Live-cell Imaging of Single-Cell Arrays (LISCA) - a Versatile Technique to Quantify Cellular Kinetics

10.3791/62025 ◽  
2021 ◽  
Author(s):  
Anita Reiser ◽  
Daniel Woschée ◽  
Simon Maximilian Kempe ◽  
Joachim Oskar Rädler
Keyword(s):  
Single Cell ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Cellular Kinetics ◽  
Cell Arrays ◽  
Versatile Technique

scholarly journals Monitoring Cannabinoid CB2 -Receptor Mediated cAMP Dynamics by FRET-Based Live Cell Imaging

2020 ◽  
Vol 21 (21) ◽  
pp. 7880
Author(s):  
Leonore Mensching ◽  
Sebastian Rading ◽  
Viacheslav Nikolaev ◽  
Meliha Karsak
Keyword(s):  
Single Cell ◽  
G Protein ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Single Cell Level ◽  
Cb2 Receptor ◽  
Cell Level ◽  
Cb2 Receptors ◽  
G Protein Coupled

G-protein coupled cannabinoid CB2 receptor signaling and function is primarily mediated by its inhibitory effect on adenylate cyclase. The visualization and monitoring of agonist dependent dynamic 3′,5′-cyclic adenosine monophosphate (cAMP) signaling at the single cell level is still missing for CB2 receptors. This paper presents an application of a live cell imaging while using a Förster resonance energy transfer (FRET)-based biosensor, Epac1-camps, for quantification of cAMP. We established HEK293 cells stably co-expressing human CB2 and Epac1-camps and quantified cAMP responses upon Forskolin pre-stimulation, followed by treatment with the CB2 ligands JWH-133, HU308, β-caryophyllene, or 2-arachidonoylglycerol. We could identify cells showing either an agonist dependent CB2-response as expected, cells displaying no response, and cells with constitutive receptor activity. In Epac1-CB2-HEK293 responder cells, the terpenoid β-caryophyllene significantly modified the cAMP response through CB2. For all of the tested ligands, a relatively high proportion of cells with constitutively active CB2 receptors was identified. Our method enabled the visualization of intracellular dynamic cAMP responses to the stimuli at single cell level, providing insights into the nature of heterologous CB2 expression systems that contributes to the understanding of Gαi-mediated G-Protein coupled receptor (GPCR) signaling in living cells and opens up possibilities for future investigations of endogenous CB2 responses.

scholarly journals An Optically Decodable Bead Array for Linking Imaging and Sequencing with Single-Cell Resolution

2018 ◽  
Author(s):  
Jinzhou Yuan ◽  
Jenny Sheng ◽  
Peter A. Sims
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Living Cells ◽  
Live Cell ◽  
Proof Of Concept ◽  
Rna Seq ◽  
Phenotypic Analysis

AbstractOptically decodable beads link the identity of an analyte or sample to a measurement through an optical barcode, enabling libraries of biomolecules to be captured on beads in solution and decoded by fluorescence. This approach has been foundational to microarray, sequencing, and flow-based expression profiling technologies. We have combined microfluidics with optically decodable beads to link phenotypic analysis of living cells to sequencing. As a proof-of-concept, we applied this to demonstrate an accurate and scalable tool for connecting live cell imaging to single-cell RNA-Seq called Single Cell Optical Phenotyping and Expression (SCOPE-Seq).

Fabrication of Silicon on Borosilicate Glass Microarrays for Quantitative Live Cell Imaging

2011 ◽  
Vol 1346 ◽  
Author(s):  
David T. Martin ◽  
Sergio Sandoval ◽  
Andy Carter ◽  
Mark Rodwell ◽  
Stefan Smith ◽  
...  
Keyword(s):  
Single Cell ◽  
Fluorescence Imaging ◽  
Borosilicate Glass ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Silicon Etching ◽  
Deep Reactive Ion Etching ◽  
Planar Arrays ◽  
Si Wafer

ABSTRACTPlanar arrays of microwells were fabricated in Silicon on borosilicate glass (pyrex) substrates in order to facilitate live cell fluorescence imaging experiments for cells sequestered inside their own individual microenvironments for incubation and quantification of single cell seceretions. Two methods of deep silicon etching were compared: cryogenic deep reactive ion etching (DRIE) and time multiplexed DIRE (Bosch Process). A 200um Si wafer was bonded to a 500um pyrex substrate. Cryogenic DRIE allowed for the reliable fabrication of 75-100um deep microwells with 60x60um openings across a 10x10mm substrate while the Bosh Process allowed for etching entirely through the Si layer, producing 200um deep microwells with transparent bottoms and steep sidewalls while maintaining the target 60x60um opening geometry.

High-throughput single-cell quantification using simple microwell-based cell docking and programmable time-course live-cell imaging

Lab on a Chip ◽  
2011 ◽  
Vol 11 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Min Cheol Park ◽  
Jae Young Hur ◽  
Hye Sung Cho ◽  
Sang-Hyun Park ◽  
Kahp Y. Suh
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Live Cell Imaging ◽  
Time Course ◽  
Cell Imaging ◽  
Live Cell ◽  
Cell Quantification

scholarly journals Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data

2017 ◽  
Vol 4 (8) ◽  
pp. 170811 ◽  
Author(s):  
Sayak Mukherjee ◽  
David Stewart ◽  
William Stewart ◽  
Lewis L. Lanier ◽  
Jayajit Das
Keyword(s):  
Single Cell ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Single Cells ◽  
Cell Signalling ◽  
Live Cell ◽  
Multidimensional Space ◽  
Matching Problems ◽  
Wide Range ◽  
Slow Variables

Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to probe single-cell signalling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 to more than 40) of signalling species but are unable to track single cells. Thus, cytometry experiments provide detailed time-stamped snapshots of single-cell signalling kinetics. Is it possible to use the time-stamped cytometry data to reconstruct single-cell signalling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signalling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signalling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations, live-cell imaging measurements, and, synthetic flow cytometry datasets. The application of invariants and slow variables to reconstruct trajectories provides a radically different way to track objects using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines.

scholarly journals Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

2019 ◽  
Author(s):  
Erick Moen ◽  
Enrico Borba ◽  
Geneva Miller ◽  
Morgan Schwartz ◽  
Dylan Bannon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Live Cell Imaging ◽  
Cell Tracking ◽  
Cell Imaging ◽  
Single Cells ◽  
Live Cell ◽  
Imaging Data ◽  
Cell Nuclei ◽  
Data Types

AbstractLive-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained on those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org.

Single-cell analysis of cytoskeleton dynamics: From isoelectric focusing to live cell imaging and RNA-seq

2019 ◽  
Vol 323 ◽  
pp. 119-124 ◽  
Author(s):  
Illana Gozes ◽  
Yanina Ivashko-Pachima ◽  
Oxana Kapitansky ◽  
Carmen Laura Sayas ◽  
Tal Iram
Keyword(s):  
Single Cell ◽  
Isoelectric Focusing ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Single Cell Analysis ◽  
Live Cell ◽  
Cell Analysis ◽  
Rna Seq ◽  
Cytoskeleton Dynamics
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