Real Time Single Cell Analysis of Bid Cleavage and Bid Translocation during Caspase-dependent and Neuronal Caspase-independent Apoptosis

Real-time single cell analysis is necessary to understand dynamic cellular functions in time and space. Such analyses require the simultaneous measurement of multiple variables in real-time, due to heterogeneity in cellular populations. We report the application of using a micro-environmental chamber on an automatic laser scanning confocal microscope to observe murine macrophage cells in incubation conditions for more than 18 hours. The motorized stage of the microscope was programmed to scan through pre-defined monitoring locations to increase the observation throughput. The acquired images were post-processed to extract the information of each cell. In contrast to current single-cell technologies, such as fluorescence-activated cell sorter (FACS) based systems, the reported architecture records the history of the physiological responses of individual cells.

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Calpain-mediated pathway dominates cisplatin-induced apoptosis in human lung adenocarcinoma cells as determined by real-time single cell analysis

International Journal of Cancer ◽

10.1002/ijc.23378 ◽

2008 ◽

Vol 122 (10) ◽

pp. 2210-2222 ◽

Cited By ~ 36

Author(s):

Lei Liu ◽

Da Xing ◽

Wei R. Chen ◽

Tongsheng Chen ◽

Yihui Pei ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Single Cell ◽

Real Time ◽

Human Lung ◽

Single Cell Analysis ◽

Cell Analysis ◽

Adenocarcinoma Cells ◽

Human Lung Adenocarcinoma Cells ◽

Lung Adenocarcinoma Cells ◽

Induced Apoptosis

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Single Cell Analysis: Label-Free Optofluidic Nanobiosensor Enables Real-Time Analysis of Single-Cell Cytokine Secretion (Small 26/2018)

Small ◽

10.1002/smll.201870119 ◽

2018 ◽

Vol 14 (26) ◽

pp. 1870119 ◽

Cited By ~ 2

Author(s):

Xiaokang Li ◽

Maria Soler ◽

Crispin Szydzik ◽

Khashayar Khoshmanesh ◽

Julien Schmidt ◽

...

Keyword(s):

Single Cell ◽

Real Time ◽

Single Cell Analysis ◽

Cytokine Secretion ◽

Cell Analysis ◽

Label Free ◽

Time Analysis ◽

Real Time Analysis

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Immobilization of Nonactivated Unfixed Platelets for Real-Time Single-Cell Analysis

Methods in Molecular Biology - Platelets and Megakaryocytes ◽

10.1007/978-1-4939-8585-2_1 ◽

2018 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Alexander P. Bye ◽

Zeki Ilkan ◽

Amanda J. Unsworth ◽

Chris I. Jones

Keyword(s):

Single Cell ◽

Real Time ◽

Single Cell Analysis ◽

Cell Analysis

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An automated real-time microfluidic platform to probe single NK cell heterogeneity and cytotoxicity on-chip

Scientific Reports ◽

10.1038/s41598-021-96609-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Nikita Subedi ◽

Laura C. Van Eyndhoven ◽

Ayla M. Hokke ◽

Lars Houben ◽

Mark C. Van Turnhout ◽

...

Keyword(s):

Nk Cells ◽

Single Cell ◽

Real Time ◽

Target Cell ◽

Single Cell Analysis ◽

Nk Cell ◽

Cell Interactions ◽

Cellular Interaction ◽

Cell Analysis ◽

Microfluidic Platform

AbstractCytotoxicity is a vital effector mechanism used by immune cells to combat pathogens and cancer cells. While conventional cytotoxicity assays rely on averaged end-point measures, crucial insights on the dynamics and heterogeneity of effector and target cell interactions cannot be extracted, emphasizing the need for dynamic single-cell analysis. Here, we present a fully automated droplet-based microfluidic platform that allowed the real-time monitoring of effector-target cell interactions and killing, allowing the screening of over 60,000 droplets identifying 2000 individual cellular interactions monitored over 10 h. During the course of incubation, we observed that the dynamics of cytotoxicity within the Natural Killer (NK) cell population varies significantly over the time. Around 20% of the total NK cells in droplets showed positive cytotoxicity against paired K562 cells, most of which was exhibited within first 4 h of cellular interaction. Using our single cell analysis platform, we demonstrated that the population of NK cells is composed of individual cells with different strength in their effector functions, a behavior masked in conventional studies. Moreover, the versatility of our platform will allow the dynamic and resolved study of interactions between immune cell types and the finding and characterization of functional sub-populations, opening novel ways towards both fundamental and translational research.

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scSVAS: CNV clonal visualization online platform for large scale single-cell genomics

10.1101/2021.06.10.437122 ◽

2021 ◽

Author(s):

Lingxi Chen ◽

Yuhao Qing ◽

Ruikang Li ◽

Chaohui Li ◽

Hechen Li ◽

...

Keyword(s):

Copy Number Variation ◽

Single Cell ◽

Real Time ◽

Copy Number ◽

Large Scale ◽

Single Cell Analysis ◽

Interactive Visualization ◽

Cell Analysis ◽

Single Cell Genomics ◽

Number Variation

The recent advance of single-cell copy number variation analysis plays an essential role in addressing intra-tumor heterogeneity, identifying tumor subgroups, and restoring tumor evolving trajectories at single-cell scale. Pleasant visualization of copy number analysis results boosts productive scientific exploration, validation, and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, scSVAS, for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell analysis. Compared with other tools, scSVAS manifests the most comprehensive functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may make scientific discoveries, share interactive visualization, and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing, and publishing single-cell copy number variation profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

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