scholarly journals Label-free image flow cytometry

2016 ◽  
Vol 13 (3) ◽  
pp. 200-200
Keyword(s):  
Flow Cytometry ◽  
Label Free ◽  
Image Flow

Combination Image Flow Cytometry Reveals Novel Methods for Isolating Megakaryocyte Progenitor Cells

2019 ◽  
Author(s):  
Lucas M. Bush ◽  
Connor P. Healy ◽  
James E. Marvin ◽  
Tara L. Deans
Keyword(s):  
Flow Cytometry ◽  
Progenitor Cells ◽  
Image Flow ◽  
Novel Methods

A deep learning-based concept for high throughput image flow cytometry

2021 ◽  
Vol 118 (12) ◽  
pp. 123701
Author(s):  
Julie Martin-Wortham ◽  
Steffen M. Recktenwald ◽  
Marcelle G. M. Lopes ◽  
Lars Kaestner ◽  
Christian Wagner ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
High Throughput ◽  
Image Flow

scholarly journals Multi-pronged approach to human mesenchymal stromal cells senescence quantification with a focus on label-free methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weichao Zhai ◽  
Jerome Tan ◽  
Tobias Russell ◽  
Sixun Chen ◽  
Dennis McGonagle ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
Preclinical Model ◽  
Label Free ◽  
Current Standard ◽  
Differentiation Potential ◽  
Human Mesenchymal Stromal Cells ◽  
Endogenous Fluorophores

AbstractHuman mesenchymal stromal cells (hMSCs) have demonstrated, in various preclinical settings, consistent ability in promoting tissue healing and improving outcomes in animal disease models. However, translation from the preclinical model into clinical practice has proven to be considerably more difficult. One key challenge being the inability to perform in situ assessment of the hMSCs in continuous culture, where the accumulation of the senescent cells impairs the culture’s viability, differentiation potential and ultimately leads to reduced therapeutic efficacies. Histochemical $$\upbeta $$ β -galactosidase staining is the current standard for measuring hMSC senescence, but this method is destructive and not label-free. In this study, we have investigated alternatives in quantification of hMSCs senescence, which included flow cytometry methods that are based on a combination of cell size measurements and fluorescence detection of SA-$$\upbeta $$ β -galactosidase activity using the fluorogenic substrate, C$${_{12}}$$ 12 FDG; and autofluorescence methods that measure fluorescence output from endogenous fluorophores including lipopigments. For identification of senescent cells in the hMSC batches produced, the non-destructive and label-free methods could be a better way forward as they involve minimum manipulations of the cells of interest, increasing the final output of the therapeutic-grade hMSC cultures. In this work, we have grown hMSC cultures over a period of 7 months and compared early and senescent hMSC passages using the advanced flow cytometry and autofluorescence methods, which were benchmarked with the current standard in $$\upbeta $$ β -galactosidase staining. Both the advanced methods demonstrated statistically significant values, (r = 0.76, p $$\le $$ ≤ 0.001 for the fluorogenic C$${_{12}}$$ 12 FDG method, and r = 0.72, p $$\le $$ ≤ 0.05 for the forward scatter method), and good fold difference ranges (1.120–4.436 for total autofluorescence mean and 1.082–6.362 for lipopigment autofluorescence mean) between early and senescent passage hMSCs. Our autofluroescence imaging and spectra decomposition platform offers additional benefit in label-free characterisation of senescent hMSC cells and could be further developed for adoption for future in situ cellular senescence evaluation by the cell manufacturers.

Deep Learning-enabled Holographic Imaging Flow-Cytometry for Label-Free Detection of Giardia Lamblia in Water Samples

2021 ◽  
Author(s):  
Zoltán Göröcs ◽  
David Baum ◽  
Fang Song ◽  
Kevin de Haan ◽  
Hatice Ceylan Koydemir ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
Water Samples ◽  
Giardia Lamblia ◽  
Label Free ◽  
Holographic Imaging ◽  
Imaging Flow Cytometry ◽  
Label Free Detection

scholarly journals Simultaneous acoustic and photoacoustic microfluidic flow cytometry for label-free analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Vaskar Gnyawali ◽  
Eric M. Strohm ◽  
Jun-Zhi Wang ◽  
Scott S. H. Tsai ◽  
Michael C. Kolios
Keyword(s):  
Flow Cytometry ◽  
Label Free ◽  
Microfluidic Flow

scholarly journals Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering

2019 ◽  
Vol 116 (32) ◽  
pp. 15842-15848 ◽  
Author(s):  
Yuta Suzuki ◽  
Koya Kobayashi ◽  
Yoshifumi Wakisaka ◽  
Dinghuan Deng ◽  
Shunji Tanaka ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Raman Scattering ◽  
Cancer Biology ◽  
High Speed ◽  
Stimulated Raman Scattering ◽  
Chemical Imaging ◽  
Fluorescent Labeling ◽  
Label Free ◽  
Imaging Flow Cytometry ◽  
Stimulated Raman

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

scholarly journals Label-free impedance flow cytometry for nanotoxicity screening

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Melanie Ostermann ◽  
Alexander Sauter ◽  
Ying Xue ◽  
Eivind Birkeland ◽  
Julia Schoelermann ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Human Health ◽  
Trypan Blue ◽  
Label Free ◽  
Trigger Level ◽  
Lymphoma Cells ◽  
Human Lymphoma ◽  
Buffer Composition ◽  
Cost Efficient

AbstractThe development of reliable and cost-efficient methods to assess the toxicity of nanomaterials (NMs) is critical for the proper identification of their impact on human health and for ensuring a safe progress of nanotechnology. In this study, we investigated the reliability and applicability of label-free impedance flow cytometry (IFC) for in vitro nanotoxicity screening, which avoids time-consuming labelling steps and minimizes possible NM-induced interferences. U937 human lymphoma cells were exposed for 24 h to eight different nanomaterials at five concentrations (2, 10, 20, 50, and 100 μg/mL). The NMs’ effect on viability was measured using IFC and the results were compared to those obtained by trypan blue (TB) dye exclusion and conventional flow cytometry (FC). To discriminate viable from necrotic cells, the IFC measurement settings regarding signal trigger level and frequency, as well as the buffer composition, were optimised. A clear discrimination between viable and necrotic cells was obtained at 6 MHz in a sucrose-based measurement buffer. Nanomaterial-induced interferences were not detected for IFC. The IFC and TB assay results were in accordance for all NMs. The IFC was found to be robust, reliable and less prone to interferences due to the advantage of being label-free.

scholarly journals Impedance spectroscopy flow cytometry: On-chip label-free cell differentiation

2005 ◽  
Vol 65A (2) ◽  
pp. 124-132 ◽  
Author(s):  
Karen Cheung ◽  
Shady Gawad ◽  
Philippe Renaud
Keyword(s):  
Flow Cytometry ◽  
Cell Differentiation ◽  
Impedance Spectroscopy ◽  
Free Cell ◽  
Label Free ◽  
On Chip

Application of machine learning-driven label-free flow cytometry to analyze T cell products

Cytotherapy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. S132-S133
Author(s):  
H. Ochiai ◽  
K. Teranishi ◽  
K. Toda ◽  
K. Sugimoto ◽  
S. Ota
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
T Cell ◽  
Free Flow ◽  
Label Free
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