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.

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 Virus Reduction Neutralization Test: A Single-Cell Imaging High-Throughput Virus Neutralization Assay for Dengue

2018 ◽  
Vol 99 (6) ◽  
pp. 1430-1439 ◽  
Author(s):  
Melissa C. Whiteman ◽  
Leah Bogardus ◽  
Danila G. Giacone ◽  
Leonard J. Rubinstein ◽  
Joseph M. Antonello ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Neutralization Test ◽  
Neutralization Assay ◽  
Virus Neutralization ◽  
Virus Neutralization Assay ◽  
Single Cell Imaging

scholarly journals High-throughput Automated Single Cell Imaging Analysis Reveals Dynamics Of Glioblastoma Stem Cell Population During State Transition

2018 ◽  
Author(s):  
Anastasia P. Chumakova ◽  
Masahiro Hitomi ◽  
Erik P. Sulman ◽  
Justin D. Lathia
Keyword(s):  
Stem Cell ◽  
Protein Expression ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
State Transition ◽  
Imaging Analysis ◽  
Sox2 Expression ◽  
Single Cell Imaging ◽  
Stem Cell State

ABSTRACTCancer stem cells (CSCs) are a heterogeneous and dynamic population that stands at the top of tumor cellular hierarchy and is responsible for maintenance of the tumor microenvironment. As methods of CSC isolation and functional interrogation advance, there is a need for a reliable and accessible quantitative approach to assess heterogeneity and state transition dynamics in CSCs. We developed a High-throughput Automated Single Cell Imaging Analysis (HASCIA) approach for quantitative assessment of protein expression with single cell resolution and applied the method to investigate spatiotemporal factors that influence CSC state transition using glioblastoma (GBM) CSC as a model system. We were able to validate the quantitative nature of this approach through comparison of the protein expression levels determined by HASCIA to those determined by immunoblotting. A virtue of HASCIA was exemplified by detection of a subpopulation of SOX2-low cells, which expanded in fraction size during state transition. HASCIA also revealed that CSCs were committed to loose stem cell state at an earlier time point than the average SOX2 level decreased. Functional assessment of stem cell frequency in combination with quantification of SOX2 expression by HASCIA defined a stable cut-off of SOX2 expression level for stem cell state. We also developed an approach to assess local cell density and found that denser monolayer areas possess higher average levels of SOX2, higher cell diversity and a presence of a sub-population of slowly proliferating SOX2-low CSCs. HASCIA is an open source software that facilitates understanding the dynamics of heterogeneous cell population such as that of CSCs and their progeny. It is a powerful and easy-to-use image analysis and statistical analysis tool available athttps://hascia.lerner.ccf.org.

scholarly journals Dynamic analysis of MAPK signaling using a high-throughput microfluidic single-cell imaging platform

2009 ◽  
Vol 106 (10) ◽  
pp. 3758-3763 ◽  
Author(s):  
R. J. Taylor ◽  
D. Falconnet ◽  
A. Niemisto ◽  
S. A. Ramsey ◽  
S. Prinz ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Mapk Signaling ◽  
Single Cell Imaging

scholarly journals Measuring Intracellular Ca2+ Changes in Human Sperm using Four Techniques: Conventional Fluorometry, Stopped Flow Fluorometry, Flow Cytometry and Single Cell Imaging

10.3791/50344 ◽  
2013 ◽  
Author(s):  
Esperanza Mata-Martínez ◽  
Omar José ◽  
Paulina Torres-Rodríguez ◽  
Alejandra Solís-López ◽  
Ana A. Sánchez-Tusie ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Cell Imaging ◽  
Human Sperm ◽  
Stopped Flow ◽  
Single Cell Imaging ◽  
Intracellular Ca

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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