scholarly journals Microfluidic platform enables live-cell imaging of signaling and transcription combined with multiplexed secretion measurements in the same single cells

2019 ◽  
Vol 11 (4) ◽  
pp. 142-153 ◽  
Author(s):  
Ramesh Ramji ◽  
Amanda F Alexander ◽  
Andrés R Muñoz-Rojas ◽  
Laura N Kellman ◽  
Kathryn Miller-Jensen

Abstract Innate immune cells, including macrophages and dendritic cells, protect the host from pathogenic assaults in part through secretion of a program of cytokines and chemokines (C/Cs). Cell-to-cell variability in C/C secretion appears to contribute to the regulation of the immune response, but the sources of secretion variability are largely unknown. To begin to track the biological sources that control secretion variability, we developed and validated a microfluidic device to integrate live-cell imaging of fluorescent reporter proteins with a single-cell assay of protein secretion. We used this device to image NF-κB RelA nuclear translocation dynamics and Tnf transcription dynamics in macrophages in response to stimulation with the bacterial component lipopolysaccharide (LPS), followed by quantification of secretion of TNF, CCL2, CCL3, and CCL5. We found that the timing of the initial peak of RelA signaling in part determined the relative level of TNF and CCL3 secretion, but not CCL2 and CCL5 secretion. Our results support evidence that differences in timing across cell processes partly account for cell-to-cell variability in downstream responses, but that other factors introduce variability at each biological step.

mSphere ◽  
2016 ◽  
Vol 1 (4) ◽  
Author(s):  
H. M. van der Schaar ◽  
C. E. Melia ◽  
J. A. C. van Bruggen ◽  
J. R. P. M. Strating ◽  
M. E. D. van Geenen ◽  
...  

ABSTRACT Enteroviruses induce the formation of membranous structures (replication organelles [ROs]) with a unique protein and lipid composition specialized for genome replication. Electron microscopy has revealed the morphology of enterovirus ROs, and immunofluorescence studies have been conducted to investigate their origin and formation. Yet, immunofluorescence analysis of fixed cells results in a rather static view of RO formation, and the results may be compromised by immunolabeling artifacts. While live-cell imaging of ROs would be preferred, enteroviruses encoding a membrane-anchored viral protein fused to a large fluorescent reporter have thus far not been described. Here, we tackled this constraint by introducing a small tag from a split-GFP system into an RO-resident enterovirus protein. This new tool bridges a methodological gap by circumventing the need for immunolabeling fixed cells and allows the study of the dynamics and formation of enterovirus ROs in living cells. Like all other positive-strand RNA viruses, enteroviruses generate new organelles (replication organelles [ROs]) with a unique protein and lipid composition on which they multiply their viral genome. Suitable tools for live-cell imaging of enterovirus ROs are currently unavailable, as recombinant enteroviruses that carry genes that encode RO-anchored viral proteins tagged with fluorescent reporters have not been reported thus far. To overcome this limitation, we used a split green fluorescent protein (split-GFP) system, comprising a large fragment [strands 1 to 10; GFP(S1-10)] and a small fragment [strand 11; GFP(S11)] of only 16 residues. The GFP(S11) (GFP with S11 fragment) fragment was inserted into the 3A protein of the enterovirus coxsackievirus B3 (CVB3), while the large fragment was supplied by transient or stable expression in cells. The introduction of GFP(S11) did not affect the known functions of 3A when expressed in isolation. Using correlative light electron microscopy (CLEM), we showed that GFP fluorescence was detected at ROs, whose morphologies are essentially identical to those previously observed for wild-type CVB3, indicating that GFP(S11)-tagged 3A proteins assemble with GFP(S1-10) to form GFP for illumination of bona fide ROs. It is well established that enterovirus infection leads to Golgi disintegration. Through live-cell imaging of infected cells expressing an mCherry-tagged Golgi marker, we monitored RO development and revealed the dynamics of Golgi disassembly in real time. Having demonstrated the suitability of this virus for imaging ROs, we constructed a CVB3 encoding GFP(S1-10) and GFP(S11)-tagged 3A to bypass the need to express GFP(S1-10) prior to infection. These tools will have multiple applications in future studies on the origin, location, and function of enterovirus ROs. IMPORTANCE Enteroviruses induce the formation of membranous structures (replication organelles [ROs]) with a unique protein and lipid composition specialized for genome replication. Electron microscopy has revealed the morphology of enterovirus ROs, and immunofluorescence studies have been conducted to investigate their origin and formation. Yet, immunofluorescence analysis of fixed cells results in a rather static view of RO formation, and the results may be compromised by immunolabeling artifacts. While live-cell imaging of ROs would be preferred, enteroviruses encoding a membrane-anchored viral protein fused to a large fluorescent reporter have thus far not been described. Here, we tackled this constraint by introducing a small tag from a split-GFP system into an RO-resident enterovirus protein. This new tool bridges a methodological gap by circumventing the need for immunolabeling fixed cells and allows the study of the dynamics and formation of enterovirus ROs in living cells.


2017 ◽  
Vol 4 (8) ◽  
pp. 170811 ◽  
Author(s):  
Sayak Mukherjee ◽  
David Stewart ◽  
William Stewart ◽  
Lewis L. Lanier ◽  
Jayajit Das

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.


2019 ◽  
Vol 8 (27) ◽  
Author(s):  
Asha A. Philip ◽  
Brittany E. Herrin ◽  
Maximiliano L. Garcia ◽  
Andrew T. Abad ◽  
Sarah P. Katen ◽  
...  

ABSTRACT A collection of recombinant rotaviruses that express the fluorescent markers UnaG, mKate, mRuby, TagBFP, CFP, or YFP as separate proteins was generated. Genes for the fluorescent proteins were inserted into genome segment 7 without compromising expression of the protein NSP3. These recombinant rotaviruses are valuable for analyzing rotavirus biology by fluorescence-based live-cell imaging.


Proceedings ◽  
2020 ◽  
Vol 50 (1) ◽  
pp. 140
Author(s):  
Thejaswi Nagaraju ◽  
Arthur Sugden ◽  
Bill Sugden

Most DNA viruses must amplify their DNA to form new viral particles. To kickstart their DNA amplification, herpesviruses alter the host cell cycle dynamics by halting G1/S progression. Soon after, the viruses begin amplifying their DNA and halt any detectable cellular DNA synthesis. Viral DNA amplification takes place in specialized regions of the cell known as replication compartments. The genesis and maturation of replication compartments are not well understood. While replication compartments can only be visualized via microscopy, examining DNA synthetic events requires ensemble approaches. We have therefore exploited single-cell assays, including live-cell imaging, fluorescence in situ hybridization (FISH), and EdU-pulse labeling, in combination with computational simulations and ensemble approaches, to study the role of replication compartments in the DNA amplification of the Epstein–Barr virus (EBV). FISH revealed that each replication compartment initially contained a single DNA molecule which did not travel between compartments. DNA amplification lasted for 13–14 h in single cells, as shown by live cell imaging. Replication compartments eventually grew to occupy 30% of the nucleus, which itself grew by 50%. We found that early in the lytic phase, the availability of DNA templates limited DNA synthesis, while late in the lytic phase, the majority of viral DNA molecules no longer served as templates, which correlated with a drop in the levels of the replication protein. The eventual decline in DNA synthesis did not result from encapsidation; only 1–2% of the viral DNA was encapsidated. The levels of viral DNA synthesis in each compartment were similar. Therefore, the number of compartments determined the total amount of DNA synthesized and, consequently, the levels of amplified DNA. This finding was predicted by computational simulations of the amplification of the two distinct EBV derived replicons that we studied. Our results establish that replication compartments represent clonal factories for DNA amplification that are regulated coordinately during the lytic phase.


Cells ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 268
Author(s):  
Parivash Nouri ◽  
Anja Zimmer ◽  
Stefanie Brüggemann ◽  
Robin Friedrich ◽  
Ralf Kühn ◽  
...  

Advances in the regenerative stem cell field have propelled the generation of tissue-specific cells in the culture dish for subsequent transplantation, drug screening purposes, or the elucidation of disease mechanisms. One major obstacle is the heterogeneity of these cultures, in which the tissue-specific cells of interest usually represent only a fraction of all generated cells. Direct identification of the cells of interest and the ability to specifically isolate these cells in vitro is, thus, highly desirable for these applications. The type VI intermediate filament protein NESTIN is widely used as a marker for neural stem/progenitor cells (NSCs/NPCs) in the developing and adult central and peripheral nervous systems. Applying CRISPR-Cas9 technology, we have introduced a red fluorescent reporter (mScarlet) into the NESTIN (NES) locus of a human induced pluripotent stem cell (hiPSC) line. We describe the generation and characterization of NES-mScarlet reporter hiPSCs and demonstrate that this line is an accurate reporter of NSCs/NPCs during their directed differentiation into human midbrain dopaminergic (mDA) neurons. Furthermore, NES-mScarlet hiPSCs can be used for direct identification during live cell imaging and for flow cytometric analysis and sorting of red fluorescent NSCs/NPCs in this paradigm.


2019 ◽  
Author(s):  
Erick Moen ◽  
Enrico Borba ◽  
Geneva Miller ◽  
Morgan Schwartz ◽  
Dylan Bannon ◽  
...  

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.


2021 ◽  
Author(s):  
Francesco Padovani ◽  
Benedikt Mairhoermann ◽  
Pascal Falter-Braun ◽  
Jette Lengefeld ◽  
Kurt M Schmoller

Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. The open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation or downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC


ChemBioChem ◽  
2019 ◽  
Vol 21 (1-2) ◽  
pp. 98-102
Author(s):  
Brandon T. Cisneros ◽  
Neal K. Devaraj

2012 ◽  
Vol 52 (supplement) ◽  
pp. S117
Author(s):  
Yuki Shindo ◽  
Kazunari Mouri ◽  
Kayo Hibino ◽  
Masaru Tomita ◽  
Yasushi Sako ◽  
...  

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