scholarly journals SCOPE-Seq: a scalable technology for linking live cell imaging and single-cell RNA sequencing

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinzhou Yuan ◽  
Jenny Sheng ◽  
Peter A. Sims
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Single Cell Rna Sequencing

scholarly journals Ins2 gene bursting activity defines a mature β-cell state

2019 ◽  
Author(s):  
Honey Modi ◽  
Søs Skovsø ◽  
Cara Ellis ◽  
Nicole A.J. Krentz ◽  
Yiwei Bernie Zhao ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Gene Activity ◽  
Insulin Gene ◽  
Insulin Production ◽  
Β Cells ◽  
Β Cell ◽  
Single Cell Rna Sequencing

AbstractHeterogeneity within specific cell types is common and increasingly apparent with the advent of single-cell transcriptomics. Transcriptional and functional cellular specialization has been described for insulin-secreting β-cells of the endocrine pancreas, including so-called extreme β-cells exhibiting >2 fold higher insulin gene activity. However, it is not yet clear whether β-cell heterogeneity is stable or reflects dynamic cellular states. We investigated the temporal kinetics of endogenous insulin gene activity using live-cell imaging, with complementary experiments employing FACS and single-cell RNA sequencing, in β-cells from Ins2GFP knock-in mice. In vivo staining and FACS analysis of islets from Ins2GFP mice confirmed that at a given moment, ∼25% of β-cells exhibited significantly higher activity at the conserved insulin gene Ins2(GFP)HIGH. Live-cell imaging captured on and off ‘bursting’ behaviour in single β-cells that lasted hours to days. Single cell RNA sequencing determined that Ins2(GFP)HIGH β-cells were enriched for markers of β-cell maturity and had reduced expression of anti-oxidant genes. Ins2(GFP)HIGH β-cells were also significantly less viable at all glucose concentrations and in the context of ER stress. Collectively, our results demonstrate that the heterogeneity of extreme insulin production, observed in mouse and human β-cells, can be accounted for by dynamic states of insulin gene activity. Our observations define a previously uncharacterized form of β-cell plasticity. Understanding the dynamics of insulin production has relevance for understanding the pathobiology of diabetes and for regenerative therapy research.

Live cell imaging analysis of the epigenetic regulation of the human endothelial cell migration at single-cell resolution

Lab on a Chip ◽  
2012 ◽  
Vol 12 (17) ◽  
pp. 3063 ◽  
Author(s):  
Chunhong Zheng ◽  
Zhilong Yu ◽  
Ying Zhou ◽  
Louis Tao ◽  
Yuhong Pang ◽  
...  
Keyword(s):  
Cell Migration ◽  
Endothelial Cell ◽  
Single Cell ◽  
Epigenetic Regulation ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Endothelial Cell Migration ◽  
Human Endothelial Cell ◽  
Imaging Analysis

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).

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

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.

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