scholarly journals Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry

2018 ◽  
Vol 96 ◽  
pp. 147-156 ◽  
Author(s):  
Yuqian Li ◽  
Bruno Cornelis ◽  
Alexandra Dusa ◽  
Geert Vanmeerbeeck ◽  
Dries Vercruysse ◽  
...  
2021 ◽  
Author(s):  
Zoltán Göröcs ◽  
David Baum ◽  
Fang Song ◽  
Kevin de Haan ◽  
Hatice Ceylan Koydemir ◽  
...  

2021 ◽  
Vol 9 (2) ◽  
pp. 025004
Author(s):  
Linting Lv ◽  
Li Dong ◽  
Jiajia Zheng ◽  
Tuohutaerbieke Maermaer ◽  
Xiangbo Huang ◽  
...  

2019 ◽  
Vol 116 (32) ◽  
pp. 15842-15848 ◽  
Author(s):  
Yuta Suzuki ◽  
Koya Kobayashi ◽  
Yoshifumi Wakisaka ◽  
Dinghuan Deng ◽  
Shunji Tanaka ◽  
...  

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.


Lab on a Chip ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1743-1756 ◽  
Author(s):  
Andy K. S. Lau ◽  
Ho Cheung Shum ◽  
Kenneth K. Y. Wong ◽  
Kevin K. Tsia

Optical time-stretch imaging is now proven for ultrahigh-throughput optofluidic single-cell imaging, at least 10–100 times faster.


2017 ◽  
Vol 139 (2) ◽  
pp. AB163
Author(s):  
Justyna Piasecka ◽  
Holger Hennig ◽  
Fabian J. Theis ◽  
Paul Rees ◽  
Huw D. Summers ◽  
...  

2018 ◽  
Author(s):  
Mohammad Tanhaemami ◽  
Elaheh Alizadeh ◽  
Claire Sanders ◽  
Babetta L. Marrone ◽  
Brian Munsky’

Abstract—Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells(Picochlorum soloecismus)during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.


Lab on a Chip ◽  
2018 ◽  
Vol 18 (14) ◽  
pp. 2065-2076 ◽  
Author(s):  
Jun-Chau Chien ◽  
Ali Ameri ◽  
Erh-Chia Yeh ◽  
Alison N. Killilea ◽  
Mekhail Anwar ◽  
...  

This work presents a microfluidics-integrated label-free flow cytometry-on-a-CMOS platform for the characterization of the cytoplasm dielectric properties at microwave frequencies.


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