.

scholarly journals Quantitative, Label-Free Characterization of Stem Cell Differentiation at the Single-Cell Level by Broadband Coherent Anti-Stokes Raman Scattering Microscopy

2014 ◽  
Vol 20 (7) ◽  
pp. 562-569 ◽  
Author(s):  
Young Jong Lee ◽  
Sebastián L. Vega ◽  
Parth J. Patel ◽  
Khaled A. Aamer ◽  
Prabhas V. Moghe ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Raman Scattering ◽  
Single Cell ◽  
Stem Cell Differentiation ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Anti Stokes

scholarly journals Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ali Rohani ◽  
Jennifer A. Kashatus ◽  
Dane T. Sessions ◽  
Salma Sharmin ◽  
David F. Kashatus
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Data Science ◽  
Structural Information ◽  
Morphological Changes ◽  
Mitochondrial Morphology ◽  
Single Cell Level ◽  
Mitochondrial Network ◽  
Cell Level ◽  
Wide Range

Abstract Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

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.

Surface-Enhanced Raman Scattering-Based Sensing In Vitro: Facile and Label-Free Detection of Apoptotic Cells at the Single-Cell Level

2013 ◽  
Vol 85 (5) ◽  
pp. 2809-2816 ◽  
Author(s):  
Xiangxu Jiang ◽  
Ziyun Jiang ◽  
Tingting Xu ◽  
Shao Su ◽  
Yiling Zhong ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Single Cell ◽  
Single Cell Level ◽  
Label Free ◽  
Cell Level ◽  
Label Free Detection ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Enhanced Raman Scattering

scholarly journals High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Filippos Porichis ◽  
Meghan G. Hart ◽  
Morgane Griesbeck ◽  
Holly L. Everett ◽  
Muska Hassan ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Level ◽  
Cell Level ◽  
High Throughput Detection ◽  
Specific Mrna

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.

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
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