Single-cell Fourier-transform light scattering analysis by high- throughput label-free imaging flow cytometry

2020 ◽  
Author(s):  
Ziqi Zhang ◽  
Queenie T.K Lai ◽  
Kelvin C.M. Lee ◽  
Kenneth K Y. Wong ◽  
Kevin K Tsia
Keyword(s):  
Flow Cytometry ◽  
Fourier Transform ◽  
Light Scattering ◽  
Single Cell ◽  
High Throughput ◽  
Label Free ◽  
Imaging Flow Cytometry

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 ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Label Free ◽  
Imaging Flow Cytometry

Optofluidic time-stretch imaging – an emerging tool for high-throughput imaging flow cytometry

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
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Imaging Flow Cytometry ◽  
Single Cell Imaging ◽  
Optical Time ◽  
High Throughput Imaging

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

scholarly journals Label Free Identification of Peripheral Blood Eosinophils Using High-Throughput Imaging Flow Cytometry

2017 ◽  
Vol 139 (2) ◽  
pp. AB163
Author(s):  
Justyna Piasecka ◽  
Holger Hennig ◽  
Fabian J. Theis ◽  
Paul Rees ◽  
Huw D. Summers ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Peripheral Blood ◽  
Label Free ◽  
Imaging Flow Cytometry ◽  
Blood Eosinophils ◽  
High Throughput Imaging

scholarly journals Tracking expression and subcellular localization of RNA and protein species using high-throughput single cell imaging flow cytometry

RNA ◽  
2012 ◽  
Vol 18 (8) ◽  
pp. 1573-1579 ◽  
Author(s):  
S. Borah ◽  
L. A. Nichols ◽  
L. M. Hassman ◽  
D. H. Kedes ◽  
J. A. Steitz
Keyword(s):  
Flow Cytometry ◽  
Subcellular Localization ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Protein Species ◽  
Imaging Flow Cytometry ◽  
Single Cell Imaging

A high-throughput flow cytometry-on-a-CMOS platform for single-cell dielectric spectroscopy at microwave frequencies

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 ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Dielectric Properties ◽  
Single Cell ◽  
High Throughput ◽  
Dielectric Spectroscopy ◽  
Free Flow ◽  
Label Free ◽  
Microwave Frequencies

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.

scholarly journals High-throughput label-free molecular fingerprinting flow cytometry

2019 ◽  
Vol 5 (1) ◽  
pp. eaau0241 ◽  
Author(s):  
Kotaro Hiramatsu ◽  
Takuro Ideguchi ◽  
Yusuke Yonamine ◽  
SangWook Lee ◽  
Yizhi Luo ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Fluorescent Labeling ◽  
Live Cells ◽  
Molecular Fingerprinting ◽  
Label Free ◽  
Indispensable Tool

Flow cytometry is an indispensable tool in biology for counting and analyzing single cells in large heterogeneous populations. However, it predominantly relies on fluorescent labeling to differentiate cells and, hence, comes with several fundamental drawbacks. Here, we present a high-throughput Raman flow cytometer on a microfluidic chip that chemically probes single live cells in a label-free manner. It is based on a rapid-scan Fourier-transform coherent anti-Stokes Raman scattering spectrometer as an optical interrogator, enabling us to obtain the broadband molecular vibrational spectrum of every single cell in the fingerprint region (400 to 1600 cm−1) with a record-high throughput of ~2000 events/s. As a practical application of the method not feasible with conventional flow cytometry, we demonstrate high-throughput label-free single-cell analysis of the astaxanthin productivity and photosynthetic dynamics ofHaematococcus lacustris.

scholarly journals Label-free cell cycle analysis for high-throughput imaging flow cytometry

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Thomas Blasi ◽  
Holger Hennig ◽  
Huw D. Summers ◽  
Fabian J. Theis ◽  
Joana Cerveira ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Flow Cytometry ◽  
High Throughput ◽  
Free Cell ◽  
Cell Cycle Analysis ◽  
Label Free ◽  
Imaging Flow Cytometry ◽  
High Throughput Imaging

scholarly journals Large-scale label-free single-cell analysis of paramylon in Euglena gracilis by high-throughput broadband Raman flow cytometry

2020 ◽  
Vol 11 (4) ◽  
pp. 1752 ◽  
Author(s):  
Kotaro Hiramatsu ◽  
Koji Yamada ◽  
Matthew Lindley ◽  
Kengo Suzuki ◽  
Keisuke Goda
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Euglena Gracilis ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Label Free

scholarly journals Review: imaging technologies for flow cytometry

Lab on a Chip ◽  
2016 ◽  
Vol 16 (24) ◽  
pp. 4639-4647 ◽  
Author(s):  
Yuanyuan Han ◽  
Yi Gu ◽  
Alex Ce Zhang ◽  
Yu-Hwa Lo
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Imaging Technologies ◽  
Imaging Flow Cytometry ◽  
Single Cell Imaging

Imaging flow cytometry combines the single-cell imaging capabilities of microscopy with the high-throughput capabilities of conventional flow cytometry. This article describes recent imaging flow cytometry technologies and their challenges.

scholarly journals Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning

2020 ◽  
Vol 48 (20) ◽  
pp. 11335-11346
Author(s):  
Nikolaos-Kosmas Chlis ◽  
Lisa Rausch ◽  
Thomas Brocker ◽  
Jan Kranich ◽  
Fabian J Theis
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Imaging Flow Cytometry ◽  
Myeloid Progenitor ◽  
Myeloid Progenitor Cells ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cell-populations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe single-cell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.

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