scholarly journals How to Perform a Microfluidic Cultivation Experiment—A Guideline to Success

Biosensors ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 485
Author(s):  
Sarah Täuber ◽  
Julian Schmitz ◽  
Luisa Blöbaum ◽  
Niklas Fante ◽  
Heiko Steinhoff ◽  
...  
Keyword(s):  
Live Cell Imaging ◽  
Cell Imaging ◽  
Life Science ◽  
Single Cells ◽  
Small Cell ◽  
End User ◽  
Custom Made ◽  
Ongoing Development ◽  
Spatio Temporal ◽  
Cultivation Experiment

As a result of the steadily ongoing development of microfluidic cultivation (MC) devices, a plethora of setups is used in biological laboratories for the cultivation and analysis of different organisms. Because of their biocompatibility and ease of fabrication, polydimethylsiloxane (PDMS)-glass-based devices are most prominent. Especially the successful and reproducible cultivation of cells in microfluidic systems, ranging from bacteria over algae and fungi to mammalians, is a fundamental step for further quantitative biological analysis. In combination with live-cell imaging, MC devices allow the cultivation of small cell clusters (or even single cells) under defined environmental conditions and with high spatio-temporal resolution. Yet, most setups in use are custom made and only few standardised setups are available, making trouble-free application and inter-laboratory transfer tricky. Therefore, we provide a guideline to overcome the most frequently occurring challenges during a MC experiment to allow untrained users to learn the application of continuous-flow-based MC devices. By giving a concise overview of the respective workflow, we give the reader a general understanding of the whole procedure and its most common pitfalls. Additionally, we complement the listing of challenges with solutions to overcome these hurdles. On selected case studies, covering successful and reproducible growth of cells in MC devices, we demonstrate detailed solutions to solve occurring challenges as a blueprint for further troubleshooting. Since developer and end-user of MC devices are often different persons, we believe that our guideline will help to enhance a broader applicability of MC in the field of life science and eventually promote the ongoing advancement of MC.

scholarly journals Quantification of protein mobility and associated reshuffling of cytoplasm during chemical fixation

2018 ◽  
Author(s):  
Jan Huebinger ◽  
Jessica Spindler ◽  
Kristin J. Holl ◽  
Björn Koos
Keyword(s):  
Technological Progress ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Cultured Cells ◽  
Chemical Fixation ◽  
Cellular Processes ◽  
Cytoplasmic Proteins ◽  
Resolution Imaging ◽  
Spatio Temporal ◽  
Key Parameter

AbstractTo understand cellular functionalities, it is essential to unravel spatio-temporal patterns of molecular distributions and interactions within living cells. The technological progress in fluorescence microscopy now allows in principle to measure these patterns with sufficient spatial resolution. However, high resolution imaging comes along with long acquisition times and high phototoxicity. Physiological live cell imaging is therefore often unfeasible and chemical fixation is employed. However, fixation methods have not been rigorously reviewed to preserve patterns at the resolution at which they can be nowadays imaged. A key parameter for this is the time span until fixation is completed. During this time, cells are under unphysiological conditions and patterns decay. We demonstrate here that formaldehyde fixation takes more than one hour for cytosolic proteins in cultured cells. Associated with this, we found a distinct displacement of proteins and lipids, including their loss from the cells. Other small aldehydes like glyoxal or acrolein showed inferior results. Fixations using glutaraldehyde were faster than four minutes and retained most cytoplasmic proteins. Surprisingly, autofluorescence produced by glutaraldehyde was almost completely antagonized by supplementary addition of formaldehyde without compromising fixation speed. These findings indicate, which cellular processes can actually be reliably imaged after a certain chemical fixation.

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
Keyword(s):  
Live Cell Imaging ◽  
Cell Imaging ◽  
Nuclear Translocation ◽  
Single Cells ◽  
Live Cell ◽  
Relative Level ◽  
Fluorescent Reporter ◽  
Cytokines And Chemokines ◽  
Cell Processes ◽  
Cell To Cell Variability

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.

scholarly journals Forced apart: a microtubule-based mechanism for equidistant positioning of multiple nuclei in single cells

2021 ◽  
Vol 136 (8) ◽  
Author(s):  
Juliane Teapal ◽  
Leander J. Schuitman ◽  
Bela M. Mulder ◽  
Marcel E. Janson
Keyword(s):  
Molecular Motors ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Single Cells ◽  
Control Cell ◽  
Stochastic Simulations ◽  
Daughter Cells ◽  
Multiple Copies ◽  
Microtubule Growth ◽  
Regular Patterns

AbstractCells can position multiple copies of components like carboxysomes, nucleoids, and nuclei at regular intervals. By controlling positions, cells, for example, ensure equal partitioning of organelles over daughter cells and, in the case of nuclei, control cell sizes during cellularization. Mechanisms that generate regular patterns are as yet poorly understood. We used fission yeast cell cycle mutants to investigate the dispersion of multiple nuclei by microtubule-generated forces in single cells. After removing internuclear attractive forces by microtubule-based molecular motors, we observed the establishment of regular patterns of nuclei. Based on live-cell imaging, we hypothesized that microtubule growth within internuclear spaces pushes neighbouring nuclei apart. In the proposed mechanism, which was validated by stochastic simulations, the repulsive force weakens with increasing separation because stochastic shortening events limit the extent over which microtubules generate forces. Our results, therefore, demonstrate how cells can exploit the dynamics of microtubule growth for the equidistant positioning of organelles.

scholarly journals Quantitative live-cell imaging reveals spatio-temporal dynamics and cytoplasmic assembly of the 26S proteasome

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Chan-Gi Pack ◽  
Haruka Yukii ◽  
Akio Toh-e ◽  
Tai Kudo ◽  
Hikaru Tsuchiya ◽  
...  
Keyword(s):  
Live Cell Imaging ◽  
Cell Imaging ◽  
Temporal Dynamics ◽  
Live Cell ◽  
26S Proteasome ◽  
Spatio Temporal ◽  
Cytoplasmic Assembly

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 4D Analyses Show That Replication Compartments Are Clonal Factories in Which Epstein–Barr Viral DNA Amplification Is Coordinated

Proceedings ◽  
2020 ◽  
Vol 50 (1) ◽  
pp. 140
Author(s):  
Thejaswi Nagaraju ◽  
Arthur Sugden ◽  
Bill Sugden
Keyword(s):  
Dna Synthesis ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Single Cells ◽  
Dna Amplification ◽  
Live Cell ◽  
Computational Simulations ◽  
Viral Dna ◽  
Barr Virus ◽  
Epstein Barr

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.

scholarly journals Spatio-temporal modeling and live-cell imaging of proteolysis in the 4D microenvironment of breast cancer

2019 ◽  
Vol 38 (3) ◽  
pp. 445-454 ◽  
Author(s):  
Kyungmin Ji ◽  
Mansoureh Sameni ◽  
Kingsley Osuala ◽  
Kamiar Moin ◽  
Raymond R. Mattingly ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Temporal Modeling ◽  
Spatio Temporal

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.

scholarly journals A live-cell imaging system for visualizing the transport of Marburg virus nucleocapsid-like structures

2019 ◽  
Author(s):  
Yuki Takamatsu ◽  
Takeshi Noda ◽  
Stephan Becker
Keyword(s):  
Live Cell Imaging ◽  
Ebola Virus ◽  
Cell Imaging ◽  
Temporal Dynamics ◽  
Imaging System ◽  
Marburg Virus ◽  
Live Cell ◽  
Infected Cells ◽  
Spatio Temporal ◽  
Living Organisms

AbstractLive-cell imaging is a powerful tool for visualization of the spatio-temporal dynamics of living organisms. Although this technique is utilized to visualize nucleocapsid transport in Marburg virus (MARV)- or Ebola virus-infected cells, the experiments require biosafety level-4 (BSL-4) laboratories, which are restricted to trained and authorized individuals. To overcome this limitation, we developed a live-cell imaging system to visualize MARV nucleocapsid-like structures using fluorescence-conjugated viral proteins, which can be conducted outside BSL-4 laboratories. Our experiments revealed that nucleocapsid-like structures have similar transport characteristics to nucleocapsids observed in MARV-infected cells. This system provides a safe platform to evaluate antiviral drugs that inhibit MARV nucleocapsid transport.

scholarly journals Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

2021 ◽  
Author(s):  
Francesco Padovani ◽  
Benedikt Mairhoermann ◽  
Pascal Falter-Braun ◽  
Jette Lengefeld ◽  
Kurt M Schmoller
Keyword(s):  
Cell Cycle ◽  
Image Analysis ◽  
Deep Learning ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
State Of The Art ◽  
Single Cells ◽  
Live Cell ◽  
Cell Segmentation ◽  
User Friendly

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

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