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

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.

scholarly journals Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeremy A. Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy H. Nguyen ◽  
Kai Kessenbrock ◽  
Jered B. Haun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Tissue Processing ◽  
Single Cell Rna Sequencing ◽  
Culture Models ◽  
Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

High-Throughput, Long-Term Imaging of Salmonella Infecting Macrophages in a Micro-Environmental System

2006 ◽  
Author(s):  
Shih-Hui Chao ◽  
Tim J. Strovas ◽  
Ting-She M. Wang ◽  
Kendan A. Jones-Isaac ◽  
Susan L. Fink ◽  
...  
Keyword(s):  
Single Cell ◽  
Real Time ◽  
Laser Scanning ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Cellular Functions ◽  
Laser Scanning Confocal Microscope ◽  
History Of ◽  
Multiple Variables

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.

Parallel single-cell analysis microfluidic platform

2011 ◽  
Vol 32 (22) ◽  
pp. 3094-3100 ◽  
Author(s):  
Floris T. G. van den Brink ◽  
Elmar Gool ◽  
Jean-Philippe Frimat ◽  
Johan Bomer ◽  
Albert van den Berg ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Microfluidic Platform

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

2005 ◽  
Vol 281 (9) ◽  
pp. 5837-5844 ◽  
Author(s):  
Manus W. Ward ◽  
Markus Rehm ◽  
Heiko Duessmann ◽  
Slavomir Kacmar ◽  
Caoimhin G. Concannon ◽  
...  
Keyword(s):  
Single Cell ◽  
Real Time ◽  
Single Cell Analysis ◽  
Cell Analysis

scholarly journals Novel microfluidic platform accelerates processing of tissue into single cells for molecular analysis and primary culture models

2020 ◽  
Author(s):  
Jeremy Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy Nguyen ◽  
Kai Kessenbrock ◽  
Jered Haun
Keyword(s):  
Single Cell ◽  
Stress Responses ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Types ◽  
Tissue Preparation ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Wide Range

Abstract Tissues are composed of highly heterogeneous mixtures of cell subtypes, and this diversity is increasingly being characterized using high-throughput single cell analysis methods. However, these efforts are hindered by the fact that tissues must first be dissociated into single cell suspensions that are viable and still accurately represent phenotypes from the original tissue. Current methods for breaking down tissues are inefficient, labor-intensive, subject to high variability, and potentially biased towards cell subtypes that are easier to release. Here, we present a microfluidic platform consisting of three different tissue processing technologies that can perform the complete tissue to single cell workflow, including digestion, disaggregation, and filtration. First, we developed a new microfluidic digestion device that can be loaded with minced tissue specimens quickly and easily, and then use the combination of proteolytic enzyme activity and fluid shear forces to accelerate tissue breakdown. Next, we integrated dissociation and filter technologies into a single device, which enhanced single cell numbers and fully prepared the sample for single cell analysis. The final multi-device platform was then evaluated using a diverse array of tissue types that exhibited a wide range of properties. For murine kidney and mammary tumor, we found that microfluidic processing produced 2.5-fold more single, viable cells. Single cell RNA sequencing (scRNA-seq) further revealed that device processing enriched for endothelial cells, fibroblasts, and basal epithelium, and did not increase stress responses. For murine liver and heart, which are softer tissues containing fragile cell types, processing time could be reduced to 15 min, and even as short as 1 min. We also demonstrated that periodic recovery at defined time intervals produced substantially more hepatocytes and cardiomyocytes than continuous operation, most likely by preventing damage to fragile cell types. In future work, we will seek to integrate additional operations such as upstream tissue preparation and downstream microfluidic cell sorting and detection to create powerful point-of-care single cell diagnostic platforms.

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

Small ◽  
2018 ◽  
Vol 14 (26) ◽  
pp. 1870119 ◽  
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

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

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.

Sign in / Sign up

Export Citation Format

Share Document